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PMC10010331.txt

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+==== Front
+Cancer Res Commun
+Cancer Res Commun
+Cancer Research Communications
+2767-9764
+American Association for Cancer Research
+
+CRC-22-0200
+10.1158/2767-9764.CRC-22-0200
+Version of Record
+Research Article
+Immunology
+Immune Responses to Cancer
+Immunomodulation
+Hematological Cancers
+Leukemias
+Immunotherapy
+Engineered/CAR T cells
+Enhanced Costimulatory Signaling Improves CAR T-cell Effector Responses in CLL
+CLL Costimulatory Phenotype Modulates CAR T-cell Responses
+Collins McKensie A. Conceptualization Data curation Formal analysis Investigation Writing - original draft 123
+Jung In-Young Investigation Writing - review and editing 24
+https://orcid.org/0000-0002-8349-5322
+Zhao Ziran Investigation Writing - review and editing 123
+Apodaca Kimberly Investigation Writing - review and editing 23
+Kong Weimin Investigation Methodology Writing - review and editing 24
+Lundh Stefan Investigation Writing - review and editing 12
+https://orcid.org/0000-0001-7900-8993
+Fraietta Joseph A. Writing - review and editing 12345
+https://orcid.org/0000-0003-3190-1891
+Kater Arnon P. Conceptualization Resources Writing - review and editing 6
+https://orcid.org/0000-0001-8498-4729
+Sun Clare Conceptualization Resources Writing - review and editing 7
+Wiestner Adrian Conceptualization Resources Writing - review and editing 7
+https://orcid.org/0000-0001-7677-537X
+Melenhorst J. Joseph Conceptualization Supervision Funding acquisition Methodology Writing - review and editing 1235
+1 Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania.
+2 Center for Cellular Immunotherapies, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania.
+3 Parker Institute for Cancer Immunotherapy, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania.
+4 Department of Microbiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania.
+5 Abramson Cancer Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania.
+6 Amsterdam UMC, University of Amsterdam, Department of Hematology, Cancer Center Amsterdam, Lymphoma and Myeloma Center Amsterdam, Amsterdam, the Netherlabds.
+7 National Heart, Lung, and Blood Institute, NIH, Bethesda, Maryland.
+Corresponding Author: J. Joseph Melenhorst, Center for ImmunoTherapy and Precision Immuno-Oncology, Cleveland Clinic Lerner Research Institute, 2111 East 96th Street, Cleveland, OH 44106. Phone: 216-215-5725; E-mail: melenhj@ccf.org
+9 2022
+30 9 2022
+2 9 10891103
+21 5 2022
+17 8 2022
+22 8 2022
+© 2022 The Authors; Published by the American Association for Cancer Research
+2022
+Copyright held by the owner/author(s).
+https://creativecommons.org/licenses/by/4.0/ This open access article is distributed under the Creative Commons Attribution 4.0 International (CC BY 4.0) license.
+
+CD19-redirected chimeric antigen receptor (CAR) T cells have shown remarkable activity against B-cell cancers. While second-generation CARs induce complete remission in >80% of patients with acute lymphoblastic leukemia, similar monotherapy induces long-term remissions in only 26% of patients with chronic lymphocytic leukemia (CLL). This disparity is attributed to cell-intrinsic effector defects in autologous CLL-derived T cells. However, the mechanisms by which leukemic cells impact CAR T-cell potency are poorly understood. Herein we describe an in vitro assay that recapitulates endogenous CLL-mediated T-cell defects in healthy donor CAR T cells. Contact with CLL cells insufficiently activates, but does not irreversibly impair, CAR T-cell function. This state is rescuable by strong antigenic stimulation or IL2, and is not driven by immune suppression. Rather, this activation defect is attributable to low levels of costimulatory molecules on CLL cells, and exogenous costimulation enhanced CAR T-cell activation. We next assessed the stimulatory phenotype of CLL cells derived from different niches within the same patient. Lymph node (LN)-derived CLL cells had a strong costimulatory phenotype and promoted better CAR T-cell degranulation and cytokine production than matched peripheral blood CLL cells. Finally, in vitro CD40L-activated CLL cells acquired a costimulatory phenotype similar to the LN-derived tumor and stimulated improved CAR T-cell proliferation, cytokine production, and cytotoxicity. Together, these data identify insufficient activation as a driver of poor CAR T-cell responses in CLL. The costimulatory phenotype of CLL cells drives differential CAR T-cell responses, and can be augmented by improving costimulatory signaling.
+
+Significance:
+
+CLL cells insufficiently activate CAR T cells, driven by low levels of costimulatory molecules on the tumor. LN-derived CLL cells are more costimulatory and mediate enhanced CAR T-cell killing. This costimulatory phenotype can be modeled via CD40 L activation, and the activated tumor promotes stronger CAR T-cell responses.
+
+http://dx.doi.org/10.13039/100014547 Parker Institute for Cancer Immunotherapy (Parker Institute) Collins McKensie A. Jung Inyoung Zhao Ziran Apodaca Kimberly Kong Weimin Lundh Stefan Fraietta Joseph A. Kater Arnon P. Sun Clare Wiestner Adrian Melenhorst J. Joseph http://dx.doi.org/10.13039/100000002 HHS | National Institutes of Health (NIH) Sun Clare Wiestner Adrian crossmarktrue
+==== Body
+pmcIntroduction
+
+Chronic lymphocytic leukemia (CLL) is a mature B-cell malignancy that accounts for nearly one-third of adult leukemia diagnoses in the West (1). Standard-of-care chemoimmunotherapies and small molecules are initially efficacious but the majority of patients inevitably relapse with progressive disease (2). The only reliably curative therapy is allogeneic hematopoietic stem cell transplantation, but this comes with its own challenges, namely high morbidity and mortality, especially with a mainly elderly population and the difficulties associated with finding HLA-matched donors. CD19-directed cell therapies have shown promise, but with the exception of recent combination therapy trials where complete response rates can reach 40+% (3–5), only around one-fourth of patients with CLL treated with CD19-directed CAR T-cell therapy will achieve a complete remission (6–10). This was unexpected as the same therapy in pediatric acute lymphoblastic leukemia (ALL) has a complete response rate >80% (11–18). Further study to understand both tumor and T-cell biology in CLL is therefore warranted.
+
+CLL is phenotypically, clinically, and genetically a highly heterogeneous disease. Patients stratify into fast and slow-progressing groups based on tumor immunoglobulin heavy chain variable region mutation status (19), driver and second-hit mutation status (20–23), and expression of ZAP70 (24–26) and CD38 (27–30). In addition, patients with CLL suffer from immune dysfunction mediated by maintenance of a protumor microenvironment (31–37) and endogenous T-cell dysfunctions including dysregulated cytokine production (38), reduced proliferation and cytotoxicity (39), and unstable immune synapse formation (40–42). The combined effects of CLL disease biology and impaired T-cell function influence the efficacy of autologous CAR T-cell therapies. Alterations in T-cell biology including terminal differentiation (43), exhaustion (39, 43–45), inverted CD4/CD8 ratios (43), impaired T-cell metabolic fitness (46, 47), and the advanced age of the patient population all impact clinical response rates (6, 48–50).
+
+Significant work has been done to understand clinical progression in CLL. However, the field lacks a complete understanding of how tumor cells and engineered T cells interact and the subsequent impact on cell-based therapies. CLL cells residing in different biological niches have different phenotypes and activation profiles. The lymph node (LN), considered the “birth place” of CLL, is a highly organized, B-cell supportive environment (31–34), wherein CLL cells proliferate and maintain an activated profile (51–53) with higher expression of costimulatory and adhesion molecules (54–56). Peripheral blood (PB) CLL cells on the other hand, are generally unactivated and noncycling (50). Most CLL studies use PB-derived tumor cells, as these are the easiest to obtain. However, the differences in tumor phenotype between the LN and PB demonstrate that using only PB-derived CLL cells may inaccurately predict antitumor responses in other compartments.
+
+CD40L-expressing, CD28ζ-signaling CAR T cells show enhanced CAR T-cell activation and improved the antigen-presenting cell (APC) phenotype of CLL cells (57). We therefore sought to use CD40L-mediated CLL activation to mimic the phenotype of LN-derived cells and directly test the impact of enhanced costimulation on 4-1BB–signaling CAR T-cell function. We address the gap in knowledge in understanding how CLL cells negatively impact allogeneic CAR T-cell products and how tumor cells from different compartments interact with these therapies. To study this, we developed an in vitro system wherein we show that CLL-mediated T-cell defects are also seen in a healthy donor–derived CAR T-cell setting; these dysfunctions are therefore not dependent on the endogenous T-cell defects seen in a patient setting, as detailed above. This is of interest as allogeneic therapies become more developed, particularly in a disease such as CLL where patients may benefit from a more-potent allogeneic CAR T-cell product. We show that these defects are rescuable, and attributable to poor costimulation by CLL cells, rather than permanent dysfunction or immune suppression. Furthermore, we show that LN-resident CLL cells are better targets for CAR T-cell lytic activity than circulating tumor cells, and this costimulatory phenotype can be modeled in vitro. Herein we describe the consequences of CLL/CAR T-cell interactions and show that the activated CLL compartment drives antitumor CAR T-cell responses. This represents a rational point of departure to design the next generation of cell-based therapeutics for this disease.
+
+Materials and Methods
+
+Antibodies and Flow Cytometry
+
+The following antibodies and fluorescent reagents were used in this study: Live/Dead Fixable Aqua Dead Cell Stain Kit (Thermo Fisher Scientific, #L34957), Biotin-SP (long spacer) AffiniPure Goat Anti-Mouse IgG F(abʹ)2 Fragment Specific (Jackson ImmunoResearch, #115-065-072, RRID:AB_2338565), PE-streptavidin (BioLegend, #405204, RRID:AB_2921282), and PE-anti-CAR19 (Novartis, custom antibody). Additional antibodies can be found in Supplementary Table S1. Cell sorts were performed on a FACS Aria II machine managed by the University of Pennsylvania Flow Core.
+
+Cell Lines
+
+The artificial APC (aAPC) cell lines were created and validated in-house by transducing K562 cells (ATCC, CCL-243, RRID: CVCL_0004) to stably express CD64, CD86, 4-1BBL, and either ROR1 or CD19. The CD40 ligand–expressing Ltk cell line (Ltk-CD40L; refs. 58, 59) was obtained from Dr. Cees van Kooten (Leiden University Medical Center, Leiden, the Netherlands). Cells were tested for Mycoplasma using the Cambrex MycoAlert kit (Promega, #LT07-118). The most recent test was in February, 2022. Furthermore, the cell lines have been regularly authenticated by the University of Arizona Genetics Core using short tandem repeat profiling.
+
+Paired LN and PB CLL Samples
+
+Patients were enrolled in the Institutional Review Board–approved protocol: Natural History Study of monoclonal B-cell lymphocytosis, CLL/small lymphocytic lymphoma, lymphoplasmacytic lymphoma/Waldenstrom macroglobulinemia, and splenic marginal zone lymphoma (ClinicalTrials.gov number NCT00923507). Samples were obtained after written informed consent in accordance with the Declaration of Helsinki, and applicable federal regulations. PB mononuclear cells (PBMC) were isolated by density gradient centrifugation and cryopreserved. LN biopsies were mechanically disaggregated into single-cell suspensions and cryopreserved.
+
+CAR T-cell Generation
+
+Normal donor apheresis products were obtained from the University of Pennsylvania Human Immunology Core. PBMCs were isolated via Ficoll density gradient centrifugation and cryopreserved in 70% OpTmizer 5 (OpT5) medium, 20% human AB serum, and 10% DMSO. T cells were isolated using the Miltenyi Pan T cell Isolation Kit (Miltenyi Biotec, #130-096-535) and cultured in OpT5 medium with T-cell expansion supplement (Thermo Fisher Scientific, #A1048501), 2 mmol/L Glutamax, and 5% human AB serum supplemented with 100 U/mL hIL2 unless otherwise stated. Cells were activated using anti-CD3/anti-CD28 Dynabeads at a ratio of 3 beads:1 T cell. T cells were transduced with CAR-expressing lentivirus on day 1. Cells were counted and resuspended at 5.0 × 105 cells/mL on days 3, 5, and 7. On day 9, cells were counted and cryopreserved. Absolute cell counts were obtained using the Luna fluorescence-based automatic cell counter (Logos Biosystems).
+
+Lentivirus Production
+
+VSVg-pseudotyped anti-CD19 or anti-ROR1 CAR (CAR19 and CAR-ROR1, respectively) lentivirus was produced using HEK293T cells (ATCC, CRL-11268). Cells were seeded on day −1 in R10 medium (RPMI + 10% FBS + 1% Penicillin/Streptomycin). On day 0, the cells were transfected with VSVg, RSV/Rev, Gag/Pol, and CAR plasmids using lipofectamine 2000 (Thermo Fisher Scientific, #11668019). Supernatant was collected at 24 and 48 hours and concentrated using an ultracentrifuge overnight at 4°C and 8,861 Relative Centrifugational Force (RCF), followed by 2.5 hours at 4°C at 76,800 RCF. Virus was aliquoted and stored at −80°C.
+
+B-CLL Isolation
+
+Primary CLL PBMCs were obtained from the Stem Cell and Xenograft Core at the University of Pennsylvania (Philadelphia, PA). B-CLL cells were isolated using the Miltenyi B-CLL Isolation Kit (Miltenyi Biotec, #130-103-466). A full list of CLL donors used can be found in Supplementary Table S2.
+
+B-CLL Cell Activation/Activated CLL Generation
+
+Primary B-CLL cells were activated by plating them over confluent Ltk-CD40 L cells at 3.0 × 106 cells/mL in Iscove's modified Dulbecco's medium (Thermo Fisher Scientific, #12440049) + 10% GemCell FBS (GeminiBio, #100-500). Cells were placed at 37°C for 6 hours before harvest.
+
+Cytotoxicity Assay
+
+CAR T cells and donor-matched untransduced T cells were thawed and stained with 0.1 μmol/L carboxyfluorescein diacetate succinimidyl ester solution (Thermo Fisher Scientific, #C34554) according to manufacturer's instructions. Cells were counted and resuspended at 1.0 × 106 cells/mL in OpT5 medium. B-CLL cells were isolated as described above and stained with 0.1 μmol/L Cell Trace Violet solution (Thermo Fisher Scientific, #C34557) according to manufacturer's instructions. CLL cells were counted, resuspended at 1.0 × 106 cells/mL in OpT5 medium and cocultures were set up at the desired effector:target ratios. Untransduced cells were used as an alloreactivity control and aAPCs were used as a positive control. Assays using activated CLL cells or IL2 used CLL cells activated for 6 hours as described or had 100 U/mL IL2 added to the cocultures, respectively.
+
+Restimulation Assay
+
+CAR T cells were sorted to obtain the live, CAR+ T-cell fraction and plated with aAPCs or primary CLL cells at a ratio of 3:1 APCs/CLL to CAR T cells at 1.0 × 106 total cells/mL in OpT5 medium without cytokines. Stimulator cells were irradiated with 100 Gy prior to use. T cells were counted and stimulated on days 0, 5, and 10, and harvested on day 15.
+
+Trans-costimulation of CAR T Cells
+
+CAR T-cell and CLL cocultures were set up as indicated in the restimulation assay. aAPCs expressing CD86 and 4-1BBL but an irrelevant CAR target antigen were added at a ratio of 3 aAPCs: 1 CAR T cell. Cocultures were left for 5 days and then assessed for CAR activation by flow cytometry.
+
+Intracellular Cytokine Analysis
+
+CAR T cells were thawed, counted, and resuspended in OpT5 medium. CLL cells were isolated as described above. Cocultures were set up at a final concentration of 1.0 × 106 total cells/mL in OpT5 medium with a 1:20 dilution of CD107a-FITC (BD Biosciences, #555800), 1 μg/mL brefeldin (BD Biosciences, #555029), and 2 μg/mL monensin (BD Biosciences, #554724) at a ratio of 3:1 Stimulator:CAR T cell. Cells were harvested after 6 or 12 hours and stained with Live/Dead Blue followed by surface staining for CAR, CD3, CD4, CD8, and CD19. Cells were fixed using the BD Intracellular Cytokine Staining Kit and Protocol (BD Biosciences, #554715) and stained intracellularly for IL2, TNFα, and IFNγ. Additional antibody information is given in Supplementary Table S1.
+
+Secreted Cytokine Detection
+
+CAR T cells were cocultured with aAPCs or primary CLL cells for 24 hours. Supernatant was collected and stored at −80°C. Cytokine levels were assessed using the Luminex Human Th17 25-plex platform (Millipore) according to manufacturer's instructions.
+
+Statistical Analysis
+
+Statistical analysis was performed using GraphPad Prism version 9 (GraphPad Software, San Diego, CA). All data were subjected to the D'Agostino-Pearson normality test. Nonparametric statistics were used where the sample size was too small or the data were non-normally distributed. Data were assessed for outliers using the ROUT method with a Q value of 1%. When outliers were identified they were removed from subsequent analysis. Statistical tests are indicated in the relevant figure legends along with exact P value. P values and adjusted P values as determined by the Holm-Sidak method to correct for multiple comparisons can be found in Supplementary Table S3. A P value <0.05 was considered statistically significant. Data are shown as mean ± SD.
+
+Data Availability Statement
+
+The data generated during this study can be found within the article and Supplementary Data.
+
+Results
+
+CLL Cells Fail to Elicit Strong Effector Responses From Healthy Donor–derived CAR T Cells
+
+To study CLL/CAR T-cell interactions, second-generation CD19- and ROR1-directed CAR T cells with a 4-1BB intracellular signaling domain (Fig. 1A) were serially stimulated with primary CLL cells every 5 days for three total stimulations (Fig. 1B). A ROR1-directed CAR was selected since this molecule is under development for treatment of CLL and other malignancies (60, 61), and the CD19-directed CAR was chosen to validate the study results as it remains the gold standard for the field. An irradiated K562-based aAPC expressing the relevant CAR target antigen was used as a positive control. These aAPCs stimulate potent effector activity including proliferation and cytokine production (62–64). Because the aAPCs are transduced to express the CAR target antigen, expression of these proteins may differ from endogenous expression levels (Supplementary Fig. S1A). The expression of CD19 is comparable between the aAPCs and the endogenous expression on the CLL cells. However, the expression level of ROR1 on the aAPCs is higher than on the CLL tumor. However, variability in expression of both proteins is seen on the tumor cells, with more variable expression of ROR1 than CD19. The aAPCs also express CD86 and 4-1BBL in addition to either ROR1 or CD19. However, CAR19 T cells proliferate similarly in response to both aAPCs with CD86/4-1BBL and aAPCs that only express CD19 (Supplementary Fig. S1B). Furthermore, we chose to use a 4-1BB–signaling CAR due to the clinical success of Kymriah, a CD19-directed, 4-1BB–signaling CAR, and the observation that a CD28-signaling CD19-directed CAR also demonstrated similarly reduced proliferation in response to CLL cells (Supplementary Fig. S1C).
+
+FIGURE 1 CAR-ROR1 and CAR19 T cells serially stimulated with CLL cells recapitulate clinically observed effector hypofunction. A, A schematic of the second-generation CAR construct used in these studies depicting the promoter, scFv, hinge, transmembrane, and intracellular signaling domains (ICD). B, An experimental schematic depicting the serial restimulation assay. CAR T cell indicates normal donor-derived CAR T cells, aAPC indicates the positive control cell line, and CLL indicates a primary tumor stimulus. C, Proliferation measured as population doublings of CAR-ROR1 and CAR19 T cells following restimulation with either aAPCs or CLL cells. The frequency of ROR1- or CD19-specific CAR T cells expressing the activation-associated molecules LAG-3 (D), TIM-3 (E), CTLA-4 (F), and PD-1 (G) at each stimulation shows robust induction of expression with the aAPC but not CLL cells. CLL refers to CLL-stimulated cells while aAPC refers to CAR T cells stimulated with aAPCs. H, Cytotoxic function of CAR-ROR1 and CAR19 T cells against primary CLL cells or aAPCs after a 48-hour coincubation. UTD refers to the untransduced donor-matched control. I, Secreted cytokine production as measured by Luminex 24-hours after each stimulation. Concentration is given as pg/mL. Analyses were performed using the multiple Mann–Whitney test with Holm-Sidak correction. CAR-ROR1: n = 2 experiments, 5 CLL donors: (2655, 2761, 3416, 4487, 4625), CAR19: n = 5 experiments, 22 CLL donors: (1993, 3507, 3578, 3935, 3955, 4045, 4048, 4129, 4265, 4276, 4288, 4444, 5071, 5083, 5108, 5131, 5267, 5574, 5597, 5786, 5798, 5963). P values are as follows: (D) [ROR1: **, P = 0.002451; CAR19: ****, P = 0.000050 (left); ***, P = 0.000226; ****, P = 0.000025 (right)]. E, [ROR1: **, P = 0.002451; *, P = 0.036765; CAR19: ***, P = 0.000050; ns P = 0.365669; *, P = 0.021909]. F, [ROR1: **, P = 0.002451; ns P = 0.056373; CAR19: ns P = 0.746761 (left), 0.844946 (middle), 0.376935 (right)]. G, [ROR1: *, P = 0.004902; ns P = 0.352941 (middle); P = 0.654412 (right)]. H, [ROR1: **, P = 0.004396; CAR19: **, P = 0.008081]. I, IL2: [ROR1: **, P = 0.002451; CAR19: **, P = 0.004040 (left); P = 0.009524 (middle); P = 0.006061 (right)]. IFNγ: [ROR1: **, P = 0.002451; CAR19: **, P = 0.004040]. TNFα: [ROR1: **, P = 0.002451; CAR19: **, P = 0.004040 (left, middle), P = 0.006061 (right)]. (*, P < 0.05; **, P < 0.01; ***, P < 0.001; ****, P < 0.0001).
+
+Following three consecutive CLL stimulations, we found that both CD19- and ROR1-directed CAR T cells proliferated significantly less than aAPC-stimulated cells (Fig. 1C). Furthermore, both CAR T cells showed delayed, low-level upregulation of the inhibitory receptors LAG3 (Fig. 1D) and TIM3 (Fig. 1E) while these proteins were strongly induced following aAPC stimulus. CAR-specific differences were seen in expression patterns of CTLA4 and PD-1 (Fig. 1F and G), but the overall reduction in activated phenotype was consistent. Cytotoxic function after a 48-hour coculture was also impaired in CLL-stimulated CAR T cells but remained intact in aAPC stimulated cells (Fig. 1H). Finally, aAPC-stimulated CAR T cells produced higher levels of cytokines than the matched CLL-stimulated cells (Fig. 1I). Together, these data confirmed that we can model CLL-induced T-cell nonresponsiveness in vitro using primary CLL cells and healthy donor–derived CAR T cells, and that this phenotype is inducible irrespective of CAR target antigen. Furthermore, this confirmed that CLL cells induce hypofunction in a CAR T-cell setting, not just in autologous CLL-derived T cells. Having determined that these functional defects were CAR independent, we chose to focus our efforts on the relatively understudied ROR1-directed CAR, validating major findings with CAR19 as needed.
+
+CLL-induced CAR T-cell Dysfunction is Due to Insufficient Activation
+
+We next sought to determine the causes of this hypofunction. On the basis of the body of work detailing T-cell exhaustion in CLL, we hypothesized that CLL-mediated dysfunction was a sustained, defective state, stabilized by successive interactions with CLL cells. We tested this by stimulating CAR-ROR1 T cells with CLL cells once or multiple times and then switched to an aAPC stimulus (Fig. 2A). Sustained hyporesponsiveness of CAR T cells against strongly stimulatory targets following one or more encounters with CLL cells would be highly suggestive of permanent dysfunction. However, no differences in the magnitude of the proliferative response were observed after switching to an aAPC stimulus in any of the stimulation conditions (Fig. 2B and C). Switching to an aAPC also restored the breadth of the secreted cytokine repertoire, although a slight decrease in cytokine level was observed following repeated CLL stimulations (Fig. 2D). In addition, we observed a near absence of cells entering the cell cycle as denoted by Ki-67 positivity, a phenotype which was rescued by aAPC stimulation (Fig. 2E). We then used flow cytometry to assess the differentiation state of the CAR T cells at the end of the coculture. While all aAPC-stimulated CAR T cells underwent memory or effector differentiation, CLL cells failed to induce progressive T-cell differentiation (Fig. 2F and G). Together, these data indicated that CLL cells insufficiently activate even second-generation CAR T cells, explaining the observed lack of effector responses. This suggested that CLL cells either lack a positive regulator of CAR T-cell responses which was then provided by an aAPC, or that CLL cells negatively regulate CAR T cells which can be rescued by removing the T cells from a CLL-dense environment. The ability to rescue CAR T-cell function indicated that we could improve anti-CLL responses if we enhanced T-cell activation.
+
+FIGURE 2 CLL-mediated CAR-ROR1 dysfunction is due to insufficient activation as opposed to permanent defects. A, An experimental schematic depicting the APC switching assay. Red and black circles indicate whether CAR T cells were challenged with CLL cells or aAPCs, respectively for a total of three times. AAA: Three successive aAPC stimulations. CAA: One CLL stimulation followed by two successive aAPC stimulations. CCA: Two CLL stimulations followed by one aAPC stimulation. CCC: Three successive CLL stimulations. B, Proliferation of CAR-ROR1 T cells as measured on days 5, 10, and 15 of the APC switching restimulation assay. Proliferation is given as population doublings. C, The number of population doublings each group underwent after receiving one aAPC stimulus. For the AAA condition, this was day 5, for CAA this was day 10, and for CCA this was day 15. No significant difference is observed between the groups, indicating no sustained defect after successive CLL interactions. D, The levels of IFNg, TNFa, and IL2 produced by each group after receiving one aAPC stimulus as described in C. **, P = 0.013225; ***, P = 0.000535. E, The frequency of Ki67+ CAR-ROR1 T cells amongst the different restimulation conditions. CD3/CD28 stimulated indicates CAR T cells stimulated with anti-CD3/CD28 beads as a positive control and “Rested” indicates CAR T cells that were thawed and rested overnight as an unactivated cell control. F, Representative flow cytometry plots depicting the differentiation state of AAA, CAA, CCA, CCC, unstimulated CAR-ROR1 T cells, and CD3/CD28 bead-activated CAR-ROR1 T cells as determined by CD27 and CD45RO staining. G, Quantification of the frequencies of each differentiation state defined as follows: Effector: CD27−CD45RO−. Effector Memory: CD27−CD45RO+. Central Memory: CD27+CD45RO+. Naïve-like: CD27+CD45RO−. CD4+ CAR-ROR1 T cells are depicted on the left and CD8+ CAR-ROR1 T cells are depicted on the right. C was analyzed using an unpaired, two-tailed t test. D and E were analyzed using multiple Mann–Whitney test with Holm-Sidak correction. n = 3 experiments, 5 CLL donors: (3578, 3955, 4288, 4444, 4279). (*, P < 0.05; **, P < 0.01).
+
+Strong Antigenic Stimulation is Sufficient to Overcome CAR T-cell Activation Defects
+
+We next hypothesized that impaired CAR T-cell activation was due to active immunosuppression. To test this, we developed a mixed coculture assay wherein CAR T cells were stimulated with aAPCs and CLL cells mixed at graded ratios (Fig. 3A). All cultures containing any proportion of aAPCs proliferated (Fig. 3B) and produced cytokines at similar levels (Fig. 3C) regardless of the frequency of CLL cells in the culture (Fig. 3B). This suggested that CLL cells do not prevent CAR T-cell activation in the presence of a potent stimulus.
+
+FIGURE 3 Strong antigenic stimulation partially rescues CLL-mediated insufficient activation. A, An experimental schematic depicting the mixed APC assay. aAPCs and CLL cells were mixed together and CAR-ROR1 T cells were stimulated with the indicated ratios of aAPC to CLL cells. 4:0 and 0:4 indicate that CAR T cells were stimulated with aAPC or CLL cells only, respectively. 3:1 indicates a mix of 75% aAPCs and 25% CLL cells and 2:2 indicates a 50/50 mix of the two cell types CLL cells do not inhibit the proliferative (B) or cytokine secretion response (C) of CAR T cells to aAPC. D, Mid-to-high dose IL2 rescues the proliferative response of CAR T cells against CLL cells. Proliferation of CAR-ROR1 T cells (left) and CAR19 T cells (right) is given as population doublings during the restimulation assay with the given quantity of IL2 supplemented into the culture medium. E, Proliferation of CAR-ROR1 T cells incubated with the given quantity of IL2 alone in the absence of antigen. Proliferation is given as population doublings. F, Cytokine production of CAR-ROR1 T cells on day 15 of the restimulation assay with the indicated amount of supplemented IL2 (left). Background levels of IFNg, TNFa, and IL2 present in the media with exogenous IL2 added (right). The dotted line on the left graph indicates the background IL2 levels for the 100 U/mL condition with no CAR T cells present. B was analyzed using the multiple Wilcoxon matched-pairs signed rank test with Holm-Sidak correction. B: n = 3 experiments, 4 CLL donors: (4265, 4516, 4567, 5574). C: n = 5 CAR T-cell donors. D (left): n = 3 experiments, 3 CLL donors: (4526, 4567, 5574). D (right): n = 1 experiment, 1 CLL donor: (4625). E: n = 1 experiments, 4 CLL donors: (4265, 4516, 4567, 5574). F: n = 3 CAR T-cell donors (***, P < 0.001).
+
+Exogenous IL2 Supplementation Overcomes Impaired CAR T-cell Proliferation in CLL Cocultures
+
+Although proliferation was unaffected by CLL cells in the mixed culture assay (Fig. 3B), we observed a trend wherein IL2 production decreased with increasing numbers of CLL cells (Fig. 3C, right). We hypothesized that this limited IL2 secretion may drive insufficient T-cell activation. To determine whether IL2 was limiting, we performed a restimulation assay while supplementing graded doses of IL2 to the cultures. We found that this restored both CAR-ROR1 and CAR19 proliferation in response to CLL cells in a dose-dependent manner (Fig. 3D). This response was at least partially antigen-driven as IL2 supplementation alone did not result in the same levels of T-cell proliferation (Fig. 3E). Finally, we assessed cytokine production in the IL2 supplemented cultures and observed partial restoration of the secreted cytokine repertoire (Fig. 3F).
+
+IL2 Enhances the Stimulatory Phenotype of CLL Cells
+
+To discern a mechanism for the improved CAR T-cell responses against CLL cells with IL2 addition, we immunophenotyped primary CLL cells incubated for 5 days with high-dose (100 U/mL) IL2 (Supplementary Fig. S2A). We found that the IL2R alpha-chain (CD25) was expressed on >80% of the tumor cells in 18 of 20 CLL donors (Supplementary Fig. S2B). This suggested that these CLL cells may have a functional, signaling IL2R; this could lead to phenotypic changes in response to IL2. We found that while IL2 stimulation did not alter the expression of inhibitory ligands PD-L1 and PD-L2 (Supplementary Fig. S2C), expression of the costimulatory molecules CD80 and CD86 was significantly augmented (Supplementary Fig. S2D). The expression of the adhesion molecules CD54 and CD58 was similarly increased (Supplementary Fig. S2E). Furthermore, we observed induction of CLL cell proliferation by IL2 (Supplementary Fig. S2F). Together, these data show that CLL cells acquire a phenotype consistent with B-cell activation when incubated with IL2. We postulated that this prostimulatory phenotype could explain the improved CAR T-cell responses observed in Fig. 3.
+
+Additional Costimulation Enhances Second-generation CAR T-cell Activation
+
+Given that IL2 significantly boosted the expression of costimulatory molecules on CLL cells, we sought to determine whether enhanced costimulatory receptor signaling could facilitate better activation of second-generation CAR T cells. To test this, we provided stimulation via CD86 and 4-1BBL using a bystander aAPC that did not express the CAR target antigen (Supplementary Fig. S3A). We referred to this system as trans-costimulation to differentiate from costimulation provided by the target cell itself (costimulation in cis). Trans-costimulation enhanced CAR-ROR1 T-cell proliferation (Supplementary Fig. S3B) and the activation phenotype of both CAR-ROR1 and CAR19 T cells as assessed by PD-1, TIM-3, and CTLA-4 upregulation in CLL cocultures (Supplementary Fig. S3C–S3E). Furthermore, a shift toward a central memory immunophenotype indicated rescued CAR T-cell differentiation (Supplementary Fig. S3F and S3FG). Together, these data show that additional costimulation positively impacts second-generation CAR T cells and implicates an improved APC phenotype in driving anti-CLL T-cell responses.
+
+LN-derived CLL Cells Have an Improved Stimulatory Phenotype Compared with PB-derived CLLs
+
+It is well established that a significant fraction of LN-resident CLL cells expresses the cell-cycle marker Ki67, while PB CLL cells are largely quiescent (50, 51, 53, 54, 56). We hypothesized that LN-resident CLL cells also express higher levels of the adhesion and costimulatory molecules these second-generation CAR T cells still rely on. To assess this, we examined 10 paired LN and PB samples from patients with treatment-naïve CLL (Table 1) for expression of costimulatory and inhibitory ligands on tumor cells and their matching receptors on T cells. As expected, LN-derived CLL cells had a more activated, stimulatory phenotype than the matched PB CLL cells (Fig. 4A). The LN-derived tumor cells expressed higher levels of costimulatory and adhesion molecules (Fig. 4B) as well as molecules associated with B-cell activation, including Ki67, PD-1, CD200, and CD47 (Fig. 4C). In addition, both CD4+ and CD8+ T cells from the LN were more activated than the matching PB T cells (Fig. 4D). Both CD4+ and CD8+ T cells taken from the CLL LN had higher expression of PD-1 (Fig. 4E), CTLA-4 (Fig. 4F), and HLA-DR (Fig. 4G) than those from the PB. Coexpression of these molecules is highly suggestive of an activated T-cell phenotype. We therefore hypothesized that in contrast to quiescent PB CLL cells, strong stimulation provided by this LN-resident CLL population drives anti-tumor CAR T-cell responses in patients.
+
+TABLE 1 Paired LN/PB patient samples. List of the 10 paired samples for which we have both PB- and LN-derived CLL cells. Age, sex, Rai Stage, IGHV mutational status, and cytogenetic abnormalities for each donor are indicated
+
+			Rai	IGHV		
+Sample ID	Sex	Age	stage	status	FISH	
+1	F	55	1	U	13q	
+2	M	43	2	U	17p	
+3	M	48	3	U	17p	
+4	M	48	4	U	13q	
+5	M	50	3	U	11q	
+6	F	49	1	M	13q	
+7	F	57	3	U	13q	
+8	M	73	4	M	11q	
+9	F	55	3	U	t12	
+10	M	47	4	M	17p	
+
+FIGURE 4 The CLL LN provides a pro-T-cell stimulatory environment. A, Heatmap showing the phenotype of 10 matched LN- and PB-derived CLL cells given as mean fluorescence intensity (MFI). B, APC phenotype given as comparisons of expression of costimulatory molecules CD80, CD86, CD54, and CD58 between PB- and LN-derived CLL cells. Expression is given as MFI. C, Comparison of B-CLL activation molecules Ki67, PD-1, CD47, and CD200 on PB compared with LN-derived CLL cells. Expression is given as MFI. D, Heatmap depicting the T-cell activation phenotype of CD4 T cells (top) and CD8 T cells (bottom) derived from the LN compared with the PB. E, Expression of PD-1 on CD4+ (left) and CD8+ (right) endogenous T cells from the LN and PB from the 10 matched samples in A. Expression is given as MFI. F, Expression of CTLA-4 on CD4+ (left) and CD8+ (right) endogenous T cells from the LN and PB from the 10 matched samples in A. Expression is given as MFI. G, Expression of HLA-DR on CD4+ (left) and CD8+ (right) endogenous T cells from the LN and PB from the 10 matched samples in A. Expression is given as MFI. B (top left and bottom left), C (top right and bottom right), F, and G were analyzed using the two-tailed Wilcoxon matched-pairs signed rank test. The remaining analyses in B and C and E were analyzed using a two-tailed paired t test. (n = 10 LN/PB pairs, *, P < 0.05; **, P < 0.01; ***, P < 0.001).
+
+LN-derived CLL Cells Elicit Stronger CAR T-cell Effector Responses Than PB-derived CLLs
+
+Next, we sought to directly test whether CLL cells from the LN stimulated improved CAR T-cell responses compared with those from the PB. We stimulated CAR19 T cells for 6 hours with the matched LN/PB CLL pairs described in Table 1. The ROR1-directed CAR was not used because none of the 10 donors expressed the ROR1 antigen in the LN compartment. While most samples had ROR1+ cells, this population was entirely restricted to the PB tumor cells (Fig. 5A). We assessed cytokine production and degranulation as a proxy to cytolytic activity using flow cytometry. We observed an increase in degranulation of both CD4 and CD8 CAR19 T cells following a LN CLL stimulation compared with a PB CLL stimulation (Fig. 5B). Furthermore, LN-derived CLL cells elicited more IL2, IFNg, and TNFa production than their paired PB-derived counterparts (Fig. 5C–E). CD4+ CAR19 T cells also showed strong activation responses as indicated by CD40 L upregulation (Fig. 5F). Together, these data show that the enhanced costimulatory phenotype of CLL tumor in the LN corresponds to improved CAR T-cell activation and effector responses, further highlighting the CLL costimulatory phenotype as having a major impact on CAR T-cell activation.
+
+FIGURE 5 LN-derived CLL cells induce stronger CAR T-cell degranulation and cytokine production than PB-derived CLLs. A, The frequency of ROR1+ tumor cells in the LN and PB compartments. Raw numbers are given on the right.B, Degranulation of CD4+ and CD8+ CAR19 T cells as measured by frequency of CD107a following a 6-hour stimulus with paired LN- or PB-derived CLL cells as described in Table 1. C–E, Frequency of IL2+, IFNγ+, and TNFα+ CAR19 T cells following a 6-hour stimulus with the LN and PB CLL pairs. F, CAR T-cell activation as measured by frequency of CD40L+ cells. Each data point represents an average of four CAR T-cell donors stimulated with each LN/PB CLL pair over the course of two experiments. B (left), C (right), D (right), E, and F (left) were analyzed using a two-tailed Wilcoxon matched-pairs signed rank test. A, B (right), C (left), D (left), and F (right) were analyzed using a two-tailed paired t test. (n = 10 LN/PB pairs, 4 CAR T-cell donors, two experiments. *, P < 0.05; **, P < 0.01; ****, P < 0.0001).
+
+A Strong CLL Costimulatory Phenotype can be Induced In Vitro
+
+Because donation of LN-derived CLL cells is an invasive procedure, we sought to model the LN phenotype in vitro. To achieve this, we tested whether CD40L-mediated activation would approximate the LN-resident CLL phenotype by stimulating primary CLL cells for only 6 hours with a human CD40 ligand–expressing epithelial cell line. Our results revealed that a brief 6-hour stimulation resulted in upregulation of costimulatory, adhesion, and activation-associated molecules in CLL cells, in line both with prior CD40L activation studies (51, 56, 65) and the patient LN CLL phenotype (Fig. 6A). CD40L-activated CLL cells also maintained expression of both ROR1 and CD19 antigens, although we observed a slight decrease in ROR1 expression (Fig. 6B). However, based on cell phenotype, we determined that CD40L-activated CLL cells (aCLL) mimic the LN-resident CLL cell costimulatory phenotype and can be used to study the impact of improved costimulation on CAR T-cell effector responses.
+
+FIGURE 6 Strongly costimulatory CLL cells stimulate robust CAR T-cell effector responses. A, Heatmaps showing the phenotype of aCLLs compared with resting CLL cells, looking at both frequency (top) and MFI (bottom) of the markers indicated. B, Changes in expression level of ROR1 (left, middle) and CD19 on resting and aCLL cells. C, Proliferation of CAR-ROR1 (left) and CAR19 (right) T cells following serial stimulations with resting CLLs, a, or aAPCs. Proliferation is given as population doublings. D, Cytotoxicity of CAR-ROR1 and untransduced donor-matched control (UTD) T cells after a 2-day stimulation with either aCLLs or resting CLLs. Asterisks indicate comparisons between aCLL/CAR-ROR1 versus aCLL/UTD (left). Comparison of AUC for each condition from the data on the left. E, Cytotoxicity of CAR19 and UTD T cells after a 2-day stimulation with either aCLLs or resting CLLs. Asterisks indicate comparisons between aCLL/CAR19 versus aCLL/UTD (left). Comparison of AUC for each condition from the data on the left. B, C (right), D (left), and E (left) were analyzed using the Wilcoxon matched-pairs signed rank test with Holm-Sidak correction. C (right), D (right), and E (right) were analyzed using a paired two-tailed t test. [B (left): n = 4 experiments, 6 CLL donors: (2655, 4167, 4516, 4556, 4625, 4794), B (right): n = 2 experiments, 5 CLL donors: (2655, 4405, 4556, 4625, 6342), C: n = 2 experiments, 7 CLL donors: (2655, 2656, 4121, 4405, 4567, 4625, 4684), D, E: n = 2 experiments/CAR, 8 CLL donors: (2655, 2761, 2771, 3416, 4405, 4419, 4556 4625), *, P < 0.05; **, P < 0.01; ***, P < 0.001; ****, P < 0.0001].
+
+Activated CLL Cells Drive Potent Antitumor CAR T-cell Responses
+
+To test our hypothesis that enhanced costimulation on LN-derived CLL cells directly mediates CAR T-cell effector function, we performed repeat stimulation assays using aCLL cells and found that both CAR-ROR1 and CAR19 T cells had enhanced proliferation with an aCLL stimulus compared to unactivated CLL cells (Fig. 6C). We further assessed the ability of CAR T cells to degranulate (Supplementary Fig. S4A) and produce cytokines (Supplementary Fig. S4B–S4D). CAR19 CD8+ T cells displayed increased degranulation after a 6-hour aCLL stimulus indicating enhanced cytotoxic function while CAR ROR1 T cells had a mixed phenotype (Supplementary Fig. S4A). We also observed inconsistent expression of IL2, IFNγ, or TNFα between the CARs at the 6-hour timepoint (Supplementary Fig. S4B–S4D). We next determined whether aCLL cells were susceptible to CAR-mediated lysis. Here we confirmed that that both CAR-ROR1 (Fig. 6D) and CAR19 T cells (Fig. 6E) preferentially lysed aCLL cells over the matched unactivated CLL cells. Furthermore, we observed enhanced lysis at lower effector:target ratios with aCLL stimulation. Our results demonstrate that CAR T cells effector responses are directly affected by costimulatory molecule expression on the CLL tumor. Furthermore, these data suggest that CAR T cells may preferentially target activated CLL cells, such as those found in the LN. Thus, the clinical efficacy of CART19 therapy in CLL may be explained by the lysis of LN-resident CLL cells as PB-derived CLL cells are poor stimulators of CAR T-cell activity.
+
+Discussion
+
+Despite the relative success of CD19-directed CAR T-cell therapy in treating B-cell malignancies, only a minority of patients with CAR T cell–treated CLL will be classed as complete responders, highlighting a remaining unmet need for curative therapies. Although there exists a body of work on how CLL cells impact endogenous T-cell function, little work has been done to understand how primary CLL cells affect cell-based therapies. With the advent of transgenic-TCR (T-cell receptor) and CAR-modified T cells, as well as the rising popularity of allogeneic or “off-the-shelf” cell therapies, there remains a need to understand how CLL cells impact healthy donor–derived T cells. Furthermore, understanding the differences between the immune compartments in which CLL cells reside and how each impacts the efficacy of cell-based therapies is critically important. This study aimed to understand how primary CLL cells negatively impact allogeneic CAR T-cell products and, subsequently, identify how the tumor microenvironment (TME) drives antitumor responses in a patient population. This work will allow the field to enhance existing therapies to capitalize on TME modulation, improve CAR T-cell therapeutic responses, and ultimately provide more successful treatments for patients with CLL.
+
+It is well documented that CLL cells negatively impact both endogenous and allogeneic T cells, regardless of TCR specificity. Herein, we demonstrate that even second-generation CAR T cells exhibit dysfunctions against primary tumor cells. However, these dysfunctions are not permanent; rather they are the result of insufficient CAR T-cell activation. While immunosuppression mediated by CLL cells may abrogate CAR T-cell function, in the presence of strong, positive activation signals such as stimulation with an aAPC or addition of high-dose IL2, CAR T-cell effector functions remain intact. This improvement is mediated by enhanced APC phenotypes as defined by increased expression of costimulatory and adhesive molecules. We show that this APC phenotype is maintained by LN-resident CLL cells and is lacking in the PB-derived CLL population. Furthermore, this phenotype directly impacts the activation state of T cells derived from these compartments. By stimulating CAR T cells with aCLL cells, we enhanced anti-CLL effector functions, including proliferation and cytotoxicity, both of which are predictive of better clinical response. These findings further highlight an apparent paradox—that 26% of patients with CLL will have a durable complete response to CAR T-cell therapy. Our data suggest that in this setting, CAR T cell–mediated remissions are largely driven by a LN response, initially eliminating the proliferative progenitor CLL cells and subsequently becoming highly activated, allowing for peripheral tumor clearance.
+
+Autologous CLL T cells are well documented to have impaired proliferative and effector responses, dysregulation of the CD4/CD8 immune balance, and a terminally differentiated phenotype (39, 40, 66–68). While most studies and clinical trials have used autologous T cells, the results are mixed, likely due to disease heterogeneity between patients. By using normal donor-derived CAR T cells, we are able to focus on tumor-mediated mechanisms without the confounding variable of autologous T-cell function. Furthermore, this work is directly applicable to allogeneic cell therapy, which is a rapidly growing field of study. Understanding how CLL tumor negatively impacts even healthy T-cell activation will further enable these allogeneic cell therapies.
+
+The heterogeneity of primary CLL tumors has made it challenging to engineer representative murine or cell-based models. While murine systems to study CLL do exist, they are complicated and time consuming to develop (69, 70). Another difficulty in studying primary CLL is that the cells do not grow autonomously in vitro without concomitant CD40 ligation and cytokine/CpG addition (58, 71–74) or Epstein-Barr Virus (EBV) transformation, such as with the OSU-CLL cell line (75). Our study addressed this gap by focusing on primary CLL cells as a biologically and translationally relevant tool to study this disease. Using our primary tumor cell bank, we consistently observed that CLL cells prevent CAR T-cell activation and that the poor stimulatory phenotype of CLL cells explains this impairment. The consistency between CLL donors and CAR19 and CAR-ROR1 cells validates our conclusions, suggesting a conserved activation defect despite sample heterogeneity. Further our finding that the LN compartment drives CAR T-cell responses highlights a need for the CLL field to better understand the TME in this disease.
+
+It is of note that, clinically, large LNs are cleared more slowly than the PB or bone marrow. This begs the question, if the LN is in fact the site of active T-cell killing is it also a sanctuary site for the tumor? Although we can model a LN-like CLL phenotype in vitro, we cannot model the entire associated TME. Thus, this in vitro system lacks cells that may provide support to the tumor or negatively regulate CAR T-cell function, via either secreted or contact-dependent mechanisms. It is logical to assume that the LN, traditionally a home for mature B cells, provides prosurvival signals to the tumor. Furthermore, being a tumor-supportive environment, it is likely that the in vivo LN has T-cell immune-suppressive qualities. More complete models of the tumor microenvironment both in vivo and in vitro are required to systematically address the relative contributions of cell types and secreted factors from this tissue.
+
+The data presented in our study bring to mind prior work wherein vaccination of patients with CD40L-expressing CLL cells enhanced tumor killing by endogenous T cells (76, 77). However, the effects of these treatments were time limited, and ceased to be effective once the CD40L-expressing tumor was no longer present. These data suggest that priming of CD4+ T cells by CD40 L likely drove this response, but once the activating signal was removed, T cells again became unresponsive. In our study, we preactivated CLL cells with CD40 L before removing the source of CD40L, such that the CAR T cells were impacted by the tumor rather than the CD40L-expressing cell line. However, the sensitization of CLL cells to T-cell killing in the prior studies is in accordance with our own findings, wherein CAR T cells better proliferate and kill CD40L-activated CLL cells (see Fig. 6C–E). Interestingly, we also see bystander activation of untransduced T cells as measured by cytotoxicity in response to the activated CLL stimulus. This supports the findings of the above studies, wherein not only are T cells activated allowing for cytotoxic function, but the activated CLL cells themselves serve as better targets for T-cell killing. This highlights the requirement of costimulatory signaling for activation of TCR-directed T cells. The CAR T-cell field then capitalized on such findings to create second-generation CARs that contain costimulatory domains, enabling killing of target cells even in the absence of costimulatory molecules on the cell surface—as is the case in CLL. The low costimulatory ligand expression on CLL tumor greatly limits native T-cell killing capacity, and, as our data indicate, can impact CAR T-cell function, since enhancing expression of costimulatory ligands rescues CAR T-cell responses.
+
+Surprisingly, this study identifies a need for additional costimulatory signaling even in the context of a second-generation CAR that already contains a 4-1BB costimulatory signaling domain. This is highlighted by the improved activation and killing observed with aCLL-stimulated CAR T cells as well as the improved activation phenotype of CAR T cells in a trans-costimulation setting. The introduction of costimulatory molecules on CAR T cells to allow for cross-stimulation of CAR T-cell products will both enhance CAR T-cell function and stimulate the endogenous immune system (78). This finding also calls into question whether CAR design has been fully optimized to provide adequate costimulatory signaling. Studies have shown that the addition of the costimulatory molecules CD80 or 4-1BBL to CAR19-BBζ or CAR19-28z T cells, respectively, enhanced CAR T-cell persistence and tumor clearance in a mouse model of ALL (79). The addition of 4-1BBL extended persistence of the 28z CAR compared with the 28z CAR alone, and also maintained higher numbers of CD8+ CAR T cells long term. Both the CD80-expressing and 4-1BBL–expressing CARs demonstrated marked reduction in exhaustion. These findings, while shown in an ALL model, demonstrate that improving the type and level of costimulation has positive impacts on second-generation CAR T-cell function, as measured by T-cell killing, persistence, and overall antitumor activity. This study confirms the importance of optimal costimulation, even in a setting where CAR T-cell function is “optimized” and yields impressive clinical responses. Extrapolating out to a CLL setting where responses to CAR T-cell therapy are lacking, our data support the investigation of adding costimulatory molecules to CAR lentiviral construct design as a method to improve response rates. CD40 L has also been added to CAR19 T cells and demonstrated improved antitumor efficacy via endogenous immune system activation in murine models of leukemia and/or lymphoma (78). In a CLL setting, this could also function as an indirect mechanism to enhance the native immune response and may activate CLL cells to upregulate costimulatory molecules in vivo. Combining cellular therapies with small molecules that enhance either CAR T-cell or CLL activation such as lenalidomide (40–42, 80–82) or CD40 agonists (78, 83) may improve therapeutic responses in CLL. Future investigation is needed to understand how we can optimize CAR design to improve costimulation as well as understand how combination therapies may improve responses in CLL.
+
+Supplementary Material
+
+Supplemental Figures 1-4, Tables 1-3 Supplemental Figure 1: An overview of study design choices. Supplemental Figure 2: IL-2 Supplementation Enhances the APC phenotype of CLL cells. Supplemental Figure 3: Exogenous Co-stimulation Improves Second-generation CAR T cell Activation. Supplemental Figure 4: aCLL Stimulation Shows Inconsistent Cytokine Production After a 6hr Incubation. Supplemental Table 1: Full Antibody List. Supplemental Table 2: CLL Donor List. Supplemental Table 3: Full list of adjusted P-values from Holm-Sidak multiple comparisons.
+
+Click here for additional data file.
+
+Acknowledgments
+
+We are grateful to the University of Pennsylvania Stem Cell and Xenograft Core for providing primary CLL samples, the Flow Core for their assistance in cell sorting, the Human Immunology Core for providing healthy donor PBMCs, and the Translational Correlative Studies Laboratory for their assistance with Luminex assays. We acknowledge Carl H. June for his part in scientific discussions related to this manuscript and Wei-Ting Hwang for discussions regarding statistical analyses. We acknowledge Armando van Bruggen for scientific discussions. Finally, we are thankful to the patients and their families without whose generous donations of samples to research this work would not be possible. Funding: This work was funded by the Parker Institute for Cancer Immunotherapy in support of M.A. Collins and J.J. Melenhorst. The Intramural Research Program of the NIH, NHLBI supported A. Wiestner and C. Sun. Degree Candidacy: M.A. Collins is a PhD candidate at the University of Pennsylvania. This work is submitted in partial fulfillment of the requirement for the PhD.
+
+Authors’ Disclosures
+
+M.A. Collins reports grants from Parker Institute for Cancer Immunotherapy during the conduct of the study. A.P. Kater reports grants and other from Abbvie, AstraZeneca, BMS; and other from Janssen and LAVA outside the submitted work; in addition, A.P. Kater has a patent to Janssen pending and a patent to LAVA pending. C. Sun reports grants from Genmab outside the submitted work. A. Wiestner reports grants from Pharmacyclics, Acerta, Merck, Nurix, Verastem, and Genmab outside the submitted work. J.J. Melenhorst reports grants from Parker Institute for Cancer Immunotherapy during the conduct of the study; personal fees from IASO Biotherapeutics, Poseida Therapeutics, and Kite Pharma outside the submitted work; in addition, J.J. Melenhorst has a patent to Methods for improving the efficacy and expansion of immune cells issued, a patent to Biomarkers predictive of therapeutic responsiveness to chimeric antigen: issued, a patent to Methods of making chimeric antigen receptor - expressing cells issued, a patent to Methods for improving the efficacy and expansion of chimeric antigen receptor: issued, a patent to CAR T-cell therapies with enhanced efficacy issued, and a patent to Biomarkers predictive of cytokine release syndrome issued. No disclosures were reported by the other authors.
+
+Authors’ Contributions
+
+M.A. Collins: Conceptualization, data curation, formal analysis, investigation, writing-original draft. I.-Y. Jung: Investigation, writing-review and editing. Z. Zhao: Investigation, writing-review and editing. K. Apodaca: Investigation, writing-review and editing. W. Kong: Investigation, methodology, writing-review and editing. S. Lundh: Investigation, writing-review and editing. J.A. Fraietta: Writing-review and editing. A.P. Kater: Conceptualization, resources, writing-review and editing. C. Sun: Conceptualization, resources, writing-review and editing. A. Wiestner: Conceptualization, resources, writing-review and editing; J.J. Melenhorst: Conceptualization, supervision, funding acquisition, methodology, writing-review and editing.
+
+Note: Supplementary data for this article are available at Cancer Research Communications Online (https://aacrjournals.org/cancerrescommun/).
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+

PMC10016846.txt

@@ -0,0 +1,83 @@
+
+==== Front
+Postgrad Med J
+Postgrad Med J
+pmj
+Postgraduate Medical Journal
+0032-5473
+1469-0756
+Oxford University Press
+
+32727887
+postgradmedj-2020-137901
+10.1136/postgradmedj-2020-137901
+Images
+AcademicSubjects/MED00160
+AcademicSubjects/MED00790
+AcademicSubjects/MED00530
+Heterogeneity of clinical and radiological findings of COVID-19
+https://orcid.org/0000-0002-3807-7287
+D’Arena Giovanni Hematology Service, S. Luca Hospital, Vallo Della Lucania (SA), Vallo Della Lucania, Italy
+
+Penna Augusto La Radiology, S. Luca Hospital, Vallo Della Lucania (SA), Vallo Della Lucania, Italy
+
+Crocamo Antonino Infectious Disease, S. Luca Hospital, Vallo Della Lucania, Italy
+
+Sguazzo Francesca Emergency, S. Luca Hospital, Vallo Della Lucania, Italy
+
+Viceconti Roberto Infectious Disease, S. Luca Hospital, Vallo Della Lucania, Italy
+
+Barlotti Vincenzo Infectious Disease, S. Luca Hospital, Vallo Della Lucania, Italy
+
+Gambardella Michele Infectious Disease, S. Luca Hospital, Vallo Della Lucania, Italy
+
+Correspondence to Giovanni D’Arena, Hematology Service, ‘S. Luca’ Hospital, ASL Salerno, Vallo Della Lucania, Italy; giovannidarena@libero.it
+4 2021
+29 7 2020
+29 7 2020
+97 1146 268269
+14 4 2020
+01 6 2020
+22 4 2020
+© Author(s) (or their employer(s)) 2021. No commercial re-use. See rights and permissions. Published by BMJ.
+2021
+https://creativecommons.org/licenses/by/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (https://creativecommons.org/licenses/by/4.0/), which permits non-commercial reuse, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
+==== Body
+pmcThe pandemic COVID-19 caused by the 2019 novel coronavirus called severe acute respiratory syndrome coronavirus-2 displays a very heterogeneous clinical behaviour. The majority of patients (>85%) are asymptomatic or have mild symptoms, while few others show a very aggressive and life-threatening disease. Imaging spectrum of COVID-19 is very heterogenous as well: from normal picture in patients with mild symptoms such as fever and dry cough (figures 1 and 2) to pneumonia with multiple patchy, peripheral, bilateral areas of ground-glass opacity (GGO) and consolidation as in more severe illness (figures 3–8). A quick evolution of the disease is also seen. Involvement of both lungs seems to be the main imaging feature (75–100% of cases)1  2 usually with GGOs (77–91%)1  3 and consolidations (55–69%)1  3 in peripheral regions. Pleural effusions may occur in a minority of cases (4.1% of cases vs 39% in non-COVID-19 viral pneumonia)1; lymphadenopathy is rare, and pulmonary nodules and cavitation are not described.1, 2, 3 These imaging characteristics must be taken into account because they may help clinicians to better diagnose COVID-19 especially in an early phase4 and differentiate it from other viral cases of pneumonia (central distribution of lesions was observed in 80% vs 57% of cases in non-COVID-19 viral pneumonia in one study)1 or bacterial infections (usually with lobar or segmental consolidation).5
+
+Figure 1 Thin slice (1 mm) lung CT of patients.
+
+Figure 2 Singular intralobular GGO of a 31-year-old male patient with fever and cough. GGO, ground-glass opacity.
+
+Figure 3 Bilateral patchy subpleural GGOs and small peripheral consolidation of a 56-year-old female with fever, cough and dyspnoea. GGO, ground-glass opacity.
+
+Figure 4 CT at presentation of a 62-year-old female with fever, cough and dyspnoea: perifissural GGOs in posterior segment of right upper lobe and small bilateral pleural effusion. GGO, ground-glass opacity.
+
+Figure 5 Follow-up at day 5: increased size and density of previous lesions, onset of new multiple bilateral GGOs and consolidations in both subpleural and central localisation, with interlobular septal thickening, and increased pleural effusion. GGO, ground-glass opacity
+
+Figure 6 Follow-up at day 19: partial regression of the lesions with residual smaller GGOs, irregular parenchymal bands and interlobular septal thickening; reduction of bilateral pleural effusion with residual pleural fissure thickening and/or distortion. GGO, ground-glass opacity.
+
+Figure 7 CT of a 38-year-old female with fever, cough, dyspnoea and anosmia at presentation: subpleural/peripheral GGOs. GGO, ground-glass opacity.
+
+Figure 8 CT at day 9: increasing size and density of previous lesions, onset of new irregular consolidations with parenchymal bands and architectural distortion.
+
+Footnotes
+
+Contributors: GDA, AC, FS, RV, VB, MG followed patients and wrote the paper. AL performed diagnostic imaging. All authors reviewed and approved the manuscript.
+
+Funding: The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.
+
+Competing interests: None declared.
+
+Patient consent for publication: Not required.
+
+Provenance and peer review: Not commissioned; internally peer reviewed.
+==== Refs
+REFERENCES
+
+1 Bai  HX, Hsieh  B, Xiong  Z, et al.  Performance of radiologists in differentiating COVID-19 from viral pneumonia on chest CT. Performance of radiologists in differentiating COVID-19 from viral pneumonia on chest CT. Radiology  2020.10.1148/radiol.2020200823
+2 Wang  D, Hu  B, Hu  C, et al.  Clinical characteristics of 138 hospitalized patients with 2019 novel coronavirus-infected pneumonia in Wuhan, China. JAMA  2020.10.1001/jama.2020.1585
+3 Song  F, Shi  N, Shan  F, et al.  Emerging 2019 novel coronavirus (2019-nCoV) pneumonia. Radiology  2020;295 :210–7.10.1148/radiol.2020200274 32027573
+4 Zu  ZY, Jiang  MD, Xu  PP, et al.  Coronavirus disease 2019 (COVID-19): a perspective from China. Radiology  2020;21 :200490.10.1148/radiol.2020200490
+5 Simpson  S, Kay  FU, Abbara  S, et al.  Radiological Society of North America expert consensus statement on reporting chest CT findings related to COVID-19. Endorsed by the Society of Thoracic Radiology, the American College of Radiology, and RSNA. J Thorac Imaging  2020;21 .10.1097/RTI.0000000000000524
+

PMC10017628.txt

@@ -0,0 +1,347 @@
+
+==== Front
+Eur Radiol
+Eur Radiol
+European Radiology
+0938-7994
+1432-1084
+Springer Berlin Heidelberg Berlin/Heidelberg
+
+36350390
+9225
+10.1007/s00330-022-09225-0
+Computed Tomography
+Myeloma bone disease imaging on a 1st-generation clinical photon-counting detector CT vs. 2nd-generation dual-source dual-energy CT
+http://orcid.org/0000-0002-3945-3686
+Winkelmann Moritz T. moritz.winkelmann@med.uni-tuebingen.de
+
+1
+Hagen Florian 1
+Le-Yannou Lucie 1
+Weiss Jakob 2
+Riffel Philipp 3
+Gutjahr Ralf 4
+Faby Sebastian 4
+Nikolaou Konstantin 1
+Horger Marius 1
+1 grid.411544.1 0000 0001 0196 8249 Department for Diagnostic and Interventional Radiology, University Hospital Tübingen, Hoppe-Seyler-Straße 3, 72076 Tübingen, Germany
+2 grid.7708.8 0000 0000 9428 7911 Department for Diagnostic and Interventional Radiology, University Hospital Freiburg, Freiburg, Germany
+3 grid.7700.0 0000 0001 2190 4373 Institute of Clinical Radiology and Nuclear Medicine, University Hospital Mannheim, Heidelberg University, Mannheim, Germany
+4 grid.5406.7 000000012178835X Siemens Healthcare GmbH, Forchheim, Germany
+9 11 2022
+9 11 2022
+2023
+33 4 24152425
+17 5 2022
+22 9 2022
+19 10 2022
+© The Author(s) 2022
+https://creativecommons.org/licenses/by/4.0/ Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
+Objective
+
+Subjective and objective image quality comparison of bone microstructure and disease-related abnormalities in multiple myeloma patients using a 1st-generation dual-source photon-counting detector CT(DS-PCD-CT) and a 2nd-generation dual-source dual-energy (energy-integrating detector) CT (DS-EID-CT).
+
+Methods
+
+Fifty multiple myeloma patients (mean age 67.7 ± 10.9 years,16 females) were prospectively enrolled. Unenhanced whole-body CTs were clinically indicated and performed on DS-EID-CT and DS-PCD-CT (median time difference: 12 months). DS-PCD-CT was performed in Quantumplus UHR mode and DS-EID-CT was performed using dual-energy mode. DS-PCD-CT kernel was set at Br64 with Quantum iterative reconstruction strength Q1; for DS-EID-CT a comparable I70f kernel with SAFIRE iterative reconstruction strength 1 was used. Two independent radiologists assessed image quality subjectively using a 5-point Likert scale considering delineation and sharpness of trabecular bone and lytic bone lesions in the spine and pelvic bones. Additionally, ImageJ was used for quantification of bony septa inside the cancellous bone and through or the edges of osteolysis.
+
+Results
+
+Overall quality as well as detectability and sharpness in the delineation of lytic bone lesions were superior for DS-PCD-CT compared with DS-EID-CT (p < 0.0001). The inter-reader agreement for subjective image quality readings showed excellent consistency(α = 94.2–98.8). CTDI and DLP mean values for DS-PCD-CT and DS-EID-CT were 1107.4 ± 247.6 mGy*cm and 8.2 ± 1.8 mGy vs. 1344.3 ± 204.6 mGy*cm and 10.1 ± 1.9 mGy. The quantitative metric for bone microstructure in the femoral head showed significantly better visualization of trabeculae in DS-PCD-CT compared with DS-EID-CT (p < 0.0001). Quantitative analyses of edge sharpness of osteolysis showed significant steeper edges for DS-PCD-CT (p < 0.0001).
+
+Conclusion
+
+DS-PCD-CT significantly improves spatial resolution of bony microstructure and lytic bone lesions compared to DS-EID-CT.
+
+Key Points
+
+• Application of photon-counting detector CT is superior to dual-source dual-energy integrating detector in clinical workup of multiple myeloma patients.
+
+• Compared to energy integrating detectors, photon-counting detectors significantly increase the spatial resolution of bone microstructure including disease-related lytic bone lesions in patients with multiple myeloma.
+
+Keywords
+
+Photon counting CT
+Dual source CT
+Multiple myeloma
+Spatial resolution
+Image quality
+Baden-Württemberg Ministry of Economic Affairs, Labor and Tourism issue-copyright-statement© European Society of Radiology 2023
+==== Body
+pmcIntroduction
+
+Multiple myeloma is a malignant hematologic disease of the mature B-cells primarily affecting the bone marrow. However, early in the disease course, medullary tumor cell expansion paired by complex paraneoplastic pathophysiologic mechanisms affecting bone metabolism aggresses also the bone (myeloma bone disease) leading to bone destruction in a diffuse or focal manner, mostly combined [1]. Affection of the skeleton has two major implications [2]. One is the tumor staging according to the Salmon and Durie classification from 1975 (bony lysis as a surrogate marker for marrow infiltration; staging depends on the number of lesions) which has immediate therapeutic implications whereas the other one is related to the risk of fracture and related neurologic complications [3]. For this purpose, an accurate skeletal evaluation is mandatory. Classically, this task was managed by plane radiographs, but in modern times CT has advanced to the method of first choice due to its superior spatial resolution and absence of image superimposition [4–6]. Nevertheless, even CT has its limitations which are in part methodically inherent (inferior bone marrow sensitivity, e.g., over MRI) and in part derived from protocol constrains (low dose, high pitch, low spatial resolution, etc.) [7, 8]. However, a more accurate and earlier detection of bone abnormalities would be desirable for decision making. As whole-body CT-surveillance implies a large field-of-view, in-plane resolution is thereby diminished whereas the use of comb filters to achieve high-resolution imaging implies decreased radiation dose efficiency [9].
+
+At this point, the advent of photon-counting CT-technology might represent a promising approach to overcome these limitations. The photon-counting detector measures each individual x-ray that passes through the patient’s body eliminating electronic noise and is, thus, presumed to deliver comparable image quality with that of previous generations of dual-source dual-energy integrating detector CT (DS-EID-CT) by significantly reduced radiation exposure or to increase image quality including spatial resolution by comparable dose exposure [10–14]. It has smaller detector elements which improve visualization of fine details (e.g., in the cancellous bone) reducing simultaneously the image noise. Other contributors to the superior image quality of DS-PCD-CT include improvements in noise reduction algorithms and iterative reconstruction techniques [15, 16]. Small detector element size may translate into improved visualization of fine detail and image noise reduction through anti-alising, particularly for high-spatial resolution images as with our protocol [17, 18].
+
+This study, to the best of our knowledge, is the first to address the potential superiority of PCDs over energy-integrating detectors (EID) for increased spatial resolution of bone microstructure including disease-related bony defects in the clinical workup of multiple myeloma patients.
+
+Material and methods
+
+Subjects
+
+This prospective data evaluation was approved by the institutional review board which was assigned the approval number 696/2021B01. Between October 2021 and February 2022, a total of 50 consecutive multiple myeloma patients who were referred to treatment monitoring purposes to our radiology department were enrolled. Patient consent was obtained in all cases.
+
+Inclusion criteria were as follows: all patients underwent DS-PCD-CT using a standardized scan protocol; all patients had prior DS-EID-CT-examination performed on the same 2nd-generation DECT using also a standardized unenhanced imaging protocol with similar adjustable protocol parameters to the DS-PCD-CT. Exclusion criteria were as follows: different examinational protocols either at the DS-PCD-CT or 2nd-generation DS-EID-CT (n = 2) or on other scanners (n = 3); different examinational protocols; use of I.V. contrast agent. Eleven patients were excluded from the study.
+
+For each patient, the levels of serum and urine M-protein were determined at the time of both CT-examinations for assessment of the degree of tumor activity. The normal values for hematologic parameters determined by the laboratory at our institution were IgG, 700–1600 mg/dL; IgA, 70–400 mg/dL; IgM, 40–230 mg/dL; serum light chains λ, 8.1–33.0 mg/L; and light chains κ, 3.6–15.9. Current myeloma-specific treatment at the time of both CT examinations was documented for each patient.
+
+DS-PCD-CT imaging protocol (parameters)
+
+All examinations were performed on a 1st-generation photon-counting CT (NAEOTOM Alpha, VA40 SP1, Siemens Healthineers). Acquisition mode was ultra-high-resolution (UHR) Quantumplus, meaning an ultra-high-resolution acquisition with 2752 detector pixels (0.151 × 0.176 mm at iso-center) and 120 × 0.2 mm collimation (24 mm z-axis coverage). The UHR morphologic image information was reconstructed as T3D images from the lowest threshold. All examinations were performed without use of contrast media. Following parameters were set: 120 kVp, tube current eff. mAs 99 (current modulation by CARE Dose4D), focal spot 0.6/0.7 mm, single collimation width 0.2 mm, total collimation width 24, table speed 40.8 cm/s, table feed per rotation 20.4, spiral pitch factor 0.8, matrix size 1024 × 1024, DS-PCD-CT kernel was set at Br64 with Quantum iterative reconstruction strength Q1. Slice thickness of MPRs (sagittal and coronal) was 2 mm; single collimation width was 0.2 mm with a total collimation width of 24 mm. FOV of multiplanar reconstructions (MPR) differed according to the patient’s body size: cervical spine (125 × 175 mm), thoracic spine (300 × 325 mm), lumbar spine (125 × 145 mm), pelvis 400 × 280 mm) (Table 1). Table 1 Acquisition parameters
+
+Variables	DS-EID-CT	DS-PCD-CT	
+Single collimation [mm]	0.6	0.2	
+Total collimation [mm]	19.1	24	
+Table speed [cm/s]	34.8	40.8	
+Spiral pitch factor	0.6	0.8	
+Slice thickness MPR [mm]	2	2	
+FOV axial [mm]	500 × 500	500 × 500	
+FOV MPR [mm]			
+  Cervical spine	205 × 143	125 × 175	
+  Thoracic spine	385 × 296	300 × 325	
+  Lumbar spine	330 × 164	125 × 145	
+  Pelvis	364 × 398	400 × 280	
+Kernel	I70f	Br64	
+Iterative reconstruction strength	1	Q1	
+Matrix size axial	512 × 512	1024 × 1024	
+Matrix size MPR	691 × 512	1024 × 1024	
+
+DS-EID-CT imaging protocol (parameters)
+
+DS-EID-CT was performed on a dual-source dual-energy scanner (SOMATOM Definition Flash, VA48A, Siemens Healthineers). The tube voltage was 100 kVp for the A tube and Sn140 kVp for the B tube. Corresponding tube current was 225 mAs (100 kVp) and 174 mAs (Sn140 kVp). Single collimation width was 0.6 mm, total collimation width was 19.1 mm, table speed 34.8 cm/s, spiral pitch factor of 0.6, kernel I70f with iterative reconstruction strength 1, and matrix 512 × 512 (axial images). Slice thickness of MPRs was 2 mm with a matrix of 691 × 512 (Table 1).
+
+Subjective image analysis
+
+Image analysis was performed on a dedicated workstation (syngo.via, VA30A; Siemens Healthineers) and focused on MPRs and not axial images, as the latter were acquired with different kernels and were, thus, not optimally comparable. Therefore, MPRs were used to mitigate this at least partially, although in one direction image impression is still dominated by the kernel. A senior radiologist with 25 years of experience and a junior radiologist with 5 years of experience assessed subjective overall image quality in a blinded and independent fashion using a 5-point Likert scale: 1, very poor; 2, poor; 3, moderate; 4, good; 5, very good. Subjective image quality in terms of detectability and sharpness of bone lesions was assessed using the Likert scale depending on lesion size: smaller than 5 mm, between 5 and 10 mm, and larger than 10 mm. All cases were anonymized, randomized, and evaluated with user-adjustable windowing in separate sessions to minimize recall bias.
+
+Quantitative image analysis
+
+The modulation-transfer-function (MTF) was collected for both kernels used, Br64u for DS-PCD-CT and I70 h for DS-EID-CT. The MTF is a widely used measure of resolution. The data are in line pairs per centimeter which can be translated into the corresponding object size.
+
+Quantification of bone microstructure (trabeculization)
+
+The morphometry of the bones was analyzed using ImageJ (version 1.53 k, US National Institutes of Health, Bethesda, MD, USA). ImageJ can be used to measure distances and angles to create line profiles [19, 20]. The scan slices were imported into ImageJ software, and a standardized straight line (standardized length: 3.0 ± 0.2 cm) was segmented at a previously defined position in the spongiosa of the femoral head in all patients included in the study (Fig. 1 a + b). A plot profile was created showing a two-dimensional representation of the intensities of the pixels along the drawn line in the femoral head. The x-axis shows the length of the line and the y-axis shows the CT values. The serrations in the line profile represent the trabeculae within the spongiosa of the femoral head (Fig. 1 c + d). To measure the number of trabeculae along the standardized line, the number of serrations was counted and recorded twice by the same observer. Fig. 1 Standardized line profile in the right femoral head in coronal MPRs of the pelvis in DS-EID-CT (a) and DS-PCD-CT (b) with corresponding graphics showing plot profile of bone trabeculization. The numbered serrations correspond to the trabeculae and were obtained and evaluated for each patient. Note also the much higher blurring effect of the trabeculae on DS-EID-CT with in part indistinction of singular lines
+
+Quantification of edge sharpness
+
+Edge sharpness between the intact cancellous bone and adjacent lytic bone lesions was quantified by CT value plots along a sampling line drawn manually in sagittal or coronal MPRs at defined positions using ImageJ software. Previous studies have shown that ImageJ can successfully measure the edge sharpness of bone surfaces on CT [21]. Osteolytic lesions were identified by visual inspection with identical window-setting (bone window), and subsequently a line profile was drawn from the bone marrow of the vertebral body or pelvic bone perpendicular through the complete extent of the lytic lesion until the reentry into the regular bone surface on the opposite side of the lesion (Fig. 2 a + b). A total of 250 osteolytic lesions smaller than 5 mm and larger than 5 mm were measured in all patients who were confirmed to have osteolysis on CT. The criterion was that the lesions were the same ones on DS-EID-CT and DS-PCD-CT and that there were no size dynamics or therapeutic alterations of the bone and bone marrow at interval. For this purpose, a side-by-side analysis of submillimeter axial sizes was used for confirmation and or exclusion of new osteolysis in our study. Fig. 2 The following formula was used to quantitatively calculate the slope at the margin of the osteolytic lesions, with an example of a plot profile with the corresponding variables
+
+The slope or gradient of the CT value plot at the entry and exit points of the osteolytic lesion was used as a measure of edge sharpness, which was defined as the difference of the CT values per millimeter [21] (Fig. 3). Fig. 3 Example of a manually drawn line to quantify the edge sharpness of an osteolytic lesion in the coronary MPR of the cervical spine in DS-EID-CT (a) and DS-PCD-CT (b) with graphs corresponding to the line profile and representing the slopes of the CT values (c + d). The decrease and increase of the CT values are highlighted in red/green. Note the higher steepness of the slopes at DS-PCD-CT
+
+Statistics
+
+The available data were analyzed using SPSS (SPSS Statistics, version 26, IBM Corp.) and JMP (version 14, SAS institute Inc.). Continuous variables are presented as the mean ± standard deviation, and relative frequencies are presented as n (%). Descriptive statistics of the data are presented as median (25th; 75th percentile or range). The Wilcoxon pairing test was used to calculate the differences in subjective image analysis. Intraclass correlation (ICC) was used to calculate the interrater agreement. ICC values ≤ 0.5 were defined as poor, those 0.51–0.75 were defined as moderate, those 0.76–0.90 were defined as good, and those > 0.90 were defined as excellent consistency. Normality of data was tested with the Shapiro-Wilk test. Data not normally distributed were analyzed with nonparametric test (Wilcoxon test). Differences of clinical status between different time points were calculated by McNemar’s chi-square test or Wilcoxon signed-rank test. A p value < 0.05 was defined as significant.
+
+Results
+
+Between October 2021 and February 2022, 50 myeloma patients (34 male, 16 females, mean age: 67.7 ± 10.9 years) were included. Hematologic diagnosis based on M gradient was IgG (n = 24), IgA (n = 9), light chain MM (n = 14), and non-secretory MM (n = 3) (Table 2). The median time interval between clinically indicated DS-EID-CT and DS-PCD-CT was 12 months (range: 4–81 months). The radiation exposure associated with DS-EID-CT resulted in a mean dose-length product (DLP) of 1344.3 ± 204.6 mGy*cm and mean volume mean volume CT dose index (CTDIvol) of 10.1 ± 1.9 mGy. Radiation exposure related to DS-PCD-CT had a mean DLP of 1107.4 ± 247.6 mGy*cm and CTDIvol of 8.2 ± 1.8 mGy. Elevated laboratory values of serum IgG, IgA, serum or urine light-chain λ, or light-chain κ alone or in combination at date of examination were identified in 17 patients (34%) prior to DS-EID-CT and in 16 patients (32%) prior to DS-PCD-CT (p = 1.0). Urine M-protein was increased in 9 patients (18%) before DS-EID-CT and in 6 patients before DS-PCD-CT (p = 0.453). All patients with active disease experienced minimal changes in the serum (p = 1.0) and urine levels of M-gradient (p = 0.453) at interval. Most patients were in complete remission or had no disease activity at the time of DS-EID-CT (n = 32) and DS-PCD-CT (n = 33), and non-parametric analyses revealed no significant difference in disease status (complete response, partial response, and disease progression) between the two time points (p = 0.308). All patients with lytic bone lesions received monthly bisphosphonates. At the time of DS-EID-CT, 21 patients (42%) and during DS-PCD-CT, 19 patients (38%) received immune-based therapy. Three patients (6%) were respectively undergoing intensive cytotoxic chemotherapy at the time of DS-EID-CT and DS-PCD-CT. There was no significant difference in the distribution of the different types of treatment (p = 0.608) and CT imaging features (p = 1.0) between the two study time points. Clinical parameters on both examination days are summarized in Table 3. Table 2 Patients’ demographics
+
+Variables	N	%	
+No. of patients	50		
+Age, years, mean ± SD	67.7 ± 10.9 years		
+Gender	
+  Male	34	68	
+  Female	16	32	
+Serological type	
+  IgG	24	48	
+  IgA	9	18	
+  Light-chain multiple myeloma	14	28	
+  Non-secretory multiple myeloma	3	6	
+
+Table 3 Clinical parameters on both examination dates
+
+Variables	DS-EID-CT	DS-PCD-CT	p-value	
+n	%	n	%		
+Elevated serum IgG, IgA, serum or urine light-chain λ or light-chain κ alone or in combination	17	34	16	32	1.0	
+Elevated urine M-protein	9	18	6	12	0.453	
+Disease status					0.308	
+  Complete response/no disease activity	32	64	33	66		
+  Partial response	9	18	12	24		
+  Progressive disease	9	18	5	10		
+Treatment					0.608	
+  Biphosphonate	41	82	42	84		
+  Immune-based therapy	21	42	19	38		
+  Intensive cytotoxic chemotherapy	3	6	3	6		
+CT imaging features					1.0	
+  Infiltration of marrow space	7	14	5	10		
+  Normal bone marrow	9	18	8	16		
+  Lytic lesions	41	82	42	84		
+    Number of lesions: 1–3	6	12	7	14		
+    More than 3 lytic lesions and/or pathologic fracture	35	70	35	70		
+
+Subjective image analysis
+
+Likert score ratings for overall image quality were significantly superior for both readers for DS-PCD-CT compared with measurements for DS-EID-CT (all p < 0.0001).
+
+Likert scores for detectability and sharpness of bone lesion edges were significantly higher with DS-PCD-CT compared to DS-EID-CT (all p < 0.0001). Detectability and sharpness of lesions were independent of lesion size (p = 0.1–1.1) (Table 4). Table 4 Overview of data from qualitative image analysis
+
+Parameter	Pelvic bone		Cervical spine		Thoracic spine		Lumbar spine		
+DS-EID-CT	DS-PCD-CT	p-value	DS-EID-CT	DS-PCD-CT	p-value	DS-EID-CT	DS-PCD-CT	p-value	DS-EID-CT	DS-PCD-CT	p-value	
+Overall image quality	
+  Reader 1	4 (3–4)	5 (5–5)	< 0.0001	3 (2–3)	4 (4–5)	< 0.0001	4 (3–4)	5 (4–5)	< 0.0001	4 (3–4)	5 (4–5)	< 0.0001	
+  Reader 2	4 (3–4)	5 (5–5)	< 0.0001	3 (2–3)	4 (4–5)	< 0.0001	4 (3–4)	5 (4–5)	< 0.0001	4 (3–4)	5 (4–5)	< 0.0001	
+Detectability of bone lesions	
+  Reader 1													
+    Lesion size													
+      < 5 mm	4 (4–4)	5 (5–5)	< 0.0001	3 (3–4)	4 (4–5)	< 0.0001	4 (3–4)	4 (4–5)	< 0.0001	4 (3–4)	5 (4–5)	< 0.0001	
+      5–10 mm	4 (3–4)	5 (5–5)	< 0.0001	3 (3–4)	4 (4–5)	< 0.0001	4 (3–4)	5 (4–5)	< 0.0001	4 (3–4)	5 (4–5)	< 0.0001	
+      > 10 mm	4 (3–4)	5 (5–5)	< 0.0001	3 (3–3)	4 (4–5)	< 0.0001	3 (3–4)	5 (5–5)	< 0.0001	4 (3–4)	5 (5–5)	< 0.0001	
+  Reader 2													
+    Lesion size													
+      < 5 mm	4 (4–4)	5 (5–5)	< 0.0001	3 (3–3)	4 (4–5)	< 0.0001	3 (3–4)	5 (4–5)	< 0.0001	4 (3–4)	5 (4–5)	< 0.0001	
+      5–10 mm	4 (3–4)	5 (5–5)	< 0.0001	3 (3–3)	4 (4–5)	< 0.0001	4 (3–4)	5 (4–5)	< 0.0001	4 (3–4)	5 (4–5)	< 0.0001	
+      > 10 mm	4 (3–4)	5 (5–5)	< 0.0001	3 (2–3)	4 (4–5)	< 0.0001	4 (3–4)	5 (5–5)	< 0.0001	4 (4–4)	5 (5–5)	< 0.0001	
+Delineation (sharpness) of bone lesions	
+  Reader 1													
+    Lesion size													
+      < 5 mm	4 (3–4)	5 (5–5)	< 0.0001	3 (2–3)	4 (4–5)	< 0.0001	3 (3–3)	5 (5–5)	< 0.0001	3 (3–4)	5 (5–5)	< 0.0001	
+      5–10 mm	4 (3–4)	5 (4–5)	< 0.0001	3 (3–3)	4 (4–5)	< 0.0001	3 (3–4)	5 (5–5)	< 0.0001	3 (3–4)	5 (5–5)	< 0.0001	
+      > 10 mm	4 (3–4)	5 (5–5)	< 0.0001	3 (2–3)	5 (4–5)	< 0.0001	3 (3–4)	5 (5–5)	< 0.0001	3 (3–4)	5 (5–5)	< 0.0001	
+  Reader 2													
+    Lesion size													
+      < 5 mm	3 (3–4)	5 (5–5)	< 0.0001	3 (2–3)	4 (4–5)	< 0.0001	3 (3–4)	5 (5–5)	< 0.0001	3 (3–4)	5 (5–5)	< 0.0001	
+      5–10 mm	3 (3–4)	5 (4–5)	< 0.0001	3 (2–3)	4 (4–5)	< 0.0001	3 (3–4)	5 (5–5)	< 0.0001	3 (3–4)	5 (5–5)	< 0.0001	
+      > 10 mm	3 (3–4)	5 (4–5)	< 0.0001	3 (2–3)	5 (4–5)	< 0.0001	3 (3–4)	5 (5–5)	< 0.0001	3 (3–4)	5 (5–5)	< 0.0001	
+Data are presented as median qualitative image analysis score; data in parentheses are interquartile ranges. DS-EID-CT, dual-source dual-energy integrating detector CT; DS-PCD-CT, dual-source photon-counting detector CT
+
+Interreader agreement showed excellent consistency between readers 1 and 2 for the metrics’ overall image quality, detectability of lytic lesions, and sharpness of lytic lesions (α values ranged from 94.2 to 98.8).
+
+Quantitative image analysis
+
+MTF values for the reconstruction kernels used as a direct technical measurement of spatial resolution are outlined in Table 5. Based on the 2% MTF, this results in the minimum detectable object size of 0.36 mm for the Br64u kernel and 0.41 mm for the I70 h kernel. Table 5 Modulation-transfer function values for the employed reconstruction kernels
+
+MTF in Ip/cm	Br64u	I70h	
+50%	10.1	9.2	
+10%	12.7	11.3	
+2%	14.0	12.3	
+MTF, modulation-transfer-function; Ip/cm, line pairs per centimeter
+
+Quantification of trabeculization
+
+Quantification of trabeculization of bone in the femoral head revealed that serrations were significantly more frequent in DS-PCD-CT MPRs than in DS-EID-CT MPRs (16.6 ± 2.9 vs. 11.8 ± 2.4, p < 0.0001) (Fig. 4). Depending on the length of the line profile, 5.5 ± 0.9 serrations per cm were detected in DS-PCD-CT and 3.9 ± 0.7 serrations per cm in DS-EID-CT (p < 0.0001). Fig. 4 The number of serrations on plot profiles of DS-EID-CT and DS-PCD-CT
+
+Quantification of edge sharpness
+
+Quantitative analyses of edge sharpness of osteolysis showed significant differences between DS-EID-CT and DS-PCD-CT with consistently steeper edges for DS-PCD-CT (all p's < 0.001). For lesions smaller than 5 mm, DS-PCD-CT measured steeper values compared to DS-EID-CT both for decrease (median [interquartile range]: −393 [−537.5, −243.0] vs. −225 [−335.5, −149.0]) and increase (388 [300.5–548.5] vs. 234 [115.8–334.0]) in slope. For osteolytic lesions larger than 5 mm, DS-PCD-CT also showed steeper values in comparison with DS-EID-CT for decrease (−378 [−545.5, −288.0] vs. −253 [−391, −163) and increase (379 [−266.0, −526.0] vs. 258 [163.0–377.0]) in slope (Fig. 5). Fig. 5 Boxplots show median and quantiles of edge slope decrease and increase for osteolytic lesions smaller and larger than 5 mm
+
+Discussion
+
+In this study, an ultra-high-resolution scan mode on a DS-PCD-CT scanner with a detector pixel size of 0.2 mm at the isocenter was compared with a DS-EID-CT with a detector pixel size of 0.2 × 0.2 mm2 for whole-body skeletal imaging in patients with multiple myeloma focusing on the trabecular microstructure of the cancellous bone and on the disease-related lytic bone lesions. The scan parameters were set as far as possible at similar levels for both scanners in order to achieve similar radiation doses which finally did not differ significantly from each other although they proved slightly lower (10–15%) for the DS-PCD-CT. As on DS-EID-CT only the sagittal and coronal MPRs were reconstructed using a sharp kernel, we decided to focus our image analysis for both scanners on these reformates due to the better comparability of these image kernels. Most other adjustable parameters were also similar.
+
+The results of our study show significant qualitative and quantitative image quality improvement on the DS-PCD-CT compared to the DS-EID-CT. Hence, the delineation of bony trabeculae as well as the sharpness of their edges and the transitions between the cancellous bone and focal lytic bone lesions was more easily and reliably accomplished on DS-PCD-CT. Notably, this trend stayed unimpaired even in bone lesions smaller than 5 mm. Despite the use of a sharp kernel and submillimeter single slice collimation, depiction of densely packed trabeculae in the femoral heads exhibited a blurred, unsharp appearance on the DS-EID-CT. Knowingly, the trabecular density varies significantly between subjects being dependent on many factors like age, gender, bone metabolism, medication, physical activity, etc. Therefore, only an intra-individual comparison is allowed in such cases which we have accomplished in this study showing an interreader agreement of between 94.2 and 98.8%.
+
+For more objective data analysis, we additionally compared the delineation (sharpness) of the contours of lytic bone lesions by the maximal slope within CT value plots and found a significantly steeper slope with DS-PCD-CT as with DS-EID-CT. Here again, quantitation of the number of serrations (trabecular structures) was significantly higher for DS-PCD-CT compared to DS-EID-CT and these results were proven once more independent on the lesion size. This latter aspect may lead in the clinical routine while comparing CT-images from different scanners to the false visual impression of detecting more bone lesions on the DS-PCD-CT because they become more conspicuous. A side-by-side analysis of submillimeter axial sizes was used for confirmation and or exclusion of new osteolysis in our study.
+
+Improvement of spatial resolution has been always in the focus of radiological imaging. Different approaches have been proposed and tested in the clinical routine; however, the main limitations with the conventional DS-EID-CT are the radiation dose and the size of the detector elements. The photon-counting detectors have opened new possibilities for improved spatial resolution without loss of dose efficiency. Conducting a study on a DS-PCD-CT using two high-resolution scan modes (sharp and UHR), Leng et al reported a 69% and 87% improvement in in-plane resolution over an EID-scanner for a detector pixel size of 0.25 mm at the isocenter [12]. The use of filters consisting of many septa decreases the size of detector elements with EIDs, but at the same time it increases dose as the blocked photons have already contributed to patient dose, but not to the reconstructed image. On the contrary, no septa are present in DS-PCD-CT and therefore reduced efficiency is not expected. The superiority of the DS-PCD-CT scanner for decreasing image noise could be already demonstrated in a cadaveric study by Zhou et al, focusing on temporal bone imaging [22]. These authors indicated the potential of a 64% reduction in dose using a DS-PCD-CT UHR mode for clinical bone imaging. Besides first attempts to improve spatial resolution with aid of PCD-technology for smaller structures (small FOV), Rajendran et al reported their initial experience with a DS-PCD-CT using a full FOV in UHR mode (0.2 mm resolution) [23]. Their study reported a 37% lower radiation dose and 46% lower image noise compared with EID-CT. The authors thus demonstrated that larger body parts can be imaged in the UHR mode. Similarly, but in a phantom study, Yu et al investigated and confirmed the clinical feasibility of a whole-body DS-PCD-CT-scanner [24]. Using the same quantification software, Bette et al, in an animal study, demonstrated the subjective and objective image quality superiority of DS-PCD-CT over DS-EID-CT for delineation of tiniest bone details [13]. Thus, PCD improves image quality and also spatial resolution because of the more efficient conversion of x-rays into light and by minimizing electron noise [25]. Also improvements with the iterative reconstruction techniques contribute to the observed improvements in image quality [15]. In line with these previous reports, our study shows that a significant improvement in spatial resolution is possible with the new DS-PCD-CT by comparable or lower radiation dose as with DS-EID-CT even for whole-body studies with full FOV.
+
+Our study has some limitations. First, the number of patients was too small in order to allow for a more sophisticated subgroup analysis of patients with different degrees of disease activity affecting the contrast between the infiltrated bone marrow in the background and the focal osteolysis. Second, we did not have additional information on the underlying bone density of our patients which would be expected to also impact the contrast between bone marrow and trabeculae of the cancellous bone as well as their thicknesses. Third, slight differences with respect to the used kernels as well as the matrix could have had a certain impact on image comparability, but at least the former is no more available for the DS-PCD-CT. We used MPRs to mitigate the differences in the kernels, but in either the x- or y-direction, the image impression is still dominated by the kernel.
+
+To the best of our knowledge, this study is the first to confirm the superiority of DS-PCD-CT over DS-EID-CT for assessment of bone microstructure in humans, in particular in the clinical setting of myeloma bone disease.
+
+In conclusion, DS-PCD-CT significantly improves spatial resolution of bony microstructure and disease-related (lytic bone lesions) compared to 2nd-generation DS-EID-CT.
+
+Abbreviations
+
+CT Computed tomography
+
+CTDI Computed Tomography Dose Index
+
+DLP Dose length product
+
+DS-EID-CT Dual-source dual-energy integrating detector CT
+
+DS-PCD-CT Dual-source photon-counting detector CT
+
+EID Energy-integrating detectors
+
+EID-CT Energy-integrating detector CT
+
+FOV Field of view
+
+ICC Intraclass correlation
+
+IgA Immunoglobulin A
+
+IgG Immunoglobulin G
+
+MPR Multiplanar reconstruction
+
+MRI Magnetic resonance imaging
+
+MTF Modulation transfer function
+
+PCD Photon-counting detector
+
+UHR Ultra-high-resolution
+
+Acknowledgements
+
+Thanks to all co-authors for their contributions to this manuscript.
+
+Funding
+
+Open Access funding enabled and organized by Projekt DEAL. This project is funded by the Baden-Württemberg Ministry of Economic Affairs, Labor and Tourism as part of the “Forum Gesundheitsstandort Baden-Württemberg”.
+
+Declarations
+
+Guarantor
+
+The scientific guarantor of this publication is Dr. Moritz Winkelmann.
+
+Conflict of interest
+
+Marius Horger received institutional research funds and speaker’s honorarium from Siemens Healthineers and is a scientific advisor of Siemens Healthcare Germany. Konstantin Nikolaou received institutional research funds and speaker’s honorarium from Siemens Healthineers and is a scientific advisor of Siemens Healthcare Germany. Ralf Gutjahr and Sebastian Faby are employees of Siemens Healthcare GmBH. No financial support or provision of equipment had been given by Siemens Healthcare GmbH. All other authors declare that they have no conflict of interest.
+
+Statistics and biometry
+
+Dr. Moritz Winkelmann has significant statistical expertise.
+
+Informed consent
+
+Written informed consent was obtained from all patients in this study.
+
+Ethical approval
+
+Institutional Review Board approval was obtained.
+
+Methodology
+
+• prospective
+
+• diagnostic or prognostic study
+
+• performed at one institution
+
+Publisher’s note
+
+Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
+==== Refs
+References
+
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+3. Burks JD Elarjani T Jamshidi AM Govindarajan V Levi AD Vertebral multiple myeloma with pathological fracture: the most common etiology for emergency spine surgery in patients with no cancer diagnosis on admission Neurosurg Focus 2021 50 E2 10.3171/2021.2.FOCUS201038 33932927
+4. Durie BG Salmon SE A clinical staging system for multiple myeloma correlation of measured myeloma cell mass with presenting clinical features, response to treatment, and survival Cancer 1975 36 842 854 10.1002/1097-0142(197509)36:3<842::AID-CNCR2820360303>3.0.CO;2-U 1182674
+5. Pierro A Posa A Astore C Whole-body low-dose multidetector-row CT in multiple myeloma: guidance in performing, observing, and interpreting the imaging findings Life 2021 11 1320 10.3390/life11121320 34947851
+6. Chantry A Kazmi M Barrington S Guidelines for the use of imaging in the management of patients with myeloma Br J Haematol 2017 178 380 393 10.1111/bjh.14827 28677897
+7. Horger M Claussen CD Bross-Bach U Whole-body low-dose multidetector row-CT in the diagnosis of multiple myeloma: an alternative to conventional radiography Eur J Radiol 2005 54 289 297 10.1016/j.ejrad.2004.04.015 15837412
+8. Lambert L Ourednicek P Meckova Z Gavelli G Straub J Spicka I Whole-body low-dose computed tomography in multiple myeloma staging: superior diagnostic performance in the detection of bone lesions, vertebral compression fractures, rib fractures and extraskeletal findings compared to radiography with similar radiation exposure Oncol Lett 2017 13 2490 2494 10.3892/ol.2017.5723 28454425
+9. Flohr TG Stierstorfer K Süss C Schmidt B Primak AN McCollough CH Novel ultrahigh resolution data acquisition and image reconstruction for multi-detector row CT Med Phys 2007 34 1712 1723 10.1118/1.2722872 17555253
+10. Ippolito D, Giandola T, Maino C et al (2021) Whole body low dose computed tomography (WBLDCT) can be comparable to whole-body magnetic resonance imaging (WBMRI) in the assessment of multiple myeloma. Diagnostics 11(5):857
+11. Tore D Rampado O Guarnaccia C Ultra-low-dose whole-body computed tomography protocol optimization for patients with plasma cell disorders: diagnostic accuracy and effective dose analysis from a reference center Front Oncol 2021 11 769295 10.3389/fonc.2021.769295 34869000
+12. Leng S Rajendran K Gong H 150-μm spatial resolution using photon-counting detector computed tomography technology: technical performance and first patient images Invest Radiol 2018 53 655 662 10.1097/RLI.0000000000000488 29847412
+13. Pourmorteza A Symons R Henning A Ulzheimer S Bluemke DA Dose efficiency of quarter-millimeter photon-counting computed tomography: first-in-human results Invest Radiol 2018 53 365 372 10.1097/RLI.0000000000000463 29595753
+14. Rajendran K Voss BA Zhou W Dose reduction for sinus and temporal bone imaging using photon-counting detector CT with an additional tin filter Invest Radiol 2020 55 91 10.1097/RLI.0000000000000614 31770297
+15. Sartoretti T Landsmann A Nakhostin D Quantum iterative reconstruction for abdominal photon-counting detector CT improves image quality Radiology 2022 303 339 348 10.1148/radiol.211931 35103540
+16. Klein L Dorn S Amato C Effects of detector sampling on noise reduction in clinical photon-counting whole-body computed tomography Invest Radiol 2020 55 111 119 10.1097/RLI.0000000000000616 31770298
+17. Baek J Pineda AR Pelc NJ To bin or not to bin? The effect of CT system limiting resolution on noise and detectability Phys Med Biol 2013 58 1433 1446 10.1088/0031-9155/58/5/1433 23399724
+18. Pourmorteza A SR, Schöck F et al (2017) Image quality assessment and dose-efficiency of quarter-millimeter photon-counting CT of humans: first in vivo experience [abstr] In: Radiological Society of North America scientific assembly and annual meeting program. Oak Brook, Ill: Radiological Society of North America, 2017; 101
+19. Seleţchi ED, Şutac V (2006) Image analysis in x-ray computed tomography
+20. Doube M Kłosowski MM Arganda-Carreras I BoneJ: free and extensible bone image analysis in ImageJ Bone 2010 47 1076 1079 10.1016/j.bone.2010.08.023 20817052
+21. Bette SJ, Braun FM, Haerting M et al (2021) Visualization of bone details in a novel photon-counting dual-source CT scanner—comparison with energy-integrating CT. Eur Radiol. 10.1007/s00330-021-08441-4
+22. Zhou W Lane JI Carlson ML Comparison of a photon-counting-detector CT with an energy-integrating-detector CT for temporal bone imaging: a cadaveric study AJNR Am J Neuroradiol 2018 39 1733 1738 10.3174/ajnr.A5768 30093479
+23. Rajendran K Petersilka M Henning A Full field-of-view, high-resolution, photon-counting detector CT: technical assessment and initial patient experience Phys Med Biol 2021 66 205019 10.1088/1361-6560/ac155e
+24. Yu Z Leng S Jorgensen SM Evaluation of conventional imaging performance in a research whole-body CT system with a photon-counting detector array Phys Med Biol 2016 61 1572 10.1088/0031-9155/61/4/1572 26835839
+25. Grunz J-P Huflage H Heidenreich JF Image quality assessment for clinical cadmium telluride-based photon-counting computed tomography detector in cadaveric wrist imaging Invest Radiol 2021 56 785 790 10.1097/RLI.0000000000000789 33882030
+

PMC10022857.txt

@@ -0,0 +1,548 @@
+
+==== Front
+J Clin Oncol
+J Clin Oncol
+jco
+JCO
+Journal of Clinical Oncology
+0732-183X
+1527-7755
+Wolters Kluwer Health
+
+36413710
+JCO.21.02734
+10.1200/JCO.21.02734
+00014
+3
+ORIGINAL REPORTS
+Hematologic Malignancy
+Overall Survival With Daratumumab, Bortezomib, and Dexamethasone in Previously Treated Multiple Myeloma (CASTOR): A Randomized, Open-Label, Phase III Trial
+https://orcid.org/0000-0002-0808-2237
+Sonneveld Pieter MD, PhD 1
+Chanan-Khan Asher MD 2
+Weisel Katja MD 3
+https://orcid.org/0000-0003-4165-6869
+Nooka Ajay K. MD 4
+https://orcid.org/0000-0003-2322-9863
+Masszi Tamas MD 5
+https://orcid.org/0000-0003-1797-8657
+Beksac Meral MD 6
+Spicka Ivan MD, PhD 7
+https://orcid.org/0000-0002-4327-1957
+Hungria Vania MD 8
+https://orcid.org/0000-0003-4947-8796
+Munder Markus MD 9
+https://orcid.org/0000-0003-2390-1218
+Mateos Maria-Victoria MD, PhD 10
+https://orcid.org/0000-0001-6996-0497
+Mark Tomer M. MD 11
+https://orcid.org/0000-0003-2139-3547
+Levin Mark-David MD, PhD 12
+Ahmadi Tahamtan MD 13
+Qin Xiang MS 14
+Garvin Mayo Wendy MSN 15
+https://orcid.org/0000-0003-4774-0607
+Gai Xue MMeD 16
+Carey Jodi BSN 14
+Carson Robin MD 14
+https://orcid.org/0000-0002-0531-7424
+Spencer Andrew MD 17
+1 Erasmus MC Cancer Institute, Rotterdam, the Netherlands
+2 Mayo Clinic Florida, Jacksonville, FL
+3 Department of Oncology, Hematology and Bone Marrow Transplantation with Section of Pneumology, University Medical Center of Hamburg-Eppendorf, Hamburg, Germany
+4 Winship Cancer Institute, Emory University, Atlanta, GA
+5 Department of Internal Medicine and Haematology, Semmelweis University, Budapest, Hungary
+6 Ankara University, Ankara, Turkey
+7 1st Department of Medicine – Department of Hematology, First Faculty of Medicine, Charles University and General Hospital in Prague, Prague, Czech Republic
+8 Clínica Médica São Germano, São Paulo, Brazil
+9 Third Department of Medicine, University Medical Center of the Johannes Gutenberg University, Mainz, Germany
+10 University Hospital of Salamanca/IBSAL/Cancer Research Center-IBMCC (USAL-CSIC), Salamanca, Spain
+11 Department of Medicine, University of Colorado, Aurora, CO
+12 Albert Schweitzer Hospital, Dordrecht, the Netherlands
+13 Genmab US Inc, Plainsboro, NJ
+14 Janssen Research & Development, LLC, Spring House, PA
+15 Janssen Research & Development, LLC, Raritan, NJ
+16 Janssen Research & Development, LLC, Beijing, China
+17 Malignant Haematology and Stem Cell Transplantation Service, Alfred Health-Monash University, Melbourne, Australia
+Pieter Sonneveld, MD, PhD, Erasmus MC Cancer Institute, PO Box 2040, 3000 CA Rotterdam, the Netherlands; e-mail: p.sonneveld@erasmusmc.nl.
+10 3 2023
+22 11 2022
+22 11 2022
+41 8 16001609
+23 11 2021
+4 5 2022
+27 9 2022
+© 2022 by American Society of Clinical Oncology
+2022
+American Society of Clinical Oncology
+https://creativecommons.org/licenses/by-nc-nd/4.0/ Creative Commons Attribution Non-Commercial No Derivatives 4.0 License: http://creativecommons.org/licenses/by-nc-nd/4.0/
+
+PURPOSE
+
+At the primary analysis of CASTOR (median follow-up, 7.4 months), daratumumab plus bortezomib and dexamethasone (D-Vd) significantly prolonged progression-free survival versus bortezomib and dexamethasone (Vd) alone in relapsed or refractory multiple myeloma (RRMM). We report updated efficacy and safety results at the final analysis for overall survival (OS).
+
+METHODS
+
+CASTOR was a multicenter, randomized, open-label, phase III study during which eligible patients with ≥ 1 line of prior therapy were randomly assigned to Vd (up to eight cycles) with or without daratumumab (until disease progression). After positive primary analysis and protocol amendment, patients receiving Vd were offered daratumumab monotherapy after disease progression.
+
+RESULTS
+
+At a median (range) follow-up of 72.6 months (0.0-79.8), significant OS benefit was observed with D-Vd (hazard ratio, 0.74; 95% CI, 0.59 to 0.92; P = .0075). Median OS was 49.6 months with D-Vd versus 38.5 months with Vd. Prespecified subgroup analyses demonstrated an OS advantage with D-Vd versus Vd for most subgroups, including patients age ≥ 65 years and patients with one or two prior lines of therapy, International Staging System stage III disease, high-risk cytogenetic abnormalities, and prior bortezomib treatment. The most common (≥ 10%) grade 3/4 treatment-emergent adverse events with D-Vd versus Vd were thrombocytopenia (46.1% v 32.9%), anemia (16.0% v 16.0%), neutropenia (13.6% v 4.6%), lymphopenia (10.3% v 2.5%), and pneumonia (10.7% v 10.1%).
+
+CONCLUSION
+
+D-Vd significantly prolonged OS in patients with RRMM, with the greatest OS benefit observed in patients with one prior line of therapy. To our knowledge, our results, together with the OS benefit observed with daratumumab plus lenalidomide and dexamethasone in the phase III POLLUX study, demonstrate for the first time an OS benefit with daratumumab-containing regimens in RRMM (ClinicalTrials.gov identifier: NCT02136134 [CASTOR]).
+
+OPEN-ACCESSTRUE
+==== Body
+pmcINTRODUCTION
+
+Daratumumab is a human IgGκ monoclonal antibody targeting CD38 with a direct on-tumor1-4 and immunomodulatory5-7 mechanism of action, demonstrating greater cytotoxicity toward multiple myeloma (MM) cells ex vivo compared with analogs of other CD38 antibodies.8 Daratumumab induces higher levels of complement-dependent cytotoxicity, similar levels of antibody-dependent cell-mediated cytotoxicity and antibody-dependent cellular phagocytosis, and, in the presence of Fc receptor crosslinking, which occurs physiologically in vivo, daratumumab elicits similar levels of cell death.8 In phase III studies in newly diagnosed MM (NDMM) and relapsed or refractory MM (RRMM), the addition of daratumumab to standard-of-care regimens significantly reduced the risk of disease progression or death and achieved deep and durable responses, including significantly higher complete response or better (≥ CR) rates and minimal residual disease (MRD)-negativity rates, versus standard of care alone.9-14 On the basis of these results, daratumumab is approved in combination with standard-of-care regimens for patients with RRMM or NDMM.15,16
+
+CONTEXT
+
+Key Objective
+
+To report updated efficacy and safety results, including overall survival (OS), from the phase III CASTOR study of daratumumab, bortezomib, and dexamethasone (D-Vd) in patients with relapsed or refractory multiple myeloma after approximately 6 years of follow-up.
+
+Knowledge Generated
+
+At a median follow-up of 72.6 months, D-Vd significantly prolonged OS, with a 26% reduction in the risk of death versus bortezomib and dexamethasone alone (median, 49.6 v 38.5 months, respectively; hazard ratio, 0.74; 95% CI, 0.59 to 0.92; P = .0075). Prespecified subgroup analyses showed OS improvement with D-Vd versus bortezomib and dexamethasone across most patient subgroups, with the greatest benefit observed in patients with one prior line of therapy.
+
+Relevance
+
+To our knowledge, these results, together with the OS benefit observed with daratumumab plus lenalidomide and dexamethasone in the phase III POLLUX study, demonstrate for the first time an OS benefit with daratumumab-containing regimens in relapsed or refractory multiple myeloma, with the greatest benefit observed in patients with one prior line of therapy.
+
+At the primary analysis of the phase III CASTOR study (median follow-up, 7.4 months), daratumumab plus bortezomib and dexamethasone (D-Vd) significantly prolonged progression-free survival (PFS; hazard ratio [HR], 0.39; 95% CI, 0.28 to 0.53; P < .001) and induced higher rates of deeper responses than bortezomib and dexamethasone (Vd) alone in patients with RRMM.17 After longer follow-up (median, 19.4 months), D-Vd reduced the risk of disease progression or death by 69% (median PFS, 16.7 v 7.1 months; HR, 0.31; 95% CI, 0.24 to 0.39; P < .0001) and responses deepened, resulting in higher ≥ CR rates (28.8% v 9.8%; P < .0001) and MRD-negativity rates (10−5 sensitivity; 11.6% v 2.4%; P = .000034) versus Vd.18 In the most recent analysis of the study at a median follow-up of 50.2 months, D-Vd continued to demonstrate significant efficacy benefits versus Vd alone, inducing deep and more durable responses and improved MRD-negativity rates.19 Efficacy benefits were most pronounced in patients who received one prior line of therapy, regardless of prior treatment with lenalidomide.
+
+We report updated efficacy and safety results at the time of the final overall survival (OS) analysis of CASTOR after approximately 6 years of follow-up.
+
+METHODS
+
+Trial Design and Oversight
+
+CASTOR (ClinicalTrials.gov identifier: NCT02136134) was a multicenter, randomized, open-label, active-controlled, phase III study in patients with RRMM. The study design has been published previously.17 Eligible patients had progressive disease per International Myeloma Working Group criteria20,21 during or after completion of their last regimen, received ≥ 1 prior line of therapy, and had a partial response or better to ≥ 1 previous line of therapy.17 Patients refractory to bortezomib or another proteasome inhibitor were ineligible. The trial protocol was approved by independent ethics committees or institutional review boards at each center. Patients provided written informed consent, and the trial was conducted in accordance with the principles of the Declaration of Helsinki and the International Conference on Harmonisation Good Clinical Practice guidelines.
+
+Random Assignment and Study Treatment
+
+Patients were randomly assigned (1:1) to D-Vd or Vd and stratified by International Staging System disease stage (I, II, or III), number of prior lines of therapy (1 v 2 or 3 v > 3), and previous bortezomib treatment (yes v no). All patients received up to eight cycles (21 days/cycle) of bortezomib (1.3 mg/m2 subcutaneously once on days 1, 4, 8, and 11) and dexamethasone (20 mg orally or intravenously once on days 1, 2, 4, 5, 8, 9, 11, and 12). For patients in the D-Vd group, daratumumab (16 mg/kg intravenously) was administered once weekly (days 1, 8, and 15) in cycles 1-3, once every 3 weeks (on day 1) in cycles 4-8, and once every 4 weeks thereafter until disease progression or unacceptable toxicity. After positive primary analysis and protocol amendment, patients receiving Vd were offered daratumumab monotherapy after disease progression or after a washout period if they already experienced disease progression and were receiving subsequent therapy.
+
+End Points and Assessments
+
+PFS was the primary efficacy end point.17 Secondary end points included time to disease progression, overall response rate, MRD negativity, and OS. Exploratory secondary analyses examined subpopulations by number of lines of therapy, prior treatment exposure, and cytogenetic risk assessed by next-generation sequencing, as described previously.18 Tumor response and disease progression were assessed using a validated computerized algorithm in accordance with International Myeloma Working Group response criteria.20,21 MRD was assessed using bone marrow aspirate samples and evaluated via next-generation sequencing using the clonoSEQ assay (v.2.0; Adaptive Biotechnologies, Seattle, WA). MRD was assessed in patients with suspected CR, in patients who achieved CR (including patients with very good partial response and suspected daratumumab interference) at cycle 9 day 1 and cycle 15 day 1 (6 months after the end of the Vd backbone), and every 12 months (± 3 months) after CR until the end of treatment. Patients were considered to be MRD-positive if they had an MRD-positive test result or had no MRD assessment. Cytogenetic risk was evaluated locally using local fluorescence in situ hybridization or karyotyping. High-risk patients had t(4;14), t(14;16), or del17p cytogenetic abnormalities.
+
+Statistical Analysis
+
+Unless otherwise specified, efficacy analyses were based on the intention-to-treat population. Time-to-event end points were compared between groups using a stratified log-rank test. HRs and 95% CIs were estimated using a stratified Cox regression model with treatment as the sole explanatory variable. The Kaplan–Meier method was used to estimate the distributions. Binary end points, including overall response rate, were assessed using the stratified Cochran–Mantel–Haenszel test. MRD-negativity rates were compared using Fisher's exact test.
+
+RESULTS
+
+Patients
+
+Patients were recruited between September 4, 2014, and September 24, 2015. In total, 498 patients were randomly assigned to D-Vd (n = 251) or Vd (n = 247; Fig 1). Patient demographics and baseline clinical characteristics were generally well balanced and have been published previously.17 Median (range) age was 64 (30-88) years, and median (range) number of prior lines of therapy was 2 (1-10) (Appendix Table A1, online only). Prior therapies included bortezomib (65.5%), thalidomide (49.4%), lenalidomide (42.0%), and both a proteasome inhibitor and an immunomodulatory drug (48.4%).
+
+FIG 1. CONSORT diagram for CASTOR.
+
+A total of 87 Vd patients subsequently received single-agent daratumumab after disease progression (or after a washout period if they already experienced disease progression and were receiving subsequent therapy), provided per study protocol. The median (range) number of daratumumab cycles received during monotherapy was 11.0 (1-63), and median (range) duration of daratumumab monotherapy was 9.2 months (0.2-57.1). An additional 38 patients received daratumumab as subsequent therapy not provided in the study.
+
+Efficacy
+
+At the clinical cutoff (June 28, 2021), at a median (range) follow-up of 72.6 months (0.0-79.8), a total of 148 (59.0%) of 251 patients in the D-Vd group and 171 (69.2%) of 247 patients in the Vd group died. The HR for death for D-Vd compared with Vd was 0.74 (95% CI, 0.59 to 0.92; P = .0075; Fig 2A), crossing the prespecified stopping boundary of P = .0323 and representing a 26% reduction in the risk of death. Median OS was 49.6 months (95% CI, 42.2 to 62.3) for D-Vd versus 38.5 months (95% CI, 31.2 to 46.2) for Vd. Prespecified subgroup analyses showed OS benefit for patients in the D-Vd group versus patients in the Vd group across most patient subgroups, including patients age ≥ 65 years, patients who have received one or two prior lines of therapy, and patients with poor prognosis, such as those with advanced-stage disease (International Staging System stage III), high-risk cytogenetic abnormalities, and prior bortezomib treatment (Fig 3). It is noteworthy that OS benefit was most pronounced in patients who received one prior line of therapy (HR, 0.56; 95% CI, 0.39 to 0.80).
+
+FIG 2. Kaplan–Meier estimates of (A) OS and (B) PFS2 in the ITT population, which included all patients who underwent random assignment. D-Vd, daratumumab, bortezomib, and dexamethasone; HR, hazard ratio; ITT, intention-to-treat; OS, overall survival; PFS2, progression-free survival on the subsequent line of therapy; Vd, bortezomib and dexamethasone.
+
+FIG 3. The results of OS in prespecified subgroups of the ITT population defined by baseline characteristics. The ISS disease stage is derived on the basis of the combination of serum β2-microglobulin and albumin levels. Higher stages indicate more severe disease. Cytogenetic risk was assessed locally by fluorescence in situ hybridization or karyotype testing; high risk was defined as the presence of t(4;14), t(14;16), or del17p. The subgroup analysis of the type of MM was performed on data from patients who had measurable disease in serum. CrCl, creatinine clearance; D-Vd, daratumumab, bortezomib, and dexamethasone; ECOG PS, Eastern Cooperative Oncology Group performance status; EU, European Union; HR, hazard ratio; IgG, immunoglobulin G; IMiD, immunomodulatory drug; ISS, International Staging System; ITT, intention-to-treat; MM, multiple myeloma; NE, not estimable; OS, overall survival; Vd, bortezomib and dexamethasone.
+
+Significantly higher MRD-negativity rates (10−5 sensitivity threshold) were achieved with D-Vd versus Vd (15.1% v 1.6%; P < .0001). MRD negativity was associated with improved OS, regardless of treatment group (Fig 4).
+
+FIG 4. Kaplan–Meier estimates of OS by MRD status among patients in the ITT population. D-Vd, daratumumab, bortezomib, and dexamethasone; ITT, intention-to-treat; MRD, minimal residual disease; OS, overall survival; Vd, bortezomib and dexamethasone.
+
+A total of 161 (66.3%) of 243 patients in the D-Vd group and 200 (84.4%) of 237 patients in the Vd group received subsequent therapy; median (range) number of subsequent lines of therapy was 2 (1-9) and 3 (1-10), respectively. Median time to subsequent therapy was significantly increased in the D-Vd arm versus the Vd arm (25.4 v 9.7 months; HR, 0.27; 95% CI, 0.21 to 0.34; P < .0001). The most common subsequent anticancer therapies included dexamethasone (59.3%), lenalidomide (39.9%), cyclophosphamide (28.0%), pomalidomide (23.0%), carfilzomib (21.0%), and bortezomib (19.8%) in the D-Vd arm and dexamethasone (66.2%), daratumumab (52.7%), lenalidomide (46.0%), cyclophosphamide (33.8%), pomalidomide (32.1%), carfilzomib (24.9%), and bortezomib (23.2%) in the Vd arm. PFS2 (defined as the time from random assignment to progression on the next line of therapy) was significantly prolonged with D-Vd versus Vd (median, 37.7 v 19.9 months; HR, 0.43; 95% CI, 0.34 to 0.54; P < .0001; Fig 2B). The most common first subsequent therapy was lenalidomide and dexamethasone (Rd) in the D-Vd arm (23.9%) and daratumumab monotherapy (22.4%) or Rd (18.9%) in the Vd arm (Appendix Table A2, online only). Median PFS for patients who started first line of subsequent therapy was 13.2 months (95% CI, 10.1 to 15.4) in the D-Vd group and 9.2 months (95% CI, 7.4 to 10.6) in the Vd group.
+
+After disease progression, median (range) time to crossover to daratumumab subsequent therapy for patients in the Vd arm was 20.5 months (5.7-68.3). Of the 87 patients in the Vd group who received subsequent daratumumab monotherapy after disease progression, provided per study protocol, 35 (40.2%) are still alive; median OS is 63.4 months (95% CI, 51.2 to 72.4).
+
+Safety
+
+With longer follow up, no new safety concerns were reported (Table 1). The most common (≥ 10%) grade 3/4 treatment-emergent adverse events (TEAEs) with D-Vd versus Vd were thrombocytopenia (46.1% v 32.9%), anemia (16.0% v 16.0%), neutropenia (13.6% v 4.6%), lymphopenia (10.3% v 2.5%), and pneumonia (10.7% v 10.1%). Grade 3/4 infections occurred in 72 (29.6%) patients in the D-Vd arm and 45 (19.0%) patients in the Vd arm. Serious TEAEs occurred in 134 (55.1%) patients who received D-Vd and 81 (34.2%) patients who received Vd, the most common being pneumonia (10.7% and 10.1%, respectively). The percentage of patients with TEAEs leading to discontinuation remained low and similar between groups (D-Vd, 10.7%; Vd, 9.3%). Seven (2.9%) patients in the D-Vd arm and 5 (2.1%) patients in the Vd arm discontinued treatment because of infections.
+
+TABLE 1. Most Common (> 15% of Patients) and Grade 3/4 (> 5% of Patients) TEAEs in the Safety Population
+
+TEAEs that resulted in death were reported in 17 (7.0%) patients in the D-Vd group and 14 (5.9%) patients in the Vd group. The most frequent TEAEs with an outcome of death were pneumonia (0.8% each) and general physical health deterioration (0.4% v 1.3%, respectively). Three patients died during the study due to COVID-19 disease (one in the D-Vd group and two in the Vd group). With extended follow up, the incidence of second primary malignancies (cutaneous, invasive, and hematologic) was 20 (8.2%) patients in the D-Vd arm (6 new cases since the 3-year follow-up analysis) and 5 (2.1%) patients in the Vd arm (no new cases). There was no predominant cancer type for second primary malignancies in either treatment arm. When adjusted for exposure to study treatment, the rate of second primary malignancies was similar between the D-Vd (0.35 events per 100 patient-months at risk) and Vd (0.46 events per 100 patient-months at risk) groups.
+
+DISCUSSION
+
+It is our understanding that these long-term results from the CASTOR study represent the first OS data with daratumumab in the RRMM population and provide further support of a survival advantage with a daratumumab-containing regimen in multiple myeloma.10,22 For the first time to our knowledge, the combination of daratumumab and a standard-of-care regimen significantly improved OS in patients with RRMM. The addition of daratumumab to Vd prolonged OS in patients with RRMM, with a 26% reduction in the risk of death versus Vd alone after a median follow-up of 72.6 months. The OS curves separated at approximately 8 months and continued to separate with time. The final MRD-negativity rate analysis results (median follow-up, 72.6 months) were consistent with those reported in the updated analysis (median follow-up, 19.4 months).18 OS was improved in patients who achieved MRD negativity compared with patients who were MRD-positive. It appears that the superiority in OS is derived from patients who achieved MRD negativity in the D-Vd group because OS was similar in the D-Vd and Vd groups in patients who were MRD-positive. This further reinforces the importance of achieving MRD negativity. A significant PFS2 benefit was maintained for the D-Vd group, with a 57% reduction in the risk of disease progression or death. These results build on the significant PFS benefit previously reported with D-Vd, with a 69% reduction in the risk of disease progression or death compared with Vd alone (median follow-up, 50.2 months).19 Median PFS was 16.7 months in the D-Vd group versus 7.1 months in the Vd group (HR, 0.31; 95% CI, 0.24 to 0.39; P < .0001).19
+
+In prespecified subgroup analyses of OS, an OS advantage was observed with D-Vd compared with Vd for most subgroups, including patients age ≥ 65 years, patients who have received one or two prior lines of therapy, and patients with prior immunomodulatory drug or bortezomib treatment. It is noteworthy that OS benefit was most pronounced in patients who received one prior line of therapy (HR, 0.56; 95% CI, 0.39 to 0.80), supporting early use of daratumumab in treatment. These OS results also reinforce those from the previous updated PFS analysis (median follow-up, 50.2 months), which showed the most pronounced PFS benefit of D-Vd in patients who received one prior line of therapy, with a 79% reduction in the risk of disease progression or death versus Vd alone (median, 27.0 v 7.9 months; HR, 0.21; 95% CI, 0.15 to 0.31; P < .0001).19
+
+Importantly, OS benefit was observed despite crossover in the Vd arm to daratumumab subsequent therapy. As noted earlier in the article, patients in the Vd group were offered daratumumab monotherapy after disease progression or a washout period. Approximately 63% of patients (125/200) in the Vd arm who received subsequent therapy received salvage daratumumab, primarily as monotherapy but also in combination regimens; 49 patients received first subsequent therapy with daratumumab (45 as monotherapy, four in various combination regimens). We realize that allowing this type of crossover per study protocol may confound OS results. Patients received daratumumab monotherapy as this was the approved and reimbursed regimen available to patients at the time this study was performed.
+
+No new safety concerns were reported after extended follow-up. Grade 3/4 infections were reported more frequently with D-Vd versus Vd, but the rate of infections leading to treatment discontinuation was low and similar between groups. Second primary malignancies were reported more commonly in the D-Vd group than in the Vd group; however, the incidence was similar between groups when adjusted for exposure to study treatment.
+
+While cross-trial comparisons should be interpreted cautiously, the CASTOR OS data reported herein (median OS, 49.6 months) are consistent with previously reported data in RRMM (ASPIRE and ELOQUENT-2),23,24 despite differences in patient demographics. In the phase III ASPIRE study of patients with RRMM and one to three prior lines of therapy, at a median follow-up of 67.1 months, median OS was 48.3 months with carfilzomib-Rd versus 40.4 months with Rd alone (HR, 0.79; 95% CI, 0.67 to 0.95; 1-sided P = .0045).23 Similarly, in the phase III ELOQUENT-2 study, at a minimum follow-up of 70.6 months, the combination of elotuzumab and Rd significantly improved OS versus Rd alone (median, 48.3 v 39.6 months, respectively; HR, 0.82; 95.4% CI, 0.68 to 1.00; P = .0408) in patients with RRMM and one to three prior lines of therapy.24 On the basis of the results of these three trials, the benchmark for survival in RRMM is approximately 4 years. It is also noteworthy that the median OS in the Vd arm of CASTOR (38.5 months) was consistent with that observed in the Vd arm of the phase III ENDEAVOR study (38.8 months).25
+
+Similar to CASTOR, the results from the phase III POLLUX study also support utilizing a daratumumab-containing regimen after first relapse in patients with RRMM who are not refractory to lenalidomide.13,26,27 Moreover, the results from the phase III ALCYONE and MAIA trials demonstrate that adding daratumumab to standard-of-care regimens significantly improves OS and PFS compared with standard of care alone in patients with transplant-ineligible NDMM.10,22 Real-world data from a patient chart review in Europe reported high attrition rates that increased with each subsequent line of therapy, with 95% of patients receiving first-line therapy and only 61% and 38% receiving second-line and third-line therapy, respectively.28 In a retrospective analysis of 3 US databases, attrition rates were high after first (57%) and each subsequent line of therapy (43%-46%) in nontransplant elderly patients with NDMM.29 Taken together, these real-world data, in conjunction with the prolonged OS observed with daratumumab-containing regimens in both the NDMM (ALCYONE and MAIA) and now RRMM (CASTOR and POLLUX) treatment settings, support early use of daratumumab to induce deep and sustained responses and prolonged disease control to potentially delay clonal evolution and subsequent drug resistance.30 As use of daratumumab increases in the frontline treatment setting, the role of retreatment with daratumumab after progression will be of growing interest. Retrospective analyses suggest clinical efficacy with daratumumab retreatment,31,32 but the results of ongoing (LYNX; ClinicalTrials.gov identifier: NCT03871829) and future studies will further define the potential role of daratumumab retreatment after progression.
+
+In conclusion, to our knowledge, these results, combined with the OS results reported with D-Rd in the phase III POLLUX study,27 demonstrate for the first time an OS benefit with daratumumab-containing regimens in patients with RRMM. Treatment of patients with RRMM with D-Vd results in a significant reduction in the risk of death compared with Vd alone, with the greatest OS benefit observed in patients who received one prior line of therapy. These results provide strong rationale for early use of daratumumab to maximize patient benefit.
+
+ACKNOWLEDGMENT
+
+The authors thank the patients who participated in the CASTOR study and their families, as well as the study coinvestigators, research nurses, and coordinators at each of the clinical sites. Medical writing and editorial support were provided by Lisa Shannon, PharmD, of Lumanity Communications Inc, and were funded by Janssen Global Services, LLC.
+
+SUPPORT
+
+CLINICAL TRIAL INFORMATION
+
+DATA SHARING STATEMENT
+
+The data sharing policy of Janssen Pharmaceutical Companies of Johnson & Johnson is available at https://www.janssen.com/clinical-trials/transparency. As noted on this site, requests for access to the study data can be submitted through Yale Open Data Access (YODA) Project site at http://yoda.yale.edu.
+
+AUTHOR CONTRIBUTIONS
+
+Conception and design: Asher Chanan-Khan, Katja Weisel, Tahamtan Ahmadi, Robin Carson
+
+Provision of study materials or patients: Asher Chanan-Khan, Katja Weisel, Tamas Masszi, Meral Beksac, Vania Hungria, Markus Munder, Maria-Victoria Mateos
+
+Collection and assembly of data: Katja Weisel, Tamas Masszi, Meral Beksac, Ivan Spicka, Vania Hungria, Maria-Victoria Mateos, Tomer M. Mark, Tahamtan Ahmadi, Wendy Garvin Mayo, Robin Carson, Andrew Spencer
+
+Data analysis and interpretation: Pieter Sonneveld, Asher Chanan-Khan, Katja Weisel, Ajay K. Nooka, Meral Beksac, Ivan Spicka, Vania Hungria, Markus Munder, Maria-Victoria Mateos, Tomer M. Mark, Mark-David Levin, Tahamtan Ahmadi, Xiang Qin, Xue Gai, Jodi Carey, Robin Carson, Andrew Spencer
+
+Manuscript writing: All authors
+
+Final approval of manuscript: All authors
+
+Accountable for all aspects of the work: All authors
+
+AUTHORS' DISCLOSURES OF POTENTIAL CONFLICTS OF INTEREST
+
+Overall Survival With Daratumumab, Bortezomib, and Dexamethasone in Previously Treated Multiple Myeloma (CASTOR): A Randomized, Open-Label, Phase III Trial
+
+The following represents disclosure information provided by authors of this manuscript. All relationships are considered compensated unless otherwise noted. Relationships are self-held unless noted. I = Immediate Family Member, Inst = My Institution. Relationships may not relate to the subject matter of this manuscript. For more information about ASCO's conflict of interest policy, please refer to www.asco.org/rwc or ascopubs.org/jco/authors/author-center.
+
+Open Payments is a public database containing information reported by companies about payments made to US-licensed physicians (Open Payments).
+
+APPENDIX
+
+TABLE A1. Baseline Demographic and Clinical Characteristics17
+
+TABLE A2. Most Common First Line of Subsequent Therapy in the ITT Population
+
+Supported by Janssen Research & Development, LLC.
+
+NCT02136134 [CASTOR]
+
+Pieter Sonneveld
+
+Consulting or Advisory Role: Celgene, Janssen, Amgen, Karyopharm Therapeutics, CARsgen Therapeutics
+
+Research Funding: Janssen (Inst), Amgen (Inst), SkylineDx (Inst), Bristol Myers Squibb/Celgene (Inst)
+
+Asher Chanan-Khan
+
+Leadership: Starton Therapeutics, Cellectar
+
+Stock and Other Ownership Interests: Matthew & Asher Inc, NanoDev Therapeutics, Starton Therapeutics, Cellectar
+
+Honoraria: BeiGene, Ascentage Pharma
+
+Research Funding: Ascentage Pharma
+
+Patents, Royalties, Other Intellectual Property: Patent on PSMB9 biomarker
+
+Katja Weisel
+
+Honoraria: Amgen, Bristol Myers Squibb/Celgene, Janssen-Cilag, GlaxoSmithKline (Inst), Adaptive Biotechnologies, Karyopharm Therapeutics, Takeda, Sanofi, AbbVie, GlaxoSmithKline, Novartis, Pfizer, Celgene, Oncopeptides, Roche
+
+Consulting or Advisory Role: Amgen, Adaptive Biotechnologies, Bristol Myers Squibb/Celgene, GlaxoSmithKline, Janssen-Cilag, Karyopharm Therapeutics, Sanofi, Takeda, Oncopeptides, Roche
+
+Research Funding: Amgen (Inst), Celgene (Inst), Sanofi (Inst), Janssen-Cilag (Inst), Bristol Myers Squibb/Celgene (Inst), GlaxoSmithKline (Inst)
+
+Travel, Accommodations, Expenses: Amgen, Celgene, Bristol Myers Squibb/Celgene, Janssen-Cilag, GlaxoSmithKline, Takeda
+
+Ajay K. Nooka
+
+Honoraria: Amgen, Janssen Oncology, Bristol Myers Squibb/Celgene, GlaxoSmithKline, Takeda, Oncopeptides, Karyopharm Therapeutics, Adaptive Biotechnologies, Genzyme, BeyondSpring Pharmaceuticals, Secura Bio
+
+Consulting or Advisory Role: Amgen, Janssen Oncology, Bristol Myers Squibb/Celgene, GlaxoSmithKline, Takeda, Oncopeptides, Karyopharm Therapeutics, Adaptive Biotechnologies, Genzyme, BeyondSpring Pharmaceuticals, Secura Bio
+
+Research Funding: Amgen (Inst), Janssen Oncology (Inst), Takeda (Inst), Bristol Myers Squibb/Celgene (Inst), Arch Oncology (Inst), GlaxoSmithKline (Inst)
+
+Travel, Accommodations, Expenses: GlaxoSmithKline
+
+Tamas Masszi
+
+Consulting or Advisory Role: AbbVie, Bristol Myers Squibb/Celgene, Janssen-Cilag, Novartis, Pfizer, Takeda
+
+Meral Beksac
+
+Consulting or Advisory Role: Janssen Oncology (Inst), Bristol Myers Squibb/Celgene Turkey (Inst), Amgen (Inst), Takeda (Inst), Oncopeptides (Inst), Sanofi (Inst)
+
+Speakers' Bureau: Amgen (Inst), Sanofi (Inst)
+
+Ivan Spicka
+
+Honoraria: Amgen, Janssen-Cilag, Takeda, Novartis
+
+Consulting or Advisory Role: Celgene, Amgen, Janssen-Cilag, Takeda, Novartis, Sanofi
+
+Speakers' Bureau: Celgene, Amgen, Janssen-Cilag, Takeda, Bristol Myers Squibb/Celgene, Novartis, Sanofi
+
+Travel, Accommodations, Expenses: Celgene, Amgen, Janssen-Cilag, Bristol Myers Squibb/Celgene
+
+Vania Hungria
+
+Consulting or Advisory Role: AbbVie, Janssen-Cilag, Takeda, Sanofi, Bristol Myers Squibb/Celgene Brazil, Amgen, Pfizer
+
+Speakers' Bureau: Janssen-Cilag, Takeda, Bristol Myers Squibb/Celgene Brazil, Sanofi, Amgen, GlaxoSmithKline
+
+Markus Munder
+
+Honoraria: Janssen Oncology, Bristol Myers Squibb/Celgene GmbH & Co KG, GlaxoSmithKline, Sanofi, Abbvie
+
+Consulting or Advisory Role: Janssen Oncology, Bristol Myers Squibb/Celgene GmbH & Co KG, GlaxoSmithKline, Sanofi, Takeda, Amgen
+
+Maria-Victoria Mateos
+
+Honoraria: Janssen-Cilag, Celgene, Amgen, Takeda, GlaxoSmithKline, AbbVie/Genentech, Sanofi
+
+Consulting or Advisory Role: Takeda, Janssen-Cilag, Celgene, Amgen, AbbVie, GlaxoSmithKline, Pfizer, Regeneron, Roche/Genentech
+
+Tomer M. Mark
+
+Employment: Karyopharm Therapeutics
+
+Stock and Other Ownership Interests: Karyopharm Therapeutics, Adaptive Biotechnologies, AbbVie
+
+Consulting or Advisory Role: Adaptive Biotechnologies, Genzyme, Bristol Myers Squibb/Celgene, Amgen, Sanofi, Takeda
+
+Research Funding: Bristol Myers Squibb/Celgene (Inst), Janssen (Inst)
+
+Mark-David Levin
+
+Honoraria: AbbVie, Celgene, Janssen, Takeda
+
+Travel, Accommodations, Expenses: Takeda, Janssen, AbbVie
+
+Tahamtan Ahmadi
+
+Employment: Genmab
+
+Leadership: Genmab
+
+Stock and Other Ownership Interests: Genmab
+
+Xiang Qin
+
+Employment: Janssen Research & Development
+
+Wendy Garvin Mayo
+
+Employment: Janssen Research & Development
+
+Xue Gai
+
+Employment: Janssen Research & Development
+
+Stock and Other Ownership Interests: Janssen Research & Development
+
+Travel, Accommodations, Expenses: Janssen Research & Development
+
+Jodi Carey
+
+Employment: Janssen Research & Development
+
+Stock and Other Ownership Interests: Johnson & Johnson
+
+Robin Carson
+
+Employment: Johnson & Johnson
+
+Stock and Other Ownership Interests: Johnson & Johnson
+
+Andrew Spencer
+
+Honoraria: Janssen-Cilag, Bristol Myers Squibb/Celgene
+
+Consulting or Advisory Role: Janssen-Cilag, Bristol Myers Squibb/Celgene
+
+Speakers' Bureau: Janssen-Cilag
+
+Research Funding: Janssen-Cilag
+
+No other potential conflicts of interest were reported.
+
+Pieter Sonneveld
+
+Consulting or Advisory Role: Celgene, Janssen, Amgen, Karyopharm Therapeutics, CARsgen Therapeutics
+
+Research Funding: Janssen (Inst), Amgen (Inst), SkylineDx (Inst), Bristol Myers Squibb/Celgene (Inst)
+
+Asher Chanan-Khan
+
+Leadership: Starton Therapeutics, Cellectar
+
+Stock and Other Ownership Interests: Matthew & Asher Inc, NanoDev Therapeutics, Starton Therapeutics, Cellectar
+
+Honoraria: BeiGene, Ascentage Pharma
+
+Research Funding: Ascentage Pharma
+
+Patents, Royalties, Other Intellectual Property: Patent on PSMB9 biomarker
+
+Katja Weisel
+
+Honoraria: Amgen, Bristol Myers Squibb/Celgene, Janssen-Cilag, GlaxoSmithKline (Inst), Adaptive Biotechnologies, Karyopharm Therapeutics, Takeda, Sanofi, AbbVie, GlaxoSmithKline, Novartis, Pfizer, Celgene, Oncopeptides, Roche
+
+Consulting or Advisory Role: Amgen, Adaptive Biotechnologies, Bristol Myers Squibb/Celgene, GlaxoSmithKline, Janssen-Cilag, Karyopharm Therapeutics, Sanofi, Takeda, Oncopeptides, Roche
+
+Research Funding: Amgen (Inst), Celgene (Inst), Sanofi (Inst), Janssen-Cilag (Inst), Bristol Myers Squibb/Celgene (Inst), GlaxoSmithKline (Inst)
+
+Travel, Accommodations, Expenses: Amgen, Celgene, Bristol Myers Squibb/Celgene, Janssen-Cilag, GlaxoSmithKline, Takeda
+
+Ajay K. Nooka
+
+Honoraria: Amgen, Janssen Oncology, Bristol Myers Squibb/Celgene, GlaxoSmithKline, Takeda, Oncopeptides, Karyopharm Therapeutics, Adaptive Biotechnologies, Genzyme, BeyondSpring Pharmaceuticals, Secura Bio
+
+Consulting or Advisory Role: Amgen, Janssen Oncology, Bristol Myers Squibb/Celgene, GlaxoSmithKline, Takeda, Oncopeptides, Karyopharm Therapeutics, Adaptive Biotechnologies, Genzyme, BeyondSpring Pharmaceuticals, Secura Bio
+
+Research Funding: Amgen (Inst), Janssen Oncology (Inst), Takeda (Inst), Bristol Myers Squibb/Celgene (Inst), Arch Oncology (Inst), GlaxoSmithKline (Inst)
+
+Travel, Accommodations, Expenses: GlaxoSmithKline
+
+Tamas Masszi
+
+Consulting or Advisory Role: AbbVie, Bristol Myers Squibb/Celgene, Janssen-Cilag, Novartis, Pfizer, Takeda
+
+Meral Beksac
+
+Consulting or Advisory Role: Janssen Oncology (Inst), Bristol Myers Squibb/Celgene Turkey (Inst), Amgen (Inst), Takeda (Inst), Oncopeptides (Inst), Sanofi (Inst)
+
+Speakers' Bureau: Amgen (Inst), Sanofi (Inst)
+
+Ivan Spicka
+
+Honoraria: Amgen, Janssen-Cilag, Takeda, Novartis
+
+Consulting or Advisory Role: Celgene, Amgen, Janssen-Cilag, Takeda, Novartis, Sanofi
+
+Speakers' Bureau: Celgene, Amgen, Janssen-Cilag, Takeda, Bristol Myers Squibb/Celgene, Novartis, Sanofi
+
+Travel, Accommodations, Expenses: Celgene, Amgen, Janssen-Cilag, Bristol Myers Squibb/Celgene
+
+Vania Hungria
+
+Consulting or Advisory Role: AbbVie, Janssen-Cilag, Takeda, Sanofi, Bristol Myers Squibb/Celgene Brazil, Amgen, Pfizer
+
+Speakers' Bureau: Janssen-Cilag, Takeda, Bristol Myers Squibb/Celgene Brazil, Sanofi, Amgen, GlaxoSmithKline
+
+Markus Munder
+
+Honoraria: Janssen Oncology, Bristol Myers Squibb/Celgene GmbH & Co KG, GlaxoSmithKline, Sanofi, Abbvie
+
+Consulting or Advisory Role: Janssen Oncology, Bristol Myers Squibb/Celgene GmbH & Co KG, GlaxoSmithKline, Sanofi, Takeda, Amgen
+
+Maria-Victoria Mateos
+
+Honoraria: Janssen-Cilag, Celgene, Amgen, Takeda, GlaxoSmithKline, AbbVie/Genentech, Sanofi
+
+Consulting or Advisory Role: Takeda, Janssen-Cilag, Celgene, Amgen, AbbVie, GlaxoSmithKline, Pfizer, Regeneron, Roche/Genentech
+
+Tomer M. Mark
+
+Employment: Karyopharm Therapeutics
+
+Stock and Other Ownership Interests: Karyopharm Therapeutics, Adaptive Biotechnologies, AbbVie
+
+Consulting or Advisory Role: Adaptive Biotechnologies, Genzyme, Bristol Myers Squibb/Celgene, Amgen, Sanofi, Takeda
+
+Research Funding: Bristol Myers Squibb/Celgene (Inst), Janssen (Inst)
+
+Mark-David Levin
+
+Honoraria: AbbVie, Celgene, Janssen, Takeda
+
+Travel, Accommodations, Expenses: Takeda, Janssen, AbbVie
+
+Tahamtan Ahmadi
+
+Employment: Genmab
+
+Leadership: Genmab
+
+Stock and Other Ownership Interests: Genmab
+
+Xiang Qin
+
+Employment: Janssen Research & Development
+
+Wendy Garvin Mayo
+
+Employment: Janssen Research & Development
+
+Xue Gai
+
+Employment: Janssen Research & Development
+
+Stock and Other Ownership Interests: Janssen Research & Development
+
+Travel, Accommodations, Expenses: Janssen Research & Development
+
+Jodi Carey
+
+Employment: Janssen Research & Development
+
+Stock and Other Ownership Interests: Johnson & Johnson
+
+Robin Carson
+
+Employment: Johnson & Johnson
+
+Stock and Other Ownership Interests: Johnson & Johnson
+
+Andrew Spencer
+
+Honoraria: Janssen-Cilag, Bristol Myers Squibb/Celgene
+
+Consulting or Advisory Role: Janssen-Cilag, Bristol Myers Squibb/Celgene
+
+Speakers' Bureau: Janssen-Cilag
+
+Research Funding: Janssen-Cilag
+
+No other potential conflicts of interest were reported.
+==== Refs
+REFERENCES
+
+1. de Weers M Tai YT van der Veer MS : Daratumumab, a novel therapeutic human CD38 monoclonal antibody, induces killing of multiple myeloma and other hematological tumors. J Immunol 186 :1840-1848, 2011 21187443
+2. Lammerts van Bueren J Jakobs D Kaldenhoven N : Direct in vitro comparison of daratumumab with surrogate analogs of CD38 antibodies MOR03087, SAR650984 and Ab79. Blood 124 :3474, 2014
+3. Overdijk MB Verploegen S Bögels M : Antibody-mediated phagocytosis contributes to the anti-tumor activity of the therapeutic antibody daratumumab in lymphoma and multiple myeloma. MAbs 7 :311-321, 2015 25760767
+4. Overdijk MB Jansen JH Nederend M : The therapeutic CD38 monoclonal antibody daratumumab induces programmed cell death via Fcγ receptor–mediated cross-linking. J Immunol 197 :807-813, 2016 27316683
+5. Krejcik J Casneuf T Nijhof IS : Daratumumab depletes CD38+ immune-regulatory cells, promotes T-cell expansion, and skews T-cell repertoire in multiple myeloma. Blood 128 :384-394, 2016 27222480
+6. Adams HC III Stevenaert F Krejcik J : High-parameter mass cytometry evaluation of relapsed/refractory multiple myeloma patients treated with daratumumab demonstrates immune modulation as a novel mechanism of action. Cytometry A 95 :279-289, 2019 30536810
+7. Casneuf T Adams HC III van de Donk NWCJ : Deep immune profiling of patients treated with lenalidomide and dexamethasone with or without daratumumab. Leukemia 35 :573-584, 2021 32457357
+8. Kinder M Bahlis NJ Malavasi F : Comparison of CD38 antibodies in vitro and ex vivo mechanisms of action in multiple myeloma. Haematologica 106 :2004-2008, 2021 33440920
+9. Moreau P Attal M Hulin C : Bortezomib, thalidomide, and dexamethasone with or without daratumumab before and after autologous stem-cell transplantation for newly diagnosed multiple myeloma (CASSIOPEIA): A randomised, open-label, phase 3 study. Lancet 394 :29-38, 2019 31171419
+10. Mateos MV Cavo M Blade J : Overall survival with daratumumab, bortezomib, melphalan, and prednisone in newly diagnosed multiple myeloma (ALCYONE): A randomised, open-label, phase 3 trial. Lancet 395 :132-141, 2020 31836199
+11. Facon T Kumar S Plesner T : Daratumumab plus lenalidomide and dexamethasone for untreated myeloma. N Engl J Med 380 :2104-2115, 2019 31141632
+12. Mateos MV Sonneveld P Hungria V : Daratumumab, bortezomib, and dexamethasone versus bortezomib and dexamethasone in patients with previously treated multiple myeloma: Three-year follow-up of CASTOR. Clin Lymphoma Myeloma Leuk 20 :509-518, 2020 32482541
+13. Bahlis NJ Dimopoulos MA White DJ : Daratumumab plus lenalidomide and dexamethasone in relapsed/refractory multiple myeloma: Extended follow-up of POLLUX, a randomized, open-label, phase 3 study. Leukemia 34 :1875-1884, 2020 32001798
+14. Dimopoulos MA Terpos E Boccadoro M : Daratumumab plus pomalidomide and dexamethasone versus pomalidomide and dexamethasone alone in previously treated multiple myeloma (APOLLO): An open-label, randomised, phase 3 trial. Lancet Oncol 22 :801-812, 2021 34087126
+15. DARZALEX® (daratumumab) [package insert]. Horsham, PA, Janssen Biotech, Inc, 2022
+16. European Medicines Agency: DARZALEX 20 mg/mL concentrate for solution for infusion [summary of product characteristics]. http://www.ema.europa.eu/docs/en_GB/document_library/EPAR_-_Product_Information/human/004077/WC500207296.pdf
+17. Palumbo A Chanan-Khan A Weisel K : Daratumumab, bortezomib, and dexamethasone for multiple myeloma. N Engl J Med 375 :754-766, 2016 27557302
+18. Spencer A Lentzsch S Weisel K : Daratumumab plus bortezomib and dexamethasone versus bortezomib and dexamethasone in relapsed or refractory multiple myeloma: Updated analysis of CASTOR. Haematologica 103 :2079-2087, 2018 30237264
+19. Weisel KC Sonneveld P Mateos MV : Efficacy and safety of daratumumab, bortezomib, and dexamethasone (D-Vd) versus bortezomib and dexamethasone (Vd) in first relapse patients (pts) with multiple myeloma (MM): Four-year update of CASTOR. Poster presented at the 61st American Society of Hematology (ASH) Annual Meeting & Exposition, Orlando, FL, December 7-10, 2019
+20. Durie BGM Harousseau JL Miguel JS : International uniform response criteria for multiple myeloma. Leukemia 20 :1467-1473, 2006 16855634
+21. Rajkumar SV Harousseau JL Durie B : Consensus recommendations for the uniform reporting of clinical trials: Report of the International Myeloma Workshop Consensus Panel 1. Blood 117 :4691-4695, 2011 21292775
+22. Facon T Kumar SK Plesner T : Daratumumab, lenalidomide, and dexamethasone versus lenalidomide and dexamethasone alone in newly diagnosed multiple myeloma (MAIA): Overall survival results from a randomised, open-label, phase 3 trial. Lancet Oncol 22 :1582-1596, 2021 34655533
+23. Siegel DS Dimopoulos MA Ludwig H : Improvement in overall survival with carfilzomib, lenalidomide, and dexamethasone in patients with relapsed or refractory multiple myeloma. J Clin Oncol 36 :728-734, 2018 29341834
+24. Dimopoulos MA Lonial S White D : Elotuzumab, lenalidomide, and dexamethasone in RRMM: Final overall survival results from the phase 3 randomized ELOQUENT-2 study. Blood Cancer J 10 :91, 2020 32887873
+25. Orlowski RZ Moreau P Niesvizky R : Carfilzomib-dexamethasone versus bortezomib-dexamethasone in relapsed or refractory multiple myeloma: Updated overall survival, safety, and subgroups. Clin Lymphoma Myeloma Leuk 19 :522-530.e1, 2019 31160237
+26. Dimopoulos MA Oriol A Nahi H : Daratumumab, lenalidomide, and dexamethasone for multiple myeloma. N Engl J Med 375 :1319-1331, 2016 27705267
+27. Dimopoulos MA Oriol A Nahi H : Overall survival with daratumumab, lenalidomide, and dexamethasone in previously treated multiple myeloma (POLLUX): A randomized, open‐label, phase III trial. J Clin Oncol 41 :1590-1599, 2023 36599114
+28. Yong K Delforge M Driessen C : Multiple myeloma: Patient outcomes in real-world practice. Br J Haematol 175 :252-264, 2016 27411022
+29. Fonseca R Usmani SZ Mehra M : Frontline treatment patterns and attrition rates by subsequent lines of therapy in patients with newly diagnosed multiple myeloma. BMC Cancer 20 :1087, 2020 33172403
+30. Suzuki K Nishiwaki K Yano S : Treatment strategies considering micro-environment and clonal evolution in multiple myeloma. Cancers (Basel) 13 :215, 2021 33435539
+31. Nooka AK Joseph NS Kaufman JL : Clinical efficacy of daratumumab, pomalidomide, and dexamethasone in patients with relapsed or refractory myeloma: Utility of re-treatment with daratumumab among refractory patients. Cancer 125 :2991-3000, 2019 31090928
+32. Hussain MJ Robinson MM Hamadeh I : Daratumumab, pomalidomide and dexamethasone combination therapy in daratumumab and/or pomalidomide refractory multiple myeloma. Br J Haematol 186 :140-144, 2019 30536372
+

PMC10026307.txt

@@ -0,0 +1,257 @@
+
+==== Front
+Cancer Rep (Hoboken)
+Cancer Rep (Hoboken)
+10.1002/(ISSN)2573-8348
+CNR2
+Cancer Reports
+2573-8348
+John Wiley and Sons Inc. Hoboken
+
+36464325
+10.1002/cnr2.1755
+CNR21755
+Original Article
+Original Articles
+Identifying monoclonal gammopathy of undetermined significance from electronic health records
+Tanenbaum et al.
+Tanenbaum Hilary C. https://orcid.org/0000-0003-2282-1662
+1 2
+Birmann Brenda M. 3
+Bertrand Kimberly A. 4
+Teras Lauren R. 5
+Krishnan Amrita Y. 6
+Pourhassan Hoda 6
+Goldsmith Scott 6
+Cannavale Kimberly 1
+Wang Sophia S. 6
+Chao Chun R. https://orcid.org/0000-0002-6072-4954
+1 chun.r.chao@kp.org
+
+1 Department of Research & Evaluation Kaiser Permanente Southern California Pasadena California USA
+2 Scientific Research & Development Embark Veterinary Ithaca New York USA
+3 Channing Division of Network Medicine, Department of Medicine Brigham and Women's Hospital and Harvard Medical School Boston Massachusetts USA
+4 Slone Epidemiology Center Boston University Boston Massachusetts USA
+5 Intramural Research Department American Cancer Society Atlanta Georgia USA
+6 City of Hope Medical Center California USA
+* Correspondence
+Chun R. Chao, Department of Research & Evaluation, Kaiser Permanente Southern California, 100 S. Los Robles, 2nd Floor, Pasadena, California 91101, USA.
+Email: chun.r.chao@kp.org
+
+04 12 2022
+3 2023
+6 3 10.1002/cnr2.v6.3 e175529 9 2022
+12 8 2022
+04 11 2022
+© 2022 The Authors. Cancer Reports published by Wiley Periodicals LLC.
+https://creativecommons.org/licenses/by/4.0/ This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
+
+Abstract
+
+Background
+
+Monoclonal gammopathy of undetermined significance (MGUS) precedes multiple myeloma (MM). Use of electronic health records may facilitate large‐scale epidemiologic research to elucidate risk factors for the progression of MGUS to MM or other lymphoid malignancies.
+
+Aims
+
+We evaluated the accuracy of an electronic health records‐based approach for identifying clinically diagnosed MGUS cases for inclusion in studies of patient outcomes/ progression risk.
+
+Methods and Results
+
+Data were retrieved from Kaiser Permanente Southern California's comprehensive electronic health records, which contain documentation of all outpatient and inpatient visits, laboratory tests, diagnosis codes and a cancer registry. We ascertained potential MGUS cases diagnosed between 2008 and 2014 using the presence of an MGUS ICD‐9 diagnosis code (273.1). We initially excluded those diagnosed with MM within 6 months after MGUS diagnosis, then subsequently those with any lymphoid malignancy diagnosis from 2007 to 2014. We reviewed medical charts for 100 randomly selected potential cases for evidence of a physician diagnosis of MGUS, which served as our gold standard for case confirmation. To assess sensitivity, we also investigated the presence of the ICD‐9 code in the records of 40 randomly selected and chart review‐confirmed MGUS cases among patients with a laboratory report of elevated circulating monoclonal (M‐) protein (a key test for MGUS diagnosis) and no subsequent lymphoid malignancy (as described above).
+
+The positive predictive value (PPV) for the ICD‐9 code was 98%. All MGUS cases confirmed by chart review also had confirmatory laboratory test results. Of the confirmed cases first identified via M‐protein test results, 88% also had the ICD‐9 diagnosis code.
+
+Conclusion
+
+The diagnosis code‐based approach has excellent PPV and likely high sensitivity for detecting clinically diagnosed MGUS. The generalizability of this approach outside an integrated healthcare system warrants further evaluation.
+
+case identification
+diagnosis code
+electronic health records
+MGUS
+monoclonal gammopathy of undetermined significance
+National Cancer Institute 10.13039/100000054 R01 CA202712 source-schema-version-number2.0
+cover-dateMarch 2023
+details-of-publishers-convertorConverter:WILEY_ML3GV2_TO_JATSPMC version:6.2.6 mode:remove_FC converted:20.03.2023
+Tanenbaum HC , Birmann BM , Bertrand KA , et al. Identifying monoclonal gammopathy of undetermined significance from electronic health records. Cancer Reports. 2023;6 (3 ):e1755. doi:10.1002/cnr2.1755
+
+Funding information National Cancer Institute, Grant/Award Number: R01 CA202712
+==== Body
+pmc1 INTRODUCTION
+
+Monoclonal gammopathy of undetermined significance (MGUS) is a largely asymptomatic obligate precursor to multiple myeloma (MM) and other lymphoproliferative disorders. Clinically, MGUS features moderately elevated serum monoclonal protein (M‐protein, <3 g/dL) and the absence of evidence of end organ damage (i.e., hypercalcemia, renal failure, anemia and bone lesions) or amyloidosis. 1 , 2 It is distinguished from smoldering MM, an intermediate stage between MGUS and MM by the M‐protein level (M‐protein ≥3 g/dl for smoldering MM) and the abundance of plasma cells in the bone marrow. 1 The prevalence of MGUS among adults aged 50 years or older in the United States is estimated at approximately 3%, with 1% of all MGUS cases advancing to MM per year. 2 , 3
+
+Overall, factors relating to the progression of MGUS to MM and other lymphoproliferative disorders remains largely unknown. Additionally, MGUS patients have demonstrated higher morbidity and mortality from bacterial and viral infections, peripheral neuropathy, thrombosis and other chronic diseases. 4 , 5 , 6 Further epidemiologic investigation of MGUS would facilitate advances in knowledge of MGUS disease progression and the development of strategies to prevent several malignant and non‐malignant outcomes.
+
+While population‐based screening may be considered the gold standard for observational research, this approach requires the availability of archived biospecimens. For MGUS, the diagnosis is not clinically actionable at present, 1 and thus widespread clinical screening to detect MGUS is not justifiable. Moreover, laboratory assays are expensive and therefore may not be feasible to use for broad‐scale screening to detect MGUS. Alternatively, manual medical chart review could be conducted to confirm MGUS diagnoses, but this approach is time‐ and labor‐intensive and would be prohibitively expensive for use in large epidemiological studies. Additionally, issues such as variation in reviewers' attention to detail can introduce ascertainment errors. 7 More recently, electronic algorithms have been developed to identify MGUS cases from electronic health records using diagnosis and utilization codes (e.g., for oncologist visit[s] and relevant lab tests without incorporating lab results). Studies of such algorithms have reported positive predictive values (PPV) between 76% and 88%. 8 , 9
+
+To build on these efforts to facilitate large scale epidemiologic research of MGUS using electronic health records—in particular, studies of factors associated with risk of progression to malignancy or other outcomes—we evaluated the performance of an electronically searchable diagnosis code‐based algorithm to identify patients with clinically diagnosed MGUS using electronic health records from a large integrated health care delivery system.
+
+2 METHODS
+
+2.1 Study setting
+
+This study was conducted at Kaiser Permanente Southern California, a large integrated healthcare system with over 4.6 million members. Data were retrieved from the system's comprehensive electronic health records, which contain chart notes from all medical encounters (including outpatient visits, emergency room visits, and hospitalizations), laboratory testing data and diagnosis codes, and Kaiser Permanente's Surveillance, Epidemiology and End Results‐affiliated cancer registry. The study was approved by Kaiser Permanente Southern California's Institutional Review Board, which also waived the requirement for informed consent.
+
+2.2 MGUS case identification algorithm and eligibility criteria for chart review confirmation
+
+The algorithm that we evaluated for identifying patients with clinically diagnosed MGUS had the following steps:We searched for patients with a first ICD‐9 diagnosis code of 273.1 between 2008–2014 (e.g., the “index” ICD‐9 code).
+
+We excluded those with a MM diagnosis within 6 months following the record of the index ICD‐9 code. 10
+
+Of the potential MGUS cases identified by steps (i) and (ii), we further restricted to those with at least 1 year of continuous health plan membership prior to the date of the index ICD‐9 code for the manual chart review confirmation (so that sufficient medical records would be available to confirm the MGUS diagnosis).
+
+We then randomly sampled 100 individuals from the remaining sample of eligible putative MGUS cases for chart review.
+
+The initial chart reviews revealed that some recorded electronic ICD‐9 codes for MGUS corresponded to a work‐up that led to diagnoses of other lymphoid malignancies (since M‐protein may also be used to monitor disease status in patients with other lymphoid malignancies 10 , 11 ). We thus subsequently revised the case‐identification algorithm outlined above to further restrict the sample of potential cases to those without evidence of other lymphoid malignancies from 2007 to 2014 and applied the same revision to the randomly selected subsample.
+
+When developing the case‐identification algorithm, we had considered developing a second algorithm based on records reporting serum M‐protein and immunofixation test results indicative of MGUS. We found that while serum M‐protein results can be queried as a discrete data field in Kaiser Permanente Southern California's electronic health records, they are sometimes not quantifiable, hindering their interpretation to determine the presence or absence of MGUS or a more advanced condition. 2 Further, immunofixation results exist only as free text and thus cannot be readily queried without language processing tools that were not available to the project. Given these limitations, we could not develop a comprehensive case‐identification algorithm based on laboratory results. Nonetheless, we used the initial M‐protein‐based efforts to identify a separate sample of plan members with clinician‐diagnosed MGUS in whom we could assess the sensitivity of the ICD‐9 diagnosis‐code based approach, as described below.
+
+2.3 Chart review confirmation for clinically diagnosed MGUS among individuals with an ICD‐9 diagnosis code for MGUS
+
+We manually reviewed medical chart notes within (±) 6 months of the first recorded ICD‐9 code (273.1) for documentation of a physician diagnosis to confirm clinically diagnosed MGUS for the randomly selected putative cases. For any unconfirmed cases, we further conducted review of the entire medical history to understand potential reasons for the inaccuracies. All reviews and confirmation dispositions were verified by a second chart reviewer. Because our purpose was to validate the diagnosis code‐based algorithm for identifying patients with clinically diagnosed MGUS (rather than to ascertain all diagnosed and undiagnosed MGUS in the Kaiser Permanente Southern California population or to determine the accuracy of the physician diagnosis against standard diagnostic criteria for MGUS2), a physician diagnosis of MGUS in the chart notes was considered the gold standard for confirmation.
+
+During chart review, we collected information on relevant test results, including serum or urine M‐protein, immunofixation and free light chain tests when available, and on the presence of clinical signs of end organ damage that contribute to a diagnosis of full‐blown MM, such as hypercalcemia, renal failure, anemia and bone lesions. The latter information was not always documented, and when present, the underlying conditions leading to the associated form of end organ damage were often not specified (e.g., renal failure could be due to long‐term diabetes rather than to MM or other malignancy). These challenges supported our decision to rely on evidence of a physician diagnosis as the gold standard for confirming clinically diagnosed MGUS in plan members with the corresponding ICD‐9 code rather than relying on reported clinical symptoms. We initially sought to determine the timing of the physician diagnosis. However, as the chart notes often had insufficient documentation of this timing, it was rarely possible to distinguish patients with newly diagnosed MGUS from those with a history of MGUS that predated our study period.
+
+To address potential misclassification between MGUS and smoldering MM, we specifically searched the chart notes to capture potential smoldering MM diagnoses for those without a quantifiable M‐protein value (e.g., whose smoldering MM would have remained undetected by a review of M‐protein test results). We also searched for and conducted chart review to confirm an electronic ICD‐9 code for MM diagnosis (203.0) within 2 years after the index date among all confirmed MGUS cases, as misclassified smoldering MM cases would have a higher probability than true MGUS cases of progressing that quickly after the MGUS diagnosis. 1 , 12
+
+2.4 Estimation of the sensitivity of the ICD‐9‐based MGUS case identification algorithm
+
+To estimate the sensitivity of the electronic ICD‐9 code‐based case‐identification algorithm, we used the same process described above (for the subsample of putative cases first identified with the ICD‐9 diagnosis code‐based approach) to perform chart review of 54 randomly selected putative MGUS patients first identified based on a first serum M‐protein test result between 0 and 3 g/dL between 2008 and 2014 (“index” M‐protein test), with at least 1 year of health plan membership prior to the index M‐protein test result and with no subsequent diagnosis of MM or other lymphoid malignancy (assessed as described for the ICD‐9‐based algorithm above). We utilized the subset of these 54 health plan members who were confirmed by medical chart review to have clinically diagnosed MGUS as a separate patient sample in whom to estimate the sensitivity of the ICD‐9 code‐based algorithm. Specifically, we searched those patients' electronic records for an ICD‐9 diagnosis code for MGUS (273.1) before or within 1 year after the index M‐protein test. A more comprehensive evaluation of algorithm sensitivity was beyond the scope of this project.
+
+2.5 Hematologist adjudication to explore the accuracy of the clinician diagnosis of MGUS
+
+As an exploratory exercise to assess the accuracy of the MGUS diagnosis made by physicians in the chart notes in comparison to current MGUS diagnostic criteria, 2 two hematologists (co‐authors Hoda Pourhassan and Scott Goldsmith) independently adjudicated 10 randomly selected chart review confirmed MGUS cases. This chart review process was a separate exercise from the steps described above to confirm the index ICD‐9 diagnosis code for MGUS via medical chart review. The two hematologists reviewed relevant clinical information available within 6 months of the index MGUS diagnosis code and provided an assessment of their certainty of the presence of MGUS by designating the putative MGUS case as “definite, probable, possible, no evidence of MGUS, or unable to determine.” They also provided notes articulating the rationales for their assessments. Discrepancy between the two hematologists was resolved by discussion. A priori, we considered the cases adjudicated as “definite” and “probable” MGUS as confirmed cases and those with “possible,” “no evidence of MGUS,” or “unable to determine” as unconfirmed cases according to current diagnostic criteria.
+
+2.6 Statistical analysis
+
+The distributions of demographic characteristics (age, sex, race/ethnicity) and the Charlson comorbidity index were obtained for the subsample of 100 ICD‐9 algorithm‐identified potential cases randomly selected for chart review. We also utilized the chart review findings to calculate the PPV for this subsample to inform the probability that an MGUS patient identified by the electronic record ICD‐9 code‐based algorithm truly had a physician diagnosis of MGUS (the gold standard for case confirmation for this project). Specifically, among the subsample of ICD‐9 algorithm‐identified putative MGUS cases subjected to chart review, the PPV was calculated as: PPV=#of putative MGUS cases confirmedbychart review tobeclinically diagnosed÷#ofallpotential MGUS cases subjected to chart review.
+
+We calculated a second PPV among the further restricted subsample after applying the exclusion criterion based on a diagnosis of any lymphoid malignancy during the study period using the same formula as above. For that second PPV calculation, the additional exclusion criterion was applied to both the denominator and the numerator of the calculation. In the separate subset of putative MGUS cases first identified by the M‐protein test result‐based alternative algorithm (described above) and subsequently confirmed by medical chart review to have a clinician diagnosis of MGUS, we estimated the sensitivity of the ICD‐9 code‐based algorithm as: Sensitivity=(#of putative MGUS cases confirmedbychart review tobeclinically diagnosed andwhohadan electronically recordedICD−9code of273.1before or within1year after theM−protein test)÷(#ofallputative MGUS cases initially identifiedbyaM−protein test and confirmedbychart review tobeclinically diagnosed).
+
+3 RESULTS
+
+3.1 Putative MGUS patients identified by the ICD‐9 code‐based case‐identification algorithm
+
+A total of 8861 potential MGUS cases who met the eligibility criteria were identified using the case‐identification algorithm. Of these, we randomly selected 100 cases for chart review confirmation. Of these 100 potential cases, 58 were male, and 42 were of non‐Hispanic white race/ethnicity (Table 1). The mean age as of the index ICD‐9 diagnostic record was 72 years (standard deviation [SD]: 10.4). The mean Charlson comorbidity index score was 2.7. Prior to applying the additional exclusion criterion related to a diagnosis of other lymphoid malignancies during the study period, 90 of the 100 randomly selected putative MGUS patients had evidence of a physician diagnosis of MGUS in the medical chart, corresponding to a PPV of 90% for the initial ICD‐9 code algorithm (Table 2). After we applied the additional exclusion criterion 92 putative cases remained. Among those, 90 (PPV = 97.8%, Table 2) were still confirmed in chart notes, and 2 (2.2%) were not confirmed (1 had an amyloidosis diagnosis and 1 had no discernible relevant diagnosis, Figure 1).
+
+TABLE 1 Demographic and clinical characteristics of the Kaiser Permanente Southern California plan members initially identified as putative MGUS patients by an ICD‐9 code‐based algorithm and subsequently randomly selected for confirmation of the MGUS diagnosis by medical chart review
+
+	Chart review confirmation subsample (N=100) a	
+Age, years, mean (SD)	72.0 (10.4)	
+Male sex	58 (58.0%)	
+Race/ethnicity		
+Non‐Hispanic white	42 (42.0%)	
+Non‐Hispanic black	24 (24.0%)	
+Hispanic	22 (22.0%)	
+Asian/Pacific islander	8 (8.0%)	
+Other/unknown	4 (4.0%)	
+Charlson comorbidity index, mean (SD)	2.7 (2.0)	
+Length of health plan membership, years, mean (SD)	24.1(14.6)	
+Abbreviations: ICD‐9, International Classification of Diseases, 9th edition; MGUS, Monoclonal Gammopathy of Undetermined Significance; SD, Standard deviation.
+
+a These 100 individuals were randomly selected from all putative MGUS cases identified by the original algorithm, that is, as health plan members with a first (“index”) ICD‐9 code in the electronic record between 2008 and 2014, no subsequent diagnosis of MM (within 6 months after the index ICD‐9 code date), and at least 1 year of continuous plan membership prior to the index ICD‐9 code.
+
+TABLE 2 Positive predictive value of the electronic record ICD‐9 code (273.1)‐based MGUS case identification algorithm in the Kaiser Permanente Southern California electronic health records system
+
+Chart review result	Full subsample (N) a	Revised subsample (N) b	
+Confirmed c	90	90	
+Non‐confirmed	10	2	
+Total	100	92	
+Algorithm PPV d	90/100 = 90%	90/92 = 97.8%	
+Abbreviations: ICD‐9, International classification of diseases, 9th Revision; MGUS, monoclonal gammopathy of undetermined significance; MM, multiple myeloma; PPV, positive predictive value.
+
+a This algorithm identified putative MGUS cases as health plan members with a first (“index”) ICD‐9 code in the electronic record between 2008‐2014, no subsequent diagnosis of MM (within 6 months after the index ICD‐9 code date), and at least 1 year of continuous plan membership prior to the index ICD‐9 code.
+
+b The revised algorithm further excluded putative MGUS cases with a diagnosis of any other lymphoid malignancy during 2007–2014.
+
+c We defined confirmed cases as those with evidence in the medical chart of a physician diagnosis of MGUS.
+
+d The PPV suggests that ~98% of health plan members identified by the algorithm summarized above truly had a physician diagnosis of MGUS (our “gold standard” for defining a confirmed case of MGUS).
+
+FIGURE 1 Flowchart for ICD‐9 code‐based case identification and chart review confirmation
+
+3.2 Chart review findings on the confirmed clinically diagnosed MGUS cases
+
+When we evaluated serum M‐protein test results for the 90 confirmed cases identified by the ICD‐9 code algorithm, we found a serum M‐protein result of <3 g/dl for 48 cases (42.4%). The charts for the 42 cases with no M‐protein lab value contained comments indicating that the serum protein electrophoresis result was not quantifiable; however, other chart information indicated that these cases were, in fact, all confirmed by immunofixation and/or free light chain testing to be true MGUS cases.
+
+When we investigated the potential misclassification of smoldering MM as MGUS among the confirmed cases with no quantifiable M‐protein in the chart, we did not find any mention of a smoldering MM diagnosis within the chart review period. Of the full set of 90 confirmed MGUS cases, 3 developed MM within 2 years after the index ICD‐9 code date.
+
+3.3 Sensitivity estimation
+
+In the analyses to estimate the sensitivity of the ICD‐9‐based algorithm, 40 of the 54 potential MGUS cases initially identified by an eligible M‐protein test results were confirmed to be true clinically diagnosed MGUS cases by manual chart review. Of those 40 confirmed clinically diagnosed MGUS cases, 35 had an ICD‐9 diagnosis code before or within 1 year after the index M‐protein test, corresponding to an estimated sensitivity of 87.5% (Table 3).
+
+TABLE 3 Sensitivity of the electronic record ICD‐9 code (273.1)‐based case identification algorithm as estimated in a separate subset of Kaiser Permanente Southern California plan members with physician‐diagnosed MGUS
+
+ICD‐9 code in the electronic record a	MGUS cases confirmed by chart review (N) b	
+Yes	35	
+No	5	
+Total	40	
+ICD‐9 code algorithm sensitivity c	35/40 = 87.5%	
+Abbreviations: ICD‐9, International classification of diseases, 9th Revision; MGUS, monoclonal gammopathy of undetermined significance; MM, multiple myeloma; PPV, positive predictive value.
+
+a ICD‐9 code in the electronic record between 2008‐2014, no subsequent diagnosis of MM (within 6 months after the index ICD‐9 code date) or of any other lymphoid malignancy (during 2007–2014), and at least 1 year of continuous plan membership prior to the index ICD‐9 code.
+
+b This subset of MGUS patients comprised health plan members with at least 1 year of continuous plan membership prior to an initial finding of an electronic record with a circulating monoclonal protein test result consistent with an MGUS diagnosis (0 to 3 g/dl) between 2007 and 2014 and with no subsequent diagnoses of MM (within 6 months of the qualifying lab result) or of any other lymphoid malignancy (during the study period) and for whom subsequent medical chart review yielded evidence of a physician diagnosis of MGUS.
+
+c This estimate suggests that ~88% of health plan members with a physician diagnosis of MGUS in the medical chart will also have an electronic record with an MGUS‐specific ICD‐9 code (273.1).
+
+3.4 Hematologist adjudication findings
+
+The adjudication of the 10 hematologist‐reviewed cases concluded 5 cases as “probable.” The remaining 5 were designated as “possible” due to the lack of complete information on the presence or absence of end organ damage (e.g., hypercalcemia, renal failure, anemia, and bone lesions) and/or the lack of findings from a bone marrow study. The unavailability of those details in the medical charts prevented the hematologists from ruling out a more advanced diagnosis than MGUS, for example, smoldering MM or MM, for the “possible” cases. (The hematologists did not note any findings that refuted the presence of either MGUS, smoldering MM or MM in any of the 10 charts they reviewed.)
+
+4 DISCUSSION
+
+We confirmed an algorithmic approach that can be used to efficiently and accurately identify clinically diagnosed prevalent MGUS cases for population‐based research to study outcomes of MGUS and factors associated with progression to malignancy. Our results suggest that the diagnosis code‐based algorithm has excellent PPV and likely also satisfactory sensitivity for ascertaining individuals with clinically diagnosed MGUS.
+
+Our findings are aligned with results of a Danish study that used ICD diagnosis codes to identify patients with MGUS who were subsequently confirmed with chart reviews. 9 That study reported an initial PPV of 82.3% but subsequently applied additional exclusion criteria to eliminate cases diagnosed with malignant monoclonal gammopathy prior to or within 1 year of the MGUS diagnosis. This change resulted in a PPV improvement of approximately 4 percentage points. Similarly, when we expanded our exclusion criteria to include other lymphoid malignancies, the PPV of our diagnosis code approach increased from 90% to 97.8%.
+
+More recently, an algorithm was developed to identify MGUS cases using electronic health records from a large, community‐based healthcare group. 8 , 13 The criteria required at least two MGUS diagnosis codes entered on different dates in a 12‐month period, as well as at least one serum protein electrophoresis or immunofixation test (regardless of the results) and an oncologist office visit within 90 days of the MGUS diagnosis. After excluding putative patients diagnosed with MM ±3 months after the first MGUS diagnosis code, this approach achieved a PPV of 75%. It is possible that amending the exclusion criteria by extending the window of time and list of relevant diagnoses (e.g., to other lymphoproliferative disorders) may improve the PPV of this approach as well.
+
+The hematologist adjudication findings suggested that MGUS diagnoses made in the community‐based practice may not always involve a complete symptom work‐up and/or bone marrow study within an applicable time window (i.e., a ±6‐month chart review window). Furthermore, other underlying conditions can contribute to the reporting of certain forms of MM‐defining end organ damage (e.g., poorly controlled diabetes for renal failure), yet the potential causes for the end organ damage are not always discussed in the chart. These practice patterns are likely also present in other community‐based practice settings. This finding suggests potential misclassification between MGUS and smoldering MM in real‐world practice, although the apparently missing clinical workups may have been completed at a time outside of the chart review window utilized for this study. We attempted to assess the magnitude of this potential misclassification by searching for MM diagnoses within 2 years after the index date. Of the 90 confirmed MGUS cases, only 3 developed MM within 2 years. This observation suggests that the potential misclassification of smoldering MM as MGUS by our ICD‐9‐based algorithm is small in magnitude given that the progression rate to MM for smoldering MM is expected to be much higher, for example, 10% per year for the first 5 years after diagnosis, whereas that for MGUS is ~1% per year. 14
+
+There are several other limitations to consider when interpreting our results. First, this study was not designed to assess the specificity or negative predictive value of our algorithms because of the focus on the reliable identification of individuals with a physician diagnosis of MGUS for inclusion in studies of outcomes of MGUS. For that purpose, the PPV and sensitivity were the most relevant parameters to evaluate. Second, we could not distinguish newly made MGUS diagnoses from those previously known due to insufficient documentation about the timing of a first MGUS diagnosis in the chart notes. Of note, since MGUS is an asymptomatic condition, the exact timing of MGUS onset cannot be determined for any patient in usual clinical care. A strength of our approach is that it allows the identification of a large proportion (potentially the vast majority) of clinically diagnosed MGUS patients at any given point in time. As an additional limitation, we note that our study period ended prior to the transition from ICD‐9 to ICD‐10 code use, which occurred in 2015. Potential differences in relevant code(s) in the ICD‐10 system should be considered when applying the ICD‐based algorithm to more recent years, as should the incorporation (around 2014) of serum free light chain testing into standard clinical workups to diagnose MGUS. 2 Validation of an updated version of the algorithm could be informative to ensure optimal sensitivity and PPV for studies that span beyond 2014, while the inclusion of cases from earlier time periods based on the present ICD‐9 code algorithm can help to maximize follow‐up time for the study of relevant outcomes. Finally, the generalizability of our findings to different settings with electronic health records (such as in health systems that are not integrated) needs further confirmation.
+
+Although we designed this study to validate ascertainment of clinically diagnosed MGUS, it should be noted that undiagnosed MGUS is prevalent in older populations; indeed, approximately 80% of prevalent MGUS cases are unrecognized. 15 Thus, identification of MGUS patients from clinical diagnoses will likely lead to under‐ascertainment of the true MGUS prevalence. As a result, this case‐identification approach should not be used to identify all incident MGUS for etiologic studies. Rather, this approach will be useful to study clinically diagnosed MGUS, particularly with regard to disease progression and factors associated with disease progression. It should also be noted that asymptomatic patients are not randomly selected to undergo the laboratory tests that comprise a MGUS work up/diagnosis. Further research is necessary to evaluate or document factors that drive laboratory testing among asymptomatic individuals and characterize the potential bias of this non‐random MGUS detection in epidemiologic studies that are not designed as screening studies. For example, it is likely that seemingly healthier individuals (i.e., those without any clinical indication for serum protein electrophoresis testing) will be systematically missed in observational studies. The impact of this potential bias on electronic health record‐based studies of MGUS will need to be considered based on the specific study objectives.
+
+Despite some limitations, our findings may be useful for certain types of epidemiologic studies, such as to investigate risk factors for MGUS malignant progression or to improve clinical surveillance for more serious non‐malignant outcomes. Such studies could accelerate identification of prevention strategies for more severe MGUS outcomes that would be considered clinically actionable, including MM and lymphoproliferative disorders. Results from the latter could be particularly useful for justifying larger screening‐based studies to develop risk‐prediction models among individuals with MGUS.
+
+AUTHOR CONTRIBUTIONS
+
+Hilary C Tanenbaum: Data curation (lead); formal analysis (lead); investigation (lead); project administration (equal); validation (equal); writing – original draft (equal). Brenda M Birmann: Conceptualization (equal); funding acquisition (equal); methodology (equal); writing – review and editing (lead). Kimberly Bertrand: Methodology (supporting); writing – review and editing (supporting). Lauren Teras: Methodology (supporting); writing – review and editing (supporting). Amrita Y Krishnan: Methodology (supporting); writing – review and editing (supporting). Hoda Pourhassan: Investigation (supporting); writing – review and editing (supporting). Scott Goldsmith: Investigation (equal); writing – review and editing (equal). Kim Cannavale: Investigation (supporting); project administration (equal); validation (equal); writing – review and editing (supporting). Sophia S Wang: Conceptualization (equal); funding acquisition (equal); methodology (supporting); writing – review and editing (supporting). Chun Chao: Conceptualization (equal); methodology (equal); supervision (lead); writing – original draft (equal).
+
+CONFLICT OF INTEREST
+
+The authors have stated explicitly that there are no conflicts of interest in connection with this article.
+
+ETHICS STATEMENT
+
+This study is approved by Kaiser Permanente Southern California IRB, Approval # 11139.
+
+ACKNOWLEDGMENTS
+
+The authors thank the patients of Kaiser Permanente for helping us improve care through the use of information collected through our electronic health record systems. This work was supported by the National Institutes of Health/National Cancer Institute grant # R01 CA202712.
+
+DATA AVAILABILITY STATEMENT
+
+Anonymized data that support the findings of this study may be made available from the corresponding author on reasonable request from qualified researchers with documented evidence of training for human subjects protections.
+==== Refs
+REFERENCES
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+2 Rajkumar SV , Dimopoulos MA , Palumbo A , et al. International myeloma working group updated criteria for the diagnosis of multiple myeloma. Lancet Oncol. 2014;15 (12 ):e538‐e548.25439696
+3 Kyle RA , Durie BG , Rajkumar SV , et al. Monoclonal gammopathy of undetermined significance (MGUS) and smoldering (asymptomatic) multiple myeloma: IMWG consensus perspectives risk factors for progression and guidelines for monitoring and management. Leukemia. 2010;24 (6 ):1121‐1127.20410922
+4 Kristinsson SY , Bjorkholm M , Andersson TM , et al. Patterns of survival and causes of death following a diagnosis of monoclonal gammopathy of undetermined significance: a population‐based study. Haematologica. 2009;94 (12 ):1714‐1720.19608666
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+

PMC10028155.txt

@@ -0,0 +1,263 @@
+
+==== Front
+Cancer Med
+Cancer Med
+10.1002/(ISSN)2045-7634
+CAM4
+Cancer Medicine
+2045-7634
+John Wiley and Sons Inc. Hoboken
+
+36221244
+10.1002/cam4.5331
+CAM45331
+CAM4-2022-08-3348.R1
+Research Article
+RESEARCH ARTICLES
+Cancer Prevention
+Remote assessment of cognitive dysfunction in hematologic malignancies using web‐based neuropsychological testing
+Franco‐Rocha et al.
+Franco‐Rocha Oscar Y. https://orcid.org/0000-0002-5547-1518
+1
+Mahaffey Misty L. 2
+Matsui William 3
+Kesler Shelli R. https://orcid.org/0000-0002-4745-8014
+1 3 srkesler@austin.utexas.edu
+
+1 Brain Health Neuroscience Lab School of Nursing, University of Texas at Austin Austin Texas USA
+2 Department of Hematology/Oncology Stanford Cancer Institute, Stanford Health Care Palo Alto California USA
+3 Department of Oncology Dell School of Medicine, University of Texas at Austin Austin Texas USA
+* Correspondence
+Shelli R. Kesler, Brain Health Neuroscience Lab, University of Texas at Austin, 1210 Red River St. Austin, TX 78712, USA.
+Email: srkesler@austin.utexas.edu
+
+11 10 2022
+3 2023
+12 5 10.1002/cam4.v12.5 60686076
+21 9 2022
+04 8 2022
+25 9 2022
+© 2022 The Authors. Cancer Medicine published by John Wiley & Sons Ltd.
+https://creativecommons.org/licenses/by/4.0/ This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
+
+Abstract
+
+Background
+
+Cognitive impairment is a frequent adverse effect of cancer and its therapies. As neuropsychological assessment is not often standard of care for patients with non‐CNS disease, efficient, practical assessment tools are required to track cognition across the disease course. We examined cognitive functioning using a web‐based cognitive testing battery to determine if it could detect differences between patients with cancer and controls.
+
+Methods
+
+We enrolled 22 patients with multiple myeloma (MM) or non‐Hodgkin lymphoma (NHL) and 40 healthy controls (mean age = 56 ± 11 years, 52% male). Participants completed the BrainCheck cognitive testing battery and online versions of select measures from the Patient Reported Outcome Measures Information System (PROMIS) during a video conference. MANOVA was used to compare BrainCheck and PROMIS scores between groups controlling for age and sex. An exploratory linear regression analysis was conducted within the cancer group to determine potential contributors to cognitive functioning.
+
+Results
+
+All participants except for one control completed the online assessment measures without difficulty. Compared to controls, the cancer group demonstrated significantly lower scores in objective and subjective cognitive function, physical functioning, and social role performance and elevated fatigue scores. Corticosteroid treatment, immunotherapy, lower physical functioning, lower income, and older age significantly contributed to lower cognitive function (adjusted R 2 = 0.925, F = 19.63, p = 0.002).
+
+Conclusion
+
+Remote assessment of cognitive and psychosocial functioning is feasible with patients with cancer following treatments. The BrainCheck cognitive testing battery has the potential to detect differences in cognition between patients with cancer and controls.
+
+Cancers and their treatments can result in impaired brain function. Evaluating brain function can be challenging due to feasibility and resource limitations. We remotely administered a web‐based, commercially available cognitive testing battery and demonstrated that patients scored significantly lower than controls. Immunotherapy, high dose steroids, older age, lower physical function, and lower income level were associated with lower cognitive function. Remote assessment of cognitive function is feasible in patients with cancer and could increase availability of brain function surveillance.
+
+cognitive function
+hematological cancer
+hematopoietic stem cell transplantation
+psychosocial functioning
+remote assessment
+National Cancer Institute 10.13039/100000054 1R01CA172145 2R01CA226080 source-schema-version-number2.0
+cover-dateMarch 2023
+details-of-publishers-convertorConverter:WILEY_ML3GV2_TO_JATSPMC version:6.2.6 mode:remove_FC converted:21.03.2023
+Franco‐Rocha OY , Mahaffey ML , Matsui W , Kesler SR . Remote assessment of cognitive dysfunction in hematologic malignancies using web‐based neuropsychological testing. Cancer Med. 2023;12 :6068‐6076. doi: 10.1002/cam4.5331
+==== Body
+pmc1 INTRODUCTION
+
+Cancer and its treatments are frequently associated with decline in cognitive functioning. However, most studies to date have involved patients with breast cancer and therefore less is known regarding cognitive effects in other malignancies. Certain hematologic cancers, including multiple myeloma (MM) and non‐Hodgkin's lymphoma (NHL) undergo more intensive treatment regimens that may be associated with higher risk for cognitive decline. 1 These treatments can include high dose chemotherapy, immunotherapy, high dose steroid, and hematopoietic stem cell transplant (HSCT) that may affect memory, attention, and executive function 2 , 3 , 4 months and years after treatment completion, 5 with worse cognitive outcomes when these treatment modalities are combined. 1 , 6
+
+Cognitive functioning encompasses multiple abilities that are essential for treatment compliance. 7 This is particularly relevant in hematologic malignancies as patients often remain on therapy for a long time, and cognitive impairment may reduce their ability to engage in treatment adherence. 8 Cognition has also been associated with worse cancer outcomes. For instance, cognitive problems are associated with less self‐confidence, and more difficulties returning to work and with social relationships, which has the potential to reduce the quality of life of patients with cancer. 9 , 10 Furthermore, emerging studies have shown an association between cognitive impairment and lower survival in hematological cancers. 11 , 12
+
+The incidence of hematological cancers varies depending on the type of cancer. For instance, NHL is one of the most common cancers in the United States, with a rate 1 in 42 for women and 1 in 52 for men. 13 In contrast, MM is relatively uncommon, affecting 1 in 132. 14 Thus, multi‐site or national recruitment may be needed to obtain adequate sample sizes to assess neuropsychological symptoms. In addition, cancer‐related cognitive impairment can be subtle in some patients, representing a decline from a previous level of functioning that may not be apparent with static testing. So, in future studies, longitudinal, repeated assessment is recommended to identify changes in neuropsychological symptoms over time. 15 This can be challenging given the high cost of formal neuropsychological assessments and limited availability of neuropsychological resources within oncology centers. Remote measurement technology could provide a means of assessing cognitive functioning in a rapid and practical way. Nonetheless, to date, there have been few if any studies involving remotely administered cognitive testing batteries in patients with cancer. In this analysis, we aimed to (1) examine the feasibility of remotely assessing cognitive functioning in patients with hematologic cancer, and (2) determine if the testing battery could detect differences in cognition between patients with cancer and healthy controls.
+
+2 METHODS
+
+Feasibility studies help to assess whether methods and interventions are appropriate for further testing. 16 Bowen et al. (2009) 16 proposed general areas of focus in feasibility studies. The present feasibility study focused on implementation, the extent to which the testing battery was administered as planned. This included how often participants could access the online assessments without difficulty and how often they completed the assessments correctly.
+
+2.1 Participants
+
+We enrolled patients with MM or NHL who had received high dose chemotherapy and autologous HSCT at least 30 days prior. Patients were referred to the study by clinicians at the Livestrong Cancer Institute in Austin, Texas, and the Stanford Cancer Institute in Palo Alto, California from October 1, 2020, to November 1, 2021. We focused on hematologic malignancies that our study team had access to and that tend to receive intensive therapies while limiting the diagnoses to reduce sample heterogeneity. Patients were excluded for history of total body or cranial radiation. Medical history was obtained via patient self‐report. We also enrolled non‐cancer controls through referrals from enrolled patients, study clinicians, and social media postings. Participants were included if they were age 21 years or older, able to read, speak and write English, and had computer and internet access. Participants were excluded for prior history of diagnosed conditions known to affect cognition. The University of Texas at Austin Institutional Review Board approved the study (protocol# 2020‐05‐0117).
+
+2.2 Cognitive assessment
+
+Participants completed BrainCheck, a standardized, web‐based cognitive testing battery that assess processing speed, visual attention (Trails A), processing speed, visual attention and cognitive flexibility (Trails B), processing speed (Digit Symbol), response inhibition (Stroop), and verbal declarative memory (Immediate and Delayed Recall). These cognitive domains are of particular interest because, as previously mentioned, they are most frequently affected by cancer and its therapies, including patients with hematologic cancer. 2 , 3 , 4 BrainCheck requires approximately 15 minutes to complete and has been shown to have strong psychometric properties and significant sensitivity for detecting mild cognitive impairment. 17 , 18 We previously demonstrated that this battery could detect mild cognitive impairment in COVID‐19 survivors 19 , 20 but it has not yet been used to evaluate cancer‐related cognitive impairment. BrainCheck provides standard scores for each test as well as for a Composite score (mean = 100, SD = 15), that are standardized for age and type of device based on normative data. To obtain the Composite score, a raw composite score is calculated first by averaging all assessment scores. Then, the raw composite score is normalized for age and type of device with normative data from BrainCheck. All scores are normally distributed (mean = 0, SD = 1) and then scaled to 100, with a standard deviation of ±15, with higher scores representing higher cognitive performance. 17 , 18
+
+2.3 Patient reported outcomes
+
+We also administered the Patient Reported Outcome Measures Information System v2.0 Cognitive Function Short Form 8a (PROMIS Cognitive) to measure subjective cognitive function. 21 The PROMIS 57 22 was administered to evaluate symptoms of depression, fatigue, anxiety, sleep disturbance, pain interference, physical functioning, and social role performance, which can contribute to cognitive effects. PROMIS measures were administered online via REDCap Survey (Vanderbilt, TN) 23 using the REDCap Shared Library, 24 which also automatically provided normative scores for each scale (mean = 50, SD = 10).
+
+2.4 Assessment administration
+
+After completing the screening process, the participants were emailed an invitation to an encrypted video conference call. During the video call, the research staff explained the study procedures and sent the link to the REDCap surveys. BrainCheck generates a link and anonymous login ID for each administration. Once the surveys were completed, the staff sent the BrainCheck link and ID to the participant so they could access the BrainCheck test. REDCap surveys include written instructions for completing each survey. BrainCheck includes written instructions for each test as well as practice versions to orient participants to the test procedures. Although BrainCheck and PROMIS are designed to be administered to examinees in a self‐directed manner, research staff remained on the video conference call, with screen sharing, to assist with any questions or technical issues and to help ensure an optimal and reliable testing environment. All participants completed the assessments at home. We required participants to use a laptop or desktop computer and the Google Chrome internet browser to reduce potential variation in testing platform.
+
+2.5 Statistical analysis
+
+Data were first examined visually for normality. Sample characteristics were compared between groups using t‐test and chi square tests, as appropriate. There was collinearity within BrainCheck and PROMIS scales, so MANCOVA was applied for each, controlling for age and sex. BrainCheck Composite score was evaluated separately using ANCOVA, also controlling for age and sex. We conducted an exploratory linear regression analysis within the cancer group to examine the effects of demographic, clinical, and psychosocial variables on cognitive function. Specifically, we included racial/ethnic minority status (minority = 1, non‐minority = 0), age (years), education (years), and sex (male = 1, female = 0) as these are known to contribute to cognitive performance in patients with cancer and other neurologic conditions, 25 , 26 corticosteroid treatment (1 = yes, 0 = no), immunotherapy (1 = yes, 0 = no), and post‐transplant days given prior studies demonstrating that these can affect cognitive function, 1 , 3 , 27 and physical functioning score, fatigue score, and income level (1 = greater than $100 K, 0 = lower than $100 K) as these differed between groups. Cancer diagnosis (lymphoma = 1, myeloma = 0) was also included given that these have different pathologies and treatment regimens which may result in different cognitive outcomes. We examined only the BrainCheck Composite score to reduce multiple comparisons in our small sample. Alpha was set at 0.05. All analyses were performed using JASP v0.16.3 (JASP Team) or the R Statistical Package v4.2.0 (R Foundation).
+
+3 RESULTS
+
+3.1 Sample characteristics
+
+During the year of our study, we enrolled 23 patients with MM (N = 11) or NHL. Eight of the patients with NHL reported a diagnosis of diffuse large B‐cell lymphoma while the others did not specify the type of NHL. Patients underwent autologous transplant 94.14 (SD = 62.48, range = 30–237) days, on average, prior to evaluation. All confirmed having received high dose chemotherapy, though only two specified which drugs, 36% reported receiving high dose corticosteroid (yes/no), and 23% reported receiving immunotherapy (yes/no). We also enrolled 40 controls. There were no significant differences between the groups in age, education, or biological sex (Table 1). In the cancer group, 32% endorsed racial/ethnicity minority status compared to 18% of controls, though this difference was not‐significant (X2 = 1.66, p = 0.197). All participants reported racial/ethnic status. Significantly more patients with cancer reported having an annual household income over $100 K compared to controls (X2 = 5.78, p = 0.016). However, six patients with cancer and four controls declined to report income information. Additionally, one participant attempted to enroll again under another name to obtain a second $25 e‐gift card honorarium, claiming their video camera was not functioning. Study staff recognized the individual's voice and the phone number so were able to prevent the fraudulent entry and this participant was administratively withdrawn from the study resulting in a final sample size of 22 in the cancer group.
+
+TABLE 1 Sample Characteristics
+
+	Cancer (N = 22)	Controls (N = 40)	Statistics	p Value	
+Age (years)	59.19 (11.87)	54.59 (9.53)	t = 1.67	0.101	
+Education (years)	16.09 (16.65)	16.65 (2.28)	t = 0.928	0.357	
+Male a	50%	52%	X 2 = 0.036	0.851	
+Racial/ethnic minority b	32%	18%	X 2 = 1.66	0.197	
+Income < $100 K	25%	61%	X 2 = 5.78	0.016	
+Multiple myeloma	50%				
+Non‐Hodgkin's lymphoma	50%				
+High dose dexamethasone	36%				
+Immunotherapy	23%				
+Note: Continuous data are shown as mean (standard deviation) and categorical data are shown as percentage.
+
+a Sex categories included male, female, non‐binary/third gender, prefer to self‐describe, prefer not to answer. All participants endorsed either male or female.
+
+b Racial/ethnic categories included Asian, Black, Caucasian, Hispanic/Latinx, Native American, Pacific Islander, prefer to self‐describe, prefer not to answer. No participants chose to self‐describe or not to answer. Racial/ethnic minority was defined as Asian, Black, Hispanic/Latinx, Native American, Pacific Islander, based on census data for the Palo Alto, CA and Austin, TX regions.
+
+3.2 Feasibility
+
+In terms of implementation, all participants were able to access the online questionnaires and cognitive testing without difficulty. All participants but one control completed the testing battery without difficulty. The control participant's session was interrupted by a phone call, resulting in the Trail Making Test timing out. Therefore, those data were excluded from analysis.
+
+3.3 Cognitive performance
+
+MANCOVA indicated a significant effect of group for BrainCheck tests (Pillai = 0.559, p < 0.001). All tests were significantly lower in the cancer group compared to controls, except for Trails B and Immediate Recall (Table 2). The ANCOVA for BrainCheck Composite score indicated significantly lower performance in the cancer group compared to controls (F = 29.16, p < 0.001).
+
+TABLE 2 Cognitive performance controlling for age and sex
+
+	Cancer Mean	%ile	Standard deviation	Control Mean	%ile	Standard deviation	F	p	
+Trails A	97.45	45	10.44	106.31	66	10.63	9.91	<0.001	
+Trails B	99.27	47	6.85	105.80	66	16.02	3.40	0.07	
+Digit symbol	87.59	21	15.31	108.51	73	12.56	29.43	<0.001	
+Stroop	89.82	25	13.50	106.86	68	12.90	13.37	<0.001	
+Immediate recall	106.41	66	11.70	110.25	75	5.48	3.08	0.08	
+Delayed recall	99.41	47	14.95	109.59	75	7.43	12.84	<0.001	
+Composite score	96.66	45	7.75	107.89	70	7.15	29.16	<0.001	
+Note: Scores have a normative mean of 100 and standard deviation of 15. Percentile (%ile) corresponds to the standard score and is interpreted as the percentage of individuals who score below the given standard score, on average, when those individuals are matched for age and sex.
+
+3.4 Patient reported outcomes
+
+MANCOVA indicated a significant effect of group for PROMIS scales (Pillai = 0.578, p < 0.001). Patients demonstrated significantly lower subjective cognitive function, physical functioning, and social role performance as well as significantly higher fatigue compared to controls (Table 3).
+
+TABLE 3 Patient reported outcomes controlling for age and sex
+
+	Cancer Mean	Standard deviation	Control Mean	Standard deviation	F	p	
+Cognitive function	46.65	6.78	52.00	7.80	7.79	0.01	
+Physical function	42.42	9.16	56.18	5.63	54.24	<0.001	
+Anxiety	51.15	6.05	50.07	7.96	0.33	0.57	
+Depression	49.75	5.49	47.13	9.21	1.43	0.24	
+Fatigue	52.96	6.62	44.82	7.08	24.15	<0.001	
+Sleep disruption	49.33	10.40	46.16	7.44	1.77	0.19	
+Social function	44.68	9.15	57.41	8.24	33.80	<0.001	
+Pain interference	52.56	10.27	48.51	6.93	3.26	0.08	
+
+3.5 Contributors to cognitive performance
+
+The overall linear regression model was significant (adjusted R 2 = 0.925, F = 19.63, p = 0.002). Dexamethasone treatment (β = −28.58, p = 0.002), immunotherapy (β = −13.33, p = 0.009), lower physical functioning (β = 1.32, p = 0.002), lower income (β = 22.94, p = 0.005), and older age (β = −1.102, p = 0.022) were significantly associated with lower cognitive function.
+
+Regression diagnostics indicated no violations of linearity, normality, or homoscedasticity. However, the high R 2 value in such a small sample suggested potential overfitting of the model. We then performed a 3‐fold cross‐validation of the regression model and observed that the mean R 2 value across the folds was much lower, R 2 = 0.551 (SD = 0.361), but explained over half the variance in Composite score. The R 2 value range across the folds explained 43%–76% of the variance.
+
+4 DISCUSSION
+
+We showed that remote cognitive assessment of patients with hematological cancers is feasible and provides several advantages including efficiency, convenience, and automated scoring. This is consistent with previous studies supporting the feasibility of remotely measuring cognitive performance in other populations. 19 , 28 Additionally, although for this pilot study we restricted enrollment to English‐speaking participants, BrainCheck can be administered in multiple languages. However, there are also several caveats to remote assessment. We supervised the testing sessions via videoconferencing and screen sharing, but it did not completely prevent interruptions in the participants' home environment. This occurred in only one case but happened even after we had instructed participants to complete testing in a quiet area, free from distractions. When using remote assessment, investigators must consider that some data might be lost due to uncontrolled factors in the participant environment, which would be much less likely in the typical laboratory or clinic in‐person scenario. Investigators must also be cautious with respect to recruitment considering our experience with the participant who attempted to enroll twice. Fraudulent enrollment may be a risk with remote assessment and therefore video conferencing is critical. However, this has certain technological requirements that may prevent subgroups of patients from participating.
+
+Our findings also showed that the BrainCheck battery has the potential to detect differences in cognition between patients with cancer and healthy controls. The cancer group had significantly lower scores in executive function, attention, processing speed, and delayed verbal memory, which is consistent with other studies. 2 , 5 , 29 Despite this, overall performance in the cancer group was clinically “average” for all tests except for Digit Symbol (processing speed) and Stroop (executive function), which were “low average,” higher than only 21%–25% of similarly aged individuals. This is consistent with previous studies suggesting that cancer‐related cognitive impairment tends to be mild to moderate. 30
+
+The cancer group performed significantly lower than the control group on all tests administered except for Trails B. Patients with cancer scored lower on this test than controls and thus, we may have lacked sufficient power to detect a difference here. Alternatively, this may have reflected a practice effect. Trails A is administered prior to Trails B and the two tests are highly similar. Although Trails B is supposedly more difficult than Trails A due to the addition of set shifting for measuring cognitive flexibility, Trails A may allow the examinee to become familiarized with the novel task and subsequently perform better on Trails B. 31
+
+Our exploratory regression analysis showed that several factors may contribute to cognitive impairment in patients with MM or NHL. Older age was associated with lower cognitive performance, consistent with previous findings. 26 We also observed that lower income was a predictor of lower cognitive function. Income may be considered an indirect measure of cognitive reserve because it reflects the lifetime experience of individuals and their socioeconomic capacity 32 including access to health insurance and higher‐quality medical care as well as stimulating environments, activities, and opportunities. On the other hand, education and racial/ethnic minority status were not significant contributors to cognitive function in our model, which contrast with previous findings. 33 , 34 This may suggest that income level is a more important predictor. However, our sample was small with most participants identifying as White and being highly educated and therefore, we may have lacked adequate power and variance to examine these effects.
+
+Previous studies have proposed that corticosteroids and immunotherapy are risk factors for cognitive impairment in patients with hematologic cancers. 35 , 36 Even though the results from our study support this association, they must be interpreted cautiously. Again, our sample was small, only 23% of our participants received immunotherapy, and 36% received high‐dose corticosteroids. Future studies with larger and more diverse samples are necessary to analyze how clinical characteristics contribute to cognitive symptoms. Also, to explore differences in cognitive performance within different treatment types, such as the type of immunotherapy received.
+
+Lower physical function was another predictor of cognitive performance and the only patient‐reported outcome associated with cognition in the present study. Physical function in this context refers to the individual's ability to carry out simple and complex physical activities, usually in social contexts. 37 It is an important factor for promoting cognitive functioning and preventing cognitive decline. 38 Furthermore, HSCT and concomitant treatment, like steroids, negatively impact physical functioning post‐transplant, 39 which is consistent with our findings.
+
+However, our results contrast with previous studies that showed an association between fatigue and cognitive performance in patients with breast cancer. 40 , 41 Post hoc analysis of our data indicated that women in the cancer group endorsed significantly more fatigue than men (F = 34.45, p < 0.001). However, given that there was no effect of sex on cognitive performance, the women may have been more resilient than the men with respect to brain health. Alternatively, we may have lacked adequate power for detecting an effect of fatigue on cognition.
+
+Unlike prior studies, we did not detect an effect of education level on cognitive outcome. 26 , 42 Most previous studies have involved female patients with breast cancer, so they are not directly comparable to our findings. However, education level is considered a proxy for cognitive reserve and is thus frequently correlated with cognitive performance across conditions of brain health and disease. 32 Our sample did not likely have adequate variance in education level to fully explore this relationship given that participants were all highly educated, with more than half the sample having college degrees.
+
+Finally, we did not observe an effect of racial/ethnic minority status on cognitive function in this sample. Previous studies have shown mixed results on the influence of race and ethnicity in cognitive performance after cancer treatment. While some studies have found that racial/ethnic minority status is associated with cognitive performance, 25 , 43 , 44 others have not. 45 , 46 However, the inclusion of racial and ethnic diverse groups in studies analyzing the influence of cancer and its treatment in cognitive health has been limited to date. Research studies analyzing these differences and the potential contributors of unique factors experienced by these groups, such as structural racism, are warranted.
+
+This study was strengthened by restricting inclusion to participants with MM or NHL, which reduced the heterogeneity of the sample and shed light on the specific cognitive and psychosocial needs of patients with these types of cancers. However, the sample size was small, the participants were highly educated, and from upper class income, limiting the generalizability to patients with different sociodemographic characteristics. Another limitation was the significant number of variables assessed as potential predictors of cognitive functioning. There are always multiple potential contributors to the complexity of cognition with cancer treatments adding further to this. Reliance on patient‐report for treatment information resulted in missing data that may have provided further insights regarding contributors to cognitive function. Additionally, future studies with larger samples should include more precise questions regarding treatments such as the specific corticosteroid and immunotherapies received. The cross‐validation of our linear regression model produced a range of R 2 values that suggested the model had strong performance but was unstable. Further validation of the potential predictors of cognitive function that we have identified here is required. There may be other testing batteries with remote capability that yield different results and remote assessment is not a substitution for comprehensive clinical evaluation. Remote assessment requires access to technology that may limit or bias the sample, especially among socially disadvantaged patients who are at highest risk for cognitive effects.
+
+In conclusion, remote assessment of cognitive and psychosocial functioning in patients with cancer is feasible. The BrainCheck cognitive testing battery has the potential to detect differences in cognition between patients with cancer and healthy controls and therefore could be more widely used in this population. Sociodemographic (age and lower income), clinical (corticosteroids and immunotherapy), and physical factors (lower physical function) may contribute to cognitive decline in people with MM or NHL after treatment. However, research studies with larger and more diverse samples are necessary to assess contributors to cognitive functioning.
+
+AUTHOR CONTRIBUTIONS
+
+Oscar Y. Franco‐Rocha: Conceptualization (equal); validation (equal); visualization (equal); writing – original draft (equal); writing – review and editing (equal). Misty L. Mahaffey: Methodology (equal); supervision (equal); validation (equal); visualization (equal); writing – review and editing (equal). William Matsui: Methodology (equal); supervision (equal); validation (equal); visualization (equal); writing – review and editing (equal). Shelli R. Kesler: Conceptualization (equal); formal analysis (equal); funding acquisition (equal); methodology (equal); resources (equal); supervision (equal); validation (equal); visualization (equal); writing – original draft (equal); writing – review and editing (equal).
+
+FUNDING INFORMATION
+
+This research was supported by funding from the National Institutes of Health (1R01CA226080, 2R01CA172145).
+
+CONFLICTS OF INTEREST
+
+The authors declare no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
+
+ACKNOWLEDGMENTS
+
+None.
+
+DATA AVAILABILITY STATEMENT
+
+All data relevant to the study are included in the article.
+==== Refs
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+20 Henneghan AM , Lewis KA , Gill E , Kesler SR . Cognitive impairment in non‐critical, mild‐to‐moderate COVID‐19 survivors. Front Psychol. 2022;13 :770459. doi:10.3389/fpsyg.2022.770459 35250714
+21 Jensen RE , Potosky AL , Moinpour CM , et al. United States population‐based estimates of patient‐reported outcomes measurement information system symptom and functional status reference values for individuals with cancer. J Clin Oncol. 2017;35 (17 ):1913‐1920. doi:10.1200/JCO.2016.71.4410 28426375
+22 Cella D , Riley W , Stone A , et al. The patient‐reported outcomes measurement information system (PROMIS) developed and tested its first wave of adult self‐reported health outcome item banks: 2005–2008. J Clin Epidemiol. 2010;63 (11 ):1179‐1194. doi:10.1016/j.jclinepi.2010.04.011 20685078
+23 Harris PA , Taylor R , Thielke R , Payne J , Gonzalez N , Conde JG . Research electronic data capture (REDCap)‐‐a metadata‐driven methodology and workflow process for providing translational research informatics support. J Biomed Inform. 2009;42 (2 ):377‐381. doi:10.1016/j.jbi.2008.08.010 18929686
+24 Obeid JS , McGraw CA , Minor BL , et al. Procurement of shared data instruments for research electronic data capture (REDCap). J Biomed Inform. 2013;46 (2 ):259‐265. doi:10.1016/j.jbi.2012.10.006 23149159
+25 Kesler SR , Petersen ML , Rao V , Harrison RA , Palesh O . Functional connectome biotypes of chemotherapy‐related cognitive impairment. J Cancer Surviv. 2020;14 (4 ):483‐493. doi:10.1007/s11764-020-00863-1 32157609
+26 Ahles TA , Saykin AJ , McDonald BC , et al. Longitudinal assessment of cognitive changes associated with adjuvant treatment for breast cancer: impact of age and cognitive reserve. J Clin Oncol. 2010;28 (29 ):4434‐4440. doi:10.1200/JCO.2009.27.0827 20837957
+27 Phillips NS , Kesler SR , Scoggins MA , et al. Connectivity of the Cerebello‐Thalamo‐cortical pathway in survivors of childhood leukemia treated with chemotherapy only. JAMA Netw Open. 2020;3 (11 ):e2025839. doi:10.1001/jamanetworkopen.2020.25839 33216140
+28 George MF , Holingue CB , Briggs FBS , et al. Feasibility study for remote assessment of cognitive function in multiple sclerosis. J Neurol Neuromedicine. 2016;1 (8 ):10‐18. doi:10.29245/2572.942x/2016/8.1084 28255581
+29 Friedman MA , Fernandez M , Wefel JS , Myszka KA , Champlin RE , Meyers CA . Course of cognitive decline in hematopoietic stem cell transplantation: a within‐subjects design. Arch Clin Neuropsychol. 2009;24 (7 ):689‐698. doi:10.1093/arclin/acp060 19767298
+30 Wefel JS , Kesler SR , Noll KR , Schagen SB . Clinical characteristics, pathophysiology, and management of noncentral nervous system cancer‐related cognitive impairment in adults. CA Cancer J Clin. 2015;65 (2 ):123‐138. doi:10.3322/caac.21258 25483452
+31 Demakis GJ . Frontal lobe damage and tests of executive processing: a meta‐analysis of the category test, stroop test, and trail‐making test. J Clin Exp Neuropsychol. 2004;26 (3 ):441‐450. doi:10.1080/13803390490510149 15512932
+32 Stern Y . Cognitive reserve. Neuropsychologia. 2009;47 (10 ):2015‐2028. doi:10.1016/j.neuropsychologia.2009.03.004 19467352
+33 Jones D , Vichaya EG , Wang XS , Sailors MH , Cleeland CS , Wefel JS . Acute cognitive impairment in patients with multiple myeloma undergoing autologous hematopoietic stem cell transplant. Cancer. 2013;119 (23 ):4188‐4195. doi:10.1002/cncr.28323 24105672
+34 Jim HSL , Small B , Hartman S , et al. Clinical predictors of cognitive function in adults treated with hematopoietic cell transplantation. Cancer. 2012;118 (13 ):3407‐3416. doi:10.1002/cncr.26645 22139882
+35 Tomasi D , Wang GJ , Volkow ND . Energetic cost of brain functional connectivity. Proc Natl Acad Sci U S A. 2013;110 (33 ):13642‐13647. doi:10.1073/pnas.1303346110 23898179
+36 Sostak P , Padovan CS , Yousry TA , Ledderose G , Kolb HJ , Straube A . Prospective evaluation of neurological complications after allogeneic bone marrow transplantation. Neurology. 2003;60 (5 ):842‐848. doi:10.1212/01.wnl.0000046522.38465.79 12629244
+37 Riley WT , Rothrock N , Bruce B , et al. Patient‐reported outcomes measurement information system (PROMIS) domain names and definitions revisions: further evaluation of content validity in IRT‐derived item banks. Qual Life Res. 2010;19 (9 ):1311‐1321. doi:10.1007/s11136-010-9694-5 20593306
+38 Erickson KI , Hillman C , Stillman CM , et al. Physical activity, cognition, and brain outcomes: a review of the 2018 physical activity guidelines. Med Sci Sports Exerc. 2019;51 (6 ):1242‐1251. doi:10.1249/MSS.0000000000001936 31095081
+39 Morishita S , Kaida K , Yamauchi S , et al. Relationship between corticosteroid dose and declines in physical function among allogeneic hematopoietic stem cell transplantation patients. Support Care Cancer. 2013;21 (8 ):2161‐2169. doi:10.1007/s00520-013-1778-7 23475197
+40 Askren MK , Jung M , Berman MG , et al. Neuromarkers of fatigue and cognitive complaints following chemotherapy for breast cancer: a prospective fMRI investigation. Breast Cancer Res Treat. 2014;147 (2 ):445‐455. doi:10.1007/s10549-014-3092-6 25138546
+41 Bekele BM , Luijendijk M , Schagen SB , de Ruiter M , Douw L . Fatigue and resting‐state functional brain networks in breast cancer patients treated with chemotherapy. Breast Cancer Res Treat. 2021;189 (3 ):787‐796. doi:10.1007/s10549-021-06326-0 34259949
+42 Perrier J , Viard A , Levy C , et al. Longitudinal investigation of cognitive deficits in breast cancer patients and their gray matter correlates: impact of education level. Brain Imaging Behav. 2020;14 (1 ):226‐241. doi:10.1007/s11682-018-9991-0 30406352
+43 Syed Alwi SM , Narayanan V , Mohd Taib NA , Che DN . Chemotherapy‐related cognitive impairment (CRCI) among early‐stage breast cancer survivors in Malaysia. J Clin Exp Neuropsychol. 2021;43 (5 ):534‐545. doi:10.1080/13803395.2021.1945539 34369307
+44 Kohler C , Chang M , Allemann‐Su YY , et al. Changes in attentional function in patients from before through 12 months after breast cancer surgery. J Pain Symptom Manage. 2020;59 (6 ):1172‐1185. doi:10.1016/j.jpainsymman.2020.01.001 31953207
+45 Chen BT , Sethi SK , Jin T , et al. Assessing brain volume changes in older women with breast cancer receiving adjuvant chemotherapy: a brain magnetic resonance imaging pilot study. Breast Cancer Res. 2018;20 (1 ):38. doi:10.1186/s13058-018-0965-3 29720224
+46 Lyon DE , Cohen R , Chen H , et al. Relationship of systemic cytokine concentrations to cognitive function over two years in women with early stage breast cancer. J Neuroimmunol. 2016;301 :74‐82. doi:10.1016/j.jneuroim.2016.11.002 27890459
+

PMC10028170.txt

@@ -0,0 +1,274 @@
+
+==== Front
+Cancer Med
+Cancer Med
+10.1002/(ISSN)2045-7634
+CAM4
+Cancer Medicine
+2045-7634
+John Wiley and Sons Inc. Hoboken
+
+36394080
+10.1002/cam4.5409
+CAM45409
+CAM4-2022-08-3480.R1
+Research Article
+RESEARCH ARTICLES
+Clinical Cancer Research
+Immune checkpoint gene VSIR predicts patient prognosis in acute myeloid leukemia and myelodysplastic syndromes
+Yao et al.
+Yao Kevin https://orcid.org/0000-0001-6369-655X
+1
+Zhou Emily https://orcid.org/0000-0002-9194-7042
+2
+Schaafsma Evelien https://orcid.org/0000-0002-3821-3568
+3 4
+Zhang Baoyi 5
+Cheng Chao https://orcid.org/0000-0002-5002-3417
+6 7 8 chao.cheng@bcm.edu
+
+1 Department of Electrical and Computer Engineering Texas A&M University College Station Texas USA
+2 Department of Biosciences Rice University Houston Texas USA
+3 Department of Molecular and Systems Biology Dartmouth College Lebanon New Hampshire USA
+4 Department of Biomedical Data Science The Geisel School of Medicine at Dartmouth College Lebanon New Hampshire USA
+5 Department of Chemical and Biomolecular Engineering Rice University Houston Texas USA
+6 Department of Medicine Baylor College of Medicine Houston Texas USA
+7 Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine Houston Texas USA
+8 Institute for Clinical and Transcriptional Research, Baylor College of Medicine Houston Texas USA
+* Correspondence
+Chao Cheng, Department of Medicine, Baylor College of Medicine, Houston, TX 77030, USA.
+Email: chao.cheng@bcm.edu
+
+16 11 2022
+3 2023
+12 5 10.1002/cam4.v12.5 55905602
+08 10 2022
+11 8 2022
+24 10 2022
+© 2022 The Authors. Cancer Medicine published by John Wiley & Sons Ltd.
+https://creativecommons.org/licenses/by/4.0/ This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
+
+Abstract
+
+Background
+
+Immune checkpoint proteins play critical functions during the immune response to cancer and have been targeted by immune checkpoint blockade therapy. V‐domain Ig suppressor of T cell activation (VSIR) is one of these immune checkpoint genes and has been investigated extensively in recent years due to its conflicting roles in cancer immunity. Specifically, in acute myeloid leukemia (AML), the prognostic value of VSIR is debated.
+
+Results
+
+In both patient tumor samples and cancer cell lines we find that VSIR has the highest expression in AML out of all cancer types and, in AML, has the highest expression out of all other immune checkpoint genes. Survival analysis indicated that AML patients with higher VSIR expression have significantly shorter survival than those patients with lower expression, even within established AML subgroups (e.g., FAB subtypes). Importantly, VSIR expression is predictive of progression from myelodysplastic syndromes (MDS) patients into AML, suggesting its potential role during the very early stage of AML development and progression. In addition to AML, VSIR also demonstrates prognostic values in other cancer types, including multiple myeloma and mesothelioma.
+
+Conclusion
+
+In summary, our analyses revealed the prognostic value of VSIR and its potential as a target for immunotherapy, especially in AML.
+
+The Kaplan‐Meier plot shows that patients with higher VSIR expression have a significantly worse prognosis. In addition, the forest plot showing that VSIR expression remains significant in predicting prognosis after adjusting for molecular and genetic variables like FAB score and cytogenetic risk.
+
+AML
+MDS
+prognosis
+VSIR
+Cancer Prevention and Research Institute of Texas 10.13039/100004917 RR180061 source-schema-version-number2.0
+cover-dateMarch 2023
+details-of-publishers-convertorConverter:WILEY_ML3GV2_TO_JATSPMC version:6.2.6 mode:remove_FC converted:21.03.2023
+Yao K , Zhou E , Schaafsma E , Zhang B , Cheng C . Immune checkpoint gene VSIR predicts patient prognosis in acute myeloid leukemia and myelodysplastic syndromes. Cancer Med. 2023;12 :5590‐5602. doi: 10.1002/cam4.5409
+
+Kevin Yao and Emily Zhou contributed equally to this work.
+==== Body
+pmc1 INTRODUCTION
+
+Acute myeloid leukemia (AML) is a blood cancer of myeloid cells and is projected to have about 20,500 new cases and 11,540 deaths in 2022. 1 AML is characterized by the presence of abnormal or poorly differentiated, proliferative, and clonal myeloid cells, leading to higher concentrations of myeloblasts in bone marrow, blood, and other tissues. These blasts consume body resources and prevent the genesis of normal healthy cells. AML often progresses quickly, and the 5‐year survival for AML patients is as low as about 30%. 1 AML is more likely to develop in patients with underlying myelodysplastic syndromes (MDS), a heterogeneous class of diseases that result in ineffective hematopoiesis. In fact, about a third of patients with MDS will develop AML. 2 The distinguishing criteria for MDS or AML diagnosis are the percentage of blasts in the bone marrow and peripheral blood, which has been arbitrarily set as 30% by the French‐American‐British (FAB) classification system 3 and 20% by the World Health Organization (WHO). 4
+
+The main treatment for AML has traditionally been chemotherapy and targeted therapy drugs. 5 In recent years, cancer treatment has been greatly advanced by the development of immunotherapy, including immune checkpoint inhibitors, adoptive cell therapies, and monoclonal antibodies. 6 Such an advancement is largely attributed to the discovery of immune checkpoint targets, especially PD‐L1 and CTLA‐4. Therapy via blockade of these targets has found success for solid cancers such as melanoma 7 and non‐small lung cancer. 8 For AML, there have also been pilot studies on the use of PD‐L1 checkpoint blockade. Ravandi et al. found that the median event‐free survival of patients treated with nivolumab in combination with idarubicin and cytarabine was not reached in the study, but a considerable number of patients experienced immune‐related adverse events. 9 In addition, Daver et al. found that the overall response rate of patients treated with nivolumab in combination with Azacitidine was 33%, with 11% of patients experiencing grade 3–4 immune‐related adverse effects. 10 Such immunotherapies have yet to be widely recommended for AML. This may be due to adverse effects or because AML has a lower mutation burden and a more suppressed immune system, 11 , 12 which tends to decrease the efficacy of immune checkpoint blockade therapy.
+
+In addition to PD‐L1 and CTLA‐4, other immune checkpoint targets like V‐domain Ig suppressor of T cell activation (VSIR, also called VISTA, C10orf54, B7‐H5, PD‐H1) have also been discovered and investigated. 13 VSIR is an integral membrane protein with an extracellular immunoglobulin domain, a stalk, a transmembrane domain, and a cytoplasmic tail. 14 Under physiological conditions, VSIR is highly expressed in hematopoietic lineages and tissues rich in infiltrating leukocytes and has lower expression in non‐hematopoietic tissue. In hematopoietic lineages, VSIR is expressed CD14+ monocytes, neutrophils, myeloid CD11c+ DCs, and CD4+ and CD8+ T cells. VSIR is not expressed on CD19+ B cells or CD56Hi NK cells. In mice, VSIR is highly expressed in tumor‐infiltrating leukocytes. Unlike PD‐1/PD‐L1, VSIR expression is restricted to cells from hematopoietic lineages. 15 VSIR not only serves as an inhibitor of T‐cell response, it also serves as a regulator for signaling and activation of innate immune cells. For example, in cancer, autoimmune, and inflammatory diseases, VSIR inhibits the production of inflammatory cytokines and chemokines in myeloid dendritic cells and macrophages. 15 It is mainly expressed in hematopoietic cells in humans. VSIR is encoded by a gene located at 10q22.1, which is entirely confined inside an intron of the CDH23 gene. 16 VSIR has attracted additional research interest due to its conflicting roles as both an inhibitory and a stimulatory immune checkpoint protein. 17 In AML, VSIR has been found to be highly expressed. 18 Knockout studies in mice found that VSIR induced immune evasion and caused the observed proliferation of AML cells. 18 This supports the candidacy of VSIR as a novel checkpoint target in AML treatment. Wang et al. found a positive correlation between the expression of VSIR in peripheral myeloid cells and the expression of PD‐1 in T cells, even though there is no evidence that these genes are directly regulated. 19 Since high PD‐1 expression has been implicated in a worse prognosis for AML patients, 20 we expect that VSIR expression might also be associated with patient prognosis. However, previous studies have reported inconsistent results on the prognostic association of VSIR expression in AML. 19 , 21 , 22 For instance, Wang et al. 19 found that VSIR expression did not correlate with prognosis, but Zhang et al. and Chen et al. 21 , 22 found that higher VSIR expression is related to poorer prognosis. In addition, there have not been reports on the prognostic value of VSIR in MDS, the pre‐AML disease.
+
+In this study, we investigated the prognostic value of VSIR gene expression in human AML and MDS. Using the TCGA (The Cancer Genome Atlas) and CCLE (Cancer Cell Line Encyclopedia) datasets, we first investigate the expression levels of VSIR in AML compared to other cancers and other immune checkpoint targets. We then establish the prognostic value of VSIR in AML based on survival analysis and show its potential for improving patient stratification in conjunction with established clinical factors, such as FAB subtype and cytogenetic risk. We also investigate the expression and prognostic value of VSIR in patients with myelodysplastic syndromes (MDS). Lastly, we perform a comprehensive analysis to examine the prognostic association of VSIR across seven different blood cancers and 33 other cancer types.
+
+2 MATERIALS & METHODS
+
+2.1 Datasets used in this study
+
+The Cancer Genome Atlas (TCGA) datasets for the various cancer types studied in this study were downloaded from Firehose (https://gdac.broadinstitute.org/). Specifically, for LAML, the dataset contains RSEM‐normalized gene expression data for 20,501 genes and 173 patient samples. Genomic mutation and copy number variation (CNV) data were downloaded from Firehose. 23 The genomic mutation data were downloaded in mutation annotation format (MAF) files and contain mutation information for 24,058 genes and 197 samples. Patient mutation burden is calculated as the sum of all non‐synonymous mutations. The CNV data were downloaded as a segmented copy number variation (sCNA) file and contain the CNV information for 23,311 genes and 380 patient samples. CNV burden is calculated as the fraction of genomic regions with abnormal chromosome copy numbers. 24
+
+Other than the transcriptomic and genomic data from TCGA, the study has investigated the following gene expression datasets. The cell line data for 33 cancers were downloaded from the Cancer Cell Line Encyclopedia (CCLE) database (https://portals.broadinstitute.org/). The de novo AML dataset contains 526 total samples and was downloaded from the Gene Expression Omnibus (GEO) under the accession ID GSE14468. The MDS dataset contains 159 MDS and 17 wild‐type samples and was downloaded from GEO under the accession ID GSE58831. The processed single‐cell RNA‐seq dataset was downloaded from GEO under accession ID GSE116256. It contained scRNA data for 38,410 cells from 40 bone marrow biopsies. Those biopsies were obtained from 16 patients with AML and five healthy donors. In addition, we have analyzed a collection of cancer gene expression datasets from the PREdiction of Clinical Outcomes from Genomic (PRECOG) (http://precog.stanford.edu) database, including a total of 166 datasets with matched gene expression profiles and patient survival information for seven different cancer types. More detailed information for these datasets can be found in Table S1.
+
+2.2 Survival analysis
+
+Overall survival (OS) event status and time were provided in the TCGA dataset. Using this data, we constructed univariate and multivariate Cox regression models using the “coxph” function with default parameters from the R library “survival.” The univariate Cox regression models were used to determine the association between overall survival and VSIR expression. The multivariate regression models were used to determine the association between OS and the following covariates: VSIR expression, FAB score, and cytogenetic risk. The results of the multivariate Cox regression models were visualized in forest plots, which was constructed using “forest_model” function from the R package “forestmodel.” Since VSIR expression is a continuous variable, we used the median VSIR expression to divide the samples into two equal‐sized groups (High and Low VSIR expression). The Kaplan–Meier method was used to plot the survival curves, and the log‐rank test was used to determine the difference between the two curves and calculate their p‐value. Survival analysis was performed using the R library “survival” with otherwise default parameters. The Kaplan Meier method was done using the function survfit from R library “survival” with default parameters, and the function “ggsurvplot” from the R library “ggplot” was used to plot the survival curves.
+
+2.3 Comparing VSIR expression in subgroups
+
+To test if VSIR expression is different between three or more subgroups, such as the eight FAB subtypes or three cytogenetic risk categories, we performed one‐way ANOVA using the R function “aov” with default parameters. To determine if VSIR expression is higher in one subgroup compared to the others, such as if VSIR expression is higher in NPM1 mut patients compared to NPM1 WT patients, we performed the one‐sided Wilcoxon test using the “wilcox.test” function in R.
+
+2.4 scRNA‐seq analysis of VSIR expression in healthy cells and AML cells
+
+Single‐cell RNA seq data of healthy cells were analyzed using data downloaded from the Bloodspot database, which provides mRNA expression profiles for a comprehensive list of hematopoietic cells. 25 We selected the HemaExp v1 dataset and filtered the genes to select the VSIR gene. Bloodspot then generated a diagram for the expression of VSIR depending on the cell type.
+
+For analysis of VSIR expression in AML patients, we utilized the GSE116256 dataset, which contains scRNA‐seq data on 38,410 cells from 40 bone marrow biopsies from 16 AML patients and five healthy patients. We chose to exclude the five healthy patients from this analysis, which left us with 30,712 cells. The cells were categorized as one of 14 cell types and determined to be cancerous or healthy. Of all the cell types, the only cancerous cells detected in this dataset were granulocyte monocyte progenitors (GMP), hematopoietic stem cells (HSC), progenitor cells (Prog), promonocytes (ProMono), classical dendritic cells (cDC), and monocytes (mono).
+
+2.5 VSIR expression in other blood cancers
+
+The PRECOG dataset was used to determine the expression of VSIR in other blood cancers. This dataset contains curated data from 166 datasets, including gene expression data and survival data for around 18,000 patients. Specifically, PRECOG contained gene expression data on the following hematopoietic cancers: two datasets for chronic lymphocytic leukemia (CLL), two datasets for multiple myeloma, one dataset for Burkitt lymphoma, seven datasets for AML, two datasets for B‐cell acute lymphoblastic leukemia (B‐ALL), three datasets for diffuse large B‐cell lymphoma (DLBCL), and one dataset for follicular lymphoma. To consolidate the survival analysis results from all datasets within a cancer type, after calculating the p‐values using the log‐rank test for each dataset individually, we used Fisher's method to calculate the meta p‐value for each cancer type.
+
+3 RESULTS
+
+3.1 VSIR is the immune checkpoint gene with the highest expression and association with prognosis in AML
+
+To investigate the implication of immune checkpoint genes in leukemia, we compared their expression in AML patient samples from TCGA and leukemia cell lines from CCLE. The TCGA data reflect the mixed expression of genes in both tumor cells and non‐tumor cells in the tumor microenvironment, while CCLE data provide a proxy of gene expression in tumor cells only. As shown in Figure 1A, VSIR has the highest expression out of all the immuno‐checkpoint genes in both TCGA and CCLE datasets for AML. This indicates that VSIR expression is prominent in both cancer cells (CCLE) and the cancer microenvironment (TCGA), including immune cells that have infiltrated into the bone marrow. Furthermore, we examined the association of these immune checkpoint genes with the prognosis of patients with AML using the TCGA data. VSIR was determined to be the most prognostic immune checkpoint gene in AML (Figure 1B).
+
+FIGURE 1 VSIR has highest expression and is prognostic in AML. (A) VSIR has the highest expression out of all cancer types in both the TCGA and CCLE datasets. (B) VSIR has the highest expression out of other known immune checkpoint genes in AML samples for both the TCGA and CCLE datasets. (C) Within TCGA AML samples, VSIR expression has the highest statistical significance and hazard ratio when predicting survival compared to other immune checkpoint genes. (D) Kaplan–Meier plot showing that TCGA AML patients with higher‐than‐median VSIR expression have significantly worser prognosis. (E) Forest plot validating the prognostic value of VSIR expression after considering the contributions of other molecular and genetic variables such as cytogenetic risk and FAB subtype in a multivariate Cox proportional hazards model.
+
+Previous studies have suggested that VSIR is highly expressed in hematopoietic tissues. 14 To confirm this, we examined its expression pattern in different cancer types. Indeed, VISA shows the highest expression in AML out of all other cancer types in both TCGA and CCLE datasets (Figure 1C). We then differentiated TCGA AML samples into High and Low VSIR expression using the median as the threshold. The survival curves of the two groups are shown in Figure 1D. As shown, the VSIR‐high expression group has significantly shorter overall survival than the VSIR‐low expression group (p = 2 e‐3), with a median survival of 273 days compared with 471 days, respectively. Multivariate regression analysis indicated that VSIR expression remains significant after considering well‐known clinical factors, including the FAB category and cytogenetic risk group (Figure 1E), suggesting that it provides additional prognostic value to these established clinical features.
+
+3.2 Validation of the prognostic value of VSIR in an independent dataset
+
+We further validate the prognostic association of VSIR in an independent AML dataset (GSE14468) with a larger number of samples (n = 526). Similar to previous results, after differentiating patients into two groups (High and Low VSIR expression) using the median as the threshold, patients with high VSIR expression exhibited significantly worse prognosis (p = 0.004) (Figure 2A), consistent with our previous observation. To account for possible confounding effects between VSIR and clinical variables, we performed a multivariate Cox regression analysis and found that high VSIR expression remained a significant prognostic indicator when clinical variables like FAB score and cytogenetic risk were considered (Figure 2B).
+
+FIGURE 2 Validation of VSIR prognostic value in GSE14468. (A) Kaplan–Meier plot shows that patients with higher VSIR expression have a significantly worse prognosis. (B) Forest plot showing that VSIR expression remains significant in predicting prognosis after adjusting for molecular and genetic variables like FAB score and cytogenetic risk. (C) VSIR expression is different depending on the FAB subtype (ANOVA p‐value = 3 e‐39). (D–F) AML patients with the FAB M1, M2, and M4 subtypes, respectively, have significantly poorer prognoses if they have high VSIR expression. (G) VSIR expression is different depending on cytogenetic risk (ANOVA p‐value = 4 e���17). (H) AML patients with an intermediate cytogenetic risk have a significantly poorer prognosis if they have high VSIR expression.
+
+Biomarkers for cancer are often only applicable within specific molecular or clinical subsets of patients. For example, the Oncotype DX assay is only applicable to ER‐positive breast cancer patients. 26 Considering this, we aimed to determine the prognostic value of VSIR within certain subsets of AML patients. One such classification system of AML is the French‐American‐British (FAB) system, which distinguishes roughly eight categories of AML based on cell type and maturity: M0 (myeloblastic without differentiation), M1 (myeloblastic with minimal maturation), M2 (myeloblastic with maturation), M3 (promyelocytic), M4 (myelomonocytic), M5 (monocytic), M6 (erythroid), and M7 (megakaryoblastic). VSIR expression varied considerably depending on the FAB subtype (ANOVA p‐value = 3 e‐39), suggesting that VSIR may play different roles within different subtypes. We thus systematically evaluated the prognostic value of VSIR within each subtype by utilizing Kaplan–Meier analysis on patients dichotomized by the median VSIR expression within that subtype. As demonstrated in Figure 2D–F, high VSIR expression in M1 (p = 0.05), M2 (p = 0.001), and M4 (p = 0.01) are associated with poor prognosis. In the other FAB subtypes, the prognostic association was not significant.
+
+The next clinical subtype we considered was a cytogenetic risk (Figure 2G), which is a classification based on whether a patient's cytogenetics confer relatively favorable, intermediate, or poor prognosis. For example, patients with the t(8;21) translocation have a favorable prognosis, whereas the del(7q) abnormality confers poorer prognosis. 27 Intermediate cytogenetic risk, which is exhibited in the majority of AML patients, 28 appeared to have the highest VSIR expression, and higher VSIR expression is associated with significantly poorer prognosis (p = 0.01) (Figure 2H). Thus, we have demonstrated that high VSIR expression is associated with poor survival in both an external AML dataset and clinical variable categories commonly associated with AML.
+
+3.3 VSIR is overexpressed in MDS patients and predicts overall and AML‐free survival
+
+MDS has been previously referred to as a preleukemia disorder, and about 30% of MDS patients eventually develop AML. 29 We, therefore, investigated the expression of VSIR in MDS samples. MDS patients exhibited significantly higher VSIR expression (p = 0.009) compared to their wild‐type or normal counterparts, which supports results from the previous work. 30 Additionally, VSIR expression remains prognostic in predicting overall survival in both a univariate (p = 0.008) and multivariate cox proportional hazards model (p = 0.02) when considering established clinical variables like age, gender, and cytogenetic risk (Table 1). Because MDS has a high frequency of transition to AML, we next investigated if VSIR is able to predict AML‐free survival, where events are defined as a patient with MDS progressing to AML. We find that higher VSIR expression puts patients at higher risk for progression to AML in a univariate model (p = 0.05) and after adjusting for possible confounding effects of age, gender, and cytogenetic risk (p = 0.02, Table 1). These results support the theory that VSIR acts as an immune checkpoint inhibitor, which may allow proliferating blasts to escape immune detection and progress to AML.
+
+TABLE 1 VSIR is prognostic for MDS patients
+
+PMID	# Samples/Cancer types	Tissue type	
+25,079,552	173	AML Hematopoietic Tissue	
+22,460,905	33	Cell Line	
+26,193,342	7	Various	
+25,574,665	176	Bone Marrow	
+30,827,681	40	Bone Marrow	
+19,171,880	526	Bone Marrow	
+Note: VSIR expression significantly predicts overall survival and AML‐free survival for MDS patients. This is validated in both a univariate cox proportional hazards model and in a multivariate cox proportional hazards model considering clinical variables like age, gender, and cytogenetic risk.
+
+3.4 Association of VSIR with the genomic landscape and biological pathways of AML patients
+
+To further investigate whether the expression of VSIR is affected by upstream genomic aberrations, we determined the correlation between the patients' VSIR expression and their mutation burden or their CNV burden in the TCGA dataset. Using Spearman's correlation test, we found that VSIR expression is significantly associated with mutation burden (p = 0.006, ρ = −0.218) but not CNV burden (p = 0.5, ρ = −0.0545). To explore specific mutations that may be associated with VSIR expression, we chose to only study NRAS, DNMT3A, IDH1, TET2, NPM1, WT1, FLT3, IDH2, TP53, CEBPA, and RUNX1 mutations, which are all present in greater than 5% of AML samples in the TCGA dataset. We then tested whether VSIR expression is different between patients with the mutation and those without the mutation. The p‐values were calculated by the Wilcoxon test and were adjusted using the Bonferroni multiple testing correction to limit the false discovery rate. We plotted that against the VSIR fold change between mutated and wild‐type samples (Figure 3A). NPM1 was the only mutation that remained significant, where patients with the mutation had higher VSIR expression (Figure 3B, p = 3 e‐5). This trend was also validated in the GSE14468 dataset (Figure 3C, p = 1 e‐23).
+
+FIGURE 3 VSIR association with the genomic landscape. (A) Volcano plot showing that NPM1 mutation is the only common genomic mutation in AML that has significantly different VSIR expressions between the group of patients with the mutation versus those without it. (B, C) Patients with the NPM1 mutation have significantly higher VSIR expression versus those without the mutation in both the TCGA (Wilcoxon p‐value = 1 e‐4) and GSE14468 (Wilcoxon p‐value = 1 e‐23) datasets. (D, E) Kaplan–Meier plots showing that patients are significantly dichotomized by their VSIR expression if they are NPM1 WT, but not if they have the NPM1 mutation, in both the TCGA and GSE14468 datasets.
+
+A previous study has reported that NPM1 mutation was associated with a favorable prognosis, 31 , 32 but another study observed no significant prognostic association. 33 Having shown that VSIR expression correlates with NPM1 mutation status, we further investigated if the prognostic value of VSIR can be explained by the prognostic value of the NPM1 mutation. We did not observe a significant survival difference between NPM1 mutant and wild‐type samples in both TCGA and GSE14468 (Figure S1). In addition, we investigated the prognostic association of VSIR expression within the NPM1 mutation and wild‐type subgroups. In the TCGA dataset, VSIR expression was negatively associated with patient prognosis (p = 0.01, HR = 1.837) in the NPM1 wild‐type subgroup, but not in the mutant subgroup (p = 0.8, HR = 1.119) (Figure 3D). In the GSE14468 dataset, similar results were observed (Figure 3E). As shown, higher VSIR expression was a poor prognostic factor for patients in the NPM1 wild‐type subgroup (p = 0.004, HR = 1.487), but high VSIR levels were not significantly associated with prognosis for NPM1 mutant patients (p = 0.2, HR = 1.470). These results seem to indicate that VSIR expression may only be prognostic for patients without the NPM1 mutation, but validation in a larger dataset is necessary to draw any definite conclusions.
+
+Although FLT3‐ITD mutations were not significantly associated with VSIR expression, since they occur in roughly 20%–25% of AML cases, 34 it is of importance to study the prognostic value of VSIR within populations with and without the FLT3‐ITD mutation. We found that VSIR expression significantly stratified patients without the mutation (p = 0.05, Figure S2A), but was unable to stratify patients with the FLT3‐ITD mutation (p = 0.4, Figure S2A), presumably due to the relatively small sample size (N = 63 for each group).
+
+Additionally, we note that NPM1 mutant AML is a clinically heterogeneous subtype due to its frequent coexistence with other mutations. Specifically, the FLT3‐ITD mutation is a about twice as frequent in patients with NPM1 mutations. 35 , 36 Thus, we next investigated the effect of FLT3‐ITD mutations on VSIR expression and prognostic value within NPM1 mutant patients. We found that patients with the FLT3‐ITD mutation in tandem with the NPM1 mutation had lower expression of VSIR (p = 0.05, Figure S2B). We then explored whether VSIR was prognostic within these subgroups and found that VSIR was not significantly prognostic within either (NPM1 mutants with FLT3‐ITD mutations p = 0.53, NPM1 mutants without FLT3‐ITD mutations p = 0.45, Figure S2C,D). The heterogeneity of NPM1 mutants may be confounded by other factors outside FLT3 mutations, such as stem cell signatures, 32 so further research in a larger dataset is needed to confirm the prognostic value of VSIR within further subtypes of NPM1 mutant samples.
+
+3.5 The expression pattern of VSIR in different cell types
+
+To understand the potential mechanisms underlying the prognostic value of VSIR at the cellular level, we examined its expression pattern using scRNA‐seq data. First, we mapped the landscape of single‐cell VSIR expression for healthy patients using the HemaExplorer dataset based on their lineage in hematopoiesis (Figure 4A). As expected, cells of the myeloid lineage tend to have higher VSIR expression, especially monocytes and polymorphonuclear cells (PMN) from the bone marrow (BM) or peripheral blood (PB).
+
+FIGURE 4 VSIR expression in single cells associated with leukemia. (A) VSIR expression in various hematopoietic cells of different cell lineages. Myeloid cells appear to have the highest expression of VSIR. (B) Single‐cell RNA sequencing data show that monocytes have the highest nonzero percent expression of VSIR. (C) Boxplots showing the distribution of VSIR expression for cDC, GMP, HSC, monocytes, progenitor cells, and pro‐monocyte cells based on whether the cell is healthy or cancerous. Malignant cells have consistently higher VSIR expression than normal cells. (D) VSIR expression correlation with monocyte marker genes such as ITGAM and CCR2. Any point above the horizontal red line has a significant nonzero Pearson correlation coefficient (p‐value <0.05).
+
+We next aimed to compare this landscape of normal VSIR expression to the VSIR expression levels of patients with AML by utilizing the GSE116256 dataset, which contains scRNA‐seq data on 30,712 cells from bone marrow biopsies from AML patients. We first aimed to identify the percentage of cells which express VSIR in these AML patients (Figure 4B). Cancerous cells generally have a higher nonzero expression of VSIR, and monocytes have the highest expression of VSIR for both normal and cancerous cells. We further explored the distribution of VSIR expression for normal vs cancerous cells with nonzero VSIR expression (Figure 4C) and found that monocytes are the only cell type where the VSIR expression in cancerous cells is significantly higher than that in normal cells. Based on this evidence, VSIR expression in monocytes is the most prominent and appears to be most affected when a person develops AML. Hypothesizing that VSIR may be co‐expressed on monocytes, we correlated the expression of VSIR with monocyte marker genes (Figure 4D) and found that every monocyte marker gene besides CCR7 is significantly correlated with VSIR expression. This further suggests that monocytes may be the major cell type that VSIR affects in AML patients.
+
+3.6 VSIR expression is prognostic in other cancer types
+
+Finally, we explored the association of VSIR expression with prognosis in other blood cancers and cancer types. Initially, we examined other types of blood cancers. As shown in Figure 5A, VSIR expression is significantly prognostic in chronic lymphocytic leukemia (CLL), AML, and multiple myeloma. We further divided the samples into two groups by using the median VSIR expression and constructed a survival curve. We revealed that low VSIR expression is significantly associated with poor prognosis in multiple myeloma (p = 2 e‐6) (Figure 5B). Similarly, low VSIR expression is significantly associated with poor prognosis in CLL (p = 8 e‐4) (Figure 5C). After examining other types of blood cancers, we expanded our scope to include all the cancer types available on TCGA. After performing a univariate Cox regression model using overall survival and VSIR expression in each cancer type, it was revealed that VSIR expression has the highest statistical significance and hazard ratio in AML (Figure 5D). As shown in Figures 5E,F similarly to previous findings, low VSIR expression is associated with poor prognosis in mesothelioma (MESO) (p = 2 e‐4) and cervical squamous cell carcinoma (CESC) (p = 0.03). These results are consistent with the previous studies. 17 , 37 , 38
+
+FIGURE 5 Prognostic value of VSIR in other blood cancer types. (A) Barplot showing the meta‐p values when predicting survival using VSIR in multiple myeloma, AML, CLL, and other blood cancers using the PRECOG dataset. (B, C) Kaplan–Meier plot showing that patients with higher VSIR expression have significantly better prognosis for both multiple myeloma and CLL, respectively. (D) Volcano plot showing that VSIR expression has high statistical significance when predicting survival in AML compared to other cancer types. (E, F) Kaplan–Meier plot showing that patients with higher VSIR expression have significantly better prognosis for both mesothelioma and cervical squamous cell carcinoma, respectively.
+
+4 DISCUSSION
+
+In this study, we demonstrated that VSIR is prognostic and has the highest expression out of all other immune checkpoint genes in AML. We validated this claim in the TCGA and GSE14468 datasets and accounted for possible confounding clinical variables like FAB category and cytogenetic risk. Favorable cytogenetic risk predicted better survival, which aligns with current guidelines. VSIR score was also found to be prognostic within the FAB M1, M2, and M4 categories, as well as patients with intermediate cytogenetic risk. We also found that VSIR is significant in predicting both overall survival and AML‐free survival for MDS patients. Specifically, MDS patients with higher VSIR expression have a higher risk of regressing into AML. Next, we considered genomic driver events that correlate with VSIR expression and found that NPM1 is the only common genomic mutation in AML that correlates significantly with VSIR expression. We then investigated the expression of VSIR in individual cells and found that monocytes have the highest expression of VSIR for both healthy cells and AML cells. Lastly, we determined that VSIR is also prognostic in other blood cancers like multiple myeloma and CLL and other cancers like mesothelioma and cervical squamous cell carcinoma.
+
+Our study supports the theory that VSIR acts as an inhibitory immune checkpoint in AML. 17 Higher VSIR expression suppresses immune responses at the cancer site and allows immune escape, leading to poorer prognosis. This should further support the ongoing efforts in determining the efficacy of VSIR blockade therapy in AML patients. 39 Interestingly, we observe the same trends in MDS patients, suggesting that VSIR also suppresses immune response for MDS patients, which would cause poorer prognosis and a higher risk of progressing to AML. These results can have implications in MDS treatment. Currently, immunotherapy is not recommended for MDS treatment, but our results suggest that VSIR blockade therapy may yield benefits. Clinical trials of administering immunotherapy to MDS patients should be evaluated more carefully.
+
+Currently, immune checkpoint inhibitors have modest efficacy in AML and MDS. The ORR rate of PD1 inhibitor combined with azacitidine in RR‐AML is about 30%, and the CR rate is about 20%. 40 Recently, a few clinical trials have been performed that evaluated the efficiency of anti‐VSIR‐based immunotherapy in multiple cancer types as monotherapy or combination therapies. Several VSIR inhibitors have undergone phase I and II clinical trials in solid tumors. 41 , 42 , 43 The VSIR inhibitors include CA‐170 (NCT02812875), CI‐8993 (NCT04475523), and W0180 (NCT04564417). CA‐170, an anti‐VSIR and anti‐PD‐L1 inhibitor, has shown very promising results. For example, in a phase II study for nonsquamous non‐small cell lung cancer, it was demonstrated that CA‐170 had a clinical benefit rate of 75% and patients had 19.5 weeks of progression‐free survival. 41 However, none of these trials were tested for treating AML or MDS. Our analysis in this study indicates that VSIR has the highest expression levels in AML compared to other cancer types; and in AML VSIR has the highest expression than the other immune checkpoint genes. These results suggest that VSIR might be a good target for delivering effective immune checkpoint blockade therapy.
+
+Importantly, for all cell types, VSIR expression increases when the cell develops AML, with monocytes having the highest VSIR expression with or without cancer. VSIR may directly bind to T cell receptors as a ligand, suppressing its activation. 44 , 45 Another possible pathway is that VSIR binds to and causes conformational changes in galectin‐9 that serve to enhance its effect. 46 Galectin‐9 has been reported to limit the activity of natural killer cells and induce apoptosis of T cells, thus limiting their ability to respond against cancer. 47 , 48 Our results suggest that the mechanism behind why higher VSIR expression leads to poorer prognosis may be that cancerous cells produce more VSIR protein, which through binding to T cells and galectin‐9, inhibits immune response. Monocytes appear to be the biggest player in the expression of VSIR, which may be understood since monocytes were the only cells (out of naive T cells, CD4 central memory T cells, CD4 effector memory T cells, CD8 EM T cells, NK cells, B cells, and basophils) that experienced significant transcriptional signature changes when VSIR was bound by anti‐VSIR antibodies. 49 Functionally, the transcriptional changes serve to enhance monocyte activity, such as increased secretion of IFNγ in a mixed lymphocyte reaction.
+
+Lastly, we note that higher VSIR expression leads to a better prognosis in multiple myeloma, CLL, MESO, and CESC. This is contrasted by the observation that higher VSIR expression leads to poorer prognosis in AML. The survival advantage of higher VSIR expression in multiple myeloma, MESO, and CESC agrees with the previous work, 17 , 37 , 38 but the prognostic value of VSIR in CLL has not been studied, to our knowledge. Both CLL and multiple myeloma are lymphoid cancers, which suggests that VSIR may play opposing roles depending on the hematopoietic lineage of cancer. Overall, this supports the consensus that VSIR plays a multifaceted role in cancer immunity across different cancers and may have co‐inhibitory or stimulatory roles in the immune response. 17
+
+Our study is currently limited by the lack of large datasets with VSIR expression data for AML patients. Our study may also be further improved through the incorporation of VSIR proteomics data, since gene expression may have a low correlation with protein expression. 50 , 51 A direct correlation between VSIR protein expression and poorer prognosis may further support the idea that VSIR should be targeted in checkpoint blockade therapy.
+
+In conclusion, we have reported that VSIR is a poor prognostic factor for overall survival in AML and MDS. These associations hold after accounting for possible confounding clinical variables. VSIR is also prognostic within specific subgroups of patients. Importantly, MDS patients with higher VSIR expression are also more likely to develop AML. Not only do these results further support efforts in administering VSIR blockade therapy for AML patients, we also suggest that VSIR blockade therapy may be a promising treatment for MDS patients, which is a relatively unexplored prospect.
+
+AUTHOR CONTRIBUTIONS
+
+Kevin Yao: Data curation (equal); formal analysis (equal); methodology (equal); writing – original draft (equal). Emily Zhou: Formal analysis (equal); visualization (equal); writing – review and editing (equal). Evelien Schaafsma: Writing – review and editing (supporting). Baoyi Zhang: Writing – review and editing (supporting). Chao Cheng: Conceptualization (lead); data curation (lead); formal analysis (supporting); funding acquisition (lead); methodology (lead); project administration (lead); supervision (lead); writing – review and editing (lead).
+
+FUNDING INFORMATION
+
+This work is supported by the Cancer Prevention Research Institute of Texas (CPRIT) (RR180061 to CC). CC is a CPRIT Scholar in Cancer Research.
+
+CONFLICT OF INTEREST
+
+The authors have declared that no conflict of interest exists.
+
+Supporting information
+
+Figure S1
+
+Click here for additional data file.
+
+Figure S2
+
+Click here for additional data file.
+
+Table S1
+
+Click here for additional data file.
+
+DATA AVAILABILITY STATEMENT
+
+All data generated or analyzed during this study are included in this published article and its supplementary information files.
+==== Refs
+References
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+

PMC10035369.txt

@@ -0,0 +1,292 @@
+
+==== Front
+Cancer Res Commun
+Cancer Res Commun
+Cancer Research Communications
+2767-9764
+American Association for Cancer Research
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+CRC-22-0022
+10.1158/2767-9764.CRC-22-0022
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+Research Article
+Hematological Cancers
+Myelomas
+Tumor Microenvironment
+Immune Cells and the Microenvironment
+Single Cell Technologies
+Comprehensive Characterization of the Multiple Myeloma Immune Microenvironment Using Integrated scRNA-seq, CyTOF, and CITE-seq Analysis
+Examining the MM TME with Complementary Single-cell Methods
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+Bhasin Manoj K. Supervision 3
+https://orcid.org/0000-0003-1517-2975
+Ding Li Conceptualization Supervision Writing - review and editing 1
+1 Washington University School of Medicine, Saint Louis, Missouri.
+2 Icahn School of Medicine at Mt. Sinai, New York, New York.
+3 Emory University, Atlanta, Georgia.
+4 Mayo Clinic, Rochester, Minnesota.
+5 Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts.
+6 Multiple Myeloma Research Foundation, Norwalk, Connecticut.
+Corresponding Authors: Li Ding, McDonnell Genome Institute, Department of Medicine, Department of Genetics, Washington University School of Medicine in St. Louis, 660 S. Euclid Ave., St. Louis, MO 63110. Phone: 314-286-1848; E-mail: lding@wustl.edu; T. Kourelis, D. Avigan; E-mail: kourelis.taxiarchis@mayo.edu; M.V. Dhodapkar; E-mail: madhav.v.dhodapkar@emory.edu; S. Gnjatic; E-mail: sacha.gnjatic@mssm.edu and M.K. Bhasin; E-mail: manoj.bhasin@emory.edu
+10 2022
+25 10 2022
+2 10 12551265
+18 1 2022
+09 6 2022
+19 8 2022
+© 2022 The Authors; Published by the American Association for Cancer Research
+2022
+Copyright held by the owner/author(s).
+https://creativecommons.org/licenses/by/4.0/ This open access article is distributed under the Creative Commons Attribution 4.0 International (CC BY 4.0) license.
+
+As part of the Multiple Myeloma Research Foundation (MMRF) immune atlas pilot project, we compared immune cells of multiple myeloma bone marrow samples from 18 patients assessed by single-cell RNA sequencing (scRNA-seq), mass cytometry (CyTOF), and cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) to understand the concordance of measurements among single-cell techniques. Cell type abundances are relatively consistent across the three approaches, while variations are observed in T cells, macrophages, and monocytes. Concordance and correlation analysis of cell type marker gene expression across different modalities highlighted the importance of choosing cell type marker genes best suited to particular modalities. By integrating data from these three assays, we found International Staging System stage 3 patients exhibited decreased CD4+ T/CD8+ T cells ratio. Moreover, we observed upregulation of RAC2 and PSMB9, in natural killer cells of fast progressors compared with those of nonprogressors, as revealed by both scRNA-seq and CITE-seq RNA measurement. This detailed examination of the immune microenvironment in multiple myeloma using multiple single-cell technologies revealed markers associated with multiple myeloma rapid progression which will be further characterized by the full-scale immune atlas project.
+
+Significance:
+
+scRNA-seq, CyTOF, and CITE-seq are increasingly used for evaluating cellular heterogeneity. Understanding their concordances is of great interest. To date, this study is the most comprehensive examination of the measurement of the immune microenvironment in multiple myeloma using the three techniques. Moreover, we identified markers predicted to be significantly associated with multiple myeloma rapid progression.
+
+http://dx.doi.org/10.13039/100001253 Multiple Myeloma Research Foundation (MMRF) Kourelis Taxiarchis Avigan David Dhodapkar Madhav V. Gnjatic Sacha Bhasin Manoj K. Ding Li crossmarktrue
+==== Body
+pmcIntroduction
+
+Single-cell sequencing technologies offer advantages over traditional bulk methods in cancer genomics research for evaluating cellular heterogeneity and investigating evolution of cellular subpopulations between the tumor and its microenvironment. For example, single-cell methods have been extensively applied to multiple myeloma, a highly heterogeneous disease marked by uncontrolled clonal expansion of plasma cells. Single-cell RNA sequencing (scRNA-seq) has been used to examine tumor and immune cell populations (1, 2) and mass cytometry (CyTOF) to evaluate the impact of drugs on immune populations in multiple myeloma (3). The third technology, cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq), is a more recent, multimodal approach with simultaneous quantification of single-cell transcriptomes and surface proteins. All three approaches enable identification of cell types, cell states, and characterization of cellular heterogeneity at transcriptomic and/or protein levels. Consequently, understanding their concordances across technologies is of great practical interest.
+
+In addition, the bone marrow microenvironment plays an important role in the evolution of premalignant multiple myeloma, multiple myeloma progression, and treatment response. Single-cell transcriptomics analysis of the tumor microenvironment (TME) revealed compositional alterations begin at the monoclonal gammopathy of undetermined significance (MGUS) stage, including enrichment of T cells, natural killer (NK) cells, and CD16+ monocytes (2). Specifically, the percentage of CD4+ T cells was significantly reduced in bone marrow of patients with multiple myeloma, leading to altered CD4+ T/CD8+ T ratio (4). When comparing the clinical status, the ratio decreased in International Staging System (ISS) stage 3 patients compared with stage 1 patients (5). With respect to treatment, the proportion of CD3+ T cells was lower in treated patients compared with patients with chemo-naïve multiple myeloma (6). Further work is needed to expand initial findings using various assays and reveal candidate markers for characterizing clinical features of patients with multiple myeloma and optimizing treatment.
+
+Combining the timeliness of the technology concordance question with furtherance of multiple myeloma research, we subjected bone marrow samples from 18 patients with multiple myeloma to scRNA-seq, CyTOF, and CITE-seq, examining the similarities across the aforementioned single-cell techniques. We used the results to investigate the relationship between immune population compositional alterations and disease stages and revealed a set of markers associated with multiple myeloma rapid progression.
+
+Materials and Methods
+
+Ethics Approval and Consent to Participate
+
+All procedures performed in studies involving human participants were in accordance with the ethical standards of the Multiple Myeloma Research Foundation (MMRF) research committee. These samples provided by MMRF were all from the MMRF's CoMMpass clinical trial (NCT NCT01454297). Written informed patient consent was obtained from all patients for the collection and analysis of their samples by the MMRF. The CoMMpass study was conducted in accordance with recognized ethical guidelines in the United States and European Union. The Institutional Review Board at each participating center approved the study protocol.
+
+Ammonium-chloride-potassium Lysis of Bone Marrow Aspirates
+
+Bone marrow aspirate (BMA) samples obtained from subjects enrolled in the MMRF CoMMpass study (NCT01454297). Any blood clots were removed from BMA samples via passage through 70 mmol/L cell strainer. BMA samples were aliquoted into 5 mL aliquots in 50 mL conical tubes and 45 mL of 22 mmol/L filtered ammonium-chloride-potassium (ACK) lysing buffer (155 mmol/L ammonium chloride/10 mmol/L potassium bicarbonate/0.1 mmol/L Ethylenediaminetetraacetic Acid (EDTA)/pH7.4) was added to each 5 mL aliquot and the tune gently inverted several times to mix. Tubes were then centrifuged at 400 × g for 5 minutes. The supernatant was removed and the cell pellet resuspended with 5 mL of RPMI1640 and transferred to a clean tube. All aliquots of ACK-lysed BMA aliquots were combined into 1 × 50 mL tube, the volume adjusted to 50 mL with RPMI1640. The cells were then mixed by gentle inversion and the tube centrifuged at 400 × g for 5 minutes. The supernatant was then removed by aspiration. Depending on the size of the BMA cell pellet, the cell pellet resuspended in 1–10 mL of EasySep buffer [PBS containing 2% FBS (v/v) and 1 mmol/L EDTA (PBS/FCS/EDTA buffer)]. A total of 25 mL of cell suspension was removed for cell counting.
+
+Isolation of CD138-positive and CD138-negative Cells from BMA
+
+CD138-negative (CD138−) immune cell mononuclear cells in BMAs from subjects enrolled in the MMRF CoMMpass study (NCT NCT01454297) were isolated via negative selection from CD138-positive (CD138+) myeloma cells using the EasySep immunomagnetic bead technology (EasySep Human CD138-Positive Selection Kit: Stem Cell Technologies) in accordance with the manufacturers protocol. Briefly, 100 × 106 cell/mL bone marrow mononuclear cell (BMMC) in a sterile 17 × 100 mm (14 mL) tube were gently mixed and incubated with 100 mL/mL CD138 selection antibody cocktail for 15 minutes at room temperature. A total of 50 mL/mL of EasySep magnetic nanoparticles was then added to the cell suspension, gently mixed, and incubated for a further 10 minutes at room temperature. The volume of the cell suspension was then adjusted to 8 mL with PBS containing 2% FBS (v/v) and 1 mmol/L EDTA (PBS/FCS/EDTA buffer) and the cell suspension mixed by gentle pipetting (2–3×). The tube was then placed in the magnetic separator. After 5 minutes incubation at room temperature, the magnet and tube were carefully inverted to pour off the supernatant into a sterile 50 mL conical tube. This supernatant contains the heterogeneous CD138− immune cell mononuclear population (MNC). The tube was then removed from the magnet and an additional 8 mL of PBS/FCS/EDTA added, gently mixed, and returned to the magnetic separator. Again, after 5 minutes incubation in the magnetic separator, the tube and magnet were carefully inverted to pour of the supernatant into the 50 mL collection tube. This PBS/FCS/EDTA “wash” step was repeated once more resulting in approximately 24 mL suspension of CD138− bone marrow MNCs. CD138− MNCs were then pelleted by centrifugation at 400 × g for 5 minutes and the supernatant removed by aspiration. The CD138− MNC pellet was resuspended in freezing medium (90% FCS/10% DMSO) at a concentration of approximately 8–10 × 106 cells/mL prior to cryogenic storage in liquid nitrogen.
+
+Processing of BMMC and Library Prep From MMRF CoMMpass Study for scRNA-seq at Washington University in St. Louis
+
+Washington University in St. Louis (WUSTL) Cell Thawing: Multiple myeloma BMMC aliquots were thawed in 37°C water bath. Cells were then pelleted by centrifugation at 300 × g for 5 minutes and all supernatant was removed. To prepare cells for the Miltenyi Dead Cell Removal Kit, cells were resuspended in 100 μL of beads and incubated at room temperature for 15 minutes. Dead cells were depleted using the autoMACSPro Separator. Live cells were pelleted by centrifugation at 450 × g for 5 minutes. Cells were finally resuspended in ice-cold PBS and 0.5% BSA and loaded onto the 10x Genomics Chromium Controller and using the Chromium Next GEM Single-Cell 3′ GEM, Library and Gel Bead Kit v3.3. Utilizing the 10x Genomics Chromium Single-Cell 3′v3 Library Kit and Chromium instrument, approximately 16,500 to 20,000 cells were partitioned into nanoliter droplets to achieve single-cell resolution for a maximum of 10,000 individual cells per sample. The resulting cDNA was tagged with a common 16nt cell barcode and 10nt Unique Molecular Identifier (UMI) during the Reverse Transcription (RT) reaction. Full-length cDNA from poly-A mRNA transcripts was enzymatically fragmented and size selected to optimize the cDNA amplicon size (∼400 bp) for library construction (10x Genomics). The concentration of the 10x single-cell library was accurately determined through qPCR (Kapa Biosystems) to produce cluster counts appropriate for the HiSeq 4000 or NovaSeq 6000 platform (Illumina). A total of 26 × 98 bp (3′v2 libraries) sequence data were generated targeting between 25K and 50K read pairs/cell, which provided digital gene expression profiles for each individual cell.
+
+Icahn School of Medicine at Mount Sinai BMMC Processing Differences From WUSTL
+
+BMMC aliquots were partially thawed in 37°C water bath. A total of 1 mL of warm thawing media (RPMI + 10% FBS) was added to the partially thawed BMMC aliquot and the entire volume was transferred to a 15 mL conical tube containing 10 mL of warm thawing media. The empty BMMC tube was rinsed with another 1 mL of thawing media which was then also transferred to the 15 mL conical tube. Cells were processed using the EasySep Dead Cell Removal (Annexin V) Kit (StemCell Technologies, catalog no. 17899).
+
+scRNA-seq Data Quantification Preprocessing
+
+For scRNA-seq analysis, the proprietary software tool Cell Ranger v3.0.0 from 10x Genomics was used for demultiplexing sequence data into FASTQ files, aligning reads to the human genome (GRCh38), and generating gene-by-cell UMI count matrix.
+
+Seurat v3.0.0 (7, 8) was used for all subsequent analysis. First, a series of quality filters was applied to the data to remove those barcodes which fell into any one of these categories recommended by Seurat: too few total transcript counts (<300); possible debris with too few genes expressed (<200) and too few UMIs (<1,000); possible more than one cell with too many genes expressed (>50,000) and too many UMIs (>10,000); possible dead cell or a sign of cellular stress and apoptosis with too high proportion of mitochondrial gene expression over the total transcript counts (>20%). Finally, predicted doublets were also removed using scrublet V0.2.3.
+
+We constructed a Seurat object using the unfiltered feature-barcode matrix for each sample. Each sample was scaled and normalized using Seurat's “SCTransform” function to correct for batch effects (with parameters: vars.to.regress = c("nCount_RNA", "percent.mito"), return.only.var.genes = F).
+
+scRNA-seq Cell Type Annotation
+
+Cell types were assigned to each cluster by manually reviewing the expression of marker genes. The marker genes for main cell types were CD79A, CD79B, MS4A1 (B cells); CD8A, CD8B, CD7, CD3E (CD8+ T cells); CD4, IL7R, CD7, CD3E (CD4+ T cells); NKG7, GNLY, KLRD1, NCAM1 (NK cells); MZB1, SDC1, IGHG1 (Plasma cells); CLEC4C, IL3RA, IRF8, GZMB (Dendritic cells); FCGR3A (Macrophages); CD14, LYZ, S100A8, S100A9 (Monocytes); AZU1, ELANE, MPO (Neutrophils); COL1A1, COL3A1, TNC, S100A4 (Fibroblasts); and AHSP1, HBA, HBB (Erythrocytes). Detailed cell type markers are listed in Supplementary Table S1A. All cells that were labeled as erythrocytes and plasma cells were removed from subsequent analysis.
+
+Processing of BMMC From MMRF CoMMpass Study for CITE-seq
+
+Samples were thawed in the water bath at 37°C for 2–3 minutes and the cell concentration, viability were determined using a Bio-Rad T20 Cell Counter (catalog no. 145-0102). Samples were blocked by incubation with TruStain fcX (BioLegend, catalog no. 422301) in a 50 μL cell labeling buffer. Next, samples were labeled with Total-seq antibodies (BioLegend; Supplementary Table S1B) for 30 minutes. Cells were washed and resuspended to obtain a cell concentration of 700–1,200 cells/μL and gently pipette mix using a regular-bore pipette tip until a single-cell suspension is achieved. We then proceed immediately to Single-Cell Gene Expression Library (3′GEX) construction using 10X Chromium Single-Cell 3′ Reagent Kits v3 (catalog no. 1000075) and Chromium i7 Sample Index Plate with Barcoding technology for Cell Surface Protein. For each sample, 5,000 cells were injected for CITE-seq. The libraries were sequenced on NovaSeq S4 platform in pair end sequencing and a single index with at least 50,000 read pairs per cell.
+
+CITE-seq Data Quantification Preprocessing
+
+We used Cell Ranger to demultiplex, map to the human reference genome (grch38), and count UMIs in the mRNA libraries, and CITE-seq-Count to count UMIs in the antibody-derived tag (ADT) libraries. We filtered out cells with more than 10% UMIs from mitochondrially encoded genes or less than 1,200 mRNA UMIs in total. We then constructed a Seurat object using the feature-barcode matrix for each sample (Seurat v3.0.0). Each sample was scaled and normalized using Seurat's “SCTransform” function to correct for batch effects (with parameters: vars.to.regress = c("nCount_RNA", "percent.mito"), return.only.var.genes = F). Next, the protein expression levels were added to the Seurat object, followed by normalization and scaling for ADT assay.
+
+CITE-seq Data Multimodal Integration and Cell Type Annotation
+
+Using Citefuse v1.2.0, expression was normalized by function normaliseExprs(sce, altExp_name = "ADT", transform = "log"). We then integrated RNA and ADT matrix by an integration algorithm called similarity network fusion (SNF) and clustered cells by Louvain clustering. Then, cell types were assigned to each cluster by manually reviewing the expression of marker genes at RNA levels (same as scRNA-seq; Supplementary Table S1A) and ADT levels (if available). All cells that were labeled as erythrocytes and plasma cells were removed from subsequent analysis.
+
+Processing of BMMC From MMRF CoMMpass Study for CyTOF at Icahn School of Medicine at Mount Sinai
+
+BMMC aliquots were thawed in a 37°C water bath and immediately transferred into RPMI + 10% FBS. Cells were pelleted by centrifugation at 300 × g for 5 minutes and all supernatant was removed. Cells were then incubated for 20 minutes in a 37°C water bath with Cell-ID Rh103 Intercalator (Fluidigm, catalog no. 201103A) to label nonviable cells. Samples were then blocked with Fc receptor blocking solution (BioLegend, catalog no. 422302) and stained with a cocktail of surface antibodies for 30 minutes on ice. All antibodies were either conjugated in-house using Fluidigm's × 8 polymer conjugation kits or purchased commercially from Fluidigm. Next, samples were fixed and barcoded using Fluidigm's 20-Plex Pd barcoding kit (catalog no. 201060) and pooled into a single tube. The pooled sample was then fixed and permeabilized using BD's Cytofix/Cytoperm Fixation/Permeabilization Kit (catalog no. 554714), blocked with heparin at a concentration of 100 U/mL to prevent nonspecific staining of eosinophils and stained with a cocktail of intracellular antibodies. Finally, the sample was refixed with freshly diluted 2.4% formaldehyde in PBS containing 0.02% saponin and Cell-ID Intercalator-Ir (Fluidigm, catalog no. 201192A) to label nucleated cells. The sample was then stored as a pellet in PBS until acquisition.
+
+Immediately prior to acquisition, the pooled sample was washed with Cell Staining Buffer (CSB) and Cell Acquisition Solution (Fluidigm, catalog no. 201240) and resuspended in Cell Acquisition Solution at a concentration of 1 million cells per mL containing a 1:20 dilution of EQ normalization beads (Fluidigm, catalog no. 201078). The sample was acquired on the Fluidigm Helios mass cytometer using the wide bore injector configuration at an acquisition speed of < 400 cells per second.
+
+Processing of BMMC From MMRF CoMMpass Study for CyTOF at Mayo
+
+BMMC aliquots were thawed in a 37°C water bath and immediately transferred into 15mL tubes and slowly diluted with 10 mL of prewarmed RPMI + 10% FBS+25 U/mL Benzonase (Sigma-Aldrich; catalog no. E1014-5KU; 250 U/mL). Cells were pelleted by centrifugation (all spins at 500 × g for 5 minutes) and supernatant was removed. Cells were then incubated for 1 hour in a 37°C water bath in 10 mL of RPMI+10% FBS. Cells were counted and 3–4 million cells were aliquoted into microfuge 2 mL conical tubes, pelleted and washed 2× with 2 mL CSB Maxpar Cell Staining Buffer (Fluidigm; catalog no. 201068; 500 mL) and resuspended in 300 μL of Cell-ID Cisplatin (Fluidigm; catalog no.: 201064) 5 minutes/RT, to label dead cells. Immediately quenched with 1.5 mL CSB, pelleted, and washed with CSB 2×.
+
+For staining, the cell pellet was gently resuspended in 50 μL CSB and the addition of an equal volume of diluted surface antibody cocktail, for a final staining volume of 100 μL. The staining reaction was incubated on a rocker platform for 45 minutes at room temperature. A total of 1 mL of CSB was used to wash and pellet the cells 2×. Cell pellet was resuspended in the residual volume and then gently resuspended in 500 μL of 1× PBS. An equal volume of 4% PFA in PBS was added to fix cells for a minimum of 20 minutes at a final concentration of 2% PFA in PBS. The sample was labeled overnight at 4°C on a rocker platform with Cell-ID Intercalator-Ir (Fluidigm, catalog no. 201192A) in Maxpar Fix and Perm Buffer (Fluidigm; catalog no. 201067; 100 mL) to label nucleated cells.
+
+The following day the sample was washed 1× with CSB (all cell pelleting performed at 800 × g for 5 minutes after fixation) and twice with Cell Acquisition Solution (Fluidigm, catalog no. 201240). Final resuspension was in Cell Acquisition Solution at a concentration of 0.7 million cells per mL containing a 1:10 dilution of EQ normalization beads (Fluidigm, catalog no. 201078). The sample was acquired on the Fluidigm Helios mass cytometer using the wide bore injector configuration at a targeted acquisition speed of 300 events per second. A cryopreserved specimen of 3–4 million Ficoll-enriched peripheral blood mononuclear cell (PBMC) derived from a pool of 4 anonymous platelet donors was included with every batch of MMRF samples (9). This sample was treated and analyzed in parallel throughout the entire experiment as a process control.
+
+Processing of BMMC From MMRF CoMMpass Study for CyTOF at Emory
+
+BMMC aliquots were thawed in a 37°C water bath and immediately transferred into RPMI+10% FBS. Cells were pelleted by centrifugation at 300 × g for 5 minutes and all supernatant was removed. Cells were then incubated for 20 minutes in a 37°C incubator. Cells were pelleted by centrifugation at 300 × g for 5 minutes and all supernatant was removed. Cells were resuspended in PBS and incubated with cisplatin for 1 minute (Fluidigm, catalog no. 201195) to label nonviable cells. Samples were washed with Maxpar cell staining buffer (Fluidigm, catalog no. 201068) and stained with a cocktail of surface antibodies for 15 minutes at room temperature. All antibodies were either conjugated in-house using Fluidigm's X8 polymer conjugation kits or purchased commercially from Fluidigm. Next, samples were washed and fixed and permed with TF Fix/Perm and Perm/Wash Kit (BD Pharmigen, catalog nos. 51-9008100 and 51-9008102) using manufacturer's recommendations. Permeabilized samples were incubated for 30 minutes in Perm/Wash with a cocktail of intracellular antibodies. After washing and centrifugation at 800 × g for 5 minutes, the sample was refixed with Maxpar Fix I buffer (Fluidigm, catalog no. 201065) and Cell-ID Intercalator-Ir (Fluidigm, catalog no. 201192A) to label nucleated cells. The sample was then stored as a pellet in PBS until acquisition. Immediately prior to acquisition, the sample was washed with Cell Staining Buffer and Maxpar Water (Fluidigm, catalog no. 201069) and resuspended in Maxpar Water at a concentration of 1 million cells per mL containing a 1:10 dilution of EQ normalization beads (Fluidigm, catalog no. 201078). The sample was acquired on the Fluidigm Helios mass cytometer using the HT injector configuration at an acquisition speed of <500 cells per second.
+
+CyTOF Data Preprocessing
+
+The resulting FCS files were normalized and concatenated using Fluidigm's CyTOF software and then demultiplexed using the Zunder lab single-cell debarocder (https://github.com/zunderlab/single-cell-debarcoder). The FCS files were further cleaned on Cytobank by removing EQ beads, low DNA debris, and gaussian multiplets. Barcoding multiplets were also removed on the basis of the Mahalanobis distance and barcode separation distance parameters provided by the Zunder lab debarcoder.
+
+CyTOF Cell Type Annotation and Expression Normalization
+
+Gating and data analysis were done using WUSTL Cytobank. Live, single cells are selected by gating out cells/debris with outlier cisplatin and DNA intercalator staining. Cell populations were determined on the basis of gating of cell type marker expression. Icahn School of Medicine at Mount Sinai (ISMMS): CD3+CD19−CD56−CD33− (T cells); CD3−CD19−CD56−CD33−CD123+HLA_DR+CD11c+ [plasmacytoid dendritic cells (pDC)]; CD3−CD19+CD56−CD33− (B cells); CD56+CD3−CD19−CD33− (NK cells); CD33+CD3−CD19−CD14+ (monocytes); CD33+CD3−CD19−CD14−CD16+ (macrophages). Mayo: CD3+CD19− (T cells); CD3− CD19+CD56− (B cells); CD56+CD3−CD16+HLADR−/CD56+CD3−CD16−CD123−CD11c− (NK cells); CD3−CD19−CD20−CD14+ (monocytes); CD3−CD19−CD20−CD14−CD16+ (macrophages); CD3−CD19−CD20−CD123+ (pDC). Emory: CD3+CD19− (T cells); CD3− CD19+ (B cells); CD3−CD19−CD14+ (monocytes); CD3−CD19−CD14−CD16+ (macrophages). For T-cell subtypes, ISMMS and Mayo used the same gating strategy: CD4+CD8− (CD4+ T cells); CD8+CD4− (CD8+ T cells); CD4+CD8−CD25+CD127− [regulatory T cell (Treg)]; CD45RA+CCR7+ (naïve T cells); CD45RA+CCR7− (EMRA T cells); CD45RA−CCR7+ (central memory T cells), CD45RA−CCR7− (effector memory T cells), Emory: CD4+CD8− (CD4+ T cells); CD8+CD4− (CD8+ T cells); CD45RO−CCR7+ (naïve T cells). Next, we performed t-SNE analysis for 18 samples from ISMMS. We used the scaled expression of markers, including CD57, CD11c, Ki67, CD19, CD45RA, KLRG1, CD4, CD8, ICOS, CD16, CD127, CD1c, CD123, CD66b, TIGIT, TIM3, CD27, PD-L1, CD33, CD14, CD56, NKG2A, CD5, CD45RO, NKG2D, CD25, CCR7, CD3, Tbet, CD38, CD39, CD28, DNAM1, HLA-DR, PD-1, Granzyme B, CD11b. For expression normalization in CyTOF analysis, we followed instructions from Cytobank and used transformed ratios itself compared with its control, which is the table's minimum of median of channel (described here https://support.cytobank.org/hc/en-us/articles/206147637-How-to-create-and-configure-a-Heatmap).
+
+Bland-Altman Analysis
+
+R package Blandr (v0.5.3) was used to calculate mean difference and 95% confidence interval (CI) in Bland-Altman analyses (10). Parameter sig.level = 0.95.
+
+Differential Expression Analysis
+
+Differential expression analysis was performed using the default test (Wilcoxon rank-sum test) of function FindMarkers (from the Seurat package) with the specified parameters: min.pct = 0.25, logfc.threshold = 0.25, and only.pos = T.
+
+Data Availability Statement
+
+The sequence data generated in this study have been submitted to the NCBI BioProject database PRJNA765009 (https://www.ncbi.nlm.nih.gov/bioproject/).
+
+Results
+
+Patient Characteristics and Overview of CD45+ Immune Cells Measured by scRNA-seq, CyTOF, and CITE-seq
+
+We used 18 cryopreserved multiple myeloma samples of CD138− “immune cell” fractions from patients enrolled in the MMRF CoMMpass study (NCT01454297). Nine were fast progressors (FP, progressed within 6 months) and nine were nonprogressors (NP, progressed >6 months but within 5 years) with patient ages ranging from 37 to 83 years. Twelve patients were in the ISS stage III, 8 underwent autologous stem cell transplantation (ASCT), 11 were females and 15 were Caucasians (Fig. 1A; Supplementary Table S1C). Each sample was subjected to scRNA-seq, CyTOF, and CITE-seq at three different respective academic research centers, namely WUSTL, ISMMS, and Beth Israel Deaconess Medical Center (BIDMC). All sites received aliquots from the same sample and technical replicates were conducted for two samples for each assay (Fig. 1A).
+
+FIGURE 1 Overview of cell populations of 18 multiple myeloma patient samples subject to scRNA-seq, CyTOF, and CITE-seq. A, Patient characteristics and single-cell data collection. FP and NP denote fast progressors and nonprogressors, respectively. ISS = International Staging System. ASCT = Autologous Stem Cell Transplantation. B, UMAP projection of integrated scRNA-seq data, with cells colored by immune cell types. C, t-SNE projection of integrated CyTOF data, with cells colored by immune cell types. D, UMAP projection of integrated CITE-seq data, with cells clustered by integrated RNA and ADT expression, colored by immune cell types. E, UMAP projection of integrated CITE-seq data, with cells clustered by transcriptional level alone, colored by immune cell identities from D. F, Comparison of canonical cell type marker gene expressions between protein level (ADT, top) and transcriptional level (RNA, bottom). Cells are colored by normalized expression. G, Concordance of sample-level average expressions of CITE-seq protein markers measured at RNA level and ADT level. The gray shaded area represents the 95% confidence interval around the line of best fit. R = Pearson correlation coefficient. H, UMAP projection of CD4+ T cells and naïve CD8+ T cells, which is the subset of integrated data in E, with cells clustered by transcriptional level alone, colored by immune cell identities from D and E.
+
+To assess immune cell composition of patients with multiple myeloma, bone marrow (BM) baseline samples (collected at the initial diagnosis) from these 18 patients were subjected to scRNA-seq, with immune cells clustered on the basis of their transcriptome profiles using the Louvain clustering algorithm implemented by Seurat (refs. 7, 8; Fig. 1B). We then investigated immune cells of these same samples by CyTOF using a 39-marker panel (Supplementary Table S1D). Cell populations were characterized by expression of markers, clustered by the flowsom algorithm (11), and visualized with vi-SNE in the Cytobank (12) platform (Fig. 1C). Given the discordance between RNA expression and protein expression that is known to exist (13), it is informative to characterize cell populations by measuring RNA and protein at the same time. Finally, we utilized CITE-seq with antibody-oligonucleotide conjugates and 29 protein markers (Supplementary Table S1B) to simultaneously quantify single-cell transcriptomes and surface proteins. Following standard scRNA-seq quality filtering protocols, immune cells were clustered on the basis of integrated multi-omic profiles by the SNF integration algorithm in CiteFuse (ref. 14; Fig. 1D). From CD138− BM aliquots, we detected, on average, 1,051 immune cells/sample using scRNA-seq, >64K CD45+ cells/sample using CyTOF, and 718 immune cells/sample using CITE-seq.
+
+Advantages of CITE-seq in Distinguishing T-cell Subtypes in Multiple Myeloma
+
+To assess the potential advantages of simultaneous quantification of RNA and protein expression in CITE-seq as compared with standard scRNA-seq, we labeled immune cell identities determined by integrated transcriptome and protein expression, but clustered cells by transcriptional profiles alone (Fig. 1E). Interestingly, most cell types, including B cells, monocytes, macrophages, neutrophils, and pDCs, formed distinct clusters, while T-cell subtypes mixed together. To further understand the difference of cell type marker expression between the RNA and protein levels, we visualized the expression of some canonical markers in Uniform Manifold Approximation and Projection (UMAP) and investigated the concordance of the sample-level average expression of the 29 CITE-seq protein markers between RNA level and ADT level (Fig. 1F and G; Supplementary Fig. S1A). As expected, expression levels of markers are generally concordant (R = 0.72, P < 10−4), with some exceptions where protein-level expression is higher than RNA-level expression and vice versa. One impressive example is CD4 (Fig. 1F and G), which is highly expressed at ADT measurement, but minimally expressed at the RNA level, mainly because mRNAs are produced at much lower rates and have much shorter half-lives than proteins (15). This observation is consistent with previous studies showing low CD4 mRNA expression compared with surface CD4 protein (16). Finally, because naïve CD8+ T cells were clustered together with CD4+ T cells based on transcriptome profiles (Fig. 1E), we investigated whether reclustering T cells alone could help to distinguish subtypes at the RNA level. Because the high similarities of transcriptional profiles among T cells (16) and different surface protein markers could be encoded by the same gene (17), reclustering CD4+ and naïve CD8+ T cells did not provide additional resolution of T-cell subtypes (Fig. 1H). Consistent with a published study about renal T subtype identification using CITE-seq (18), our observation emphasizes the advantage of integrating protein-level expression of cell type markers for multiple myeloma T-cell subtype identification in CITE-seq as compared with standard scRNA-seq.
+
+Data Reproducibility and Comparisons of Cell Populations Measured by the Same Technologies Across Different Centers
+
+To examine data reproducibility, percentages of cell subsets in CD45+ populations were compared between technical replicates for two samples in each assay. The technical replicate pairs are strongly correlated in all three assays (average Pearson correlation coefficient r = 0.94 in scRNA-seq, 0.89 in CyTOF, and 0.92 in CITE-seq; Supplementary Fig. S1B–S1D). Next, to examine the consistency of immune cell populations measured by the same techniques at different sites, we evaluated the percentage of immune populations captured by three centers using four samples. scRNA-seq data were generated in ISMMS, WUSTL, and BIDMC using aliquots of the same samples and CyTOF data were generated in ISMMS, Mayo Clinic, and Emory University (panels are shown in Supplementary Table S1D–S1F). BIDMC scRNA-seq data are from CITE-seq data analyzed with RNA signal alone (Supplementary Fig. S1E). We observed that the percentages of B cells, pre-B cells, NK cells, pDCs, monocytes and macrophages are generally consistent, while the T-cell subset varies across centers in scRNA-seq measurement (Supplementary Fig. S1F). This suggests that T-cell composition could vary by aliquots and potential sample processing differences across centers while other cell types are more similar in scRNA-seq measurement. The cell type abundance measured by CyTOF is less variable than that measured by scRNA-seq, with smaller differences observed in T-cell subsets across centers (Supplementary Fig. S1G, mean difference calculated by Bland-Altman analysis, shown in Supplementary Table S1G). Moreover, cell subset abundances of ISMMS samples tend to have less variation likely due to the benefit of barcoding samples (Materials and Methods). The cell type frequencies calculated by one center (Emory) tend to be lower overall compared with other centers in CyTOF, probably because wide bore injector assembly with cell acquisition solution was not used to maintain cell integrity (Materials and Methods). It is worthwhile noting that including reference samples in CyTOF is very helpful for identifying potential artifacts. For example, we observed a big proportion of CD66b/CD3+ cells in patient samples while these were absent in the reference sample from a healthy donor (data not shown). We hypothesized that this CD66b staining artifact (CD66b is not expressed on CD3+ T cells) was likely due to nonspecific staining from dead cells. Indeed, the percentage of CD66b/CD3+ cells dropped dramatically after dead cell depletion. Finally, to evaluate the similarity of expression profiles across different samples and centers, we calculated the Pearson correlation coefficient of expression of the B-cell markers between populations detected from different centers using scRNA-seq (Supplementary Fig. S1H). We observed that B cells clustered according to patients instead of centers, suggesting patient dependence of B-cell transcriptome profiles, likely because B cells are potential reservoirs of plasma cells (19). Overall, we observed that cell type abundances are generally consistent across centers for most cell types and that similarity of transcriptome profiles of immune populations is center independent, suggesting absence of strong batch effects across centers. These observations imply that our cross-technique comparisons should be valid.
+
+Comparisons of Cell Type Abundances and Correlations of Cell Type Marker Expression Across the Three Techniques
+
+To evaluate the concordance of cell type composition determined by the three methods, we calculated the cell subset frequency of each immune population relative to the CD45+ populations (Fig. 2A). Overall, all three approaches were concordant, though there is somewhat stronger concordance between scRNA-seq and CITE-seq for all cell types except NK cells (mean difference calculated by Bland-Altman analysis, shown in Supplementary Table S1H). Cell type abundance is especially consistent for B cells, pDC, and neutrophils. Interestingly, the cell frequency decreased and increased for T cells and macrophages/monocytes, respectively, in CyTOF as compared with scRNA-seq and CITE-seq. The mean differences between CyTOF and CITE-seq were −13.6% (95% CI: −24.02 to −3.11) for T cells and 11.07% (95% CI: 3.19–18.95) for macrophages/monocytes. This finding is consistent with a previous study where fewer T cells were detected in CyTOF compared with scRNA-seq in healthy bone marrow samples (20). To further investigate which subpopulations were discordant, the frequencies of T-cell subsets, monocytes, and macrophages were evaluated (Fig. 2B, mean difference calculated by Bland-Altman analysis). Interestingly, CITE-seq detected far more CD4+ T cells compared with CyTOF and scRNA-seq, while CyTOF detected far fewer CD8+ T cells compared with the other two techniques. In terms of T-cell subtypes, Treg frequency increased and memory CD8+ T cells reduced in scRNA-seq, as compared with CyTOF. In addition, scRNA-seq detected far more macrophages than the other two methods, while monocyte frequency was the lowest in CyTOF.
+
+FIGURE 2 Comparison of cell subset frequencies and correlations of expression of canonical cell type markers across different modalities. A, Main immune cell population (CD45+) frequencies observed by CITE-seq, CyTOF, and scRNA-seq. Each boxplot is colored by assay. CITE-seq populations are determined on the basis of integrated RNA and ADT expressions. B, Immune cell subtype frequencies for CITE-seq, CyTOF, and scRNA-seq. Each boxplot is colored by assay. CITE-seq populations are determined on the basis of integrated RNA and ADT expressions. C, Concordance of sample-level average expressions of canonical cell type markers in main cell subsets between scRNA-seq and CITE-seq. CITE-seq RNA and protein (ADT) level expressions are represented by blue and red dots, respectively. D, Spearman correlation coefficients of protein level expressions of cell type markers between CyTOF and CITE-seq. Each dot represents a marker gene and the color of the dot represents the P value of correlation. Markers are highlighted with an outer circle if the P value is less than 0.05. E, Spearman correlation coefficients of transcriptional level expressions of cell type markers between scRNA-seq and CITE-seq. Each dot represents a marker gene and the color of the dot represents the p value of correlation. Markers are highlighted with an outer circle if the P value is less than 0.05. F, Spearman correlation coefficients of cell type markers between transcriptional level and protein level expressions in CITE-seq. Each dot represents a marker gene and the color of the dot represents the P value of correlation. Markers are highlighted with an outer circle if the P value is less than 0.05. G, Spearman correlation coefficients of cell type markers between transcriptional level expressions from scRNA-seq and protein level expressions from CyTOF. Each dot represents a marker gene and the color of the dot represents the P value of correlation. Markers are highlighted with an outer circle if the P value is less than 0.05.
+
+To further evaluate concordance between scRNA-seq and CITE-seq, we examined expression of cell type marker genes, including both the RNA and ADT levels. Average expressions of each marker gene at the transcriptional level (blue dots) between scRNA-seq and CITE-seq are generally concordant (Fig. 2C). In contrast, we observed drastic differences of some marker genes between RNA and ADT expression in CITE-seq, probably due to the RNA dropout (21) and shorter half-lives of mRNAs versus proteins (15). For example, expression of CD4_adt is higher than that of transcriptional CD4, whereas CD127/IL7R tends to be highly expressed at the transcriptional level. This dynamic explains why IL7R is often differentially expressed in CD4+ T-cell population, while CD4 is weakly expressed in scRNA-seq. Taken together, these observations highlight the importance of choosing cell type marker genes best suited to particular modalities.
+
+We also correlated expressions of marker genes among scRNA-seq, CyTOF, and CITE-seq. The vast majority are positively correlated in protein–protein comparison (Fig. 2D) and RNA–RNA comparison (Fig. 2E). Next, we investigated the correlations of expressions of marker genes between the transcriptome and protein levels (Fig. 2F and G; Supplementary Fig. S2A and S2B). As expected, the overall correlation between different modalities is lower than that of the same modalities. We observed significant correlation for some markers, including CCR7 in CD4+ naïve T cells, IL7R in CD4+ memory T cells, and FCGR3A in NK cells, between RNA and protein level of CITE-seq, while no markers are significantly correlated between scRNA-seq and CyTOF (Fig. 2G). We also found that FCGR3A in macrophages has a strong correlation, while some markers are significantly anticorrelated between CITE-seq transcriptional level and CyTOF, such as CD3D, CD3G, IL7R, CD8A, etc. (Supplementary Fig. S2A–S2C; Supplementary Table S1I).
+
+Decreased Ratio of CD4+/CD8+ T Cells From ISS Stage 2 to ISS Stage 3 Patients and FP-related Gene Signatures
+
+Furthermore, we sought to investigate the relationship between clinical features and immune cell composition of patients with multiple myeloma by examining the ratio of CD4+/CD8+ T cells of patients at different disease stages. A previous study used flow cytometry to reveal that this ratio was significantly lower in PBMCs of patients with multiple myeloma as compared with that of normal controls and the ratio decreased with the multiple myeloma progression (5). By integrating three assays, we found the ratio tends to decrease from ISS stage 2 to ISS stage 3 patients (Fig. 3A). Furthermore, CITE-seq and CyTOF analyses revealed significant downregulation of CD45RA in stage 3 patients, suggesting that CD8+ T cells tend to be activated rather than naïve in stage 3 patients (Fig. 3B). In addition, we then identified several differentially expressed genes (DEG) of NK cells from FPs relative to NPs, including ARPC5, XAF1, RAC2, and PSMB9, as revealed by both scRNA-seq and CITE-seq assays (Fig. 3C). ARPC5, actin-related protein 2/3 complex subunit 5, has been revealed to be highly expressed in patients with poor overall survival and could be treated as an independent biomarker for patients with multiple myeloma (22), consistent with our observations. A previous microarray-based study found that RAC2, Rac family small GTPase 2, is significantly upregulated in multiple myeloma as compared with MGUS (23). One subunit of the proteasome (PSMB9), was remarkably highly expressed in cell groups with t(4;14) translocations versus cells from MGUS (24). In summary, previous studies indicated RAC2 and PSMB9 are associated with disease development from MGUS to multiple myeloma and our analysis suggested that they might also be related to multiple myeloma progression. Taken together, we observed the ratio of CD4+ T/CD8+ T cells decreased in stage 3 patients relative to stage 2 patients, suggesting an increased population of CD8+ T cells in bone marrow microenvironment (BMME) of patients in stage 3. We also found that RAC2 and PSMB9 are upregulated in NK cells in FPs relative to NPs at transcriptional level, which could potentially serve as multiple myeloma progression markers.
+
+FIGURE 3 Ratio of CD4+ T/CD8+ T of patients in different ISS stages and markers associated with ISS disease stages and multiple myeloma progression. A, Violin plots showing the ratio of CD4+ T/CD8+ T of patients in ISS stage 2 and 3 in scRNA-seq, CyTOF, and CITE-seq. Horizontal lines indicate the median of data points in each group. B, Violin plots showing single cell–level normalized expression of CD45RA in CITE-seq ADT measurement and CyTOF. The difference is significant at P ≤ 0.0001 based on Wilcoxon rank-sum test. C, Heatmaps showing DEGs of NK cells of FP versus NP patients in CITE-seq RNA measurement (left) and scRNA-seq measurement (right). The samples are ordered on the basis of hierarchical clustering of expression profiles of these genes in CITE-seq RNA measurement. Expression values are scaled such that for each gene, the average of the scaled expression is 0 and the SD is 1. Adjusted P values and log fold change in CITE-seq and scRNA-seq were shown on the left and right side of DEGs, respectively. FC = fold change.
+
+Discussion
+
+Single-cell sequencing technologies have been widely used in studying tissue heterogeneity, tumorigenesis, and metastasis given their advantages of being able to depict genome, transcriptome, proteome, and other mutli-omics profiles of single cells (25). However, the similarities of measurements across the various single-cell techniques remain to be fully elucidated. Herein, we integrated scRNA-seq, CyTOF, and CITE-seq to perform a detailed comparison of their measurements for multiple myeloma BMME. From CD138− BM aliquots of 20 samples from 18 patients, we detected, on average, 1,051 immune cells/sample using scRNA-seq, >64K CD45+ cells/sample using CyTOF, and 718 immune cells/sample using CITE-seq. By clustering cells with or without protein profiles in CITE-seq, we showed the advantages of multimodal measurement over transcriptional measurement alone of cell type markers when characterizing T-cell subtypes in MM (Fig. 1E–H). This observation is in line with a study to investigate renal T-cell subtypes by CITE-seq (18).
+
+Next, to examine the consistency of cell populations measured by the same techniques at different sites, we evaluated the cell subset abundances captured by three centers using four samples. Cross-center comparisons (Supplementary Fig. S1F and S1G) suggested no strong batch effect across centers and there are some important factors to consider to obtain reproducible and reliable results: (i) It is important to include reference samples in CyTOF to help identify marker nonspecific staining artifacts; (ii) Barcoding samples, sample delivery mechanism, and using lyophilized panels is important in CyTOF experiments. Furthermore, cross-technique comparisons revealed that the percentages of immune populations measured by scRNA-seq, CyTOF, and CITE-seq are generally concordant, except some variations in T cells, macrophages, and monocytes (Fig. 2A and B). Analysis revealed relatively high correlations of most markers between the same modalities, though some markers are negatively correlated. (Fig. 2C–G). This observation highlighted the importance of choosing marker genes best suited to particular modalities.
+
+Previous studies have found patients with multiple myeloma have lower CD4+ T/CD8+ T ratios relative to healthy donors and these ratios are further decreased in ISS stage 3 versus ISS stage 1 patients (5). Here, we confirmed this trend using three single-cell technologies, finding that this ratio tends to decrease even in stage 3 versus stage 2 patients (Fig. 3A). We also observed the decreased ratio in stage 2 compared with stage 1 patients based on CyTOF and CITE-seq measurement but not in scRNA-seq, probably due to the limited number of patients in stage 1. Future study could further investigate how immune cell composition changes along with ISS stages with expanded sample size. In addition, we observed upregulation of ARPC5, XAF1, RAC2, and PSMB9 in NK cells of FPs compared with those of NPs, as suggested by both scRNA-seq and CITE-seq RNA measurements (Fig. 3C). RAC2 and PSMB9 have been revealed to be associated with disease development from MGUS to multiple myeloma (23, 24) and our analysis suggested that they might also be related to multiple myeloma rapid progression, supported by both scRNA-seq and CITE-seq. Because of the limited number of protein markers in CITE-seq, we were unable to evaluate the protein-level expression of these multiple myeloma progression-related genes identified from RNA measurement, which requires further validation. It would also be interesting to investigate multiple myeloma progression-related markers after controlling for treatments in future studies.
+
+This analysis is just a small sampling of the larger work being conducted by the MMRF and their associated academic research centers to provide a sufficiently broad, deep, and technologically diverse vast dataset for accurately characterizing BMME and to help interrogate multiple myeloma TME using different single-cell technologies. We hope this study will help researchers refine cell population characterization strategies and provide insights to those considering integrating multiple single-cell methods to comprehensively address biological questions.
+
+Supplementary Material
+
+Supplementary Figure FS1 Expression of cell type markers in CITE-seq and comparison of cell subset abundance between technical replicates and across different centers
+
+Click here for additional data file.
+
+Supplementary Figure FS2 Correlation of expression of canonical cell type markers across different modalities
+
+Click here for additional data file.
+
+Supplementary Table TS1 supplementary table 1 shows cell type annotation markers, clinical information of patients, cross-technique comparison of cell subset abundance and expression of cell type marker genes in CITE-seq and CyTOF
+
+Click here for additional data file.
+
+Acknowledgments
+
+This study was funded through the MMRF Immune Atlas initiative. We thank the patients with multiple myeloma, families, and professionals who have contributed to this study. We thank Upadhyaya Bhaskar, Nicolas Fernandez, and Laura Walker for their contribution to the initial work of MMRF immune atlas pilot study. We thank John Leech for administrative support.
+
+Authors’ Disclosures
+
+S.K. Kumar reports other from Abbvie, Amgen, BMS, Janssen, Roche-Genentech, Takeda, AstraZeneca, Bluebird Bio, Epizyme, Secura Biotherapeutics, Monterosa therapeutics, Trillium, Loxo Oncology, K36, Sanofi, ArcellX; personal fees from ncopeptides, Beigene, Antengene, GLH Pharma; and grants from Abbvie, Amgen, Allogene, AstraZeneca, BMS, Carsgen, GSK, Janssen, Novartis, Roche-Genentech, Takeda, Regeneron, Molecular Templates outside the submitted work. H.J. Cho reports other from The Multiple Myeloma Research Foundation during the conduct of the study; grants from BMS and Takeda outside the submitted work. A.H. Rahman reports grants from Multiple Myeloma Research Foundation during the conduct of the study; grants from Celgene/BMS and personal fees from Fluidigm outside the submitted work. D. Avigan reports other from BMS, Chugai, Sanofi, Merk, and Paraexel; and grants from MMRF outside the submitted work. S. Gnjatic reports grants from Multiple Myeloma Research Foundation during the conduct of the study; grants from Regeneron, Boehringer Ingelheim, BMS, Genentech, Jannsen R&D, Takeda, and EMD Serono outside the submitted work. No disclosures were reported by the other authors.
+
+Authors’ Contributions
+
+L. Yao: Software, formal analysis, investigation, visualization, writing-original draft, writing-review and editing. R.G. Jayasinghe: Resources, data curation. B. Lee: Data curation, formal analysis. S.S. Bhasin: Data curation, formal analysis. W. Pilcher: Software, formal analysis. D.B. Doxie: Software, formal analysis. E. Gonzalez-Kozlova: Software, formal analysis. S. Dasari: Supervision. M.A. Fiala: Software, formal analysis. Y. Pita-Juarez: Methodology. M. Strausbauch: Software, formal analysis. G. Kelly: Software, formal analysis. B.E. Thomas: Software, formal analysis. S.K. Kumar: Software, formal analysis. H.J. Cho: Investigation. E. Anderson: Software, formal analysis. M.C. Wendl: Writing-review and editing. T. Dawson: Software, formal analysis. D. D'souza: Software, formal analysis. S.T. Oh: Supervision. G. Cheloni: Data curation. Y. Li: Investigation. J.F. DiPersio: Supervision. A.H. Rahman: Supervision. K.M. Dhodapkar: Supervision. S. Kim-Schulze: Supervision. R. Vij: Supervision. I.S. Vlachos: Supervision. S. Mehr: Project administration. M. Hamilton: Project administration. D. Auclair: Supervision. T. Kourelis: Software, formal analysis. D. Avigan: Supervision. M.V. Dhodapkar: Supervision. S. Gnjatic: Supervision. M.K. Bhasin: Supervision. L. Ding: Conceptualization, supervision, writing-review and editing.
+
+Note: Supplementary data for this article are available at Cancer Research Communications Online (https://aacrjournals.org/cancerrescommun/).
+==== Refs
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+3. Adams HC 3rd , StevenaertF, KrejcikJ, Van der BorghtK, SmetsT, BaldJ, . High-parameter mass cytometry evaluation of relapsed/refractory multiple myeloma patients treated with daratumumab demonstrates immune modulation as a novel mechanism of action. Cytometry A 2019;95 :279–89.30536810
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+5. Koike M , SekigawaI, OkadaM, MatsumotoM, IidaN, HashimotoH, . Relationship between CD4(+)/CD8(+) T cell ratio and T cell activation in multiple myeloma: reference to IL-16. Leuk Res 2002;26 :705–11.12191564
+6. Zelle-Rieser C , ThangavadivelS, BiedermannR, BrunnerA, StoitznerP, WillenbacherE, . T cells in multiple myeloma display features of exhaustion and senescence at the tumor site. J Hematol Oncol 2016;9 :116.27809856
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+

PMC10037000.txt

@@ -0,0 +1,101 @@
+
+==== Front
+Intern Med
+Intern Med
+Internal Medicine
+0918-2918
+1349-7235
+The Japanese Society of Internal Medicine
+
+35871579
+10.2169/internalmedicine.0010-22
+Case Report
+Multiple Myeloma with Hyperammonemia Treated with Novel Agents: A Case Series of Three Patients
+Nakamura Hajime 1
+Takada Kohichi 1
+Murase Kazuyuki 1
+Ikeda Hiroshi 2
+Iyama Satoshi 2
+Manabe Tatsuo 3
+Kobune Masayoshi 2
+1 Department of Medical Oncology, Sapporo Medical University School of Medicine, Japan
+2 Department of Hematology, Sapporo Medical University School of Medicine, Japan
+3 Department of Neurology, Sapporo Medical University School of Medicine, Japan
+Correspondence to Dr.　Kohichi Takada, ktakada@sapmed.ac.jp
+
+22 7 2022
+1 3 2023
+62 5 775778
+23 3 2022
+10 6 2022
+Copyright © 2023 by The Japanese Society of Internal Medicine
+https://creativecommons.org/licenses/by-nc-nd/4.0/ The Internal Medicine is an Open Access journal distributed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. To view the details of this license, please visit (https://creativecommons.org/licenses/by-nc-nd/4.0/).
+Multiple myeloma (MM) is a cancer characterized by the expansion of plasma cells in the bone marrow. Survival times of patients with MM have increased due to the development of novel therapeutic agents. We herein highlight three MM cases that had a poor prognosis despite treatment with novel therapeutic agents. Of note, all patients presented with hyperammonemia that led to a consciousness disorder. The outcome for patients with MM showing high levels of serum ammonia continues to be poor, even with the use of novel therapies. For such patients showing a consciousness disorder, hyperammonemia should be considered as a possible cause.
+
+hyperammonemia
+multiple myeloma
+==== Body
+pmcIntroduction
+
+Multiple myeloma (MM) is a cancer characterized by clonal proliferation of plasma cells in the bone marrow. The worldwide incidence of MM amounted to over 176,000 cases and mortality was over 117,000 patients for the year 2020 (1). Therapeutic strategies for MM have changed dramatically in the last 15 years due to the advent of proteasome inhibitors and immunomodulatory imide drugs (IMiDs). Furthermore, in the last 10 years, 7 new drugs carfilzomib, pomalidomide (IMiDs), panobinostat (histone deacetylase inhibitor), ixazomib (proteasome inhibitor), and elotuzumab, daratumumab, and isatuximab (immunostimulatory monoclonal antibodies) have been newly approved for the treatment of this disease. The overall survival has improved for patients with MM, although some still present with a poor prognosis, depending on the presence of genetic abnormalities, including t(4;14), t(14;16), (14;20), del 17p, p53 mutation, and gain 1q (2,3).
+
+Patients with MM often present with disease-related complications, including hypercalcemia, renal insufficiency, anemia, and bone lesions (4). However, hyperammonemia is a rarely reported complication of MM and is associated with high mortality (5). Furthermore, knowledge is scarce concerning the treatment effect of newly approved drugs on MM patients with hyperammonemia.
+
+We herein report three MM patients who displayed a consciousness disorder evoked by hyperammonemia and who were treated with the aforementioned new therapeutic agents.
+
+Case Reports
+
+Case 1
+
+A 75-year-old female was referred to our hospital for further treatment of IgG-κ-type MM with t(4;14), gain 1q, and revised international staging system (R-ISS) II. She had previously been treated with bortezomib-melphalan-prednisolone (VMP), lenalidomide-dexamethasone (Ld), pomalidomide-dexamethasone (Pd), bortezomib-dexamethasone (Bd), and panobinostat-bortezomib-dexamethasone, in this order, for two years and six months. However, the disease continued to progress, and we consequently administered carfilzomib-lenalidomide-dexamethasone (KRd) as a sixth treatment.
+
+The day after the administration of KRd, the patient presented with a disorder of consciousness. Electroencephalography revealed triphasic waves (Fig. 1), and the level of serum ammonia increased to as high as 268.6 μg/dL. No other causes existed for the consciousness disorder according to a blood examination and computed tomography (CT) of the brain. We therefore deemed the consciousness symptoms to have been caused by hyperammonemia, which was treated using a branched-chain amino acid (BCAA) agent in parallel with KRd treatment.
+
+Figure 1. Electroencephalogram findings for Case 1. Electroencephalography revealed triphasic waves that implied metabolic abnormalities, such as hyperammonemia.
+
+The level of serum ammonia gradually decreased, and her consciousness improved to a certain extent. However, the patient's general condition deteriorated quickly with the progression of MM, and she ultimately passed away seven days after the administration of KRd (Fig. 2a).
+
+Figure 2. Clinical courses for the three patients. a: Case 1, b: Case 2, c: Case 3. BCAA: branched-chain amino acid, EPd: elotuzumab-pomalidomide-dexamethasone, ERd: elotuzumab-lenalidomide-dexamethasone, Kd: carfilzomib-dexamethasone, KRd: carfilzomib-lenalidomide-dexamethasone
+
+Case 2
+
+A 61-year-old woman was admitted to our hospital for the further treatment of IgA-λ-MM with del 17p and R-ISS I. At 55 years old, she had undergone an autologous peripheral blood stem cell transplant. Three years and four months later, she relapsed and was treated with Ld, Pd, and daratumumab-bortezomib-dexamethasone, in that order. The disease continued to progress despite treatment, and we subsequently administered carfilzomib-dexamethasone. However, the serum IgA-λ level continued to increase, so we administered elotuzumab-pomalidomide-dexamethasone (EPd) as a fifth-line treatment.
+
+The patient presented with strange behavior five days after the administration of EPd. Blood examinations revealed hyperammonemia, with levels as high as 212.1 μg/dL. There were no other possible causes of the strange behavior besides hyperammonemia, according to a blood examination, so we administered BCAA agents.
+
+The serum ammonia levels subsequently decreased to 56.7 μg/dL at 11 days after the confirmation of hyperammonemia, resulting in an improvement in the patient's strange behavior. Furthermore, serum λ-light chain decreased with EPd, which was continued for two cycles. However, the patient's general condition gradually deteriorated, mainly because of infectious complications such as bacterial pneumonia, and it became difficult to continue the chemotherapy for MM. She ultimately passed away four months after the determination of hyperammonemia (Fig. 2b).
+
+Case 3
+
+A 48-year-old woman was admitted to our hospital for further treatment of IgA-λ-MM with R-ISS 1. She had previously been treated with Bd, Ld, and Pd, in that order. Despite this, the disease continued to progress, so we consequently proceeded to administer elotuzumab-lenalidomide-dexamethasone (ERd). One day before administering ERd, the patient presented with strange behavior, and the serum level of ammonia increased to 67.7 μg/dL. Treatment with ERd was started as planned, with the patient simultaneously being treated with BCAA agents for hyperammonemia.
+
+The serum ammonia level decreased two days after administering BCAA agents, and the patient's unusual behavior improved accordingly. Furthermore, the serum λ-light chain level decreased from 661.0 mg/L to 216.0 mg/L with ERd treatment. However, the patient presented with abdominal bloating after a cycle of ERd; CT revealed massive ascites and multiple liver metastases. Plasma cells were found in the ascites after a pathological evaluation. The patient's general condition deteriorated quickly, mainly due to the refractory ascites. She ultimately passed away 32 days after hyperammonemia was diagnosed (Fig. 2c).
+
+Discussion
+
+Hyperammonemia, characterized by excessive levels of ammonia in the blood, is a life-threatening metabolic condition that can cause brain injury and consciousness disorder. The most common causes of hyperammonemia are severe liver dysfunction and congenital disorders of urea metabolism (6). Notably, several representative MM cell lines reportedly produce a high amount of ammonia compared to non-MM cell lines, including human hepatic carcinoma (7).
+
+In practice, the hyperammonemia of our three cases was thought to have been caused by MM cells, as no evidence of liver disease was found by blood or imaging examinations. All three cases were classified as relapsed refractory MM that was treated with several regimens. However, a case with hyperammonemic encephalopathy as a presenting symptom of newly diagnosed MM was also reported in the literature (8). Consciousness disorder in MM is often related to hypercalcemia and hyperviscosity. Although hyperammonemia is a rare condition of MM, it should always be ruled out in patients who present with a consciousness disorder during all stages of the disease.
+
+Hyperammonemia in MM is reportedly associated with a high mortality rate (5,9,10). Recently, various novel therapeutic agents, including monoclonal antibodies, proteasome inhibitors, and IMiDs, have been introduced and have extended the survival in patients with MM. However, the clinical impact of these new therapeutic agents on MM with hyperammonemia remains uncertain. We administered new drugs, including carfilzomib, lenalidomide, pomalidomide, and elotuzumab, to the patients as outlined here. All patients received BCAA agents simultaneously and presented with a decreased level of serum ammonia. In addition, treatments elicited responses and were of benefit in cases 2 and 3, as evidenced by a decrease in the serum λ-light chain level after the administration of chemotherapy. However, the general condition of all patients deteriorated despite treatments. Clinicians should be aware of the high mortality rate of patients with MM presenting with hyperammonemia even after treatment with novel therapeutic agents.
+
+Recently, more intensive treatment strategies, including daratumumab-VMP and daratumumab plus lenalidomide, bortezomib, and dexamethasone, have been introduced clinically for untreated MM (11,12). Furthermore, B-cell maturation antigen-directed chimeric antigen receptor T-cell therapy has also induced frequent and intense responses in patients with relapsed and refractory MM (13). These new treatment strategies should be considered when MM patients present with hyperammonemia.
+
+In summary, MM should be included in the differential diagnosis of hyperammonemia. A general awareness should exist regarding the poor prognosis of patients with MM showing high levels of serum ammonia, despite various novel therapeutic agents having been approved for treatment. Further efforts are needed to improve the treatment outcomes of MM patients with hyperammonemia.
+
+The authors state that they have no Conflict of Interest (COI).
+==== Refs
+1. Sung H , Ferlay J , Siegel RL , et al . Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 71 : 209-249, 2021.33538338
+2. Mikhael JR , Dingli D , Roy V , et al . Management of newly diagnosed symptomatic multiple myeloma: updated Mayo Stratification of Myeloma and Risk-Adapted Therapy (mSMART) consensus guidelines 2013. Mayo Clin Proc 88 : 360-376, 2013.23541011
+3. Abdallah N , Greipp P , Kapoor P , et al . Clinical characteristics and treatment outcomes of newly diagnosed multiple myeloma with chromosome 1q abnormalities. Blood Adv 4 : 3509-3519, 2020.32750129
+4. Murtaza G , Lu H , Faqah A , Konowitz N , Kuruvilla A , Adhikari S . Multiple myeloma-induced hyperammonemic encephalopathy. J Hematol 6 : 29-31, 2017.32300389
+5. Lora-Tamayo J , Palom X , Sarrá J , et al . Multiple myeloma and hyperammonemic encephalopathy: review of 27 cases. Clin Lymphoma Myeloma 8 : 363-369, 2008.19064403
+6. Holahan JR . Hyperammonemia: elevated ammonia levels in multiple myeloma. Am J Med 116 : 210-211, 2004.14749172
+7. Otsuki T , Yamada O , Sakaguchi H , et al . In vitro excess ammonia production in human myeloma cell lines. Leukemia 12 : 1149-1158, 1998.9665203
+8. Gaiani A , Pompanin S , Zambello R , Briani C , Cagnin A . Steroid-responsive hyperammonemic encephalopathy as first manifestation of multiple myeloma. Neurol Sci 38 : 503-505, 2017.27718015
+9. Pham A , Reagan JL , Castillo JJ . Multiple myeloma-induced hyperammonemic encephalopathy: an entity associated with high in-patient mortality. Leuk Res 37 : 1229-1232, 2013.23932549
+10. Douedi S , Kapadia S , AlAzzawi M , Sen S . Hyperammonemic encephalopathy: a unique presentation of multiple myeloma. Cureus 13 : e12781, 2021.33628653
+11. Mateos MV , Dimopoulos MA , Cavo M , et al . Daratumumab plus bortezomib, melphalan, and prednisone for untreated myeloma. N Engl J Med 378 : 518-528, 2018.29231133
+12. Voorhees PM , Kaufman JL , Laubach J , et al . Daratumumab, lenalidomide, bortezomib, and dexamethasone for transplant-eligible newly diagnosed multiple myeloma: the GRIFFIN trial. Blood 136 : 936-945, 2020.32325490
+13. Munshi NC , Anderson LD Jr , Shah N , et al . Idecabtagene vicleucel in relapsed and refractory multiple myeloma. N Engl J Med 384 : 705-716, 2021.33626253
+

PMC10045543.txt

@@ -0,0 +1,356 @@
+
+==== Front
+PLoS One
+PLoS One
+plos
+PLOS ONE
+1932-6203
+Public Library of Science San Francisco, CA USA
+
+36480501
+10.1371/journal.pone.0274704
+PONE-D-22-09948
+Research Article
+Medicine and Health Sciences
+Oncology
+Cancers and Neoplasms
+Hematologic Cancers and Related Disorders
+Myelomas and Lymphoproliferative Diseases
+Myeloma
+Multiple Myeloma
+Medicine and Health Sciences
+Hematology
+Hematologic Cancers and Related Disorders
+Myelomas and Lymphoproliferative Diseases
+Myeloma
+Multiple Myeloma
+Medicine and Health Sciences
+Hematology
+Plasma Cell Disorders
+Multiple Myeloma
+Biology and Life Sciences
+Molecular Biology
+Molecular Biology Techniques
+Gene Mapping
+Restriction Fragment Mapping
+Electrophoretic Mobility Shift Assay
+Research and Analysis Methods
+Molecular Biology Techniques
+Gene Mapping
+Restriction Fragment Mapping
+Electrophoretic Mobility Shift Assay
+Medicine and Health Sciences
+Pharmaceutics
+Drug Therapy
+Research and analysis methods
+Bioassays and physiological analysis
+Biochemical analysis
+Colorimetric assays
+MTT assay
+Research and analysis methods
+Bioassays and physiological analysis
+Biochemical analysis
+Enzyme assays
+MTT assay
+Biology and Life Sciences
+Genetics
+Gene Expression
+Gene Regulation
+Biology and Life Sciences
+Genetics
+Gene Expression
+Biology and life sciences
+Genetics
+DNA
+DNA damage
+Biology and life sciences
+Biochemistry
+Nucleic acids
+DNA
+DNA damage
+Biology and Life Sciences
+Cell Biology
+Cellular Types
+Animal Cells
+Bone Marrow Cells
+HAPLN1 confers multiple myeloma cell resistance to several classes of therapeutic drugs
+New mode of drug resistance in multiple myeloma
+Huynh Mailee Conceptualization Data curation Funding acquisition Investigation Methodology Validation Visualization Writing – original draft 1 2
+Chang Hae Yeun Conceptualization Data curation Formal analysis Investigation Methodology Validation Visualization Writing – original draft 1 2
+Lisiero Dominique N. Data curation Investigation Methodology Writing – review & editing 1 2
+Ong Irene M. Formal analysis Methodology Software Writing – review & editing 3 4
+Kashyap Trinayan Conceptualization Resources Writing – review & editing 5
+Callander Natalie S. Conceptualization Writing – review & editing 6
+https://orcid.org/0000-0002-7827-3426
+Miyamoto Shigeki Conceptualization Funding acquisition Project administration Supervision Writing – original draft 1 2 4 *
+1 Department of Oncology, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI, United States of America
+2 McArdle Laboratory for Cancer Research, Madison, WI, United States of America
+3 Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI, United States of America
+4 University of Wisconsin Carbone Cancer Center (UWCCC), Madison, WI, United States of America
+5 Karyopharm Therapeutics, Inc., Newton, MA, United States of America
+6 Department of Medicine, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI, United States of America
+Amodio Nicola Editor
+University of Catanzaro, ITALY
+Competing Interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: T.K. is currently a full-time employee at Karyopharm Therapeutics. The remaining authors declare no competing interests exist.
+
+* E-mail: smiyamot@wisc.edu
+8 12 2022
+2022
+17 12 e02747045 4 2022
+2 9 2022
+© 2022 Huynh et al
+2022
+Huynh et al
+https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
+
+Multiple myeloma (MM), a malignant plasma cell infiltration of the bone marrow, is generally considered incurable: resistance to multiple therapeutic drugs inevitably arises from tumor cell-intrinsic and tumor microenvironment (TME)-mediated mechanisms. Here we report that the proteoglycan tandem repeat 1 (PTR1) domain of the TME matrix protein, hyaluronan and proteoglycan link protein 1 (HAPLN1), induces a host of cell survival genes in MM cells and variable resistance to different classes of clinical drugs, including certain proteasome inhibitors, steroids, immunomodulatory drugs, and DNA damaging agents, in several MM cell lines tested. Collectively, our study identifies HAPLN1 as an extracellular matrix factor that can simultaneously confer MM cell resistance to multiple therapeutic drugs.
+
+NIH R01 CA251595 https://orcid.org/0000-0002-7827-3426
+Miyamoto Shigeki NIH R01 CA155192 https://orcid.org/0000-0002-7827-3426
+Miyamoto Shigeki NIH R01 CA077474-14S1 https://orcid.org/0000-0002-7827-3426
+Miyamoto Shigeki NIH R21 CA194868 https://orcid.org/0000-0002-7827-3426
+Miyamoto Shigeki NIH T32 CA009135 Huynh Mailee NIH R01 CA077474-14S1 Huynh Mailee http://dx.doi.org/10.13039/100007015 University of Wisconsin-Madison SciMed Graduate Research Scholars Fellowship Huynh Mailee http://dx.doi.org/10.13039/100007923 University of Wisconsin Carbone Cancer Center P30 CA014520 This study was funded by NIH R01 CA251595, CA155192 and CA077474-14S1 and R21 CA194868 to S.M. NIH T32 CA009135 (to M.H.), R01 CA077474-14S1 (M.H.), and SciMed Graduate Research Scholars Fellowship at University of Wisconsin-Madison (M.H.). Cancer Informatics Shared Resource (CISR) and flow cytometry core funded by University of Wisconsin Carbone Cancer Center Support Grant P30 CA014520. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Data AvailabilityThe RNA-seq data are deposited to GEO database (accession number GSE202672). It will be released after acceptance of the manuscript for publication.
+Data Availability
+
+The RNA-seq data are deposited to GEO database (accession number GSE202672). It will be released after acceptance of the manuscript for publication.
+==== Body
+pmcIntroduction
+
+Multiple myeloma (MM) is a hematopoietic malignancy characterized by the unrestrained proliferation and accumulation of antibody secreting plasma cells in the bone marrow [1]. In the United States, MM represents ~11% of all hematological cancers and is increasing in incidence in the US (e.g., 14,400 in 1996 to 34,920 estimated in 2021) [2]. Since the first documented case of MM in 1844, the treatments available for MM have improved considerably [3], from early success with L-phenylalanine mustard (melphalan), an alkylating agent [4, 5] with addition of a corticosteroid, such as prednisone and dexamethasone [6] to highly active, more targeted agents such as the immunomodulatory drugs (IMiDs; thalidomide, lenalidomide and pomalidomide) [7–10], proteasome inhibitors (PIs; bortezomib, ixazomib, and carfilzomib) [11–13], and monoclonal antibodies (daratumumab, elotuzumab and isatuximab). Other agents, including alkylating agents (cyclophosphamide) and DNA-damaging agents (doxorubicin and bendamustine), are also employed at times in MM therapy. More recently, a selective inhibitor of nuclear export (XPO1), selinexor in combination with dexamethasone, demonstrated efficacy in MM patients previously resistant to five classes of therapies (penta-refractory) [14–17]. Drugs under development include novel agents such as cereblon E3 ligase modulator (CELMoD) such as iberdomide, the BCL2 inhibitor (venetoclax) and bispecific T cell engagers [18–20]. Finally, the recently FDA approved chimeric antigen receptor (CAR)-T cell therapy against B cell maturation antigen (BCMA) has shown promising results in relapsed/refractory MM patients. Yet patients continue to relapse [21]. Despite the vast array of currently available therapies, MM is still generally considered incurable with a median survival of 5–7 years after diagnosis. Therefore, there is a critical need to understand factors that contribute to therapy resistance and key signaling and regulatory pathways involved in therapy resistance to improve clinical outcomes [1, 3, 17, 22].
+
+Data from sequencing efforts confirmed that MM is a highly genetically heterogeneous disease. Often observed are aberrant genetic changes in critical pathways such as: nuclear factor-kappaB (NF-κB), β-catenin, insulin-like growth factor receptor (IGFR), mitogen-activated protein kinases (MAPK), AKT, KRAS, JAK/STAT and many more [23–25]. Alterations of these various pathways to induce their constitutive activation or hyperactivation is often implicated in mediating MM cell survival and drug resistance. The complex bone marrow (BM) tumor microenvironment (TME) is also an essential component of MM pathogenesis and has been the focus of intense research efforts. It is recognized that direct and indirect interactions with different cell types, such as bone marrow stromal cells (BMSCs) and the extracellular matrix (ECM) augment MM cell growth, survival, migration, and drug resistance [26–28]. Drug resistance (DR) through the TME can be divided by two major subgroups: soluble factor mediated (SFM-DR) and cell adhesion mediated (CAM-DR). SFM-DR involves soluble cell-derived cytokines, growth factors and chemokines that act on MM cells to promote growth and survival [28]. CAM-DR is dependent on the adhesive contacts of MM cells directly to BMSCs or ECM proteins. The adhesion of MM cells through molecules, such as VLA-4 or CD44, and direct contact of myeloma cells to BMSCs allow MM cells to survive the cytotoxic effects of anti-cancer therapy [28]. Like cell-intrinsic genetic alterations, SFM-DR and CAM-DR commonly deregulate key survival signaling pathways in MM cells. Numerous ECM proteins and glycosaminoglycans (GAGs) undergo enzymatic cleavage resulting in the release of peptides (matrikines) that exert biological activities, which are usually different from those of the full-length molecules. The liberated matrikines may interact with specific receptors on the cell surface to activate several signaling pathways leading to increased migration, proliferation, or cell adhesion [29, 30]. However, whether matrikines cause drug resistance in MM remains obscure.
+
+We previously reported that HAPLN1 is secreted by patient-derived bone marrow stromal cells, and HAPLN1 fragments containing proteoglycan tandem repeat 1 and 2 (PTR1/2) are present in MM patient bone marrow plasma or soluble fraction [31]. HAPLN1-PTR1 is sufficient to activate bortezomib-resistant NF-κB activity and confer bortezomib-resistance survival of MM cells [31]. Since NF-κB pathway plays a pivotal role in MM biology, there has been a persistent attempt to target NF-κB signaling and its effectors in anti-myeloma therapy, such as monoclonal antibodies against BAFF and BCMA, and small molecule inhibitors against NIK and IKK, among others [32]. In addition, blockade of PD-L1, a downstream effector of NF-κB signaling, is also heavily investigated [33, 34]. Whether HAPLN1-PTR1 can also induce resistance to other MM therapeutic drugs remain untested.
+
+In this study, we report that HAPLN1-PTR1 may induce a constellation of survival genes, and confer MM cell resistance to multiple classes of therapeutic agents used in MM treatment. Our study reveals a novel mechanism of matrikine-mediated drug resistance in MM, which could represent a novel biomarker and/or therapeutic target for MM disease.
+
+Material and methods
+
+Cell line culture
+
+RPMI8226, MM.1S, NCI-H929, and U266 human MM cell lines were obtained from American Type Culture Collection (ATCC). L363 cell line was obtained from Dr. Lixin Rui. All MM cell lines were cultured at 37°C/5% CO2 in RPMI1640 containing 10% FBS, 2% glutamax (Gibco) + 1% penicillin/streptomycin. These cells were checked for mycoplasma contamination by Universal Mycoplasma Detection Kit (30-1012K, ATCC) and confirmed to be negative.
+
+Antibodies and reagents
+
+Antibodies against IκBα (C-21), IκBβ (C-20) were obtained from Santa Cruz Biotechnology and antibody against tubulin (CP06) purchased from Calbiochem, recombinant human TNFα (654205, EMD Millipore), cycloheximide (C7698, Sigma-Aldrich), and leptomycin B (87081-35-4, Cayman Chemicals). Bortezomib (S1013), ixazomib citrate (S2181), carfilzomib (S2853), melphalan (S8266), and bendamustine (S1212) were purchased from Selleckchem. Dexamethasone (D4902), doxorubicin (D1515), and lenalidomide (CDS022536) were purchased from Sigma-Aldrich. Selinexor (KPT-330) was provided by Dr. Trinayan Kashyap (Karyopharm). MTT (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide) (M6494) was purchased from Thermo-Fisher Scientific.
+
+Purification and expression of GST-tagged proteins
+
+The details of GST-fused HAPLN1-PTR1 and assessment of bacterial LPS contamination have been published [35]. Briefly, pGEX6p-1 plasmid-based expression constructs were transformed into BL21 Rosetta 2 Escherichia coli strain and induced with 1 mM IPTG followed by lysis and purification by glutathione-agarose beads (G4510, Pierce) and elution by 50 mM reduced glutathione at pH 8.0.
+
+Electrophoretic mobility shift assays (EMSA)
+
+EMSA to measure NF-κB activity in MM cell lines were performed as previously described [36]. Briefly, cell extracts were made using TOTEX buffer, as previously described [37] and 10 μg of extracts were separated on 4% native polyacrylamide gel, dried, and exposed to phosphor screen (Amersham Biosciences) followed by quantitation of NF-κB DNA-binding through ImageQuant software (GE Healthcare). Each NF-κB lane was normalized to Oct-1 values from the same sample and then to the vehicle treated control values for each experiment to derive fold induction.
+
+SDS-PAGE and immunoblot (IB) analysis
+
+Myeloma cell lines were pelleted and lysed in TOTEX buffer, as previously described [37]. Equal amounts (100 μg) of soluble protein were run on denaturing 10 or 12.5% SDS-PAGE gel and transferred onto a polyvinylidene fluoride or nitrocellulose membrane (GE Healthcare). The membrane was then incubated with the appropriate antibodies as described previously [37]. Immunoblots were analyzed by enhanced chemiluminescence as described by the manufacturer (GE Healthcare).
+
+Immunofluorescence and ImageStream cytometry
+
+For immunofluorescence, IκBα antibody (E130, Abcam), 5 mM DRAQ5 (564902, BD Biosciences), anti-rabbit IgG (H+L), F(ab’)2 fragment (Alexa Fluor® 488 Conjugate), and viability stain (Fixable Viability Dye eFluor 780) were purchased from Thermo-Fisher Scientific. RPMI8226 cells (2 x 106) were treated with or without 10 μM selinexor for 45 min at 37°C. Cells were stained with fixable viability dye eF780 for 15 min at room temperature and subsequently fixed with 4% paraformaldehyde for 10 min. eBioscience Foxp3 transcription factor staining buffer set (Thermo-Fisher Scientific) was used to permeabilize and stain cells with IκBα antibody (1:50), then F(ab’)2 Fragment (1:1000) in permeabilization buffer with 1.5% goat serum for 45 min. Nuclear DNA was labeled with DRAQ5 (1:1000) immediately preceding acquisition on the Imagestream X Mark II Imaging Flow Cytometer. Images were acquired based on circularity (Area vs. Aspect Ratio in the brightfield channel) and exclusion of the eF780 fixable viability dye. Following acquisition, IDEAS software was used to measure the degree of IκBα nuclear translocation. Similarity scores were calculated for at least 1,000 cells. The similarity score is a log-transformed Pearson’s correlation coefficient between the pixel values of two image pairs to measure the pixel intensity correlation between the IκBα and DRAQ5 images and gives the degree to which the IκBα signal is localized to the nucleus.
+
+RNA-sequencing
+
+Total RNA was isolated from RPMI8226 cells treated with GST-PTR1 or GST control for 6 hours in 3 biological replicates following standard protocol with TRIzol reagent (15596–018, Thermo-Fisher). Full RNA-Seq workflow service (including library preparation, sequencing, and data QC) was provided by ProteinCT Biotechnologies (Madison, WI).
+
+GSEA analysis using KEGG pathways of RNA-Seq data
+
+Gene set enrichment analysis (GSEA) was performed by calculating a ranked vector as sign(FoldChange)*1/p-value and submitted to the GSEA Ranked. The statistical significance was determined by 1,000 gene set permutations [38]. The latest KEGG pathway [39] database was used for the GSEA analysis. Analyses and plots were performed and generated using R statistical package.
+
+Quantitative RT-PCR (qRT-PCR) analysis
+
+Total RNAs from treated cells were purified by a Nucleospin RNA II column (740955, Clontech) according to the manufacturer’s instruction. cDNAs were synthesized from the total RNAs using iScript cDNA synthesis kit (1708891, Bio-Rad). qRT-PCR was performed and analyzed using a Bio-Rad CFX Connect real-time system. Relative expression was determined by ΔΔCt calculation. The mRNA levels of the samples were normalized to GAPDH mRNA levels and shown as fold induction relative to GST-treated control samples. The primers for qRT-PCR analysis are: IL-8 (forward, 5’-tgcagctctgtgtgaagg-3’; reverse, 5’-ctcagccctcttcaaaaac-3’), IL-10 (forward, 5’-aggatcagctggacaacttg-3’; reverse, 5’-gatgtctgggtcttggttctc-3’), BCL2A1 (forward, 5’-tacaggctggctcaggactat-3’; reverse, 5’-cgcaacattttgtagcactctg-3’), Bcl-2 (forward, 5’-ggtggggtcatgtgtgtgg-3’; reverse, 5’-cggttcaggtactcagtcatcc-3’), and Bcl-XL (forward, 5’-gagctggtggttgactttctc-3’; reverse, 5’-tccatctccgattcagtccct-3’).
+
+Cell viability assay
+
+For the MTT assay, 104−105 cells per well were plated in triplicate in a 96-well clear bottom microassay plate and assayed per manufacturer’s instructions. Cells containing formazan (MTT) were dissolved in 50–75 μL DMSO and formazan concentration was measured by absorbance at 540 nm. For the 10-day cell viability assessment, 5 × 104 cells per well were plated in a 24-well plate and cultured for 10 days. Trypan blue assay was carried out every 2 days to assess the effect of lenalidomide on cells. The number of viable cells were counted using hemocytometer every 2 days. The assays were run in technical triplicate with GST-PTR1 treatment and the data were normalized to mean of GST control. Then, the means of three independent biological replicates were averaged to determine the mean and SD reported. The following concentrations of drugs (Table 1) were used:
+
+10.1371/journal.pone.0274704.t001 Table 1 Drug concentrations for cell viability assay.
+
+	RPMI8226	MM.1S	H929	L363	U266	
+Bortezomib	10 nM	10 nM	10 nM	10 nM	10 nM	
+Ixazomib	100 nM	100 nM	100 nM	100 nM	100 nM	
+Carfilzomib	100 nM	100 nM	100 nM	100 nM	100 nM	
+Dexamethasone	100 μM	100 μM	100 μM	100 μM	100 μM	
+Melphalan	10 μM	10 μM	10 μM	10 μM	10 μM	
+Doxorubicin	2 μM	0.5 μM	2 μM	2 μM	3 μM	
+Bendamustine	250 μM	50 μM	250 μM	250 μM	250 μM	
+Lenalidomide	1 μM	1 μM	1 μM	1 μM	1 μM	
+Selinexor	100 nM	100 nM	100 nM	100 nM	100 nM	
+
+Flow cytometric analysis of rhodamine 123 efflux
+
+Rhodamine 123 (Rh123) was obtained from Abcam (ab275545); propidium iodide (Pl) from Thermo-Fisher Scientific (ICN19545825). RPMI8226 cells pretreated with 100 nM GST-PTR1 or GST for 24 hr were stained with rhodamine 123 (10 nM) at 37°C for 60 min. Samples were analyzed by flow cytometry. The ratio of rhodamine 123 mean fluorescent intensity (MFI) was assessed.
+
+Statistical analysis
+
+Unpaired two-sided Student’s t-test was used to compare two independent groups. Two-way ANOVA analysis with multiple comparisons test was used to compare PI curves and IκB degradation curves. A p-value of <0.05 was considered statistically significant. Analysis was performed with GraphPad Prism Software (GraphPad Software Inc.).
+
+Results
+
+HAPLN1-PTR1 induces a host of cell survival genes in RPMI8226 MM cells
+
+We previously demonstrated that the PTR1 domain of HAPLN1 (HAPLN1-PTR1), produced as recombinant glutathione-S-transferase (GST) fusion (GST-PTR1), induces pro-survival NF-κB signaling in a manner resistant to the PI bortezomib and confers resistance to this clinical drug in several human MM cell lines tested relative to GST negative control [31]. To gain insights into global transcriptional changes induced by HAPLN1-PTR1 in MM cells, we performed RNA-Seq analysis of RPMI8226 cells exposed to 100 nM GST-PTR1 for 6 hr using GST exposed cells as a negative control (GSE202672). We measured bacterial LPS contamination using highly sensitive Limulus Amebocyte Lysate assay and confirmed that it was a few orders of magnitude below that which is detectable by NF-κB EMSA assay in these cells as before [31]. We performed RNA-Seq analysis in three biological replicates, which clustered well for upregulated and downregulated genes (Fig 1A), and found that HAPLN1-PTR1 reproducibly induced ≥2-fold changes in mRNA levels of >1400 genes (Fig 1B). As expected, many NF-κB regulated genes defined by Staudt et al. [40] were detected in the GST-PTR1-upregulated gene category when compared to GST control (highlighted red in Fig 1B). Gene Set Enrichment Analysis (GSEA) [38] of GST-PTR1 RNA-Seq data identified multiple positive regulated disease pathways or phenotypes significantly correlated with GST-PTR1 treatment (Fig 1C and S1 Table) [39]. Consistent with the gene signature, GST-PTR1 treatment displayed a significant correlation to the induction of NF-κB signaling by the GSEA analysis (Fig 1D). GSEA also identified TNF signaling, JAK/STAT signaling, and immunity related changes (Fig 1C and S1 Table). Notably, NF-κB regulated genes included a host of anti-apoptotic genes, such as BCL2, BCL2L1, BCL2A, BIRC2, BIRC3, CFLAR (c-FLIP) and IER3 (IEX-1L) (Fig 1E), as well as certain immune cytokines, such as IL-8 and IL-10. We confirmed the induction of some of these genes by qRT-PCR analysis (e.g., BCL2A1, 284-fold; IL-8, 224-fold; and IL-10, 153-fold) (Fig 1F).
+
+10.1371/journal.pone.0274704.g001 Fig 1 HAPLN1-PTR1 induces transcriptional changes, including an NF-κB transcriptional program.
+
+(A) Heatmap summarized all the differentially expressed genes of two-fold. Each row is a gene, each column is a sample. (B) Volcano plot of RNA-Seq data illustrating significantly (dark gray) and non-significantly (light gray) changed genes in RPMI8226 cells treated with 100 nM GST-PTR1 for 6 hr, relative to control (100 nM GST). Genes depicted in red indicate known NF-κB regulated genes. The RNA-seq analysis was done in three biological replicates. (C) Gene set enrichment analysis (GSEA) for indicated KEGG pathways and the genes differentially regulated by HAPLN1-PTR1 versus control (GST). Shown are bars indicating normalized enrichment scores (NES) for top 30 positive pathways, all with an FDR = 0. The identities of some of the pathways are indicated. For more details, see S1 Table. (D) GSEA normalized enrichment score (NES) plots of the signature of the NF-κB pathway. (E) Reads per kilobase million (RPKM) values of select NF-κB target survival genes from the RNA-Seq results in A. (F) qRT-PCR analysis of select genes detected by RNA-Seq analysis in A. RNA levels of indicated genes in GST-PTR1-treated condition were normalized to GAPDH and fold change relative to control (100 nM GST) for each gene is plotted. Results represent the mean ± SD of three biological replicates. ** p<0.01.
+
+HAPLN1-PTR1 induces RPMI8226 MM cell resistance to multiple therapeutic drugs
+
+Induction of a host of anti-apoptotic genes by GST-PTR1 suggested the possibility that HAPLN1-PTR1 might induce resistance of MM cells to not only bortezomib but also other clinically employed FDA-approved drugs. To evaluate this hypothesis, we next investigated whether GST-PTR1 could increase RPMI8226 MM cell survival in the presence of different classes of FDA-approved MM therapeutics. These include other PIs (carfilzomib, ixazomib), a glucocorticoid (dexamethasone), DNA damaging agents (melphalan, doxorubicin, bendamustine), an IMiD (lenalidomide), and a nuclear export inhibitor (selinexor). We first exposed RPMI8226 cells to varying concentrations of these drugs and identified drug doses that would yield ~50–80% toxicity using MTT assays at 24-hours (PIs, doxorubicin), 2 days (bendamustine), or 3 days (dexamethasone, melphalan, selinexor), or longer 10-day growth assay (lenalidomide). IMiD-induced toxicity is known to manifest much more slowly than that induced by other cytotoxic MM drugs [41]. The cells were then treated with 100 nM GST-PTR1 or GST in the presence of these MM drugs. These toxicity assays demonstrated that GST-PTR1 caused significant resistance of RPMI8226 MM cells to all the drugs tested, except for carfilzomib and selinexor (Fig 2). GST-PTR1 alone without the drugs did not affect the growth of the MM cells. These results demonstrate that HAPLN1-PTR1 is capable of inducing RPMI8226 cell resistance to multiple MM drugs.
+
+10.1371/journal.pone.0274704.g002 Fig 2 HAPLN1-PTR1 causes RPMI8226 MM cell resistance to different classes of therapeutic agents.
+
+RPMI8226 cells were cultured with 100 nM GST-PTR1 (H1-P1) or GST (-) in the presence or absence of indicated drugs at 50–80% cytotoxicity and the cell viability was measured as described in Materials and Methods. Results represent the mean ± SD of three biological replicates, each performed in triplicate. * p<0.05, ** p<0.01.
+
+HAPLN1-PTR1 induces clinical PI resistant NF-κB activation in RPMI8226 MM cells
+
+We previously showed that HAPLN1-PTR1 induces bortezomib-resistant NF-κB signaling, which was correlated with bortezomib resistance in RPMI8226 cells [31]. The finding that GST-PTR1 can cause resistance to ixazomib but not carfilzomib in the above study could be related to the ability of the latter to inhibit NF-κB signaling induced by HAPLN1-PTR1 but not by the former. To test this hypothesis, we treated RPMI8226 cells at different concentrations of ixazomib and carfilzomib, in the presence of GST-PTR1 or GST control. To demonstrate the efficacy of these PIs, the cells were also treated in parallel with TNFα, a canonical NF-κB inducer that involves the 26S proteasome-mediated IκBα degradation to activate NF-κB. The cell samples were then analyzed by EMSA using Igκ-κB probe with Oct-1 binding serving as a loading control. Similar to the case with bortezomib, NF-κB activation by GST-PTR1 was not inhibited by ixazomib or carfilzomib up to 30 nM (Fig 3A–3D). Significant NF-κB inhibition was only evident at 100 nM of these PIs. In contrast, significant inhibition of NF-κB activation by TNFα was evident at 30 nM of ixasomib and 10 nM for carfilzomib. The degree of inhibition was significantly greater for TNFα than that observed for GST-PTR1 at the varying PIs concentrations analyzed (Fig 3A–3D). These results suggest that, similar to bortezomib [31], NF-κB activation by HAPLN1-PTR1 is significantly more resistant to both ixazomib and carfilzomib relative to TNFα-induced activation. HAPLN1-PTR1 can reduce the MM cell toxicity of both bortezomib and ixazomib, but not carfilzomib, indicating that the latter drug can escape the resistance impact of HAPLN1-PTR1 despite its inability to inhibit NF-κB activation.
+
+10.1371/journal.pone.0274704.g003 Fig 3 HAPLN1-PTR1-induced NF-κB activation is resistant to clinical PI’s.
+
+(A) Representative EMSA analysis of RPMI8226 cells incubated with 10 ng/mL TNFα or 100 nM GST-PTR1 (H1-P1) in the absence or presence of increasing concentrations (nM) of ixazomib (Ixa). (B) Graph depicts the mean ± SD of the quantification of three independent replicates of EMSA analysis as in A. (C) Representative EMSA analysis as in A with increasing concentrations (nM) of carfilzomib (Carf). (D) Graph depicts the mean ± SD of the quantification of three independent replicates of EMSA analysis as in C. ** p<0.01, *** p<0.001.
+
+Selinexor inhibits HAPLN1-PTR1-induced IκBα degradation and NF-κB activation
+
+We previously demonstrated that NF-κB activation by HAPLN1-PTR1 involves PI-resistant degradation of IκBα and PI-sensitive IκBβ degradation [31]. Of these, inhibition of IκBα degradation by selinexor was reported to be critical for its clinical efficacy [42]. We and others also reported that IκBα, but not IκBβ, contains a nuclear export sequence (NES), critical for nuclear export of inactive NF-κB-IκBα complexes to the cytoplasm via the nuclear export receptor XPO1/CRM1 [43, 44]. We further reported that the nuclear export inhibitor Leptomycin B (LMB) or a mutation of IκBα-NES can prevent PI resistant IκBα degradation via nuclear sequestration and inhibit atypical bortezomib-resistant NF-κB activity [45]. Because both selinexor and LMB are specific chemically distinct inhibitors of XPO1, we tested whether the ability of selinexor to overcome HAPLN1-PTR1-mediated drug resistance was related to its ability to prevent HAPLN1-PTR1-induced IκBα degradation and associated NF-κB activation. RPMI8226 cells were exposed to GST-PTR1 in the presence of cycloheximide to block new IκBα synthesis to specifically determine the impact of its degradation induced by the ligand with and without selinexor. We previously demonstrated that IκBα levels are not altered by three-hour treatment with cycloheximide in RPMI8226 cells [31]. GST-PTR1 induced near complete degradation of IκBα within 1 hour, which was inhibited by selinexor (Fig 4A and 4B). This resulted in partial inhibition of NF-κB activation induced by GST-PTR1 (Fig 4C); the remaining activation is likely arising from degradation of IκBβ, which is insensitive to selinexor (Fig 4A and 4B) but sensitive to bortezomib [31]. Indeed, this is confirmed by addition of both selinexor and bortezomib that caused near complete NF-κB inhibition induced by GST-PTR1 (Fig 4D). As expected, selinexor caused nuclear accumulation of IκBα, like LMB treatment (Fig 4E). These results support the hypothesis that selinexor can block HAPLN1-PTR1-induced IκBα degradation via its nuclear sequestration causing inhibition of IκBα-associated NF-κB activation to overcome the survival effects induced by HAPLN1-PTR1 in MM cells. Moreover, these results provide additional molecular details potentially underlying the marked clinical synergy with the combination of selinexor and PIs [46].
+
+10.1371/journal.pone.0274704.g004 Fig 4 Selinexor inhibits HAPLN1-PTR1-induced IκBα degradation and NF-κB activation.
+
+(A) Representative immunoblot analysis of IκBα, IκBβ and tubulin in RPMI8226 cells pretreated for 30 min with 20 μg/mL cycloheximide (Cx) with 10 μM selinexor (Sel) and stimulated with GST-PTR1 (H1-P1) for indicated times. (B) Graph depicts the mean ± SD of the quantification of three independent replicates of western blot analysis as in A. (C) EMSA analysis of RPMI8226 cells pretreated for 30 min with increasing concentrations of selinexor (Sel) and stimulated with GST-PTR1 (H1-P1). (D) EMSA analysis of RPMI8226 cells pretreated for 30 min with 10 μM Sel or 100 nM Bort and stimulated with 100 nM GST-PTR1 (H1-P1) or 10 ng/mL TNFα. (E) Upper: Images of RPMI8226 cells control and treated with 10 μM Selinexor (Sel) or 20 μg/mL Leptomycin B (LMB) for 45 min. The similarity score for each set of representative images is included in merged image. Lower: A representative similarity histogram for control and treated cells showing the co-localization of IκBα and the nuclear dye, DRAQ5.
+
+HAPLN1-PTR1 also induces expression of multi-drug resistance (MDR) genes and function in RPMI8226 cells
+
+While the above studies focused on the potential role of NF-kB-regulated antiapoptotic genes induced by HAPLN1-PTR1, our RNA-seq analysis also identified various MDR family genes were also upregulated after GST-PTR1 treatment (Fig 5A). Moreover, using the drug efflux pump substrate, rhodamine 123, GST-PTR1 also significantly upregulated MDR efflux function (Fig 5B and 5C). These results suggest a dual mode of HAPLN1-induced drug resistance mechanism by inducing the expression of both anti-apoptotic genes and drug efflux pump MDR genes.
+
+10.1371/journal.pone.0274704.g005 Fig 5 HAPLN1-PTR1 increases expression of MDR genes and function.
+
+(A) Heatmap of upregulated MDR genes after GST-PTR1 treatment in RNA-seq analysis colored by Z-score. (B) Representative histogram of rhodamine 123 efflux assay. RPMI8226 cells were treated with 100 nM GST-PTR1 (H1-P1) or control GST for 24 h and subjected to the rhodamine 123 efflux assay. (C) Graph depicts the mean fluorescent intensity (MFI) ± SEM of three independent replicates of rhodamine 123 efflux assay as in B. **** p<0.0001.
+
+HAPLN1-PTR1 induces variable levels of drug resistance in several myeloma cell lines
+
+To determine whether the drug resistance phenotype induced by HAPLN1-PTR1 in RPMI8226 cells is not limited to this MM cell line alone, we exposed several other MM cell lines to different clinical drugs in the presence of GST-PTR1 or GST as control. As in the RPMI8226 cell study, these studies were performed in biological triplicates, each performed in three technical replicates. The results are summarized in Fig 6, which demonstrated that GST-PTR1 was able to induce variable but significant resistance to multiple classes of MM therapeutic drugs in different MM cell lines. Again, as in the case for RPMI8226 cells, GST-PTR1 failed to induce resistance to carfilzomib in all cell lines tested and to selinexor in most lines. Overall, our results identify HAPLN1-PTR1 as a novel extracellular matrix factor capable of inducing resistance in MM cells to multiple classes of MM therapeutics with notable exceptions.
+
+10.1371/journal.pone.0274704.g006 Fig 6 HAPLN1-PTR1 causes resistance of several MM cell lines to multiple clinical drugs.
+
+MM cell lines (L363, MM.1S, H929, U266) were cultured with 100 nM GST-PTR1 (H1-P1) or GST (-) in the presence or absence of indicated drugs and the cell viability was measured as described in Materials and Methods. Results represent the mean ± SD of three biological replicates, each performed in triplicate. * p<0.05, ** p<0.01, *** p<0.001.
+
+Discussion
+
+In the present study, we tested whether HAPLN1 acts as a drug resistance factor in MM cells against different classes of therapeutic drugs. We previously showed that HAPLN1 can initiate bortezomib-resistant NF-κB activity in MM cells and bortezomib-resistant cell survival [31]. We now performed an RNA-seq analysis of RPMI8226 MM cell line exposed to HAPLN1-PTR1 and found that this ligand induces significant changes in the transcriptomic landscape in these MM cells. This included induction of a group of anti-apoptotic genes that are known targets of NF-κB, among many other genes. Because these changes in expression of anti-apoptotic genes may increase the death threshold of MM cells, we tested whether HAPLN1-PTR1 could also increase resistance of MM cells to other clinically employed MM therapeutics. Indeed, this ligand was able to variably increase MM cell resistance to many of the currently employed drugs, including PIs, glucocorticoids, DNA damaging agents, and IMiDs, in several MM cell lines tested. HAPLN1 also induced the expression of a host of MDR genes and drug efflux function of MM cells. These results therefore support HAPLN1 as a novel ECM-derived multi-drug resistance inducer in MM cells.
+
+Interestingly, HAPLN1 was found to mediate resistance to apoptosis induction by bortezomib and ixazomib, but not by carfilzomib. We found that HAPLN1-PTR1-induced NF-κB activity was highly resistant to all the PIs tested, including carfilzomib, thus further demonstrating the induction of NF-κB activation by HAPLN1 proceeds via an atypical PI-resistant mechanism. Nevertheless, HAPLN1-PTR1 was unable to cause apoptosis resistance to carfilzomib in any of the MM cell lines tested even though it did so against bortezomib and ixazomib in almost all cell lines tested. The difference in HAPLN1-mediated survival could potentially be attributed to differences in pharmacological properties of the PIs. Bortezomib and ixazomib are chemically related (peptide boronates) and are both reversible inhibitors of the proteasome [47]. In contrast, carfilzomib has a different chemical structure based on four amino acids and irreversibly binds to the proteasome via epoxyketone pharmacophore [48]. Furthermore, carfilzomib and dexamethasone regimen was shown to be clinically superior to bortezomib and dexamethasone in patients with relapsed MM in a large phase III randomized trial [49]. Indeed, NF-κB activation induced by HAPLN1-PTR1 was also resistant to oprozomib, another irreversible epoxyketone structured PI [47], but it was still unable to cause resistance to this PI similar to carfilzomib (S1 Fig). Other than sharing the similar chemical structure, carfilzomib and oprozomib differ in their selectivity against target proteasome β subunits, i.e., carfilzomib inhibits β5/2/1, oprozomib only β5 [47]. This suggests that HAPLN1 can induce drug resistance only against reversible PIs, but irreversible PIs can overcome HAPLN1-induced survival effects. It is therefore likely that prolonged proteasome inhibition, possibly only with irreversible PIs, can overcome HAPLN1 effects.
+
+The differences in sensitivities to HAPLN1-induced drug resistance may also be due to differences in resistance mechanisms involved for different PIs. Unlike bortezomib, whose various resistance mechanisms including NF-κB signaling have been described in the literature [50–56], the mechanisms of resistance to other PIs are not well defined. It is possible that other signaling pathways induced by HAPLN1, such as the JAK/STAT pathway identified by the KEGG analysis of our RNA-seq data, might be playing a role in mediating differential effects against different PIs. In patients, carfilzomib shows clinical efficacy against MM resistant to bortezomib, thus demonstrating that this second generation PI is capable of overcoming bortezomib resistance mechanism(s) [57]. Besse et al., demonstrated that PI’s difference in the β subunit selectivity exerts the differential cytotoxic effect, namely, co-inhibition of β5/β2 subunits has the most cytotoxicity which can be achieved by high dose carfilzomib, while bortezomib can only abrogate β5/β1 [58]. Moreover, bortezomib and carfilzomib resistant MM cell lines manifest different PSMB5 mutation status and MDR gene expression. Unlike bortezomib resistant cell lines, carfilzomib resistant MM cells have wild-type PSMB5, but overexpress the drug efflux pump ABCB1/MDR1 [58, 59]. Interestingly, we also observed induction of a host of MDR gene family members in response to HAPLN1 stimulation of MM cells but ABCB1/MDR1 was not among those induced. Thus, further studies are required to define the mechanism by which the irreversible PI carfilzomib overcomes HAPLN1-induced survival effects in MM cells.
+
+Similar to carfilzomib, selinexor also overcame HAPLN1-induced survival effects in most MM cell lines analyzed. Selinexor is an oral first-in-class selective inhibitor of XPO1-mediated nuclear export (SINE) compound [42]. This inhibitor was able to abolish HAPLN1-PTR1 induced IκBα degradation by forcing nuclear retention and accumulation of IκBα. This is consistent with our previous findings that PI-resistant IκBα degradation requires the presence of IκBα in the cytoplasm and thus its nuclear accumulation induced by XPO1/CRM1 inhibitors prevents its degradation [60]. Consequently, selinexor suppressed HAPLN1-induced NF-κB activation. Prior studies have found that cytotoxic effects of selinexor against MM cells (and other cancer types) depend, at least part, on IκBα [61]. Thus, through the inhibition of IκBα nuclear export, selinexor appears to overcome the HAPLN1-induced NF-κB signaling and survival effects. Selinexor has been previously shown to inhibit NF-κB transcriptional activity in different cancer and inflammatory models [62–64]. However, selinexor also affects multiple additional key molecular targets, such as p53, p21, p27, STAT1, and STAT3 [65–68], which may also contribute to overcoming the survival effects conferred by HAPLN1.
+
+GSEA analysis of RNA-Seq data also showed that HAPLN1 is not only linked to pathways related to drug resistance but it may have a wider role in orchestrating the intra-tumoral inflammatory milieu and balancing anti-tumor immunity. These results pointed to increased expression of pathways correlated to T cell-mediated immunity and anti-viral responses. This is intriguing in view of recent findings showing that the regulated proteolysis of the tolerogenic versican (VCAN) generates a matrikine, versikine, with opposing, immunostimulatory activities [69, 70]. VCAN proteolysis correlates with T-cell infiltration in both hematopoietic and solid tumors and versikine triggers a type-I interferon response in myeloid cells, an essential component of the “T-cell inflamed” TME [71, 72]. Notably, HAPLN1 is a critical component of VCAN-ECM and like HAPLN1, VCAN/versikine contains two related PTR-domains. Taken together, these findings point to coordinated roles of PTR-containing proteins in regulating drug resistance and tumor immunity. It seems plausible that distinct components of the MM TME contribute diverse PTR-containing matrikines. It would be of great interest to determine whether the levels of such matrikines vary through MM disease progression and whether their levels could be predictive of therapy resistance and possibly immune microenvironment. Individualized therapeutic approaches using the HAPLN1 level in patient plasma as a prognostic marker need to be further tested in well-designed clinical settings. Such an approach would require the development of an assay to quantify HAPLN1 matrikine levels in patient samples.
+
+Conclusions
+
+The data presented here demonstrate that a HAPLN1 matrikine can induce resistance in MM cells to different classes of therapeutic agents. This matrikine induces many changes in cell signaling and gene expression in MM cells, including an activation of NF-κB signaling and induction of many cell survival genes, multi-drug resistance genes, and immune-related genes. Our study thus reveals a novel mechanism of drug resistance in MM cells, suggesting that the HAPLN1 matrikine may be a novel biomarker and/or therapeutic target for this currently incurable disease.
+
+Supporting information
+
+S1 Table Gene set enrichment analysis (GSEA) using KEGG pathways of the ranked genes differentially regulated by GST-PTR1 versus GST control.
+
+Top 30 significant (FDR<0.05) pathways are shown. KEGG, Kyoto Encyclopedia of Genes and Genomes; ES, enrichment score; NES, normalized enrichment score; NOM, nominal; FDR, false discovery rate; FWER, family-wise error rate.
+
+(TIF)
+
+Click here for additional data file.
+
+S1 Fig HAPLN1-PTR1-induced NF-κB activation is resistant to oprozomib.
+
+(A) Representative EMSA analysis of RPMI8226 cells incubated with 10 ng/mL TNFα for 15 min or 100 nM GST-PTR1 (H1-P1) for 2 hr in the absence or presence of increasing concentrations (nM) of oprozomib (Opro). (B) Graph depicts the mean ± SD of the quantification of three independent replicates of EMSA analysis as in A. (C) MM cell lines (RPMI8226, L363, MM.1S, H929, U266) were cultured with 100 nM GST-PTR1 (H1-P1) or GST (-) in the presence of oprozomib (200 nM) and the cell viability was measured as described in Materials and Methods. Results represent the mean ± SD of three biological replicates, each performed in triplicate. ** p<0.01.
+
+(TIF)
+
+Click here for additional data file.
+
+S1 Raw images Original blots and gel image data.
+
+(PDF)
+
+Click here for additional data file.
+
+We thank the members of the Miyamoto lab for their helpful comments on the project and the manuscript, Sean McIlwain for sharing his scripts used in biostatistic analysis.
+==== Refs
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+44 Huang TT , Kudo N , Yoshida M , Miyamoto S . A nuclear export signal in the N-terminal regulatory domain of IkappaBalpha controls cytoplasmic localization of inactive NF-kappaB/IkappaBalpha complexes. Proceedings of the National Academy of Sciences. 2000;97 (3 ):1014–9. papers3://publication/doi/10.1073/pnas.97.3.1014. 10655476
+45 O’Connor S , Shumway S , Miyamoto S . Inhibition of IkappaBalpha nuclear export as an approach to abrogate nuclear factor-kappaB-dependent cancer cell survival. Mol Cancer Res. 2005;3 (1 ):42–9. .15671248
+46 Grosicki S , Simonova M , Spicka I , Pour L , Kriachok I , Gavriatopoulou M , et al . Once-per-week selinexor, bortezomib, and dexamethasone versus twice-per-week bortezomib and dexamethasone in patients with multiple myeloma (BOSTON): a randomised, open-label, phase 3 trial. Lancet. 2020;396 (10262 ):1563–73. doi: 10.1016/S0140-6736(20)32292-3 .33189178
+47 Hungria VTdM Crusoé EdQ , Bittencourt RI, Maiolino A, Magalhães RJP, Sobrinho JdN, et al . New proteasome inhibitors in the treatment of multiple myeloma. Hematology, transfusion and cell therapy. 2019;41 (1 ):76–83. papers3://publication/doi/10.1016/j.htct.2018.07.003. 30793108
+48 in SPEJF. Carfilzomib: A Promising Proteasome Inhibitor for the Treatment of Relapsed and Refractory Multiple Myeloma. frontiersinorg. papers3://publication/uuid/B1C92745-3F17-438D-BBA0-229786E4E5DB.
+49 Dimopoulos MA , Goldschmidt H , Niesvizky R , Joshua D , Chng WJ , Oriol A , et al . Carfilzomib or bortezomib in relapsed or refractory multiple myeloma (ENDEAVOR): an interim overall survival analysis of an open-label, randomised, phase 3 trial. Lancet Oncol. 2017;18 (10 ):1327–37. Epub 20170823. doi: 10.1016/S1470-2045(17)30578-8 .28843768
+50 Nikesitch N , Tao C , Lai K , Killingsworth M , Bae S , Wang M , et al . Predicting the response of multiple myeloma to the proteasome inhibitor Bortezomib by evaluation of the unfolded protein response. Blood Cancer J. 2016;6 :e432. Epub 20160610. doi: 10.1038/bcj.2016.40 ; PubMed Central PMCID: PMC5141355.27284736
+51 Murray MY , Auger MJ , Bowles KM . Overcoming bortezomib resistance in multiple myeloma. Biochem Soc Trans. 2014;42 (4 ):804–8. doi: 10.1042/BST20140126 .25109961
+52 Dong H , Chen L , Chen X , Gu H , Gao G , Gao Y , et al . Dysregulation of unfolded protein response partially underlies proapoptotic activity of bortezomib in multiple myeloma cells. Leuk Lymphoma. 2009;50 (6 ):974–84. doi: 10.1080/10428190902895780 .19391038
+53 Oerlemans R , Franke NE , Assaraf YG , Cloos J , van Zantwijk I , Berkers CR , et al . Molecular basis of bortezomib resistance: proteasome subunit beta5 (PSMB5) gene mutation and overexpression of PSMB5 protein. Blood. 2008;112 (6 ):2489–99. Epub 20080618. doi: 10.1182/blood-2007-08-104950 .18565852
+54 Lü S , Wang J . The resistance mechanisms of proteasome inhibitor bortezomib. Biomark Res. 2013;1 (1 ):13. Epub 20130301. doi: 10.1186/2050-7771-1-13 ; PubMed Central PMCID: PMC4177604.24252210
+55 Xie H , Gu Y , Wang W , Wang X , Ye X , Xin C , et al . Silencing of SENP2 in Multiple Myeloma Induces Bortezomib Resistance by Activating NF-κB Through the Modulation of IκBα Sumoylation. Scientific reports. 2020;10 (1 ):766–10. papers3://publication/doi/10.1038/s41598-020-57698-0.31964975
+56 Barrio S , Stühmer T , Da-Viá M , Barrio-Garcia C , Lehners N , Besse A , et al . Spectrum and functional validation of PSMB5 mutations in multiple myeloma. Leukemia. 2019;33 (2 ):447–56. papers3://publication/doi/10.1038/s41375-018-0216-8 30026573
+57 Siegel DS , Martin T , Wang M , Vij R , Jakubowiak AJ , Lonial S , et al . A phase 2 study of single-agent carfilzomib (PX-171-003-A1) in patients with relapsed and refractory multiple myeloma. Blood. 2012;120 (14 ):2817–25. papers3://publication/doi/10.1182/blood-2012-05-425934. 22833546
+58 Besse A , Besse L , Kraus M , Mendez-Lopez M , Bader J , Xin B-T , et al . Proteasome Inhibition in Multiple Myeloma: Head-to-Head Comparison of Currently Available Proteasome Inhibitors. Cell chemical biology. 2019;26 (3 ):340–51.e3. papers3://publication/doi/10.1016/j.chembiol.2018.11.007. 30612952
+59 Besse A , Stolze SC , Rasche L , Weinhold N , Leukemia GJM . Carfilzomib resistance due to ABCB1/MDR1 overexpression is overcome by nelfinavir and lopinavir in multiple myeloma. naturecom. papers3://publication/uuid/EFBCFD72-FEFE-4896-BC8D-F1CCED1B165C. doi: 10.1038/leu.2017.212 28676669
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+65 Subhash VV , Yeo MS , Wang L , Tan SH , Wong FY , Thuya WL , et al . Anti-tumor efficacy of Selinexor (KPT-330) in gastric cancer is dependent on nuclear accumulation of p53 tumor suppressor. Scientific reports. 2018;8 (1 ):12248. papers3://publication/doi/10.1038/s41598-018-30686-1. 30115935
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+

PMC10060291.txt

@@ -0,0 +1,437 @@
+
+==== Front
+Eur J Health Econ
+Eur J Health Econ
+The European Journal of Health Economics
+1618-7598
+1618-7601
+Springer Berlin Heidelberg Berlin/Heidelberg
+
+35610398
+1463
+10.1007/s10198-022-01463-9
+Original Paper
+Assessing the treatment pattern, health care resource utilisation, and economic burden of multiple myeloma in France using the Système National des Données de Santé (SNDS) database: a retrospective cohort study
+Bessou Antoine 1
+Colin Xavier 2
+De Nascimento Julie 1
+Sopwith Will 3
+Ferrante Shannon 4
+Gorsh Boris 4
+Gutierrez Benjamin 4
+Sansbury Leah 5
+Willson Jenny 6
+Sapra Sandhya 4
+Paka Prani 7
+http://orcid.org/0000-0002-4720-580X
+Wang Feng feng.9.wang@gsk.com
+
+4
+1 grid.434277.1 IQVIA, Paris, France
+2 grid.476503.3 0000 0001 0023 6425 GlaxoSmithKline, Rueil-Malmaison, France
+3 grid.482783.2 IQVIA, London, UK
+4 grid.418019.5 0000 0004 0393 4335 Value Evidence and Outcomes, GlaxoSmithKline, Upper Providence, Collegeville, PA USA
+5 grid.418019.5 0000 0004 0393 4335 Value Evidence and Outcomes, GlaxoSmithKline, Research Triangle Park, Durham, NC USA
+6 grid.418236.a 0000 0001 2162 0389 Value Evidence and Outcomes, GlaxoSmithKline, London, UK
+7 grid.418019.5 0000 0004 0393 4335 Global Medical Affairs, GlaxoSmithKline, Upper Providence, Collegeville, PA USA
+25 5 2022
+25 5 2022
+2023
+24 3 321333
+22 6 2021
+5 4 2022
+© The Author(s) 2022
+https://creativecommons.org/licenses/by/4.0/ Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visithttp://creativecommons.org/licenses/by/4.0/.
+Background
+
+Real-world data on health care resource utilisation (HCRU) and costs for French patients with multiple myeloma (MM) are limited due to the quickly evolving MM treatment landscape. This retrospective, national-level study quantified the MM economic burden in France.
+
+Methods
+
+The study included patients with newly diagnosed MM from the Système National des Données de Santé coverage claims database between 2013 and 2018 who received active treatment within 30 days of diagnosis. HCRU included hospitalisations, drugs, consultations, procedures, tests, devices, transport, and sick leave. Costs were annualized to 2019 prices. Drug treatments, reported by line of therapy (LOT), were algorithmically defined using drug regimen, duration of therapy, and gaps between treatments. Analyses were stratified by stem cell transplantation status and LOT.
+
+Results
+
+Among 6413 eligible patients, 6229 (97.1%) received ≥ 1 identifiable LOT; most received 1 (39.8%) or 2 LOT (27.5%) during follow-up. Average annual hospitalisation was 6.3 episodes/patient/year (median duration: 11.6 days). The average annual cost/patient was €58.3 K. Key cost drivers were treatment (€28.2 K; 39.5% of total HCRU within one year of MM diagnosis) and hospitalisations (€22.2 K; 48.6% of total HCRU costs in first year). Monthly treatment-related costs increased from LOT1 (€2.447 K) and LOT5 + (€7.026 K); only 9% of patients received LOT5 + . At LOT4 + , 37 distinct regimens were identified. Hospitalisation costs were higher in patients with stem cell transplantation than total population, particularly in the first year.
+
+Conclusions
+
+This study showed a high economic burden of MM in France (€72.37 K/patient/year in the first year) and the diversity of regimens used in late-line treatments.
+
+Supplementary Information
+
+The online version contains supplementary material available at 10.1007/s10198-022-01463-9.
+
+Keywords
+
+Multiple myeloma
+HCRU
+Cost
+Line of therapy
+Economic burden
+France
+http://dx.doi.org/10.13039/100004330 glaxosmithkline 208292 Bessou Antoine issue-copyright-statement© Springer-Verlag GmbH Germany, part of Springer Nature 2023
+==== Body
+pmcIntroduction
+
+The incidence of multiple myeloma (MM) is increasing globally [1]. Western Europe is one of 3 regions with the highest age-standardised incidence of MM globally [1]. According to estimates, in 2020 France had the second-highest MM burden in the European Union (EU), with 10 cases per 100,000 population, compared with an average of 7.5 cases per 100,000 in the EU [2].
+
+The MM treatment landscape is changing rapidly, and major advances have been made in the treatment of patients with MM in the last decade, including an increase in the number of approved novel drugs and use of combination treatments [3]. Therapies including monoclonal antibodies and advanced generations of proteasome inhibitors and immunomodulatory agents have significantly improved patient outcomes, including response rates and duration of progression-free and overall survival, when compared with conventional treatments [4]. For most patients, however, MM remains incurable and patients often repeatedly relapse, with a worsening prognosis and shorter duration of treatment response with each subsequent relapse [5–8]. The distribution of patients newly diagnosed with MM ISS stage 1 (20–24%), stage 2 (38–44%), or stage 3 (33–39%) disease reflect the severity of MM at diagnosis [9, 10]. The median duration from diagnosis to first relapse is around 22.7 months [11]. The median overall survival for patients receiving first, second, third, and fourth line of treatment (LOT) was reported to be 37.5, 19.7, 13.9, and 9.2 months, respectively [12].
+
+Patients often require multiple LOTs, which come with increased costs. Currently, there is no standard of care treatment for relapsed/refractory MM (RRMM). With the emergence of more novel treatments and combination therapies, treatment decisions are likely to become even more complex. Although these new treatments offer improved care, they may be associated with higher health care costs.
+
+Drivers of the costs of managing patients with MM include stem cell transplantation (SCT), multiple drug regimens over the course of the disease, tests, and repeated hospitalisations. Current real-world data on health care resource utilisation (HCRU) costs for patients with MM in France, and in Europe in general, are limited, particularly for patients with heavily pretreated RRMM. In one estimate, the reimbursed costs of care for patients with MM or malignant tumour plasma cells reached ~ 1 billion Euros (€) in France [13]. Studies conducted in Europe between 2001 and 2015 have shown drug and hospitalisation costs to be the largest components of total HCRU-associated costs and that costs vary by LOT [14–18]. However, because the most recently approved drugs were not included, these analyses may not reflect current HCRU and costs.
+
+Understanding patient HCRU patterns, disease burden, and health-related expenditure is important when evaluating the potential value of new treatments and facilitates targeted improvements in MM management. Analyses of health insurance databases can guide public health care decisions, monitor various types of medical expenditures, inform epidemiological studies, evaluate medical practices or health system experimentations, and can be used for international comparisons [19]. The aim of this study is to describe the treatment patterns, quantify the MM economic burden in France, and identify HCRU associated with MM treatment using the Système National des Données de Santé (SNDS) national coverage claims database.
+
+Methods
+
+Study overview and data source
+
+This descriptive, retrospective cohort study used claims data in the SNDS across all regions of France, except from those affiliated with an institution in Mayotte. The database includes reimbursement claims data covering at least 99% of French residents [20]. Information is held on outpatient claims, hospital discharges, deaths, and disabilities, with records linked by pseudonymised record identification [19, 21]. Diagnoses are coded according to the International Classification of Diseases version 10 (ICD-10) and medical procedures according to the Classification Commune des Actes Médicaux [CCAM]).
+
+The study sponsors (i.e., the authors affiliated with GSK) initiated the study by contracting IQVIA France to access the SNDS database, collect and perform analysis on raw data, and develop a study report. In accordance with French medical data privacy laws, IQVIA acquired access to the SNDS database, which required ethics approval, and approval from the Comité d'Expertise pour les Recherches, les Etudes et les Evaluations dans le domaine de la Santé (CESREES) and the Commission Nationale de l'Informatique et des Libertés (CNIL). Data access was delivered by the Caisse nationale d'assurance maladie (CNAM) after signing an agreement.
+
+Study population
+
+Eligible patients from the SNDS database had a new diagnosis of MM between January 1, 2013, and December 31, 2018, were older than 18 years, and were undergoing active treatment for MM (i.e., treated within 30 days after initial diagnosis) (Fig. 1). To minimize the potential erroneous inclusion of patients with coding errors and align with other SNDS study designs, diagnosis of MM was identified by at least 2 records of MM diagnosis: ICD-10 codes from a primary, related or associated diagnosis (C90, C90.x) during hospital stays, or a MM diagnosis as long-term disease during the study time period (or both). Historical data for these patients were available from January 1, 2008. To maximise patient inclusion, minimum time of follow-up was not specified. In addition, patients were required to have a minimum of 1 year of history in the SNDS prior to MM diagnosis and be affiliated with the general reimbursement scheme, which captures salaried employees in the private sector and their dependents, representing about 76% of French inhabitants in 2015 [19]. Patients with a diagnosis of MM within 1 year before the index date (defined as the first diagnosis of MM recorded during the inclusion period) or with another malignancy (except for non-myeloma skin cancer) within 5 years of the index date were not eligible.Fig. 1 Schema of study parameters for inclusion of newly diagnosed patients with MM and outcomes assessment. aUsed for the description of patients at baseline, comorbidities, or confirming incident cases; bThe time between index date and death, end of follow-up (a gap of 12 months without reimbursed care in SNDS), or the end of the study inclusion period. MM multiple myeloma, SNDS Système National des Données de Santé
+
+Inputs
+
+All inputs, including HCRU and cost, were extracted directly from the SNDS database. Inputs included patient demographics and clinical characteristics, as well as exposures including LOT, SCT, and adverse events (AEs). Demographic and clinical characteristics were defined using the most recent record prior to the index date. Dispensed medications were identified using Anatomical Therapeutic Chemical codes, and outputs included date of prescribing, date of dispensing, setting of prescribing, prescriber specialty, and number of packs dispensed.
+
+LOT was algorithmically defined based on published criteria using information on the drug regimen, time since first administration, and a gap between treatment regimens (Supplementary Fig. 1) [22, 23]. Drugs had to be approved for MM before December 2018. All drugs dispensed within 28 days following the treatment initiation date were considered first-line therapy (LOT1). A LOT was defined as continuing until a new drug was added (excluding widely used therapies, such as corticosteroids) or the discontinuation of all drugs in the LOT, defined as a treatment gap of at least 90 days following the end of the grace period. A grace period was allowed between 2 successive administrations of drug, based on the usual duration of a full prescription administered in clinical practice in France and adapted from Palmaro et al. 2017 [23]. Date of discontinuation in this case was defined as the date the grace period ended.
+
+Fully observable drugs in the SNDS were defined as “high-cost drugs,” costly and innovative drugs dispensed in the hospital which are excluded from the diagnosis-related group system; “temporarily authorised drugs,” exceptional hospital use of products without marketing authorisation; “retrocession drugs,” dispensed to ambulatory patients within the hospital; or “drugs in community,” only available in community settings (Supplementary Table 1). Some drugs (e.g., melphalan and cyclophosphamide) that can be administered intravenously or orally were partially observable in SNDS; community-based oral administration was observable, but hospital-based intravenous administration was not.
+
+Treatment regimens incorporating drugs that were not observable in the SNDS, e.g., corticosteroids, were constructed from assumptions based on treatment recommendation guidelines [23, 24]. SCT was determined by hospitalisation with a related diagnosis-related group code or a procedure CCAM code (Supplementary Table 2). Relevant AEs for all newly diagnosed patients with MM were identified using ICD-10 codes from hospital diagnoses (Supplementary Table 3).
+
+Outcomes
+
+All-cause HCRU was assessed for the following categories: private and public hospitalisations, outpatient physician and paramedic visits, medical procedures, laboratory tests, dispensing of observable drugs and medical devices, financial sickness benefits, invalidity pensions, and reimbursed transport expenses.
+
+Events of interest (AEs and comorbidities) included keratopathy/keratitis, blurred vision, cataracts, glaucoma, light sensitivity, bleeding events, infusion reaction, dry eye, anaemia, neutropenia, thrombocytopenia, infection, blood clots, skeletal-related events, peripheral neuropathy, venous thromboembolism, diarrhoea, shingles, and pneumonia. HCRU related to events of interest was assessed for patients with incident MM from index date to end of follow-up and included hospitalisations with any of these conditions when associated with a primary diagnosis. Each hospitalisation based on a single primary diagnosis of an event of interest was counted as a unique event. The results were reported as the proportion of the study population experiencing each event and the rate per patient per year (PPPY). To adjust for varying lengths of follow-up time, costs and healthcare use were also reported as a mean Per Patient Per Month (PPPM). PPPM costs were calculated by summing all costs incurred during the observation period divided by the sum of length of the observation period for each patient.
+
+Costs of HCRU related to the administration of MM treatment were classified as MM-related treatment administration. These included costs incurred within hospital, such as costs of “high-cost drugs” indicated for MM, all stays for which the main diagnosis indicated a chemotherapy session and a related diagnosis of MM (entire cost of stay was counted), and transport following MM-related treatment or hospital stay. Costs incurred as an outpatient, including cost of drugs with an indication for MM, and sick leave within 7 days following MM-related treatment or hospital stay were also classified as MM-related treatment administration costs.
+
+HCRU and costs related directly to MM included those described for MM-related treatment administration plus all stays with a primary or associated diagnosis of MM, all rehabilitation stays directly following a MM related stay, and all home hospitalisation with a diagnosis of MM. Outpatient laboratory tests, imaging, physician visits, and medical procedures and devices were excluded from this analysis.
+
+Data analyses
+
+Costs of HCRU for each category were annualised to 2019 prices and quoted in € PPPY or per patient per month (PPPM). Cost rate was based on the total cost of a specific HCRU during the follow-up period divided by the number of person-years available for analysis, regardless of individual HCRU exposure. Descriptive analyses were performed using number and percent for categorical variables, and mean, standard deviation (SD), median, interquartile range (IQR), as well as minimum and maximum for continuous variables.
+
+Analyses were stratified by SCT status and LOT. Treatments were analysed and reported as number and percentage of patients receiving each treatment drug and regimen by LOT, with later lines (LOT5 and above) aggregated. Duration of each LOT was calculated both descriptively (from first treatment administered until end of LOT) and using Kaplan–Meier analysis. Patients were censored at the end of follow-up period, loss to follow-up, or death. A Charlson comorbidity index was calculated based on a published study, using patients’ comorbidities recorded at the index date and during the year preceding the index date [25].
+
+Results
+
+Patient characteristics
+
+Among 58,903 patients with MM diagnosis identified in the SNDS database during the inclusion period, 44,421 had either at least 2 records of MM diagnosis during hospital stays or at least 1 record during a hospital stay and 1 record of long-term disease. Of these, 25,717 patients had incident MM and 6413 patients met study eligibility criteria (Supplementary Fig. 2). Of the study eligible patients, 6257 (97.6%) had ≥ 5 years of data history prior to the index date. A total of 15,751 (26.7%) patients were excluded based on drug treatment status; 6914 (11.7%) received no treatment during follow-up and a further 8837 (15%) received no treatment within 30 days after diagnosis. The mean age at index date was 68.9 years (SD, 11.67), and 52.1% were male (Table 1). Patients were distributed across all administrative regions of France. Patient enrolment was also distributed across index years (Supplementary Table 4). Median follow-up was 22 months (IQR, 29). Two-thirds of patients were still alive at the end of the study period (n = 4217; 65.8%); 2167 patients (33.8%) died during follow-up, and 29 patients (0.5%) were lost to follow-up or disenrolled (made no claims in the period of 1 year).Table 1 Baseline characteristics and patient disposition
+
+	Patients with SCT
+(n = 1910)	Patients
+without SCT
+(n = 4503)	Total patients
+(N = 6413)	
+Age (years) at index date	
+ Mean (SD)	58 (8.1)	73.5 (9.71)	68.9 (11.67)	
+ Median (range)	60 (26–79)	74 (22–99)	69 (22–99)	
+ IQR	11	13	17	
+Age group (years) at index date (%)	
+ 18–30	4 (0.2)	4 (0.1)	8 (0.1)	
+ 31–50	334 (17.5)	121 (2.7)	455 (7.1)	
+ 51–70	1540 (80.6)	1445 (32.1)	2985 (46.5)	
+  > 70	32 (1.7)	2933 (65.1)	2965 (46.2)	
+Sex (%)	
+ Male	1070 (56.0)	2273 (50.5)	3343 (52.1)	
+ Female	840 (43.9)	2230 (49.5)	3070 (47.9)	
+Charlson score adapted to SNDS	
+ Mean (SD)	2.3 (0.61)	2.8 (1.2)	2.6 (1.1)	
+ Median (range)	2 (2–7)	2 (2–10)	2 (2–10)	
+ IQR	0	1	1	
+Charlson score per class adapted to SNDS (%)	
+ 0	–	–	–	
+ 1–2	1559 (81.6)	2578 (57.3)	4137 (64.5)	
+ 3–4	325 (17.0)	1447 (32.1)	1772 (27.6)	
+  ≥ 5	26 (1.4)	478 (10.6)	504 (7.9)	
+Number of LOT per patient during follow-up (%)	
+ Undetermined	27 (1.4)	157 (3.5)	184 (2.9)	
+ 1	663 (34.7)	1891 (42.0)	2554 (39.8)	
+ 2	565 (29.6)	1214 (27.0)	1779 (27.7)	
+ 3	306 (16.0)	604 (13.4)	910 (14.2)	
+ 4	129 (6.8)	278 (6.2)	407 (6.3)	
+ 5 + 	220 (11.5)	359 (8.0)	579 (9.0)	
+Follow-up duration, months (%)	
+ Mean (SD)	33.2 (19.0)	23.1 (18.5)	26.1 (19.2)	
+ Median (range)	30 (4–72)	19 (0–72)	22 (0–72)	
+ IQR	31	28	29	
+Reason for end of follow-up (%)	
+ Death	279 (14.6)	1888 (41.9)	2167 (33.8)	
+ Disenrollmenta	4 (0.2)	2 (0.04)	6 (0.1)	
+ Lost to follow-upa	4 (0.2)	19 (0.4)	23 (0.4)	
+ End of observation period	1623 (85.0)	2594 (57.6)	4217 (65.8)	
+aDefined as cases with a gap of 12 months without claims registered in the SNDS for which no death was registered
+
+IQR interquartile range, LOT line of treatment, SCT stem cell transplant, SD standard deviation, SNDS Système National des Données de Santé
+
+The median Charlson comorbidity score for the study cohort was 2 (IQR, 1). The most common comorbidities were diabetes (n = 1065; 16.6%), moderate or severe renal disease (n = 1035; 16.1%), and chronic pulmonary disease (n = 809; 12.6%) (Supplementary Table 5). The number of patients was evenly distributed across the study inclusion period.
+
+In total, 1910 patients (29.8%) received SCT, 97.7% (n = 1866) of which was autologous SCT. The majority (98%) of patients received their first transplant within the first year of follow-up. Patients in the SCT subgroup were younger and had fewer comorbidities than patients without SCT (median age, 60 vs 74 years, and 81.6% vs 57.6% with Charlson comorbidity score < 3, respectively) (Table 1). Median follow-up for patients with SCT was longer than for those without SCT (30 vs 19 months, respectively), with a smaller proportion of patients lost to follow-up because of death (14.6% vs 41.9%, respectively).
+
+Overall, 2554 patients (39.8%) received a single LOT during follow-up, and 579 patients (9%) received at least 5 LOTs (Table 1; Supplementary Table 6). Fewer patients with SCT received only 1 LOT compared with those without SCT (34.7% vs 42.0%, respectively), and fewer patients who received SCT died compared with those who did not (14.6% vs 41.9%, respectively).
+
+Treatment regimens used across lines of therapy by transplantation status
+
+Almost all the study cohort received treatment with an identifiable drug during follow-up (n = 6229; 97.1%). Patients without an identifiable drug (n = 184; 2.9%) were referred to as undetermined LOT (Table 1). The most frequently administered drug regimens based on treatment guidelines are presented by LOT and SCT status in Fig. 2 and Supplementary Table 7.Fig. 2 Treatment regimens used across LOTs by SCT status. Dexamethasone and prednisone were not always observed (owing to incomplete data availability), so for some patients use in combination with other treatments is assumed. LOT line of treatment, SCT stem cell transplant
+
+Overall, bortezomib-based regimens were the most commonly prescribed regimen (n = 8865, 62.2%), with 6026 patients (96.7%) receiving this combination at LOT1. Lenalidomide-based regimens were the most frequently administered regimen at LOT2 (n = 1213, 17%) and LOT3 (583, 15%). Treatment choice was more diverse for later LOTs, with 37 distinct regimens identified at LOT4 + . Regimens based on lenalidomide, pomalidomide, or daratumumab were most frequently administered at later LOTs. Of note, dexamethasone and prednisone were only partially observed in the database and use in combination with the other treatments was assumed for some patients. The median duration of treatment decreased with each subsequent LOT from 9.3 months in SCT recipients (5.6 in non-SCT recipients) for LOT1 to approximately 2 months for LOT5 + (Supplementary Fig. 3, Supplementary Table 8).
+
+All-cause HCRU and associated costs
+
+Total MM HCRU cost during the study was €816 million (M), with more than half (€464 M) accrued during the first year following MM diagnosis (Table 2). The mean annual cost per patient was €58.3 thousand (K), and the bulk of this cost was attributed to treatment (€28.2 K) and hospitalisation (€22.2 K). The mean total annual cost per patient in the first year exceeded €72.4 K, with the monthly cost more than €7.1 K. Almost all patients in the study cohort (n = 6194, 96.6%) underwent some type of all-cause hospitalisation during follow-up, including 5968 patients (93.1%) who experienced at least one overnight stay in hospital (Table 2). The overall rate was 6.3 hospitalisations PPPY. Hospitalisations accounted for a greater proportion of total cost in the first year (48.6%) than the average of all years analysed (38.1%) (€35.2 K of €72.4 K total vs €48.5 K of €127.2 K total, respectively). Of €311.1 M total hospitalisation cost, €225.6 M was accrued in the first year.Table 2 All-cause HCRU and costs for the overall MM population (N = 6413)
+
+HCRU	Total cost	Annual cost	1st year cost	Monthly cost in 1st year	
+Sum, M€	Mean per person, K€	%	PPPY, K€	Sum, M€	Mean, K€	%	PPPM, K€	
+Hospitalisations excl. chemotherapies	206.14	32.14	25.3	14.74	145.17	22.64	31.3	2.23	
+Chemotherapy sessions	71.84	11.20	8.8	5.14	54.69	8.53	11.8	0.84	
+Hospitalisations at home	14.62	2.28	1.8	1.05	11.09	1.73	2.4	0.17	
+Hospitalisation in rehabilitation centre	18.40	2.87	2.3	1.32	14.65	2.28	3.2	0.23	
+Emergency stays	0.10	0.02	0.0	0.01	0.05	0.01	0.0	0.00	
+All hospitalisation	311.10	48.51	38.1	22.24	225.64	35.19	48.6	3.47	
+“High-cost drugs”1 including:	137.17	21.39	16.8	9.81	114.09	17.79	24.6	1.76	
+ Bortezomib	119.80	18.68	14.7	8.56	106.28	16.57	22.9	1.64	
+ Bendamustine	0.83	0.13	0.1	0.06	0.22	0.03	0.0	0.00	
+ Carfilzomib	2.91	0.45	0.4	0.21	0.21	0.03	0.0	0.00	
+ Doxorubicine	0.26	0.04	0.0	0.02	0.17	0.03	0.0	0.00	
+“Temporary Authorized drugs”2 including	42.04	6.56	5.2	3.01	4.92	0.77	1.1	0.08	
+ Daratumumab	41.43	6.46	5.1	2.96	4.63	0.72	1.0	0.07	
+“Retrocession drugs”3 including:	163.92	25.56	20.1	11.72	38.15	5.95	8.2	0.59	
+ Lenalidomide	109.02	17.00	13.4	7.79	26.79	4.18	5.8	0.41	
+ Dexamethasone	2.80	0.44	0.3	0.20	1.32	0.21	0.3	0.02	
+ Thalidomide	4.71	0.73	0.6	0.34	4.42	0.69	1.0	0.07	
+ Pomalidomide	41.70	6.50	5.1	2.98	3.44	0.54	0.7	0.05	
+“Drugs in community”4 including:	51.76	8.07	6.3	3.70	26.04	4.06	5.6	0.40	
+ Melphalan	0.94	0.15	0.1	0.07	0.88	0.14	0.2	0.01	
+ Ixazomib	0.80	0.13	0.1	0.06	0.06	0.01	0.0	0.00	
+All treatmenta	394.89	61.58	48.4	28.23	183.21	28.57	39.5	2.82	
+Laboratory tests	12.61	1.97	1.5	0.90	6.00	0.94	1.3	0.09	
+Medical procedures	8.93	1.39	1.1	0.64	4.50	0.70	1.0	0.07	
+Physician visitsb	5.82	0.91	0.7	0.42	2.38	0.37	0.5	0.04	
+Other health professional visitsc	18.90	2.95	2.3	1.35	8.44	1.32	1.8	0.13	
+Transport	31.51	4.91	3.9	2.25	18.91	2.95	4.1	0.29	
+Medical devices	10.44	1.63	1.3	0.75	4.90	0.76	1.1	0.08	
+Sick leave & invalidity	21.79	3.40	2.7	1.56	10.10	1.58	2.2	0.16	
+All other costs	110.00	17.15	13.5	7.86	55.23	8.61	11.9	0.85	
+Total	816.00	127.24	100	58.34	464.09	72.37	100	7.14	
+aIncludes sum of 1–4, bGP and specialist, cNurse, physiotherapist, and other
+
+GP general practitioner, HCRU health care resource utilisation, K thousands, M millions, MM multiple myeloma, PPPM per person per month analysis, PPPY per person per year
+
+Among treatment costs during follow-up, retrocession drugs accrued the highest cost (€11.72 K PPPY, including €5.95 K during the first year), but the “high-cost drugs” contributed the greatest cost during the first year (€17.8 K PPPY). Among the “high-cost drugs”, bortezomib cost €8.56 K PPPY, while the “retrocession drug” lenalidomide cost €7.79 K PPPY (Table 2). Almost all patients (6241 of 6413 total patients, 97.3%) received “high-cost drugs”, where the “high cost” was linked to the use of bortezomib-based regimens in LOT1 (data not shown).
+
+In general, HCRU was lower for patients with SCT compared with total population (Table 3). While the average duration of hospital stays was similar between groups (11.1 days for SCT sub-group compared with 11.8 days in no SCT), hospitalisations were less frequent among patients with SCT compared with total patient population (4.7 vs 6.3 per PPPY) but accrued higher costs. This was largely attributable to the more expensive SCT procedure (€24 K per transplant vs €0.9 K for average medicine, surgery, or obstetrics hospitalisation). The rate of sick-leave payment was also much greater for those with SCT compared with the total population (64.4 vs 28.5 days PPPY, respectively), as were rates of laboratory tests and medical procedures.Table 3 HCRU per person per year (in days) by LOT
+
+HCRU	LOT1
+(n = 6229)	LOT2
+(n = 3675)	LOT3
+(n = 1896)	LOT4
+(n = 986)	LOT5 + 
+(n = 579)	Patients with SCT
+(n = 1910)	Total
+(N = 6413)	
+n (%)	PPPY	n (%)	PPPY	n (%)	PPPY	n (%)	PPPY	n (%)	PPPY	n (%)	PPPY	n (%)	PPPY	
+Total MCO hospitalisations	4692 (75.3)	6.3	2327 (37.4)	5.6	1169 (18.8)	6.1	582 (9.3)	7.3	476 (7.6)	10.3	1910 (100.0)	4.7	6194 (96.6)	6.3	
+Complete MCO hospitalisations	4002 (64.2)	1.9	1834 (29.4)	1.3	906 (14.5)	1.5	429
+
+(6.9)
+
+	2.0	411 (6.6)	2.5	1895 (99.2)	1.6	5968 (93.1)	1.9	
+Chemotherapy sessions	5893 (94.6)	19.3	2487 (39.9)	10.9	970 (15.6)	8.7	568 (9.1)	11.8	433 (7.0)	14.8	1901 (99.5)	22.1	6199 (96.7)	18.9	
+Hospitalisations at home	857 (13.8)	1.7	325 (5.2)	1.0	106 (1.7)	0.4	64 (1.0)	0.9	67 (1.1)	0.7	392 (20.5)	0.7	1159 (18.1)	0.9	
+Hospitalisation in rehabilitation centre	1033 (16.6)	0.3	283 (4.5)	0.1	144 (2.3)	0.2	61 (1.0)	0.2	60 (1.0)	0.2	590 (30.9)	0.1	3096 (48.3)	0.2	
+Emergency stays	1071 (17.2)	0.3	632 (10.1)	0.3	259 (4.2)	0.3	104 (1.7)	0.3	102 (1.6)	0.3	759 (39.7)	0.2	2360 (36.8)	0.3	
+GP physician visits	5055 (81.2)	8.8	3068 (49.3)	9.1	1378 (22.1)	8.8	678 (10.9)	8.7	460 (7.4)	8.6	1862 (97.5)	8.5	5959 (92.9)	8.7	
+Specialist physician visits	4429 (71.1)	5.7	2957 (47.5)	6.7	1357 (21.8)	7.3	629 (10.1)	6.2	412 (6.6)	6.2	1889 (98.9)	6.1	5671 (88.4)	6.1	
+Transport	5309 (85.2)	22.7	2837 (45.5)	17.3	1360 (21.8)	15.9	813 (13.1)	18.9	813 (13.1)	25.2	1789 (93.7)	14.1	5885 (91.8)	17.4	
+High-cost medical devices	184 (3.0)	0.2	92 (1.5)	0.1	28 (0.4)	0.1	17 (0.3)	0.1	11 (0.2)	0.1	129 (6.8)	0.1	494 (7.7)	0.2	
+Sick leavea & invalidity	836 (13.4)	56.0	259 (4.2)	14.3	112 (1.8)		56 (0.9)		55 (0.9)		764 (40.0)	64.4	989 (15.4)	28.5	
+an  number of days
+
+GP general practitioner, HCRU health care resource utilisation, LOT line of treatment, MCO medicine, surgery, and obstetrics, PPPY per person per year, SCT stem cell transplant
+
+When evaluating the type of HCRU utilized by LOT, hospitalisation rates declined from LOTs 1–4, but increased in the LOT5 + group (Supplementary Table 9). As expected, ambulatory chemotherapy sessions were most common in LOT1 (92.2%) and were favoured over hospitalisations for chemotherapy (22.0%).
+
+AE-related HCRU and associated costs
+
+In total, 2901 patients (45.2%) had an event of interest at primary diagnosis of MM. The most commonly reported were infections that were not otherwise specified (n = 1200; 18.7%) (Supplementary Fig. 4). During follow-up, there were 7397 hospitalisations due to events of interest associated with MM (4463 within the first year after diagnosis), at the total cost of €29.6 M (9.5% of all hospitalisation costs) (Supplementary Table 10). The share of hospitalisation cost due to an event of interest was lower (8.0%) in patients with SCT than the total study cohort, but the monthly rate in the first year was the same (€0.28 K PPPM). The proportional cost of AE hospitalisations increased with subsequent LOTs from 8.5% of total cost of hospitalisations at LOT1 to over 14.6% of all hospitalisations during LOT5 and beyond.
+
+HCRU related to the administration of MM treatment only and associated costs
+
+The sum resource cost of MM-related treatment administration was €412 M (50.5% of the total HCRU cost for the study cohort), and during the first year following diagnosis the cost was €3.3 K PPPM (Table 4). The table provides a breakdown of drugs by categories; “high-cost drugs” which includes bortezomib, “temporarily authorised drugs” which includes daratumumab and “retrocession drugs” which includes lenalidomide, pomalidomide, and thalidomide (Supplementary Table 1). The largest component of the cost was treatment with “retrocession” and “high-cost” drugs. This is consistent with the high costs associated with lenalidomide and bortezomib (€7.79 K and €8.56 K PPPY, respectively; Table 2) reflected in the all-cost HCRU analysis. However, as noted above, regimens based on lenalidomide, pomalidomide, or daratumumab were most frequently administered at later LOTs. It is important to note that lenalidomide was removed from the “high-cost drugs” category in 2013 and daratumumab wasn’t added to “high-cost drugs” category until after the end of this study period. Monthly costs of MM-related treatment administration per patient in the first year in patients with SCT were similar to the total population (€3.314 K and €3.302 K, respectively). The monthly cost of MM-related treatment administration per patient in the first year increased with each subsequent LOT from €2.447 K in LOT1 to €7.026 K in LOT5 + . This increase could largely be attributed to greater use of “temporarily authorised” and “retrocession drugs” between LOT1 and later LOTs.Table 4 Costs of MM-related treatment administration by LOT
+
+	LOT1
+(n = 6229)	LOT2
+(n = 3675)	LOT3
+(n = 1896)	LOT4
+(n = 986)	LOT5 + 
+(n = 579)	Patients with SCT
+(n = 1910)	Total
+(N = 6413)	
+Mean resource cost, K€	
+ Chemotherapy sessions (related to MM diagnosis)	6.43	3.88	2.31	2.46	5.75	13.74	10.72	
+ MM “high-cost drugs”a	12.94	6.47	2.65	2.86	5.91	20.28	19.19	
+ MM “temporarily authorised drugs”b	0.09	0.90	4.75	10.20	27.61	9.82	6.49	
+ MM “retrocession drugs”c	2.47	17.75	22.37	16.28	28.19	32.21	24.73	
+ MM “drugs in community”	0.09	0.13	0.22	0.15	0.22	0.23	0.29	
+ MM treatment	15.60	25.26	30.00	29.48	61.93	62.54	50.71	
+ Transport (following chemotherapy)	1.11	0.70	0.37	0.42	1.13	1.99	1.83	
+ Sick leave (7 days following chemotherapy)	0.60	0.30	0.26	0.19	0.72	2.80	0.98	
+ Total cost treatment related MM	23.75	30.15	32.94	32.56	69.53	81.07	64.25	
+ Share of treatment related MM in total cost	48%	61%	66%	69%	69%	49%	50%	
+Sum resource cost, M€	
+ Chemotherapy sessions (related to MM diagnosis)	40.07	14.28	4.39	2.42	3.33	26.24	68.77	
+ MM “high-cost drugs”a	80.62	23.78	5.02	2.82	3.42	38.73	123.09	
+ MM “temporarily authorised drugs”b	0.58	3.30	9.01	10.05	15.99	18.75	41.65	
+ MM “retrocession drugs”c	15.40	65.25	42.42	16.05	16.32	61.25	158.62	
+ MM “drugs in community”	0.59	0.49	0.42	0.15	0.13	0.45	1.88	
+ MM treatment	97.20	92.82	56.88	29.07	35.86	119.45	352.23	
+ Transport (following chemotherapy)	6.93	2.58	0.69	0.42	0.65	3.81	11.75	
+ Sick leave (7 days following chemotherapy)	3.75	1.12	0.49	0.19	0.42	5.34	6.26	
+ Total cost treatment related MM	147.96	110.80	62.45	32.10	40.26	154.84	412.01	
+Monthly resource cost in the first year, K€	
+ Chemotherapy sessions (related to MM diagnosis)	0.663	0.367	0.307	0.454	0.581	0.852	0.812	
+ MM “high-cost drugs”a	1.334	0.612	0.351	0.528	0.597	1.435	1.636	
+ MM temporarily “authorised drugs”b	0.010	0.085	0.631	1.883	2.790	0.053	0.072	
+ MM “retrocession drugs”c	0.255	1.679	2.968	3.007	2.849	0.679	0.556	
+ MM “drugs in community”	0.010	0.013	0.030	0.027	0.022	0.001	0.016	
+ MM treatment	1.608	2.389	3.980	5.446	6.258	2.168	2.279	
+ Transport (following chemotherapy)	0.115	0.066	0.049	0.078	0.114	0.121	0.140	
+ Sick leave (7 days following chemotherapy)	0.062	0.029	0.034	0.035	0.073	0.172	0.071	
+ Total cost treatment related MM	2.447	2.852	4.369	6.014	7.026	3.314	3.302	
+aIncludes bortezomib, bIncludes daratumumab, cIncludes lenalidomide, pomalidomide, and thalidomide
+
+K thousands, LOT line of treatment, M millions, MM multiple myeloma, SCT stem cell transplant
+
+HCRU related only to MM and associated costs
+
+During follow-up, the cost of HCRU related to MM specifically was €611.5 M (74.9% of the total HCRU cost), with over half of the cost accrued within the first year after MM diagnosis (annual rate of €367.11 K per patient) (Supplementary Table 11). Costs per patient increased with later LOTs, more than doubling from €3.9 K PPPM at LOT1 to €8.5 K PPPM at LOT5 + , mainly attributable to the increased cost of drugs between LOT1 and LOT5 + . However, fewer patients received later lines of treatment (LOT1 n = 6229 vs LOT5 + n = 579).
+
+Discussion
+
+This analysis of a comprehensive HCRU database with national coverage in France demonstrates that MM represents a substantial economic burden to health care systems. For patients diagnosed between 2013 and 2018 receiving active treatment, the overall cost in France of treating MM was estimated to be €58.3 K PPPY, with more than half of the costs accrued in the first year after diagnosis. The greatest costs were attributed to treatment and hospitalisation. This study extends findings of earlier research in Europe that reported lower HCRU costs for MM, but most used smaller cohorts or modelled data [14, 16, 26, 27]. In one study reporting MM costs for 2018 in Portugal, the average overall annual MM cost burden per patient was lower (€31 K PPPY) than reported here (€58.3 K PPPY) [27]. Yet, the difference in the average annual cost of treatment administration was smaller (€28 K PPPY reported here vs €25 K PPPY reported in Portugal) and both studies show that the cost were mainly driven by the hospitalisations and treatments [27]. However, the Portuguese study was based on a lower number of patients than the current study (n = 1941 vs n = 6413) and did not take into account costs associated with sick leave and invalidity. Another French study looking at MM HCRU costs in patients who received at least 1 prior treatment before the period 2004–2005 estimated monthly costs of treatments to be €2.1 K (vs €2.8 K reported here) [15].
+
+The higher costs reported in this study reflect directly reported HCRU from a nationwide database and are likely to be a more representative estimate of the burden of MM in France [16, 28]. Although the current results are consistent with the recent French Health Insurance Fund report, the higher global costs found in the current study may be due to differences in the studied populations: the current study included actively treated incident patients (2013–2018), whereas the previous report evaluated costs in the prevalent population [13]. Furthermore, the greater hospitalisation costs in the current analysis were found to be higher in the first year of follow-up, which likely is due to a difference between an incident population vs a prevalent population [13]. Approximately 30% of patients underwent SCT, consistent with recently published data [29, 30]; these patients accounted for greater financial impact, particularly in the first year after diagnosis, than the average for the total population. They were generally younger, in line with published data [30], and experienced fewer hospitalisations during follow-up than the total population in a manner that was constant over time. The expensive SCT procedure contributed to higher hospitalisation costs. Extended hospitalisation required for SCT and the younger age of these patients may also have contributed to increased costs related to sick leave.
+
+In France, as in the broader global context [31], no regimen is currently recommended as a standard of care for heavily pretreated and relapsed MM. Consistent with previous findings [29], LOT1 treatment was quite uniform during the study period (with 97% of patients receiving bortezomib-based regimens). However, published global data showed lower use of bortezomib based regimens in non-SCT population than in this study (54% vs 96%, respectively) [30]. Treatment diversity in later LOTs, reflected earlier real-word evidence [30], was associated with a substantial increase in patient costs. Monthly costs related to MM for patients who received LOT5 + were twice those for patients who received up to 2 LOTs, presumably due to more severe disease and often higher costs of newer therapies; earlier studies have also indicated that later LOTs may be more expensive [15, 18]. In the current analysis, use of retrocession drugs such as immunomodulatory drugs (especially pomalidomide) at LOT3 was a major contributor to increased cost of later treatment regimens, consistent with practice reported in other European regions [16, 17]. However, drugs such as pomalidomide, lenalidomide, and daratumumab are increasingly used in earlier treatment regimens until disease progression [32, 33]. These treatment indication changes will impact the economic burden of MM. The facility for newer therapies, such as daratumumab, to be recorded during the follow-up period means that this study captured more complete drug costs and hospitalisations data than earlier studies, including a multi-country European study which did not cover treatments approved post-2015 [16]. Of note, the price of daratumumab in the French system has decreased since the analysis of this database, so this may contribute to increased use of this drug in the future [34].
+
+Increase in hospitalisation costs associated with subsequent LOT was likely related to the increasing age and decreasing health of patients requiring ongoing treatment for MM. This is supported by a retrospective study of hospitalised French patients, which identified an association between age and duration of hospital stay [35]. The cost of hospitalisations for an event of interest constituted a greater proportion of hospitalisation costs among patients at later LOT, indicating that declining patient condition conferred a greater burden to the health care system beyond the cost of drugs. Ultimately, more effective treatments are needed to avoid multiple LOTs and reduce the economic burden of MM.
+
+Strengths
+
+Unlike several published case review studies, this study analysed data from the SNDS, a large comprehensive database with national coverage of health care costs throughout France, which allows most patients to be studied from birth to death. Although the database allowed to identify a cohort of newly diagnosed patients with MM, with no patients excluded based on demographic or health status [36], because of the stringent inclusion criteria the sample may not be representative of all newly diagnosed patients. Previous European studies used model estimates to analyse HCRU costs (e.g., a Dutch study of patients treated up to LOT3 [28]), the costs reported in this study came from a database of actual costs accrued, representing an accurate estimate for routine clinical care for MM in both inpatient and outpatient settings in France. Furthermore, this study assessed patients up to LOT5 + , allowing the substantial increase in per patient costs in later LOTs to be fully characterised. To account for the absence of clinical or paraclinical test results in the database, a validated algorithmic definition of LOT allowed accurate identification of LOT for this large cohort, despite missing details on combination therapies [23]. This analysis not only provided all-cause HCRU for patients with incident MM but also confirmed the costs specifically associated with an MM diagnosis.
+
+Limitations
+
+Patients with pre-existing MM, those with a single record of MM, as well as those treated later than 30 days after the initial diagnosis were excluded. This approach was undertaken to increase the precision of the MM-related costs; however, in so doing, the size of the study cohort was substantially reduced, potentially excluding clinically eligible patients. In addition, the focus on actively treated patients may have skewed the results towards higher cost patients and excluded those for whom early treatment was not possible, e.g., due to underlying complexities. Therefore, the stringent eligibility criteria and the inherent lack of clinical and pathological information in health insurance databases may have reduced ability to accurately identify all patients with newly diagnosed MM. Furthermore, although our study was designed to limit the included patients to only those with newly diagnosed MM, there is the possibility that some patients with RRMM may have been miscoded or mistakenly included in the sample.
+
+As not all drugs were observable or fully observable in the database, the LOT was derived algorithmically, and some assumptions may not be accurate for a small proportion of LOT definitions. Although the algorithm is robust in identifying LOT and its duration, it cannot provide the granular detail of all the components of the regimen, since not all regimens are observable in SNDS database. Consequently, the algorithm is not precise enough for analysing specific regimens (combination of drugs); for example, the 40-day overlap criterion may not be optimal for some regimens at LOT3 + . Inclusion of some drugs that were only partially observable (melphalan and cyclophosphamide) may have resulted in overestimation of the number of LOT per patient identified by the algorithm and underestimation of LOT1 treatment duration. This may especially apply to the number of patients without SCT who received bortezomib/melphalan in LOT2. In addition, the lack of care plans availability in the database means that for some patients some drugs may have been used in combination with other treatments without being observed in the database. Therefore, the results should be interpreted with caution because true HCRU costs may have been underestimated. The study design introduced a follow-up time bias because patients who died before receiving a transplant were included in the non-SCT rather than the SCT subgroup in the database, leading to patients with a short follow-up being recorded as non-SCT patients.
+
+Fewer patients who received later LOTs than those who received LOT1 were captured in the study, consistent with previous findings [29]. Thus the relatively short period of the study may have not fully captured the economic impact of SCT as patients who undergo the SCT are younger, live longer, and are more likely to need multiple LOT.
+
+Conclusions
+
+This comprehensive analysis of the SNDS database demonstrates high HCRU and costs associated with treating MM in France and high variation in treatment patterns for late-line treatment strategy.
+
+These data provide a valuable, up-to-date resource to inform stakeholders around healthcare costs in MM and demonstrate the significant disease burden in this patient population. The high HCRU and cost reflect the high MM burden in France. In the future, as more treatments become available, the cost of MM treatment is expected to grow further, especially if the incidence of MM continues to rise. Therefore, development of effective and safe new treatments is critical to help mitigate those costs. Further studies would be needed to improve the treatment-defining algorithm and to accurately analyse the treatment patterns of MM patients.
+
+Supplementary Information
+
+Below is the link to the electronic supplementary material.Supplementary file1 (EPS 2204 KB)
+
+Supplementary file2 (EPS 1689 KB)
+
+Supplementary file3 (EPS 1194 KB)
+
+Supplementary file4 (EPS 1309 KB)
+
+Supplementary file5 (EPS 1617 KB)
+
+Supplementary file6 (DOCX 126 KB)
+
+Acknowledgements
+
+Medical writing support was provided by Joanna Nikitorowicz-Buniak, PhD, of Fishawack Indicia Ltd, part of Fishawack Health, and funded by GlaxoSmithKline.
+
+Author contributions
+
+All authors contributed to writing this manuscript. AB, XC, JDN, and JW contributed to the conception or design of this study, and data analysis and interpretation. WS, SF, BGorsh, BGutierrez, LS, SS, PP, and FW contributed to data analysis and interpretation.
+
+Funding
+
+This study was funded by GlaxoSmithKline (Study 208292). GlaxoSmithKline contributed to study design, implementation, data collection, interpretation, and analysis. Medical writing support was funded by GlaxoSmithKline.
+
+Availability of data and materials
+
+GlaxoSmithKline makes available anonymised individual participant data upon approval of proposals submitted to www.clinicalstudydatarequest.com. To access data for other types of GlaxoSmithKline sponsored research, for study documents without patient-level data, and for clinical studies not listed, please submit an enquiry via the website.
+
+Code availability
+
+Not applicable.
+
+Declarations
+
+Conflicts of interest
+
+AB, JDN, and WS are employees of IQVIA, which has received funding from GlaxoSmithKline. XC, SF, BGorsh, BGutierrez, LS, JW, SS, PP, and FW are employees of and hold stocks and shares in GlaxoSmithKline.
+
+Ethics approval
+
+Not applicable.
+
+Consent to participate
+
+Not applicable.
+
+Publisher's Note
+
+Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
+==== Refs
+References
+
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+2. ECIS. Estimates of cancer incidence and mortality in 2020 European Cancer Information System; 2020. https://ecis.jrc.ec.europa.eu/explorer.php?0-01-All4-1,23-516-0,855-2008,20087-7,82-AllCEstByCountryX0_8-3X0_19-AE27X0_20-NoCEstRelativeX1_8-3X1_9-AE27X1_19-AE27CEstByCountryTableX2_19-AE27. Accessed December 2020
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+6. Chari A Vogl DT Gavriatopoulou M Nooka AK Yee AJ Huff CA Oral selinexor-dexamethasone for triple-class refractory multiple myeloma N Engl J Med. 2019 381 727 738 10.1056/NEJMoa1903455 31433920
+7. Cho SF Anderson KC Tai YT Targeting B cell maturation antigen (bcma) in multiple myeloma: Potential uses of BCMA-based immunotherapy Front Immunol. 2018 9 1821 10.3389/fimmu.2018.01821 30147690
+8. Kumar SK Therneau TM Gertz MA Lacy MQ Dispenzieri A Rajkumar SV Clinical course of patients with relapsed multiple myeloma Mayo Clin Proc. 2004 79 867 874 10.4065/79.7.867 15244382
+9. Shah V Sherborne AL Walker BA Johnson DC Boyle EM Ellis S Prediction of outcome in newly diagnosed myeloma: a meta-analysis of the molecular profiles of 1905 trial patients Leukemia 2018 32 102 110 10.1038/leu.2017.179 28584253
+10. Abdallah N Greipp P Kapoor P Gertz MA Dispenzieri A Baughn LB Clinical characteristics and treatment outcomes of newly diagnosed multiple myeloma with chromosome 1q abnormalities Blood Adv. 2020 4 3509 3519 10.1182/bloodadvances.2020002218 32750129
+11. Wang C Soekojo CY Mel S Ooi M Chen Y Goh AZK Natural history and prognostic factors at first relapse in multiple myeloma Cancers (Basel). 2020 10.3390/cancers12071759 33419310
+12. Verelst SGR Blommestein HM De Groot S Gonzalez-McQuire S DeCosta L de Raad JB Long-term outcomes in patients with multiple myeloma: a retrospective analysis of the dutch population-based haematological registry for observational studies (PHAROS) Hemasphere. 2018 2 e45 10.1097/HS9.0000000000000045 31723779
+13. Expenses and income report for the year 2021. https://www.ameli.fr/: L’Assurance Maladie; 2020. https://www.ameli.fr/fileadmin/user_upload/documents/2020-07_rapport-propositions-pour-2021_assurance-maladie.pdf. Accessed March 2021
+14. Koleva D Cortelazzo S Toldo C Garattini L Healthcare costs of multiple myeloma: an Italian study Eur J Cancer Care (Engl). 2011 20 330 336 10.1111/j.1365-2354.2009.01153.x 20148933
+15. Armoiry X Fagnani F Benboubker L Facon T Fermand JP Hulin C Management of relapsed or refractory multiple myeloma in French hospitals and estimation of associated direct costs: a multi-centre retrospective cohort study J Clin Pharm Ther. 2011 36 19 26 10.1111/j.1365-2710.2009.01153.x 21198717
+16. Gonzalez-McQuire S Yong K Leleu H Mennini FS Flinois A Gazzola C Healthcare resource utilization among patients with relapsed multiple myeloma in the UK, France, and Italy J Med Econ. 2018 21 450 467 10.1080/13696998.2017.1421546 29278014
+17. Gaultney JG Franken MG Tan SS Redekop WK Huijgens PC Sonneveld P Real-world health care costs of relapsed/refractory multiple myeloma during the era of novel cancer agents J Clin Pharm Ther. 2013 38 41 47 10.1111/jcpt.12020 23126374
+18. Blommestein, H., Verelst, S., Zagorska, A., Stevanovic, J., Engstrom, A., Sonneveld, P, et al.: Value in Health; November 01, 2016. Value in Health2016. p. PA751
+19. Tuppin, P., Rudant, J., Constantinou, P., Gastaldi-Menager, C., Rachas, A., de Roquefeuil, L., et al.: Value of a national administrative database to guide public decisions: From the systeme national d'information interregimes de l'Assurance Maladie (SNIIRAM) to the systeme national des donnees de sante (SNDS) in France. Rev Epidemiol Sante Publique. 65 Suppl 4, S149–S167 (2017) 10.1016/j.respe.2017.05.004
+20. Scailteux LM Droitcourt C Balusson F Nowak E Kerbrat S Dupuy A French administrative health care database (SNDS): The value of its enrichment Therapie. 2019 74 215 223 10.1016/j.therap.2018.09.072 30392702
+21. SNDS French National Health Database. https://snds.gouv.fr/2020. https://snds.gouv.fr/SNDS/Qu-est-ce-que-le-SNDS. Accessed October 2020.
+22. Palmaro, A., Gauthier, M., Conte, C., Grosclaude, P., Despas, F., Lapeyre-Mestre, M.: Identifying multiple myeloma patients using data from the French health insurance databases: Validation using a cancer registry. Medicine (Baltimore). 96, e6189 (2017) 10.1097/MD.0000000000006189
+23. Palmaro A Gauthier M Despas F Lapeyre-Mestre M Identifying cancer drug regimens in French health insurance database: An application in multiple myeloma patients Pharmacoepidemiol Drug Saf. 2017 26 1492 1499 10.1002/pds.4266 28745019
+24. Moreau, P., San Miguel, J., Ludwig, H., Schouten, H., Mohty, M., Dimopoulos, M., et al.: Multiple myeloma: ESMO clinical practice guidelines for diagnosis, treatment and follow-up. Ann Oncol. 24 Suppl 6, vi133–137 (2013) 10.1093/annonc/mdt297.
+25. Bannay A Chaignot C Blotiere PO Basson M Weill A Ricordeau P The best use of the charlson comorbidity index with electronic health care database to predict mortality Med Care. 2016 54 188 194 10.1097/MLR.0000000000000471 26683778
+26. Fonseca R Abouzaid S Bonafede M Cai Q Parikh K Cosler L Trends in overall survival and costs of multiple myeloma, 2000–2014 Leukemia 2017 31 1915 1921 10.1038/leu.2016.380 28008176
+27. Neves M Trigo F Rui B Joao C Lucio P Mariana N Multiple myeloma in portugal: burden of disease and cost of illness Pharmacoeconomics 2021 39 579 587 10.1007/s40273-020-00993-5 33517511
+28. Blommestein HM Verelst SG de Groot S Huijgens PC Sonneveld P Uyl-de Groot CA A cost-effectiveness analysis of real-world treatment for elderly patients with multiple myeloma using a full disease model Eur J Haematol. 2016 96 198 208 10.1111/ejh.12571 25892333
+29. Touzeau C Quignot N Meng J Jiang H Khachatryan A Singh M Survival and treatment patterns of patients with relapsed or refractory multiple myeloma in France - a cohort study using the French National Healthcare database (SNDS) Ann Hematol. 2021 10.1007/s00277-021-04522-y 33884454
+30. Mohty M Terpos E Mateos MV Cavo M Lejniece S Beksac M Multiple myeloma treatment in real-world clinical practice: results of a prospective, multinational, noninterventional Study Clin Lymphoma Myeloma Leuk. 2018 18 e401 e419 10.1016/j.clml.2018.06.018 30030033
+31. Mikhael J Treatment options for triple-class refractory multiple myeloma Clin Lymphoma Myeloma Leuk. 2020 20 1 7 10.1016/j.clml.2019.09.621 31767529
+32. EEIG B.-M.S.P. Imnovid Summary of Product Characteristics. 2021. https://www.ema.europa.eu/en/documents/product-information/imnovid-epar-product-information_en.pdf. Accessed 08 December 2021
+33. NV J.-C.I. DARZALEX Summary of Product Characteristics. 2021. https://www.ema.europa.eu/en/documents/product-information/darzalex-epar-product-information_en.pdf. Accessed 08 December 2021
+34. Vidal. Multiple Myeloma: DARZALEX (daratumumab), the first anti-CD38 monoclonal antibody. Vidal: Vidal; 2019. https://www.vidal.fr/actualites/23429/myelome_multiple_darzalex_daratumumab_premier_anticorps_monoclonal_anti_cd38/. Accessed October 2020.
+35. Dumontet C Couray-Targe S Teisseire M Karlin L Maucort-Boulch D Real life management of patients hospitalized with multiple myeloma in France PLoS One 2018 13 e0196596 10.1371/journal.pone.0196596 29715281
+36. Conte C Vaysse C Bosco P Noize P Fourrier-Reglat A Despas F The value of a health insurance database to conduct pharmacoepidemiological studies in oncology Therapie. 2019 74 279 288 10.1016/j.therap.2018.09.076 30824175
+

PMC10067049.txt

@@ -0,0 +1,383 @@
+
+==== Front
+Cancer Med
+Cancer Med
+10.1002/(ISSN)2045-7634
+CAM4
+Cancer Medicine
+2045-7634
+John Wiley and Sons Inc. Hoboken
+
+36377601
+10.1002/cam4.5422
+CAM45422
+CAM4-2022-02-0586.R2
+Research Article
+RESEARCH ARTICLES
+Clinical Cancer Research
+Comparison between ixazomib+cyclophosphamide+dexamethasone regimen and ixazomib+dexamethasone regimen for elderly and frail patients having newly diagnosed multiple myeloma
+Li et al.
+Li Shutan https://orcid.org/0000-0002-3020-9425
+1
+Zhang Duanzhong 2
+Yang Lihua 2
+Huang Chunlan 3
+Niu Ting 1 lishutanfcc@163.com
+
+Gong Yuping https://orcid.org/0000-0002-2437-9348
+1 gongyuping2010@aliyun.com
+
+1 Department of Hematology, West China Hospital Sichuan University Chengdu Sichuan Province People's Republic of China
+2 Department of Hematology, Dazhou Central Hospital Dazhou city Sichuan Province People's Republic of China
+3 Department of Hematology Affiliated Hospital of Southwest Medical University Luzhou Sichuan People's Republic of China
+* Correspondence
+Yuping Gong and Ting Niu, Professor Department of Hematology, West China Hospital, Sichuan University, No.37 GuoXue Xiang, Chengdu, Sichuan Province 610041, People's Republic of China.
+Email: gongyuping2010@aliyun.com and lishutanfcc@163.com
+
+15 11 2022
+3 2023
+12 6 10.1002/cam4.v12.6 65236535
+27 9 2022
+11 5 2022
+23 10 2022
+© 2022 The Authors. Cancer Medicine published by John Wiley & Sons Ltd.
+https://creativecommons.org/licenses/by/4.0/ This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
+
+Abstract
+
+Aims
+
+The purpose of this prospective, randomized study was to investigate the effectiveness and safety of the ixazomib+cyclophosphamide+dexamethasone (ICd) and ixazomib+dexamethasone (Id) regimens in newly diagnosed multiple myeloma (NDMM) who were elderly and frail and to compare the two regimens.
+
+Methods
+
+Patients were randomly grouped into ICd and Id group. The primary end point was ORR, and patients who received at least two cycles were analyzed. The median follow‐up was 13.5 months. After nine induction cycles, patients were instructed to take single ixazomib for maintenance.
+
+Results
+
+The overall response rate in the ICd and Id groups was 78.9% and 70.6%, respectively, whereas the very good partial remission or better rate was 47.4% and 23.5%, respectively. For the ICd and Id groups, the response rate after 4 cycles was 76.5% and 57.1%, and the median duration to response was 2 and 4 months, respectively. Adverse events (AEs) included gastrointestinal intolerance, rash, fatigue, and thrombocytopenia, with severe AEs occurring in 21.1% and 23.5% patients in the ICd and Id groups, respectively, and the AEs were manageable. Both the QLQ‐C30 and QLQ‐MY20 scales indicated that ICd and Id regimens could help maintain and improve the quality of life(QoL).
+
+Conclusions
+
+The ICd and Id regimens might be effective and well‐tolerated in elderly and frail patients with NDMM. In addition, a favorable outcome was observed that ICd might tend to cause faster and higher remission than Id regimen without increasing the risk of AEs. The long‐term effectiveness and safety of the two regimens need further investigation.
+
+ICd regimen followed by ixazomib maintenance therapy is effective, safe, convenient, and well‐tolerated in elderly and transplant‐ineligible NDMM patients.
+
+elderly
+frail
+multiple myeloma
+oral ixazomib
+Foundation of the Science and Technology Department of Sichuan Province2019YFS0026 source-schema-version-number2.0
+cover-dateMarch 2023
+details-of-publishers-convertorConverter:WILEY_ML3GV2_TO_JATSPMC version:6.2.7 mode:remove_FC converted:02.04.2023
+Li S , Zhang D , Yang L , Huang C , Niu T , Gong Y . Comparison between ixazomib+cyclophosphamide+dexamethasone regimen and ixazomib+dexamethasone regimen for elderly and frail patients having newly diagnosed multiple myeloma. Cancer Med. 2023;12 :6523‐6535. doi: 10.1002/cam4.5422
+
+Shutan Li and Duanzhong Zhang are the co‐author.
+==== Body
+pmc1 INTRODUCTION
+
+Multiple myeloma (MM) generally occurs in senior individuals. At diagnosis, the median age of MM patients is 69 years, with 33% of them having an age over 75 years. 1 Many individuals having newly diagnosed MM (NDMM) are frail, exhibit severe comorbidities, and are commonly not eligible for intensive treatments including autologous stem cell transplantation (ASCT). Age is an important factor to assess frailty for MM, while the geriatric assessment (GA) is a more sensitive predictor of frailty. The International Myeloma Working Group (IMWG) develops GA for elderly MM patients, 2 , 3 , 4 which includes age, Lawton and Brody's instrumental ADL (IADL) scale, Katz and Akpom's basic activities of daily living (ADL) scale, and the Charlson Comorbidity Index (CCI). Based on GA, Palumbo A provides an easy and quick online score system to calculate the frailty score. Patients with MM are classified into fit (score = 0), intermediate‐fit (score = 1), and frail (score ≥ 2). 2 Compared with the fit and intermediate‐fit patients, the frail has a worse prognosis. 2 , 3
+
+There are no standard regimens for the elderly and frail patients with NDMM. 1 Hence, new, effective, safe, and suitable regimens for older frail MM patients are needed. 5 With the development of immune modulators (IMIDs; lenalidomide, pomalidomide) and proteasome inhibitors (PIs; bortezomib, ixazomib and carfilzomib), the management of MM has considerably improved. Triplet regimens comprising PIs, IMIDs and dexamethasone have become the common treatment for NDMM patients, 6 for example, the bortezomib +lenalidomide+dexamethasone (RVD) regimen has achieved very good efficacy. 7 As the standard frontline therapy for MM, RVD is generally effective and safe, but it may be not well tolerated and convenient in some elderly and frail. For example, IMIDs may be not preferred for some patients because of severe renal impairment, potential toxicity, 8 venous thromboembolism, 9 probability of second primary malignancies, 10 and so on. Combinations without IMIDs, such as carfilzomib or bortezomib with melphalan‐prednisone (KMP or VMP) or cyclophosphamide‐dexamethasone (KCD or VCD), as well as a combination of ixazomib and MP, are efficacious in NDMM treatment. 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 However, these different combinations may have limited feasibility in varying aspects for some elderly and frail patients because of the potential risk of peripheral neuropathy (PN) and renal or cardiac injury, the requirement of regular intravenous or subcutaneous administration, 11 , 14 , 18 or the limitation of medical insurance in China. In addition, the related guidelines for the elderly and frail patients are also limited after progression. Elderly and frail NDMM patients are prone to adverse events (AEs), and may be less tolerant to the same regimens than the fit, or intermediate‐fit patients, which may cause early discontinuations of treatments or reductions in drug dosage. 16 , 17 , 18 , 19 Meanwhile, MM in the elderly and frail patients is heterogeneous, and different individuals may have different tolerance to the different regimens. Clinical trials for these patients are underrepresented, 1 and there are no unified regimens at present for induction and progression.
+
+As the first PI administered orally, ixazomib has been approved for MM patients who have undergone prior treatments with fewer AEs. 15 , 20 Combinations of weekly ixazomib regimens have indicated good therapeutic effects in MM patients. 21 , 22 Meanwhile, maintenance therapy is important in the current MM therapeutic approaches for better clinical outcomes. 23 , 24 , 25 , 26 Efficacious regimens with limited toxicity, easy administration for prolonged periods, and the ability to maintain the quality of life (QoL) are needed. Oral ixazomib has the advantages of safety and convenient administration, but it is more expensive than bortezomib in China, and only one of the IMIDs and PIs can be included in medicare reimbursement. Hence, the initial regimen of an oral ixazomib‐based combination comprising ixazomib, low‐dose dexamethasone (ICd) and cyclophosphamide (ICd) or low‐dose dexamethasone and ixazomib (Id) is probably more tolerable and convenient for the elderly and frail patients with NDMM. Herein, we recruited the frail patients and investigated the efficacy and adverse effects of ICd and Id in the induction period administered along with ixazomib as single‐agent maintenance therapy in senior and fragile patients with NDMM. The treatment for progression need another investigation in the future, which is not the intention in this preliminary study.
+
+2 METHODOLOGY
+
+2.1 Trial design
+
+Our study was a prospective and randomized study. The ICd group was the experimental group, and the Id was the control group. Patients were randomly assigned to receive the ICd or Id regimen according to a random number table. Neither the investigator nor the patients could speculate on which group the next patients would be assigned to, and the person who determined the random number table would not participate in the inclusion of the patients. The grouping information was kept strictly.
+
+Patients who received at least two cycles were analyzed in this initial study. During the treatment, patients would withdraw from this study once there was poor response, progression of disease, death or drug intolerance. Patients who experienced progression would be assessed further, and subsequently be administered the appropriate regimens according to the possible mechanisms of resistance, 27 potential adverse effects and the related contraindications. The detailed design is shown in Figure 1.
+
+FIGURE 1 Trial flow diagram. Id, ixazomib+dexamethasone; ICd, ixazomib+cyclophosphamide+dexamethasone. ORR, Partial remission or better. Withdrawal standard*, poor response, disease progression, death, and severe toxicity or requirement of patient. a,one died during the first course of chemotherapy because of heart and kidney failure caused by multiple myeloma. b, switched to cheaper regimens for economic considerations.
+
+2.2 Patients
+
+This prospective, randomized trial was conducted at three centers: West China Hospital of Sichuan University, Affiliated Hospital of Southwest Medical University, and Dazhou Central Hospital. The inclusion criteria were as follow: ≥60 years of age, frail (frailty score ≥ 2), newly diagnosed MM meeting the IMWG 27 criteria and patients who had not received any prior therapy for myeloma. We used the website (http://www.myelomafrailtyscore calculator.net/) to calculate the frailty score, based on which all the patients in this study were frail (score ≥ 2).
+
+As ixazomib was more expensive than bortezomib in China, many patients who met the eligibility criteria refused to enroll in the clinical trial because of financial and health insurance concerns. Overall, 42 patients were included in this study from July 2020 to June 2022. Among them, 36 patients (19 in the ixazomib+cyclophosphamide+Dexamethasone [ICd] group and 17 in the ixazomib+dexamethasone [Id] group) had received at least two cycles of therapy, and they were further assessed. The last day of the observation period was June 30, 2022, and the study is ongoing.
+
+The study was approved by the Ethics Committee of West China Hospital of Sichuan University and review boards at all institutions that participated in this study. The research was performed in compliance with the the Guidelines for Good Clinical Practice, the Guidelines of the International Conference on Harmonization, and the Declaration of Helsinki. Informed consent was obtained from all the participants before the initiation of the study.
+
+2.3 Treatments
+
+The patients were randomly grouped to receive either the ICd or Id regimen during the induction stage. Because the patients were old and frail, dexamethasone and cyclophosphamide were administered with reduced doses. The dosage of ixazomib was adjusted to 3 mg for elderly patients with worse comorbidities and more adverse effects to the drug to select an appropriate dose. Detailed regimens were as follows: patients in the ICd group received 4 or 3 mg ixazomib (on the 1st, 8th and 15th days), 10 mg dexamethasone (on the 1st, 2nd, 8th, 9th, 15th, 16th, 22nd, and 23rd days), and 200 mg/m2 cyclophosphamide (on the 1st, 8th, 15th, and 22th days). In the Id group, the doses of ixazomib and dexamethasone were the same as those in the ICd group; each cycle consisted of 28 days. After 9 cycles of induction, patients with partial response (PR) or better were administered single‐agent ixazomib as maintenance therapy on the 1st, 8th and 15th days per cycle, 28 days per cycle, until the disease progressed, death, or intolerable toxicity.
+
+2.4 M protein estimation
+
+The levels of monoclonal protein were important in the diagnosis and assessment of efficiency, so serum protein electrophoresis (SPE), immunofixed electrophoresis (IFE), serum free light chain quantification, urine light chain quantification and 24 h urine M protein quantification were used for M protein estimation. M protein type of IgG, IgA, IgM, IgD, IgE, λ or κ was identified by immunofixation electrophoresis (IFE). The M protein levels of the patients were examined every two cycles, and the change of M protein level was used to judge the therapeutic effect.
+
+According to IMWG, 27 M protein criteria for NDMM was monoclonal protein≥10 g/L for IgG, ≥5 g/L for IgD, IgE, IgM and IgA, in the serum; Or urinary level of monoclonal protein ≥200 mg/24 h; Or free light chain (FLC) concentration ≥ 100 mg/L and abnormal FLC ratio of Kappa/lambda in the serum. The main standard of M protein for efficacy evaluation was complete remission (CR: M protein was negative in the serum and urine for immunofixation); very good partial remission (VGPR: positive serum and urinary M proteins were detected by immunofixation, and electrophoresis detected a reduction of ≥90% in serum M protein and a reduction in urinary M protein ≤100 mg/24 h); partial remission (PR: serum M protein decreased by ≥50%, 24 h urinary level of M protein decreased by ≥90% or reduced to ≤200 mg/24 h); minimal remission (MR: serum M protein reduced by ≥25% but ≤49%, and reduction of urinary M protein 50%–89% in 24 h).
+
+2.5 Assessment of effectiveness and safety
+
+Diagnosis, disease stage, therapeutic efficacy, and the progression of disease were all evaluated based on the IMWG criteria. 27 The required baseline assessments performed before randomization included physical examination; evaluation of the Eastern Cooperative Oncology Group performance status and medical history; Durie‐Salmon, International Staging System (ISS), and revised‐ISS stage; and hematological, biochemical, and bone marrow sample tests. The participants received regimens unless the following events occurred: poor response, progression of disease, death, drug intolerance or toxicity, or withdrawal by consent. During the treatment, the regimen was changed if no MR was observed after the fourth cycle or no PR was observed after the sixth cycle or in case of disease progression or intolerance. The patients who received at least two courses were included in the safety and efficacy analyses. The primary outcomes were the combined overall rate of response (ORR, also being referred to as partial response or better [≥PR]) and the rate of very good partial response or better (≥VGPR) after ICd or Id administration. Other data such as the rate of complete response, duration from the first drug administration to the onset of a response, decreased degree of M‐protein response, progression‐free survival, safety, and associated AEs were recorded. We graded the AEs based on the National Cancer Institute Common Terminology Criteria for Adverse Events (CTCAE‐Version 5.0).
+
+In addition, the participants completed the questionnaires of EORTC QLQ‐C30 QoL and QLQ‐MY20 questionnaires before and after therapy, which helped in assessing the change in quality of life (QoL). The EORTC QLQ‐C30 contains 30 items. These items are included the scale of Global Health QoL, 3 scales evaluating the symptoms (pain, fatigue, and nausea or vomiting), 5 scales assessing functioning of patients (physical functions, emotional functions, cognitive functions, role functioning and social functioning), and 6 items determining insomnia, dyspnea, appetite loss, constipation, diarrhea, and financial difficulties of patients. Higher scores of the functional and QoL scales represented better QoL; whereas for the symptom scale and 6 independent items, a higher score denotes poor patients' QoL. The EORTC QLQ‐MY20 is a questionnaire specifically adjusted for patients with MM, and it can supplement the QLQ‐C30 questionnaire. 28 It consists of 20 items concerning four HRQoL domains specific to myeloma: 1. “Disease Symptoms” covering pain in the chest, back, arm or shoulder, hip, or bone and pain intensifying with activity; 2: “Side Effects of Treatment,” covering tingling in the hands or feet, restlessness or agitation, sleepiness, ill feeling, dry mouth, thirst, acid indigestion or heartburn, burning or sore eyes, hair loss, and upset by hair loss; 3: “Future Perspectives,” covering thinking about illness and worrying about future health and death; and 4: “Body Image.” A higher score for each item indicates poor functioning. 28 , 29
+
+2.6 Statistical analyses
+
+Categorical variables were described as counts and percentages, and continuous variables were described as medians and ranges. Comparisons of categorical variables were performed by Fisher's exact test, and continuous variables were compared by the Mann–Whitney's U test. The Wilcoxon signed rank test was used for defining significant changes in QoL before versus after therapy. PFS was calculated by the Kaplan–Meier test for univariate analyses. The value of α was 0.05, two‐tailed, and p < 0.05 was considered to indicate statistical significance. Based on the formulas [N = (μα + μβ)2 (1 + 1/k) P (1−P)/(P e −P c )2, P = (P e+ kP C )/(1 + k)], we calculated the power value of the ORR between the two groups. SPSS v.21.0 and GraphPad Prism 9 were utilized to carry out the statistical analyses.
+
+3 RESULTS
+
+3.1 Basic characteristics of patients
+
+According to the inclusion criteria, we enrolled 42 patients. The patients who received at least two‐cycle therapy were assessed. Following patients were excluded: one patient died during the first cycle of chemotherapy because of heart and kidney failure caused by multiple myeloma, three patients switched to cheaper regimens for economic reasons after one cycle, and two patients had not finished the first or second cycle, respectively. As patients who received at least two cycles were analyzed in this study, these six patients were excluded. Therefore, a total of 36 patients from three centers in China were assessed in this study. Among them, 19 patients were included in the ICd group and 17 in the Id group. The median follow‐up duration was 13.5 months (range: 2–24 months). The median age was 75 years (range: 60–87 years); 52.8% of patients were ≥ 75 years old and 41.7% of the patients were men. Furthermore, 75.0% and 61.1% of patients had anemia and renal impairment in various degrees, respectively. The ISS III stage was identified in 55.6% of the patients. The detailed characteristics of the patients at baseline are presented in Table 1.
+
+TABLE 1 Baseline clinical characteristics of the patients
+
+Characteristic	ICd (n = 19)	Id (n = 17)	p	Overall (n = 36)	
+Age					
+X ± SD	73.7 ± 8.2	76.2 ± 6.9	0.320	74.9 ± 7.6	
+Median (years, range)	74(60–83)	77 (63–87)	0.446	75(60–87)	
+Years < 65 (n, %)	5 (26.3)	2 (11.8)	0.956 a	7 (19.4)	
+65 ≤ years < 75 (n, %)	5 (26.3)	4(29.4)		10 (27.8)	
+Years ≥ 75 (n,%)	9 (47.4)	11(58.8)	0.789	19(52.8)	
+Male sex‐(n, %)	9 (47.4)	6(35.3)	0.516	15 (41.7)	
+ECOG score‐n(%)					
+1	5 (26.3)	3 (17.6)	0.695	8 (22.2)	
+2	9 (47.4)	10 (58.8)	0.525	19 (52.8)	
+3	4 (21.1)	4 (23.5)	1.000	8(22.2)	
+4	1 (5.3)	—	—	1 (2.8)	
+≥2	14(73.7)	14(82.4)	0.695	28(77.8)	
+Myeloma disease type‐n (%)					
+IgG	13 (68.4)	8 (47.1)	0.311	21(58.3)	
+IgA	4 (21.1)	4 (23.5)	1.000	8 (22.2)	
+Kappa	—	2 (11.8)	—	2 (5.6)	
+Lamda	2 (10.5)	3 (17.6)	0.650	5 (13.9)	
+DS stage‐n (%)					
+I	1 (5.3)	—	—	1 (2.8)	
+II	5 (26.3)	6 (35.3)	0.721	11(30.6)	
+III	13 (68.4)	11 (64.7)	1.000	24 (66.7)	
+ISS stage‐n (%)					
+I	1 (5.3)	2 (11.8)	0.593	3 (8.3)	
+II	8 (42.1)	5 (29.4)	0.502	13(36.1)	
+III	10 (52.6)	10 (58.8)	0.749	20 (55.6)	
+R‐ISS stage‐n (%)					
+I	—	1 (6.7)	—	1 (2.8)	
+II	5 (26.3)	5 (29.4)	1.000	10 (27.8)	
+III	9 (47.4)	6(35.3)	0.516	15(41.7)	
+Data not available	5 (26.3)	5 (29.4)	1.000	10(27.8)	
+Cytogenetic features‐n (%)					
+Standard‐risk abnormalities b	7 (36.8)	5 (29.4)	0.732	12 (33.3)	
+High‐risk abnormalities c	7 (36.8)	7 (41.2)	1.000	14 (38.9)	
+Data not available	5 (26.3)	5 (29.4)	1.000	10 (27.8)	
+Complications before treatment					
+eCCR (ml/min)‐n(%)					
+50–80	7 (36.8)	3 (17.6)	0.274	10 (27.8)	
+31–50	2 (10.5)	5 (29.4)	0.219	7 (19.4)	
+<30	2 (10.5)	3 (17.6)	0.650	5 (13.9)	
+Cardiovascular/pulmonary
+
+Comorbidity‐n(%)
+
+	9 (47.3)	10 (58.8)	0.525	19 (52.8)	
+Hemoglobing/L, ‐n (%))					
+≥90	6 (31.6)	2 (11.8)	0.236	8 (22.2)	
+60–90	6 (31.6)	7 (41.2)	0.730	13(36.1)	
+<60	3 (15.8)	3 (17.6)	1.000	6 (16.7)	
+≥3 Lytic bone disease‐n (%)	9 (47.4)	8 (47.1)	0.000	17 (47.2)	
+Median time (range) ‐mo	14 (2–24)	13(2–18)	0.332	13.5 (2–24)	
+Note: eCCr = (140−Age) × Mass (kg) × [0.85 if female]/72 × [Serum Creatinine (mg/dl)].
+
+Abbreviations: DS, Durie‐Salmon; eCCr, estimated creatinine clearance rate; ECOG, Eastern Cooperative Oncology Group; Ig, immunoglobulin; ISS, International Staging System; R‐ISS, Revised International Staging System.
+
+a <75 years.
+
+b Includes Trisomy, t (11, 14), t (6;14), and others.
+
+c Includes t (4;14), t (14;16), t (14;20), deletion 17p, gain 1q, or p53 mutation.
+
+4 RESPONSE TO THERAPY AND EFFICIENCY
+
+4.1 ORR, VGPR and better response rate
+
+All the patients were evaluated and administered at least two courses of treatment. In addition, the ixazomib dose was adjusted to 3 mg for 4 and 5 patients from the ICd and Id groups, respectively. Meanwhile, patients would withdraw from this trial after progression. Progression in both groups during the induction was considered to be disease progression (PD) when we analyzed the response in the induction phase. In fact, in the induction phase, two patients in the ICd group progressed after 5 and 8 cycles, respectively, and three patients in the Id group progressed after 2,3 and 7 cycles, respectively. Regardless of their disease gauge after switching to other regimens, we considered them as PD (Table 2), which ensures the rational evaluation of response in induction therapy of both groups.
+
+TABLE 2 Assessment of confirmed response to ICd or Id induction therapy
+
+Response –n (%)	ICd (n = 19)	Id (n = 17)	p	Total (n = 36)	
+sCR	2 (10.5)	—	0.408 a	2 (5.6)	
+CR	3 (15.8)	2 (11.8)		5 (13.9)	
+VGPR	4 (21.1)	2 (11.8)	0.662	6(16.7)	
+PR	6(31.6)	8(47.1)	0.495	14 (38.9)	
+MR	1 (5.3)	2(11.8)	0.593	3(8.3)	
+SD	1(5.3)	—	1.000	1(2.8)	
+PD	2(10.5)	3 (17.6)	0.650	5(13.9)	
+≥VGPR	9(47.4)	4 (23.5)	0.177	13(36.1)	
+ORR b (≥PR)	15 (78.9)	12(70.6)	0.706	27(75.0)	
+Note: ORR, PR, VGPR, CR, sCR, MR, SD, PD 27 ; n, the total number of patients.
+
+a CR + sCR.
+
+b The power of ORR (ICd vs Id) was 0.875, which was calculated by using the formulas, N = (μ α + μ β)2 (1 + 1/k) P (1−P)/(P e −p c )2, P = (P e  + kP C ) / (1 + k); [P e  = 0.789, P C  = 0.706, N = 36, the alpha was 0.05, two‐ tailed, μ α = 1.96].
+
+Overall, 75.0% of patients achieved the ORR in the entire trial, and 15 (78.9%) and 12 (70.6%) patients from the ICd and Id groups achieved a confirmed ORR (≥PR), respectively, and the value of the power between the two groups was 0.875 (Table 2); 47.4% and 23.5% patients of the ICd and Id groups displayed ≥VGPR, respectively. However, no p value was <0.05. The detailed responses for the patients are shown in Table 2.
+
+4.2 ORR with disease stage and age
+
+The ORR of the ICd group in ISS stages III, II, and I was 70.0%, 87.5%, and 100.0%, respectively, whereas the corresponding ORR of the Id group was 60.0%, 80.0%, and 100.0%. Meanwhile, patients in ISS stage III (65.0%) had lower ORR than those in stage II (84.6%) and I (100.0%) in both groups. Comparing the ORR of different stages in the two groups, it was not statistically significant (Table S1).
+
+The mean age of the ICd group is 73.7 years, while that of the Id group was 76.2 years, and the median age of ICd group was 74 years, while that of the Id group was 77 years. There were 10 (52.6%) patients in the ICd group who were aged <75 years and only 6 (35.3%) patients in Id the group who were aged <75 years. Overall, patients in ICd seemed to be younger than those in the Id group, and the percentage of patients aged ≥75 years was higher in the Id group than in the ICd group. Age is quite an important prognostic factor of MM, so we analyzed whether the difference in efficacy of ICd and Id might be attributed to the younger population in the ICd group. It seemed that the ORR was higher in the ICd group (77.8%) than in the Id group (60%) when patients were ≥75 years old, and slightly lower in the ICd group (80%) than in the Id group (85.7%) when they were <75 years old. However, there was no significant difference (p = 0.628, p = 1.000, respectively; Table S1). In addition, there was no significant difference in age between the ICd group and the Id group (p = 0.446, Table 1), and there was no difference between the two groups when the age was ≥75 (p = 0.789, Table 1) or <75 years (p = 0.956, Table 1). There were more women in both groups, especially in the Id group, while the ORR in different gender had no difference not only in ICd group but in the Id group (p = 1.000, Table S1).
+
+4.3 Response of patients at the different ends of treatment cycles
+
+The median durations for the first ≥PR were 2 (range: 2–5 months) and 4 (range: 2–6 months) months in the ICd and Id groups, respectively. An improved response was observed as the patients received more treatment cycles (Figure 2). The ICd and Id groups exhibited response rates of 76.5% and 57.1%, respectively, at the end of the fourth cycle. As prespecified criteria for efficacy were not met, one patient in the ICd group was changed to VRd, and 2 patients in the Id group were subjected to the ICd or Bd regimen (Figure 1). Thirteen patients in the ICd group and 9 patients in the Id group received single‐agent maintenance therapy. However, only 9 patients in the ICd group and 6 in the Id group were finally treated with ixazomib for maintenance, whereas 4 patients in the ICd and 3 patients in the Id group received lenalidomide for maintenance therapy for financial reasons (Figure 1).
+
+FIGURE 2 Relationship between ORR and treatment cycles in ICd group and Id group. ORR: response of PR or better, objective response rate, including PR(partial response) VGPR (very good partial response), CR(complete response) and sCR(stringent complete response); Id: ixazomib+dexamethasone. ICd: ixazomib+cyclophosphamide+dexamethasone. p > 0.05.
+
+4.4 Changes in M‐protein response
+
+Changes from the baseline to the best M‐protein response (%) in this study were as follows: M‐protein response decreased by ≥ 90% and ≥ 50% in 36.1% and 75.0% in all the patients; M‐protein response decreased by ≥90%, 50%–90%, and ≥ 50% in 47.4%, 31.6% and 78.9% patients in the ICd group, respectively, and in 23.5%, 47.1%, and 70.6% patients in the Id group, respectively (Figure 3).
+
+FIGURE 3 Changes of M‐protein from basline to best response during the treatment of ICd or Id regimen. M‐protein: monoclonal immunoglobulin.
+
+4.5 Improvement in comorbidity
+
+In the ICd group, 11 patients had kidney injury at diagnosis (Table 1), whereas 5 patients displayed CR and 1 patient displayed PR after 1–5 cycles; 4 patients were stabilized, and the condition of 1 patient deteriorated. Additionally, 15 patients had anemia at diagnosis, of which 5 recovered, 5 improved after 1–7 cycles, 4 with mild anemia were stable, and the condition of 1 patient deteriorated. In the Id group, in terms of kidney injury, 4 and 3 patients achieved CR and PR, respectively, after 2–6 cycles; 3 patients were stable, and the condition of 1 patient deteriorated (n = 11). In terms of anemia, 5 patients recovered after 2–4 cycles, 3 displayed improvement, 2 were stable, and 2 exhibited deteriorated conditions (n = 12). In conclusion, the recovery rates of renal function were 54.5% and 63.6% in the ICd and Id groups, respectively. The anemia improvement rates in both groups were 66.7%.
+
+4.6 Prognosis of the disease
+
+Because the follow‐up time was short and no patient died during the study, no overall survival analyses could be performed. Four patients in the ICd group exhibited disease progression after 5,8,17 or 19 cycles, respectively, whereas five patients in the Id group exhibited progression after 2, 3, 7,12 or 15 cycles, respectively, (Figure 4).
+
+FIGURE 4 Progression‐free survival (PFS) by ICd and Id treatment. PFS rates at the endpoint were 78.9% and 70.6% in ICd and Id, respectively. Kaplan–Meier analysis, p > 0.05.
+
+4.7 Safety and adverse events
+
+The most frequently observed toxicity events were grades 1 and 2 gastrointestinal disorders, incidences of rashes, thrombocytopenia and fatigue, followed by loss of appetite, abdominal distension, vomiting, and banded blister disease, which occurred mainly in the first three cycles. The incidence rates of grades 3 and 4 AEs in the ICd and Id groups were 21.1% and 23.5%, respectively. In the ICd group, one patient developed grade 3 thrombocytopenia, and three patients developed severe digestive AEs (two patients exhibited diarrhea, another exhibited vomiting accompanied by abdominal distension). In the Id group, one patient had abdominal distension, one exhibited vomiting and two had diarrhea (Table 3).
+
+TABLE 3 Adverse events in the ICd and Id groups
+
+n (%)	ICd (n = 19)	Id (n = 17)	Total (n = 33)	
+AEs	Grde 3 or 4 a	Total	Grade 3 or 4 a	Total	Grade 3 or 4	Total	
+Thrombocytopenia	1	4 (21.1)	0	2 (11.8)	1	6(16.7)	
+Leucopenia	0	1 (5.3)	0	2 (11.8)	0	3(8.3)	
+Loss of appetite	0	3 (15.8)	0	3 (17.6)	0	6(16.7)	
+Vomiting	1	3 (15.8)	1	3 (17.6)	2	6(16.7)	
+Diarrhea	2	8(42.1)	2	7(41.2)	4	15(41.7)	
+Abdominal distension	0	2 (10.5)	1	4 (23.5)	1	6(16.7)	
+Infection	0	1 (5.3)	0	2(11.8)	0	3(8.3)	
+Banded blister disease	0	3 (15.8)	0	2 (11.8)	0	5(13.9)	
+Peripheral neuropathy	0	2 (10.5)	0	1 (5.9)	0	3(8.3)	
+Rash	0	6 (31.6)	0	4 (23.5)	0	10(27.8)	
+Fatigue	0	4(21.1)	0	4 (23.5)	0	8(22.2)	
+Note: Adverse event criteria (CTCAE ‐ Version 5.0) to assess the severity of adverse events of chemotherapies. ICd vs Id.
+
+a p = 1.000; p > 0.05.
+
+4.8 QoL evaluated based on the scores of the EORTC QLQ‐C30 and QLQ‐MY20
+
+The EORTC QLQ‐C30 showed that the items of physical functioning, fatigue and pain had been improved significantly in both groups after chemotherapy. In addition, the values for global health status and dyspnea in the ICd group were improved. However, financial difficulties increased in the ICd group after chemotherapy (Table S2).
+
+Based on the analysis of the QLQ‐MY20 scale, we observed significant improvement in disease symptoms and body image in the ICd group after treatment. In the Id group, the improvement in disease symptoms was significant after chemotherapy (Table S3).
+
+5 DISCUSSION
+
+Studies have reported that the treatment response in elderly patients is poorer than that in young patients, 2 , 30 partially because the elderly are more fragile. 23 , 31 GA is a highly sensitive predictor of frailty. The GA recommended by the IMWG integrates the factors such age, comorbidities, cognitive and physical conditions, which can predict mortality and the risk of toxicity in elderly frail myeloma patients, and propose treatment decisions. Moreover, frail patients display poor compliance and unwillingness to visit hospitals. The outcomes of MM have been improved substantially in recent years. 31 Triplet regimens based on IMs and PIs are more efficacious than doublet regimens, 7 , 32 , 33 , 34 but PIs and IMIDs cannot be included in medical insurance together in China. Hence, effective agents are needed 23 in these patients. Ixazomib has the advantages of good efficacy, few adverse effects, convenient administration and good compliance. Cyclophosphamide provides an important choice for MM in some cases. Therefore, we investigated the efficacy and safety of ICd and Id regimens in elderly and fragile patients with NDMM.
+
+5.1 The effectiveness of the ICd and Id regimens
+
+There are few studies on ICd in NDMM, and no studies have compared ICd with Id, especially in the elderly and frail patients with NDMM. Our results indicated an overall ORR of 75.0%, indicating that oral ICd and Id could provide good tolerance and therapeutic effects in the elderly and frail patients with NDMM. In particular, comparisons between Id and ICd were conducted. The ORR of ICd and Id regimens was 78.9% and 70.6%, respectively, and the power value of the ORR between the two groups is was 0.875. Although the number of patients included is currently small, the power is >0.8. After four cycles, the rate of ≥PR in the ICd and Id groups was 76.5% and 57.1%, respectively, whereas the corresponding ≥VGPR rates were 47.4% and 23.5%, respectively. The ICd regimen seemed to result in faster remission and higher ORR than the Id regimen, and additional cycles could improve the depth of response (Figures 2 and 3 and Table 2), but the number of patients was small, and we will enroll more patients to further confirm it. In addition, we observed the younger population in the ICd group than in the Id group. Patients in ICd had a higher ORR and the ORR was higher in the ICd group than in the Id group when the age was ≥75 years. However, we could still not conclude that the ICd regimen was significantly superior to the Id regimen in different age groups significantly (Table S1). The effect of the ICd regimen on the ORR in different age groups needs further investigation with larger sample size, and further assessment will be continued.
+
+The ICd regimen exhibited promising efficacy in NDMM. 21 , 35 In the study by Dimopoulos, 67 patients were evaluated and the median age was 73 (61–87) years. Most of the patients had cardiopulmonary diseases and renal insufficiency, but the ECOG score was ≤1 in 81% of patients. In the study by Kumar, 48 patients were evaluated; the median age was 64.5 (41–88) years, and the ECOG score was 0–2. After the median treatment duration of 19 cycles by Dimopoulos and a median follow‐up of 25.6 months by Kumar, the ORR was 76% and 77%, respectively. Compared with the studies by Dimopoulos 35 and Kumar, 21 only 36 elderly and frail patients receiving at least two cycles were assessed in this study and the number of patients in our study was smaller with a shorter follow‐up. However, all the patients recruited in our study were older and frail and the frailty score was calculated. The ECOG score was ≥2 in 80% of patients. Thus, our patients might be older and more fragile and had worse conditions at diagnosis. In addition, our patients received ICd therapy with reduced doses of both cyclophosphamide and dexamethasone, but a similar ORR was acquired. In brief, ICd might be an effective regimen in the elderly and frail patients with NDMM, although the response rates might be lower than other three‐drug combinations: One trial demonstrated that the ORR was 92.1%, 36 and in another study, it was 82.1% after administration of IRd 15 ; 95.2% ORR was observed in patients treated with VCd 37 ; The ORR of KCd was 95%. 14 We suspected that the different drug combinations or inclusion criterias might affect the response; on the other hand, we will continue the study to acquire more significant results.
+
+5.2 The adverse effects (AEs) of the ICd and Id regimens
+
+Ixazomib was safe and the common toxicities included leucopenia, thrombocytopenia, nausea, diarrhea, vomiting, and erythema‐multiforme, 21 , 35 most of which were ≤2 grades. Our patients mainly presented with digestive tract discomfort, skin rash and thrombocytopenia, which were well‐tolerated and manageable. AEs occurred mainly in the first three cycles but were relieved after dose modifications and supportive care measures. The median treatment duration was 14 months, which indicated the long‐term tolerance to the therapy and the multiple cycles of therapy did not seem to result in noticeable cumulative toxicity. In addition, no more AEs were observed in the ICd group than in the Id group.
+
+Studies by Dimopoulos 35 and Kumar 21 had reported that the incidences of grade ≥3 AEs were 73% and 71%, respectively, which were higher than that of 22.2% (ICd 21.4%; Id 23.5%) in our study. Meaghan reported that the incidences of grade ≥3 AEs with RD, CRD, and VCD were 61%, 74%, and 41%, 8 respectively, and the toxicities ≥grade 3 with VRD and VCD were 76% and 79%, respectively. 12 In the TOURMALINE‐MM2 trial, 88% and 81% of patients experienced grade ≥3 AEs 15 in the IRd and Rd groups, respectively. Thus, the incidences of grade ≥3 AEs in our study seemed to be lower. This might be related to the fact that we reduced the doses of cyclophosphamide and dexamethasone. In addition, we deduced that it might be related to the bias of the patient visits, and the different observation time. Further, the QoL scores remained stable and improved after therapy in both groups (Table S2,S3). Therefore, our preliminary results showed that the tolerability to these regimens in elderly fragile patients with NDMM.
+
+5.3 Limitations of this study
+
+This was the first study to use geriatric assessment to investigate ICd and Id in elderly frail patients with NDMM. All the enrolled patients in this study were frail (score ≥2), and the results provided evidence for treatment of the elderly and frail patients with NDMM, but there were some limitations in this study. First, the sample size was small and no robust conclusions could be drawn. Second, the follow‐up was short and not sufficient to observe the long‐term outcomes and the prognosis. Third, the number of patients receiving ixazomib for maintenance therapy was small and relevant conclusions could not be drawn. Fourth, generally, gender does not affect the ORR and prognosis of MM, but our data was biased towards female. Whether gender had an effect on the results requires further study. In addition, we could not conclude the effects of cytogenetics, stages and age on the prognosis significantly. Finally, as we mainly assessed patients who received at least two cycles of treatment, there might be some bias. A larger number of patients will be enrolled to further study, which will give a more comprehensive real‐world analysis including the clinical and economic benefits. In brief, we made descriptive statistics for the study due to the small number of patients, and made no more convincing conclusions about the relevance of ICd and Id therapy. Although some results were not statistically significant, this initial study presented a real‐world analysis of the combinations, and the phenomenon between the two groups should not be ignored, which would provide potential clinical information.
+
+6 CONCLUSIONS
+
+There was a trend that the oral ICd or Id regimen exhibited good effectiveness and tolerability during the initial treatment of fragile patients with NDMM. Although no robust conclusions and comparison could be drawn with ICd and Id regimens regarding the small number of patients, a favorable outcome was observed in this trial. Moreove, the convenience and tolerability might make ixazomib effective in the long‐term treatment. To conclude, the promising clinical activity warrants further study. As an ongoing prospective study, we are expanding the samples to further study the effectiveness and safety of ICd followed by ixazomib as maintenance in elderly and fragile myeloma, as well as whether it is superior to the Id regimen.
+
+AUTHOR CONTRIBUTIONS
+
+Shutan Li: Conceptualization (lead); data curation (lead); formal analysis (lead); investigation (lead); methodology (lead); project administration (supporting); resources (lead); validation (lead); visualization (supporting); writing – original draft (lead); writing – review and editing (lead). Duanzhong Zhang: Data curation (equal); project administration (equal); supervision (equal); writing – original draft (supporting). Lihua Yang: Conceptualization (equal); data curation (equal); supervision (equal); writing – original draft (equal). Chunlan Huang: Data curation (equal); supervision (equal); writing – review and editing (equal). Ting Niu: Data curation (equal); supervision (equal); writing – original draft (equal). Yuping Gong: Conceptualization (equal); data curation (lead); formal analysis (lead); funding acquisition (lead); investigation (equal); methodology (lead); project administration (lead); resources (equal); software (equal); supervision (lead); validation (equal); visualization (equal); writing – original draft (equal); writing – review and editing (equal).
+
+All authors recruited patients; YP G designed this study; YP G and ST L collected and analyzed the data;ST L drafted the manuscript and all authors critically reviewed the manuscript and approved for publication.
+
+FUNDING INFORMATION
+
+The research is funded by the Science and Technology Department of Sichuan Province, China (No. 2019YFS0026).
+
+CONFLICT OF INTEREST
+
+We declare there are no potential conflicts of interest.
+
+THE CLINICAL TRIAL REGISTRATION
+
+This research is registered at the website of Chinese clinical trial registry (ChiECRCT20200449). http://www.chictr.org.cn/showproj.aspx?proj=64839.
+
+ETHICS APPROVAL STATEMENT
+
+The study was approved by the Ethics Committee of West China Hospital of Sichuan University and review boards at all participating institutions.
+
+PATIENT CONSENT STATEMENT
+
+Before being recruited for the study, we obtained the written informed consent from all the participants.
+
+Supporting information
+
+Table S1.
+
+Table S2.
+
+Table S3.
+
+Click here for additional data file.
+
+ACKNOWLEDGMENTS
+
+We sincerely acknowledge all of the individuals who contributed to this study for their important and quite appreciated contributions to the preparation of this report.
+
+DATA AVAILABILITY STATEMENT
+
+This trial was initiated by the investigators. As the study is still ongoing, the data can not be provided at this time. The data availability will be available from the corresponding author.
+==== Refs
+REFERENCES
+
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+3 Engelhardt M , Dold SM , Ihorst G , et al. Geriatric assessment in multiple myeloma patients: validation of the international myeloma working group (IMWG) score and comparison with other common comorbidity scores. Haematologica. 2016;101 (9 ):1110‐1119.27479825
+4 Mellqvist UH . New prognostic tools for myeloma. Blood. 2015;125 (13 ):2014‐2015.25814486
+5 Zweegman S , Engelhardt M , Larocca A . Elderly patients with multiple myeloma: towards a frailty approach? Curr Opin Oncol. 2017;29 (5 ):315‐321.28763310
+6 Kumar SK , Callander NS , Alsina M , et al. NCCN guidelines insights: multiple myeloma, version 3.2018. J Natl Compr Canc Netw. 2018;16 (1 ):11‐20.29295877
+7 Durie B , Hoering A , Sexton R , et al. Longer term follow‐up of the randomized phase III trial SWOG S0777: Bortezomib, Lenalidomide and Dexamethasone vs. Lenalidomide and Dexamethasone in patients (pts) with previously untreated multiple myeloma without an intent for immediate autologous stem cell transplant (ASCT). Blood. Cancer J. 2020;10 (5 ):1‐11.
+8 Dimopoulos MA , Sonneveld P , Leung N , et al. International myeloma working group recommendations for the diagnosis and Management of Myeloma‐Related Renal Impairment. J Clin Oncol. 2016;34 (13 ):1544‐1557.26976420
+9 Wang Y , Yang F , Shen Y , et al. Maintenance therapy with immunomodulatory drugs in multiple myeloma: a meta‐analysis and systematic review. J Natl Cancer Inst. 2016;108 (3 ):1‐10.
+10 McCarthy PL , Holstein SA , Petrucci MT , et al. Lenalidomide maintenance after autologous stem‐cell transplantation in newly diagnosed multiple myeloma: a meta‐analysis. J Clin Oncol. 2017;35 (29 ):3279‐3289.28742454
+11 Khan ML , Reeder CB , Kumar SK , et al. A comparison of lenalidomide/dexamethasone versus cyclophosphamide/lenalidomide/dexamethasone versus cyclophosphamide/bortezomib/ dexamethasone in newly diagnosed multiple myeloma. Br J Haematol. 2012;156 (3 ):326‐333.22107129
+12 Kumar S , Flinn I , Richardson PG , et al. Randomized, multicenter, phase 2 study (EVOLUTION) of combinations of bortezomib, dexamethasone, cyclophosphamide, and lenalidomide in previously untreated multiple myeloma. Blood. 2012;119 (19 ):4375‐4382.22422823
+13 Boccia RV , Bessudo A , Agajanian R , et al. A multicenter, open‐label, phase 1b study of carfilzomib, cyclophosphamide, and dexamethasone in newly diagnosed multiple myeloma patients (CHAMPION‐2). Clin Lymphoma Myeloma Leuk. 2017;17 (7 ):433‐437.28576443
+14 Bringhen S , Petrucci MT , Larocca A , et al. Carfilzomib, cyclophosphamide, and dexamethasone in patients with newly diagnosed multiple myeloma: a multicenter, phase 2 study. Blood. 2014;124 (1 ):63‐69.24855212
+15 Facon T , Venner CP , Bahlis NJ , et al. Oral ixazomib, lenalidomide, and dexamethasone for transplant‐ineligible patients with newly diagnosed multiple myeloma. Blood. 2021;137 (26 ):3616‐3628.33763699
+16 San MJ , Schlag R , Khuageva NK , et al. Bortezomib plus melphalan and prednisone for initial treatment of multiple myeloma. N Engl J Med. 2008;359 (9 ):906‐917.18753647
+17 Moreau P , Kolb B , Attal M , et al. Phase 1/2 study of carfilzomib plus melphalan and prednisone in patients aged over 65 years with newly diagnosed multiple myeloma. Blood. 2015;125 (20 ):3100‐3104.25784682
+18 Moreau P , San MJ , Sonneveld P , et al. Multiple myeloma: ESMO clinical practice guidelines for diagnosis, treatment and follow‐up. Ann Oncol. 2017;28 (suppl 4):1‐11.
+19 San‐Miguel JF , Echeveste GM , Spicka I , et al. A phase I/II dose‐escalation study investigating all‐oral ixazomib‐melphalan‐prednisone induction followed by single‐agent ixazomib maintenance in transplant‐ineligible newly diagnosed multiple myeloma. Haematologica. 2018;103 (9 ):1518‐1526.29954932
+20 Machida M , Fukunaga S , Hara T . Pharmacological characteristics and clinical study results of the oral proteasome inhibitor ixazomib (NINLARO([R]) capsules; 2.3 mg, 3 mg, and 4 mg). Nihon Yakurigaku Zasshi. 2018;151 (4 ):166‐178.29628465
+21 Kumar SK , Buadi FK , LaPlant B , et al. Phase 1/2 trial of ixazomib, cyclophosphamide and dexamethasone in patients with previously untreated symptomatic multiple myeloma. Blood Cancer J. 2018;8 (8 ):70.30061664
+22 Kumar SK , Berdeja JG , Niesvizky R , et al. Safety and tolerability of ixazomib, an oral proteasome inhibitor, in combination with lenalidomide and dexamethasone in patients with previously untreated multiple myeloma: an open‐label phase 1/2 study. Lancet Oncol. 2014;15 (13 ):1503‐1512.25456369
+23 Palumbo A , Hajek R , Delforge M , et al. Continuous lenalidomide treatment for newly diagnosed multiple myeloma. N Engl J Med. 2012;366 (19 ):1759‐1769.22571200
+24 Palumbo A , Bringhen S , Larocca A , et al. Bortezomib‐melphalan‐prednisone‐thalidomide followed by maintenance with bortezomib‐thalidomide compared with bortezomib‐melphalan‐prednisone for initial treatment of multiple myeloma: updated follow‐up and improved survival. J Clin Oncol. 2014;32 (7 ):634‐640.24449241
+25 Palumbo A , Gay F , Cavallo F , et al. Continuous therapy versus fixed duration of therapy in patients with newly diagnosed multiple myeloma. J Clin Oncol. 2015;33 (30 ):3459‐3466.26282661
+26 Benboubker L , Dimopoulos MA , Dispenzieri A , et al. Lenalidomide and dexamethasone in transplant‐ineligible patients with myeloma. N Engl J Med. 2014;371 (10 ):906‐917.25184863
+27 Kumar S , Paiva B , Anderson KC , et al. International myeloma working group consensus criteria for response and minimal residual disease assessment in multiple myeloma. Lancet Oncol. 2016;17 (8 ):e328‐e346.27511158
+28 Fayers P , Aaronson NK , Bjordal K , Sullivan M . EORTC QLQ‐C30 Scoring Manual. EORTC Publications; 1997.
+29 Ficko SL , Pejsa V , Zadnik V . Health‐related quality of life in Croatian general population and multiple myeloma patients assessed by the EORTC QLQ‐C30 and EORTC QLQ‐MY20 questionnaires. Radiol Oncol. 2019;53 (3 ):337‐347.31553711
+30 Velez R , Turesson I , Landgren O , Kristinsson SY , Cuzick J . Incidence of multiple myeloma in Great Britain, Sweden, and Malmo, Sweden: the impact of differences in case ascertainment on observed incidence trends. BMJ Open. 2016;6 (1 ):e009584.
+31 Palumbo A , Waage A , Hulin C , et al. Safety of thalidomide in newly diagnosed elderly myeloma patients: a meta‐analysis of data from individual patients in six randomized trials. Haematologica. 2013;98 (1 ):87‐94.22875621
+32 Kumar SK , Dispenzieri A , Lacy MQ , et al. Continued improvement in survival in multiple myeloma: changes in early mortality and outcomes in older patients. Leukemia. 2014;28 (5 ):1122‐1128.24157580
+33 Rosinol L , Oriol A , Teruel AI , et al. Superiority of bortezomib, thalidomide, and dexamethasone (VTD) as induction pretransplantation therapy in multiple myeloma: a randomized phase 3 PETHEMA/GEM study. Blood. 2012;120 (8 ):1589‐1596.22791289
+34 Hou J , Jin J , Xu Y , et al. Randomized, double‐blind, placebo‐controlled phase III study of ixazomib plus lenalidomide‐dexamethasone in patients with relapsed/refractory multiple myeloma: China continuation study. J Hematol Oncol. 2017;10 (1 ):137.28683766
+35 Dimopoulos MA , Grosicki S , Jedrzejczak WW , et al. All‐oral ixazomib, cyclophosphamide, and dexamethasone for transplant‐ineligible patients with newly diagnosed multiple myeloma. Eur J Cancer. 2019;106 :89‐98.30471652
+36 Li J , Bao L , Xia Z , et al. Ixazomib‐based frontline therapy in patients with newly diagnosed multiple myeloma in real‐life practice showed comparable efficacy and safety profile with those reported in clinical trial: a multi‐center study. Ann Hematol. 2020;99 (11 ):2589‐2598.32892275
+37 Jimenez‐Zepeda VH , Duggan P , Neri P , Tay J , Bahlis NJ . Bortezomib‐containing regimens (BCR) for the treatment of non‐transplant eligible multiple myeloma. Ann Hematol. 2017;96 (3 ):431‐439.28074255
+

PMC10077405.txt

@@ -0,0 +1,335 @@
+
+==== Front
+Nurs Open
+Nurs Open
+10.1002/(ISSN)2054-1058
+NOP2
+Nursing Open
+2054-1058
+John Wiley and Sons Inc. Hoboken
+
+36539384
+10.1002/nop2.1558
+NOP21558
+NOP-2022-Sep-1490.R1
+Empirical Research Qualitative
+Empirical Research Qualitative
+Haematological nurses' experiences about palliative care trajectories of patients with life‐threatening haematological malignancies: A qualitative study
+McPherson et al.
+McPherson Siobhan https://orcid.org/0000-0002-1772-7762
+1 2
+Mitchell Ann‐Kristin https://orcid.org/0000-0002-4610-0993
+1 3 akmitch7@gmail.com
+
+Sletten Ida https://orcid.org/0000-0003-1335-1486
+1 2
+Kvande Monica Evelyn https://orcid.org/0000-0003-4384-4695
+1 4
+Steindal Simen Alexander https://orcid.org/0000-0002-7676-8900
+1 5
+1 Lovisenberg Diaconal University College Oslo Norway
+2 Department of Haematology Oslo University Hospital Oslo Norway
+3 Oslo Myeloma Center Department of Haematology Oslo University Hospital Oslo Norway
+4 Department of Anaesthesiology and Surgery University Hospital of North Norway Tromsø Norway
+5 Faculty of Health Studies VID Specialized University Oslo Norway
+* Correspondence
+Ann‐Kristin Mitchell, Lovisenberg Diaconal University College, Lovisenberggata 15B, 0456 Oslo, Norway.
+Email: akmitch7@gmail.com
+
+20 12 2022
+5 2023
+10 5 10.1002/nop2.v10.5 30943103
+12 11 2022
+02 9 2022
+04 12 2022
+© 2022 The Authors. Nursing Open published by John Wiley & Sons Ltd.
+https://creativecommons.org/licenses/by/4.0/ This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
+
+Abstract
+
+Aims
+
+To explore haematological nurses' experiences about the palliative care trajectories of patients with life‐threatening haematological malignancies.
+
+Design
+
+A qualitative study with a descriptive and explorative design.
+
+Methods
+
+Data were collected through 12 individual semi‐structured interviews of nurses who work with patients with haematological malignancies from four hospitals in Norway. The data were analysed using systematic text condensation. The study was reported according to the Consolidated Criteria for Reporting Qualitative Research checklist.
+
+Results
+
+Three categories emerged from the data analysis: focus on a cure delays integration of palliative care, dialogue with patients facilitates palliative care and the need for enhanced interdisciplinary understanding.
+
+Patient or public contribution
+
+No patient or public contribution since nurses' experiences were explored.
+
+haematological malignancies
+haematology
+nursing care
+palliative care
+qualitative research
+Department of Haematology, Oslo University Hospital, Oslo, Norway source-schema-version-number2.0
+cover-dateMay 2023
+details-of-publishers-convertorConverter:WILEY_ML3GV2_TO_JATSPMC version:6.2.7 mode:remove_FC converted:06.04.2023
+McPherson, S. , Mitchell, A.‐K. , Sletten, I. , Kvande, M. E. , & Steindal, S. A. (2023). Haematological nurses' experiences about palliative care trajectories of patients with life‐threatening haematological malignancies: A qualitative study. Nursing Open, 10 , 3094–3103. 10.1002/nop2.1558 36539384
+
+Siobhan McPherson, Ann‐Kristin Mitchell and Ida Sletten shared first authorship.
+==== Body
+pmc1 INTRODUCTION
+
+There have been important advances in the treatment of patients with haematological malignancies over the past few decades. However, many patients still die from their diseases or treatment complications (Wedding, 2021). Haematological malignancies are a group of blood cancers with heterogenous disease trajectories that can be broadly categorized into four subgroups: Hodgkin's lymphoma, non‐Hodgkin's lymphoma, multiple myeloma and leukaemia (Krok‐Schoen et al., 2018; Wedding, 2021). Haematological malignancies caused approximately 7% of all global cancer deaths in 2020 (Sung et al., 2021). This number is expected to keep increasing as the world's population continues to age as haematological malignancies have a higher prevalence and mortality rate in those over 65 years of age (Krok‐Schoen et al., 2018). The increasing prevalence of haematological malignancies indicates that a large proportion of these patients could have an extensive need for palliative care (Wedding, 2021). In a recent consensus‐based definition, palliative care has been defined as the active, holistic care of individuals with serious health‐related suffering because of severe illness, and it aims to improve the quality of life of patients, their families and their caregivers. Palliative care is applicable throughout the course of an illness in conjunction with treatment (Radbruch et al., 2020).
+
+Studies have shown that combining palliative care and oncological treatment has several advantages, such as improved survival and symptom control, less anxiety and depression, reduced use of futile chemotherapy at the end of life, a better quality of life for the patient and improved family satisfaction (Dowling et al., 2020; Elliott et al., 2021; Kaasa et al., 2018). However, research shows that patients with haematological malignancies are less likely to be referred to palliative care. For those who are, this tends to occur later in the illness trajectory when compared to patients with other types of cancer (Hui et al., 2014; Vanbutsele et al., 2019; Wedding, 2021). Patients with haematological malignancies also have a higher risk of dying in the hospital due to complications from aggressive treatments. As a result, palliative care is often provided in the haematology oncology unit instead of a palliative care unit or hospice unit (Elliott et al., 2021; Hui et al., 2014; Manitta et al., 2010).
+
+2 BACKGROUND
+
+Due to the many advantages of early palliative care in oncological treatment, both patients and healthcare services would undoubtedly benefit from this integration (Kaasa et al., 2018). There are several barriers to integrating palliative care into the overall care plan of patients with haematological malignancies. These include the patient's uncertain prognosis and unpredictable illness trajectory, clinical optimism due to new therapies and clinical trials and lack of awareness of palliative care services (Manitta et al., 2010; Wedding, 2021). However, there is little research available on palliative care for patients with haematological malignancies, and even less on their palliative care trajectories (Moreno‐Alonso et al., 2018; Wedding, 2021).
+
+Previous studies have investigated haematology nurses' experiences with and perspectives on end‐of‐life care (Grech et al., 2018; McCaughan et al., 2019; McGrath & Holewa, 2006, 2007a, 2007b). A study showed that close clinician–patient bonds, delayed end‐of‐life discussions and barriers to discharge contributed to patients receiving end‐of‐life care and dying in the hospital (McCaughan et al., 2019). Other studies have explored haematology nurses' experiences to develop a model for end‐of‐life care for patients with haematological malignancies. This model describes how openness towards addressing death and an organization open to discussing palliative care services can facilitate the integration of palliative care into haematology (McGrath & Holewa, 2006, 2007a, 2007b). Studies also suggest that the lack of integration of palliative care in the medical treatment of patients with haematological malignancies, prevents a dignified end‐of‐life and leads to nurses reporting high levels of emotional distress and powerlessness (Grech et al., 2018; McGrath & Holewa, 2006).
+
+To the best of our knowledge, no studies have investigated nurses' experiences with the palliative care trajectories of patients with haematological malignancies from the early stages of the illness. Consequently, the aim of this study was to explore haematological nurses' experiences about the palliative care trajectories of patients with life‐threatening haematological malignancies.
+
+3 METHODS
+
+3.1 Design
+
+This study employed a descriptive and exploratory qualitative design, which allows for flexibility when investigating and developing new knowledge on clinical nursing research topics with limited coverage (Hunter et al., 2019). The data were collected through individual, semi‐structured interviews. This approach could give nurses time to reflect and express their experiences in their own words, and make it possible to study and understand the nurses' insight on palliative care trajectories about patients with life‐threatening haematological malignancies (Polit & Beck, 2021).
+
+3.2 Participants and recruitment
+
+Participants were recruited using purposeful sampling from the haematological wards of four hospitals at national, regional and local levels in the eastern and western regions of Norway. Purposeful sampling was chosen to gain varied and rich data about nurses' experiences (Polit & Beck, 2021). To be included in the study, the participants had to be Registered Nurses with a minimum of 2 years' work experience caring for patients with life‐threatening haematological malignancies.
+
+A total of 12 nurses working in inpatient wards agreed to participate in the study: three from a local hospital, four from two regional hospitals and five from a hospital with both regional and national functions. The scope of patients treated at these inpatient wards ranged from those undergoing milder chemotherapy to those receiving stem cell transplantation. One nurse had postgraduate training in palliative care whereas eight nurses had some palliative care training during their postgraduate education (five in cancer care) or master's degree (three in advanced practice nursing). The sample is further described in Table 1.
+
+TABLE 1 Characteristics of study participants
+
+N = 12		
+Age range (mean)	30–52 a (40.0)	
+Years of work experience as Registered Nurse (mean)	5.5–28.5 a (14.1)	
+Years of experience in haematology (mean)	5–28.5 a (13.6)	
+Bachelor of science in nursing	3	
+Postgraduate education
+
+Palliative care
+
+Cancer care
+
+	 
+
+1
+
+5
+
+	
+Master of science in advanced practice nursing	3	
+a Range.
+
+3.3 Data collection
+
+The interviews took place between November 2021 and January 2022 and lasted between 28–56 min (average of 39 min). They were conducted at the participants' workplaces and were audiotaped.
+
+A semi‐structured interview guide was used to facilitate reflection and dialogue with the participants. The guide covered multiple aspects of nurses' experiences with palliative care trajectories with a focus on competency, initiative, decision‐making, communication and cooperation. The participants were encouraged to talk about their encounters with both dignified and undignified illness trajectories and how working with this group of patients affected them (Appendix S1). A pilot interview was conducted with an experienced nurse from the first authors' workplace to ensure that the questions in the interview guide were adequately relevant and understandable. After the pilot interview, an additional question was included about how the nurses experienced working with patients with life‐threatening illnesses.
+
+Each interview was conducted by two first authors (SM, AKM or IS) who had professional relationships through work with five of the participants. One author interviewed the participants while the other took notes and ensured that the topics in the interview guide were sufficiently covered. To build trust, the first authors introduced themselves and explained their role in the interview setting. They employed an open and curious attitude to encourage the informants to share their experiences (Brinkmann & Kvale, 2015). Probing questions such as “Could you tell us a little more?” “Can you give an example?” and “How did you experience that?” were asked to prompt the participants to elaborate upon their answers (Polit & Beck, 2021). During the interviews, the first authors restated or summarized their interpretations of the nurses' answers and then further questioned them to determine the validity of the interpretations. At the end of the interview, the nurses were given an opportunity to speak freely, reflect on the topics and state any last thoughts or additions they might have. Immediately after each interview, the first authors wrote down their primary impressions from the interview to grasp what was perceived as the most important themes.
+
+3.4 Data analysis
+
+The interviews were transcribed verbatim by the first authors and inductively analysed using systematic text condensation (STC) (Malterud, 2012). STC is a descriptive and explorative method consisting of an iterative four‐step process to decontextualize and analyse interview data to gain accurate, recontextualized essences of participants' experiences as they were narrated (Malterud, 2012).
+
+The transcripts were first read several times to get an overview of the material and identify the preliminary themes guided by the aim of the study. Each first author identified six to eight preliminary themes. Through discussion, three preliminary themes from these were agreed upon: a curative focus downgrades palliative care, the need for increased patient involvement and early integration of palliative care. The transcripts were re‐read to identify meaning units that were then organized into three code groups. The first authors maintained flexibility in code labelling and continuously assessed if meaning units should be reallocated to other groups. The meaning units in each code group were further analysed and organized into two or three subgroups. Each subgroup was abstracted into a summarising text by condensing the associated meaning units. These summaries formed the basis of the development of an analytical text that resulted in three categories of data. The categories were reviewed for consistency and accuracy by comparing them with the original transcripts. The authors agreed on category headings that represented the core results of the study and served to highlight its key findings. An example of the process of analysis is shown in Table 2.
+
+TABLE 2 Example of stepwise analysis from the unit of meaning to category using STC.
+
+Meaning units	Subgroup	Category	
+We give input based on our observations, we also have a dialogue with the patients beyond the very limited timeframe the doctors are present for a visit, sometimes the patients express their fatigue, that they have had enough, and we have to communicate that to the doctors. (nurse 7)
+
+We spend a lot of time with the patient, discuss a lot with the patient and his or her relatives, and we can often get an impression of where they stand and what they want. (nurse 10)
+
+We see the patients many more hours than the doctors do. The patients may look ok during the visit of the doctor, but they are not. I believe the doctors are sometimes a bit surprised that we bring up palliative care, but it starts a process and after a while they agree with us. (nurse 9)
+
+	Nurse observations and competence are important for the patients' trajectories	The need for enhanced interdisciplinary understanding	
+To think holistic at a much earlier stage, bring in more resources and make a plan that reflects what the patient's goal is – not what our goal is. (nurse 4)
+
+In general, I experience that we talk about palliative care at the end when the patient is terminal or preterminal. (…) it could have been initiated a little earlier, because there are many aspects both the patient and relatives have to think about at the end, if things had been clarified earlier then it might have been better for everyone. (nurse 5)
+
+I firmly believe the nurse should ask the doctor about the patient's outlook (…) what is the plan, is further treatment advisable? (nurse 12)
+
+	Planning is necessary to ensure satisfactory patient trajectories	
+More interdisciplinary venues where we could decide together, plan and establish a treatment plan (…) containing details about what the patient's wishes are going forward. (nurse 10)
+
+So, I think it would be wise that the nurses are included in the decision‐making process because a joint meeting would help the nurses understand the doctors' thought processes. (nurse 2)
+
+Patients with haematological malignancies are often very complicated cases. I think communication between us and the doctors is critical, and improvements here will benefit the patient as well. (nurse 5)
+
+	There is a need for interdisciplinary cooperation and platforms for information exchange	
+
+The transcripts and analysis were not returned to the participants for comments or corrections.
+
+3.5 Trustworthiness
+
+The first authors are Registered Nurses with experience in palliative care in haematology. They agree with the observation that the strong focus on curative treatment in haematology leads to palliative care only being considered late in the disease trajectory. By identifying and discussing both individual and shared preconceptions on the matter, the first authors maintained a conscious, critical and reflective approach to data collection and interpretation (Polit & Beck, 2021).
+
+Development of the interview guide, data collection process and data analysis was discussed with co‐authors with experience in palliative care and haematology to further improve credibility (Graneheim & Lundman, 2004). The first authors and co‐authors had regular discussions on the results of each step of the analysis process and shared their different perspectives on the interpretation and relevance of the data (Malterud, 2012). A shared consensus on the final analysis and categories was reached, and a logbook of important decisions was maintained for reflexivity (Polit & Beck, 2021).
+
+The variation in the level of treatment administered to the patients in the respective hospital wards contributed to diverse experiences about palliative care trajectories and enhanced the credibility of the findings.
+
+The transferability of the study was strengthened by describing the sample, data collection and analysis process. Descriptions of the findings were supplemented with key quotations to allow the reader sufficient insight to evaluate the relevance of the answers and insights to other healthcare contexts (Graneheim & Lundman, 2004; Polit & Beck, 2021). To ensure clarity in the reporting of the study, the Consolidated Criteria for Reporting Qualitative Research checklist was followed (Tong et al., 2007).
+
+3.6 Ethical considerations
+
+The study was approved by the Norwegian Centre for Research Data (NSD) (reference number: 151084) and the data protection officer at each hospital prior to data collection. To ensure that the nurses did not feel pressured to participate in the study, they were recruited by the ward nurse manager of the individual units who then conveyed the participants' contact details to the first authors. All participants provided signed consent for their involvement in the study after receiving written information about the project. This included an assurance that all their information would remain confidential, their participation was voluntary and they could withdraw their consent at any time without consequences (Beauchamp & Childress, 2019). Prior to beginning the interview, the first authors repeated the information about voluntary participation in the study. The data collected was stored securely in accordance with the guidelines by NSD.
+
+4 FINDINGS
+
+Three categories emerged from the data analysis: focus on a cure delays integration of palliative care, dialogue with patients facilitates palliative care and the need for enhanced interdisciplinary understanding. Categories and subgroups are described in Table 3.
+
+TABLE 3 Description of categories and subgroups [Correction added on 18 March 2023 after first online publication: the first subgroup in this table has been updated.]
+
+Category	Subgroup	
+Focus on a cure delays integration of palliative care	Focus on a cure prevents palliative care planning
+
+There is always new treatment to be tested, but when is enough enough?
+
+	
+Dialogue with patients facilitates palliative care	Lack of openness around death and palliative care
+
+Communication and information are crucial for patient participation
+
+	
+The need for enhanced interdisciplinary understanding	Nurse observations and competence are important for the patients' trajectories
+
+Planning is necessary to ensure satisfactory patient trajectories
+
+There is a need for interdisciplinary cooperation and platforms for information exchange
+
+	
+
+4.1 Focus on a cure delays integration of palliative care
+
+Several of the nurses had worked with both oncological and haematological patients and reflected on the differences in the way palliative care trajectories were integrated for the two groups. In their experience, oncologists were more familiar with palliative care principles, and palliative care was introduced earlier in oncological patients' illness trajectories. Haematologists, on the other hand, often focused on a cure until the patients were dying. According to the nurses, haematologists were reluctant to introduce palliative care if there was even a slight chance that patients could recover from their cancer. They often wished to try out new medications and include patients in clinical trials, and patients and relatives often expected to be offered the newest treatments. Several nurses believed that the multitude of new treatment options made it ethically difficult to introduce palliative care. As one nurse elaborated:It's typically brought up very close to the end when you are more or less dying, far too late, it's as if there's no knowledge that palliative care can last several years. Doctors seem unable to think of palliative care at the same time as giving treatment (…) and if we bring up the need for palliative care, we are often told that it isn't necessary, we aren't at that stage yet. (Nurse 4)
+
+Two nurses emphasized that it is impossible to know in advance who will survive and that everybody should be given the opportunity to be cured. Some patients are still alive because they were offered that chance. However, many nurses felt that treatment continued for too long, which resulted in patients dying rapidly once their treatment ended or from treatment complications. The nurses felt that some doctors were better than others at drawing the line and deciding when enough is enough. They also admitted that nurses should take a more active part in raising doctors' awareness of the value of integrating palliative care, especially in the context of frail or older patients receiving high doses of chemotherapy and, in some cases, even a bone marrow transplant. The nurses questioned whether milder treatments might be a better alternative for some patients. Although such treatment would not cure the patients, it could give them a few more good months to live without the heavy burden of side effects from chemotherapy and frequent hospital stays. As one nurse put it:I think that we sometimes treat them a little too long (…) the boundaries have already been pushed far in respect to age and treatment, and it's very difficult, I'm glad I'm not a doctor who needs to decide who should receive treatment and who shouldn't (….) and maybe we give a treatment with poor prospects, in the hope of curing them, and then we end up in situations where we almost feel that we ‘kill’ the patient with the treatment. (Nurse 8)
+
+4.2 Dialogue with patients facilitates palliative care
+
+Several nurses emphasized that dialogue characterized by honest information and attentive communication was important to enable patient participation. In their opinion, this could facilitate a palliative care trajectory in accordance with the patient's wishes. However, a few nurses admitted that the ward lacked a routine for talking to patients about their preferences from the time of diagnosis. Nurses stated that a lack of openness about the possibility of dying and alternative treatments to cure could result in little time for planning and coordinating care. As a result, the patient could sometimes be too sick to be discharged from the hospital in time. Some nurses felt that patients received sufficient information that enabled them to make informed decisions about their treatment. When it was apparent that the patient might not survive, the nurses and doctors were competent in preparing them and their families for the worst outcome. Other nurses felt that doctors typically “dragged their feet,” waited too long before discussing the patient's prognosis, and that they should give the patient and their family realistic information about the situation at a much earlier stage. These nurses were concerned that the patients were not sufficiently informed about the possible risks and complications of treatment, and therefore did not have enough knowledge to comprehend how sick they could become. As one nurse described:After a while we understand that they haven't understood the consequences of what they have agreed to, and sadly we experience that when they get exhausted someone finally has the courage to ask them, and then the answer is that had I known what I know now I think I would have said no to treatment. (Nurse 4)
+
+The nurses interviewed believed that they were highly competent at creating a dialogue about palliative care with their patients. At the same time, they described that doctors and nurses were hesitant to talk to patients about palliative care and the possibility of dying early in the course of their illness. As illustrated by one nurse:In my opinion, we never think about palliative care from the beginning of treatment. We're very focused on the fact that, yes, it's a serious illness, but we're going to handle this. I think we rarely talk about death and say: you could die of this. OK, maybe we say it once, but then we don't talk about it anymore. (Nurse 11)
+
+This could lead to delayed implementation of palliative measures and prevent patients from having the opportunity to express their end‐of‐life preferences. One nurse felt that patients, deep down, were aware of their risk of dying and that such conversations did not necessarily have to be unpleasant. Many nurses reflected on the challenge of determining the right time for such a conversation. On the one hand, they did not want to take away the patient's motivation for treatment and hope of survival, but on the other hand, they did not want to rob them of time at home with a good quality of life and a dignified death.
+
+4.3 The need for enhanced interdisciplinary understanding
+
+The nurses perceived that they were a step ahead of the doctors in identifying patients who might be eligible for palliative care. They said that they have a unique insight into the patients' physical and mental state and individual preferences because they care for them at all hours and in various situations. Many nurses pointed out that while having a specialization or degree was an advantage, experience was just as important to be able to identify those patients in need of palliative care. The nurses often attempted to share their observations with the doctors and described advocating the patient's case as an important part of their work. As one nurse described:We try to put forward our points of view, and kind of, speak on behalf of the patient and his or her family. We try to suggest that it might be, purposeful is maybe not the right word, but important for the patient to have a good quality of life, or if we should continue with treatment that only torments them. (Nurse 6)
+
+Some nurses expressed frustration when doctors did not listen to their assessments and functioned as gatekeepers for introducing palliative care. Many experienced nurses, on the other hand, felt that their voices were heard:I think the dialogue is complementary: we are often the ones who bring it up, and then the doctors balance it against what they have experienced can work and what can't. (Nurse 3)
+
+One nurse spoke of discussions between fellow nurses on how palliative care planning should be considered from the moment patients receive the diagnosis. The nurses believed that it was their duty to take an active part in the establishment and evaluation of treatment plans because patients with haematological malignancies often experience acute changes in health status. Several nurses described frustrating situations during weekends where a lack of planning often caused insecurity and decision aversion in doctors who did not have adequate knowledge of the patients' medical histories. The nurses believed that a long‐term treatment plan made by the haematologist in charge of the patient would allow for a shared understanding and prepare everyone involved. As one nurse said:Maybe have a plan, that if this doesn't work, considering all the treatment, that we have a plan for when it isn't purposeful anymore, a plan for what we should do, together with the patient. That the patient also knows, is prepared maybe, for the possibility that if this doesn't work, then OK, we have reached a new phase. (Nurse 6)
+
+The nurses maintained that it is the doctor's right to have the final say in the decision‐making process, as they have the ultimate responsibility for the patient. However, they also believed that there was much to gain from nurses contributing to the decision‐making process about palliative care, as their input could improve palliative care planning.
+
+Nurses argued that there was a lack of arenas for information exchange between nurses and doctors. Segregated planning meetings were perceived as a barrier to cooperation. Furthermore, several nurses found it challenging to speak up during their daily meetings with the haematologists due to time constraints and doctors dominating these meetings. Nurses stressed how important it is to understand the reasons behind doctors' decisions about the implementation of palliative care, given that explaining medical and treatment decisions to patients was often the nurses' responsibility. To ensure satisfactory patient trajectories, the nurses expressed the need for weekly interdisciplinary meetings where they could discuss complicated patient cases. The nurses hoped that such meetings could contribute to a common understanding of the treatment objectives and expected outcomes and allow for ethical reflections on palliative care trajectories. One nurse shared positive experiences of interdisciplinary cooperation:We discuss amongst ourselves since we experience things differently and have contrasting standpoints (…) the focus is on working as a team and not as separate professions, which benefits the entire ward. (Nurse 11)
+
+5 DISCUSSION
+
+This study gives insight into nurses' experiences about palliative care trajectories of patients with life‐threatening haematological malignancies. The findings suggest that the integration of palliative care is hindered by a curative focus because there may be a chance that patients can recover. The nurses experienced a lack of openness about death and believed that enhanced dialogue with patients and interdisciplinary cooperation between doctors and nurses could improve palliative care trajectories.
+
+Our participants believed that patients should have the opportunity to benefit from medical progress and new treatments, as they could lead to an improved chance of survival or prolonged life. Nevertheless, in line with previous studies (Grech et al., 2018; McCaughan et al., 2019; McGrath & Holewa, 2006), nurses in our study experienced that this medical focus delayed the integration of palliative care into treatment pathways. In haematology, where the treatment goal is primarily to cure or prolong life, there might be insufficient knowledge of the benefits of early integration of palliative care (El‐Jawahri et al., 2020; Kaasa et al., 2018). However, patients with haematological malignancies already have extensive palliative care needs from the time of initial diagnosis, and throughout their illness trajectory, as they receive intensive treatments such as stem cell transplants and high‐dose chemotherapy regimens with a high risk of toxicity and mortality (El‐Jawahri et al., 2020).
+
+Our participants experienced the doctors' decisions on whether to continue treatment as ethically difficult and that some treatments continued for too long or were futile. The impression that patients were treated more aggressively than the participants believed should be the case could be a source of moral distress for the participants. Sanderson et al. (2019) define moral distress as “ethical unease or disquiet resulting from a situation where a clinician believes they have contributed to avoidable patient or community harm through their involvement in an action, inaction or decision that conflicts with their own values.” A quantitative literature review found that nurses experienced moral distress more frequently when they acted in a way that they felt was not in the patients' best interest such as providing futile care (Oh & Gastmans, 2015). According to Benner et al. (2011), the decision to continue or terminate treatment needs to be based on both ethical and clinical reasoning, as it is unethical to either give futile care or not offer patients the best available treatment. Reports from the Lancet Oncology Commission and Lancet Commission on Value of Death indicate that patients and doctors tend to focus on treatment in uncertain situations due to their hope for prolonged survival (Kaasa et al., 2018; Sallnow et al., 2022). Furthermore, offering curative therapies seems to have become synonymous with caring for patients (Benner et al., 2011). Our participants felt that the treatment sometimes continued for too long and questioned its aggressive nature especially when administered to frail patients. The option of not treating certain patients should be actively considered (Benner et al., 2011) as new treatments often extend life only marginally, and end‐of‐life chemotherapy produces more harm than good in patients over 80 years of age (Krok‐Schoen et al., 2018; Sallnow et al., 2022).
+
+Our participants had different experiences with how well patients were informed, and some believed that a lack of timely and honest information hindered open dialogue with patients. Honest discussions, even when the prognosis is poor, could enhance the relationship between patients and the medical team. However, avoiding prognostic discussions could lead to mistrust, anxiety, reduced quality of life and family distress (Kaasa et al., 2018). The nurses in our study believed that explaining different scenarios prepared patients for what was to come and gave them the oppourtunity to participate and make informed decisions about their treatment and care throughout the illness trajectory. This is concurrent with the results of a study indicating that early, frank discussions with the patients and their families about likely treatment outcomes could avoid unrealistic expectations (McCaughan et al., 2019). Prognostic outcomes when treating haematological malignancies are often hard to predict, and it can be challenging for patients to decide whether undergoing aggressive treatment is worth the suffering involved (Wedding, 2021). A lack of understanding of their prognosis could make patients overestimate the chance of a cure and is associated with an increased willingness to accept chemotherapy (El‐Jawahri et al., 2020). Even if patients have received adequate information about their disease and prognosis, they may not be able to understand the intention of the treatment (Sallnow et al., 2022) or remember the information later (Wedding, 2021). Kaasa et al. (2018) underlined the importance of assessing what patients already know and the level of detail that they want and using non‐technical language when explaining the prognosis, encouraging questions, verifying their understanding and tailoring communication to meet the patient's needs.
+
+Our study suggests that the nurses regard themselves as competent in discussing palliative care with patients, however, in line with the study by McCaughan et al. (2019), they hesitated to discuss death and found it challenging to find the right time for these conversations. According to our participants, this could prevent patients from expressing their end‐of‐life wishes. Each patient has individual beliefs, values and needs that should be reflected in their care (Österlind & Henoch, 2021). Through open, two‐way dialogue, nurses can enhance patients' ability to express how they view and experience their situation and preferences and enable them to be co‐creators of their palliative care trajectory. This person‐centred care approach allows the nurses to help patients live as good a life as possible during their palliative care trajectory (Österlind & Henoch, 2021). Such discussions are essential and should be seen as a professional responsibility throughout the illness trajectory (Sallnow et al., 2022). Nurses sometimes experience human and nursing failure in care when patients do not have time to prepare for death. Therefore, it is important that nurses work towards bridging this gap by discussing human aspects of illness and hospitalization, such as the patient's concerns, fears and hopes (Benner et al., 2011).
+
+To meet the complex needs of patients with life‐threatening diseases, palliative care is often provided by multidisciplinary teams with professionals from different disciplines (Leclerc et al., 2014; World Health Organization, 2016). In contrast, patients with haematological malignancies primarily receive palliative care from their haematology team (Wedding, 2021). Our participants believed that an enhanced interdisciplinary approach, where doctors and nurses discuss patients and share their assessments, can lead to a common understanding and more holistic palliative care trajectories. Interdisciplinary cooperation entails that professional groups share knowledge and plan patient care together (Klarare et al., 2013; Leclerc et al., 2014). However, our participants reported a lack of opportunities and arenas to meet and discuss with doctors to allow for this exchange. This caused frustration in the nursing team. Poor teamwork and team support, such as difficulties in maintaining open communication and a collaborative environment, may also lead to moral distress among healthcare professionals (Maffoni et al., 2019; McCarthy & Gastmans, 2015). The integrative review by Mafoni et al. (2019) suggests that collaborative and respectful relationships and opportunities to have confrontations and exchange of experiences may help healthcare professionals to cope with moral distress.
+
+Our participants described that doctors and nurses had different understandings of the palliative needs of patients with haematological malignancies. Nurses spend considerable amounts of time with their patients and can therefore contribute with vital medical information and the patient's perspective. In line with previous studies (Grech et al., 2018; McCaughan et al., 2019), our participants described advocating the patient's needs and concerns as being an important nurse responsibility. This can be explained by the fact that most participants in our study were experienced nurses. Because of their knowledge of the patients' needs and concerns, our participants believed that they could influence and improve palliative care planning. Their engagement in establishing and updating treatment plans was important for establishing a common understanding in the medical team. In accordance with the study by Grech et al. (2018), our participants saw the need to establish a treatment plan from the time of initial diagnosis, which should be a standard approach for all oncology patients (Kaasa et al., 2018). A treatment plan containing prognostic information and guidelines for different trajectories could be beneficial to organizing better clinical care. Furthermore, such a plan could help patients and their families better understand and cope with their situation (Kaasa et al., 2018).
+
+5.1 Limitations
+
+The ward managers of the different wards had sole hands in recruiting the participants and may have done so in a manner to represent their own or the ward's views about palliative care trajectories. Additionally, they could have chosen participants who they knew would represent the ward in a favourable manner.
+
+A sample of 12 nurses might be considered small. However, the participants had diverse experiences and provided rich data related to our study's aim. We perceived that during the interviews, the nurses shared their reflections and experiences openly and honestly. The sample size was therefore considered to have sufficient information power (Malterud et al., 2016). Participants were selected from both the eastern and western parts of Norway, and a majority of the nurses had extensive clinical experience; all except three had a postgraduate or master's degree that included training in palliative care. Nurses from other geographical parts of Norway, novice nurses and nurses without postgraduate training in palliative care could have had other experiences or opinions on the matter.
+
+Nurses who were familiar with the first authors and the established social norms at their mutual workplace may have been hesitant to talk about sensitive topics or challenge institutional rules and conventions. They may also have made assumptions about the first authors' preconceptions, goals and knowledge on various topics, which might have influenced the answers that they provided (McEvoy, 2002). However, these interviews were conducted by the first authors who were least acquainted with the respective participants to encourage the participants to express their thoughts more freely. When reviewing the interviews of the nurses in question, they were found to share experiences that were both critical and supportive of current practices.
+
+The interviews were conducted by two first authors, which may have affected the participants' ability to relax and feel comfortable providing candid, genuine answers since they were in the minority during the interview (Polit & Beck, 2021).
+
+6 CONCLUSION
+
+The nurses experienced that the integration of palliative care was hindered by a medical focus aiming to cure or prolong life, which could lead to patients being overtreated. The nurses described a lack of openness about the oppourtunity of palliative care and death. They believed that enhanced dialogue with patients would allow them to better understand their prognosis, include them in treatment decisions, and give them time to prepare for death. The nurses also believed that enhanced interdisciplinary cooperation could improve long‐term planning and, subsequently, patients' palliative care trajectories. However, they experienced a lack of arenas where they could share their assessments and discuss their patients with the doctors. Nurses' insights on patients' needs and concerns could contribute to more holistic palliative care trajectories and ensure a person‐centred approach to care. Future research should therefore focus on ways to improve collaboration between nurses and doctors working in haematology wards. Based on our findings, we would also recommend further research into the development of palliative care guidelines, which incorporates knowledge from both haematologists and haematological nurses. Involving patients and relatives in the development of these guidelines can further improve patients' illness trajectories.
+
+7 RELEVANCE TO CLINICAL PRACTICE
+
+To improve the palliative care trajectories of patients suffering from life‐threatening haematological malignancies, there is a need for increased openness and dialogue around death and palliative care from the time of initial diagnosis. Haematologists need to be made aware of the benefits of palliative care and how it can be applied in conjunction with standard treatment throughout the patient's illness trajectory. Furthermore, arenas that facilitate the exchange of observations and assessments between doctors and nurses are vital to improving palliative care planning. Establishing such arenas should therefore be a priority. Consequently, palliative care should be included in the official guidelines for patients with haematological malignancies, and internal procedures to secure holistic palliative care trajectories must be established in the wards.
+
+AUTHOR CONTRIBUTIONS
+
+Design: SM, AKM and IS; data collection and drafting of the manuscript: SM, AKM and IS; data analysis: SM, AKM, IS, MEK and SAS and critical reviewing of the manuscript: MEK and SAS. All authors approved the final version of the manuscript.
+
+FUNDING INFORMATION
+
+SM, AKM and IS wrote this article as part of their master's degree which was funded by the Department of Haematology, Oslo University Hospital, Oslo, Norway.
+
+CONFLICT OF INTEREST
+
+The authors declare no conflict of interest.
+
+Supporting information
+
+Appendix S1. Supporting Information.
+
+Click here for additional data file.
+
+ACKNOWLEDGEMENT
+
+The authors would like to thank all the participants for their contribution and time to help with this research.
+
+DATA AVAILABILITY STATEMENT
+
+The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.
+==== Refs
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+

PMC10084224.txt

@@ -0,0 +1,323 @@
+
+==== Front
+Asia Pac J Clin Oncol
+Asia Pac J Clin Oncol
+10.1111/(ISSN)1743-7563
+AJCO
+Asia-Pacific Journal of Clinical Oncology
+1743-7555
+1743-7563
+John Wiley and Sons Inc. Hoboken
+
+35599450
+10.1111/ajco.13783
+AJCO13783
+Original Article
+Original Articles
+Large variation in radiation therapy fractionation for multiple myeloma in Australia
+ONG et al.
+Ong Wee Loon https://orcid.org/0000-0001-6657-7193
+1 2 3 weeloonong@cantab.net
+
+MacManus Michael 4 5
+Milne Roger L. 6 7 8
+Foroudi Farshad 9
+Millar Jeremy L. 1 2
+1 Alfred Health Radiation Oncology Melbourne Victoria Australia
+2 Central Clinical School Monash University Melbourne Victoria Australia
+3 School of Clinical Medicine University of Cambridge Cambridge UK
+4 Department of Radiation Oncology Peter MacCallum Cancer Centre Melbourne Victoria Australia
+5 Sir Peter MacCallum Department of Oncology University of Melbourne Melbourne Victoria Australia
+6 Cancer Epidemiology Division Cancer Council Victoria Melbourne Victoria Australia
+7 Centre for Epidemiology and Biostatistics, School of Population and Global Health University of Melbourne Melbourne Victoria Australia
+8 Precision Medicine, School of Clinical Sciences, Monash Health Monash University Melbourne Victoria Australia
+9 Department of Radiation Oncology Olivia Newton‐John Cancer Wellness and Research Centre, Austin Health Heidelberg Victoria Australia
+* Correspondence
+Wee Loon Ong, Alfred Health Radiation Oncology, 55 Commercial Road, Melbourne, 3000 VIC, Australia.
+Email: weeloonong@cantab.net
+
+22 5 2022
+2 2023
+19 1 10.1111/ajco.v19.1 149157
+13 7 2021
+22 3 2022
+© 2022 The Authors. Asia‐Pacific Journal of Clinical Oncology published by John Wiley & Sons Australia, Ltd.
+https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.
+
+Abstract
+
+Aim
+
+To evaluate the patterns of use of different radiation therapy (RT) fractionation for multiple myeloma (MM) bone disease.
+
+Methods
+
+This is a population‐based cohort of patients with MM who had RT between 2012 and 2017 as captured in the statewide Victorian Radiotherapy Minimum Data Set in Australia. Data linkage was performed to identify subsets of RT delivered within 3 months of death. RT fractionation was classified into four groups: single‐fraction (SFRT), 2–5, 6–10, and > 10 fractions. Changes in RT fractionation use over time were evaluated with the Cochran–Armitage test for trend. Factors associated with RT fractionation were evaluated using multivariate logistic regressions.
+
+Results
+
+Nine hundred and sixty‐seven courses of RT were delivered in 623 patients. The proportion of SFRT, 2–5, 6–10 and > 10 fractions RT was 18%, 47%, 28%, and 7%, respectively. There was an increase in the use of 2–5 fractions, from 48% in 2012 to 60% in 2017 (p‐trend < .001), with corresponding decrease in the use of 6–10 fractions, from 26% in 2012 to 20% in 2017 (p‐trend = .003). Nine percent (40/430) of RT courses at private institutions were SFRT, compared to 25% (135/537) in public institutions (p < .001). In multivariate analyses, treatment in private institution was the strongest predictor of multifraction RT use. SFRT use was more common closer to the end of life–18%, 14%, and 33% of RT within 2–3, 1–2, < 1 month of death, respectively.
+
+Conclusion
+
+There is increasing use of shorter course RT (2–5 fractions) for MM over time. SFRT use remains low, with large variation in practice.
+
+Radiation therapy (RT) is effective treatment for symptomatic multiple myeloma‐related bone disease. This population‐based study showed increasing use of short course RT of 2‐5 fractions. However, the use of single‐fraction RT remains low, even at the end of life. There is large institutional variation in practice.
+
+source-schema-version-number2.0
+cover-dateFebruary 2023
+details-of-publishers-convertorConverter:WILEY_ML3GV2_TO_JATSPMC version:6.2.7 mode:remove_FC converted:10.04.2023
+Ong WL , MacManus M , Milne RL , Foroudi F , Millar JL. Large variation in radiation therapy fractionation for multiple myeloma in Australia. Asia‐Pacific Journal of Clinical Oncology. 2023;19 :149–157. 10.1111/ajco.13783 35599450
+==== Body
+pmc1 INTRODUCTION
+
+Bone involvement is common in patients with multiple myeloma (MM), with up to 80% of patients with newly diagnosed MM presenting with osteolytic lesions, with high risk of skeletal‐related events, such as pathological fractures and spinal cord compression. 1 Radiation therapy (RT) is an effective treatment modality for symptom management of these bony lesions, 2 and evidence‐based modeling estimated that approximately two in five patients with MM should receive at least one course of RT over the course of their disease. 3
+
+RT fractionation for bone metastases is an area that has been extensively investigated in the past. Meta‐analyses of multiple randomized trials have consistently shown that single fraction RT (SFRT) is as effective as multifraction RT for symptom management for uncomplicated bone metastases. 4 However, few of these studies have specifically looked into the MM cohort. A randomized prospective trial comparing 30 Gy in 10 fractions to 8 Gy in 1 fraction for symptomatic bone lesions in 101 patients with MM showed no differences in symptom response. 5 In the setting of MM‐related bone disease with spinal cord compression, in a large international multicenter retrospective pooled analysis, Rades et al. reported that long‐course RT (10–20 fractions) resulted in better functional improvement compared to short‐course RT (1–5 fractions). 6
+
+Based on the available evidence, several international guidelines and recommendations specifically on the management of MM‐related bone disease have been developed by the International Myeloma Working Group 1 and International Lymphoma Radiation Oncology Group (ILROG). 2 The ILROG consensus guidelines recommend that 8 Gy in 1 fraction, 20 Gy in 5 fractions, or 30 Gy in 10 fractions were all reasonable options for symptom control, but 8 Gy in 1 fraction is preferred for patients with poor prognosis. 2 In situations where there is spinal cord compression or bulky disease where durable control is desired, however, 30 Gy in 10–15 fractions is preferred. 2
+
+Despite these evidence and guidelines, it is unclear as to the actual pattern of practice of RT fractionation for MM in Australia. Earlier Victoria statewide population studies had evaluated the use of SFRT for the management of bone metastases, 7 but these were restricted to patients with solid tumors, excluding patients with hematological cancers, such as MM. It was unclear as to the proportion of patients with MM in a separate population‐based study in the state of New South Wales in Australia. 8 The aim of this study is to evaluate the RT fractionation schedule used in the management of MM‐related bone disease in Victoria, and to identify factors associated with multifraction RT.
+
+2 MATERIALS AND METHODS
+
+2.1 Study population
+
+This study comprised a population‐based cohort of patients with MM (ICD10: C90.0) who received RT between 2012 and 2017, as captured in the statewide Victorian Radiotherapy Minimum Data Set (VRDMS). VRDMS is an administrative dataset maintained by the Victorian Department of Health. Patients with plasma cell leukemia (ICD10: C90.1), extramedullary plasmacytoma (ICD10: C90.2), and solitary plasmacytoma (ICD10: C90.3) were excluded. We only included RT courses where the target site of RT was documented as bone. Data from VRMDS were linked with the Victorian Cancer Registry and the Registry of Births, Deaths and Marriages to capture data on death. We further analyzed a subset of RT delivered at the end of life (EOL), defined as at least one fraction palliative RT courses delivered within 90 days of death. The study was approved by our institutional Health Human Research Ethics Committee (LNR/18/34).
+
+2.2 Primary outcomes and covariables
+
+The primary outcome was the different RT fractionations used, categorized into four ordinal groups: SFRT, 2–5 fractions, 6–10 fractions, and >10 fractions. Information on radiation dose was not available in VRMDS for the study period. Factors evaluated for association with different fractionations were: age at time of RT, sex, site of treated lesion (spine or non‐spine), socioeconomic status, remoteness of residency (major cities, or regional/remote), treatment center type (public or private) and location (metropolitan or regional), and year of RT. Socioeconomic status was determined based on residential postcode using the Socio‐Economic Indexes for Areas index for Relative Socio‐Economic Disadvantage based on the Australian Bureau of Statistics data (i.e., based on 2011 Australian census data for patients treated in 2012 and 2013, and Australian census 2016 data for patients treated in 2014–2017); this was further subdivided into quintiles based on the Victorian general population. The area of residence was also dichotomized as major city or regional/remote using the Australian Statistical Geographical Standard remoteness structure. It is important to note that VRMDS does not capture information on MM‐related prognostic factors, as well as information on systemic therapy that patients received.
+
+2.3 Statistical analyses
+
+Variables associated with different RT fractionations were evaluated using Pearson's chi‐squared test for categorical variables, and Kruskal–Wallis test for continuous variables. The Cochran–Armitage test for trend was used to evaluate the changes in different fractionation use over time. Multinomial logistic regression was used to assess the factors associated with different fractionations, with SFRT as the reference group. For the subset of RT courses delivered at the EOL, multivariate logistic regression was used to evaluate factors associated with SFRT. All multivariable analyses employed the robust standard errors, with analyses clustered on patient identifiers to allow for clustering of multiple courses of RT given to the same patient. A two‐sided p‐value of < .05 was considered to indicate statistical significances. All statistical analyses were performed using STATA/SE 17 (STATA Corp, College Station, TX, USA).
+
+3 RESULTS
+
+A total of 967 courses of RT were delivered in 623 patients for MM between 2012 and 2017. The mean age at RT was 69.7 (SD = 11.7). Approximately two‐third of the RT target sites were spine. The use of advanced RT techniques, such as intensity‐modulated RT, volumetric‐modulated arc therapy, or stereotactic RT, was rare (4%). Majority of RT courses were delivered in metropolitan centers (79%), while just over half were delivered in public centers (56%).
+
+3.1 RT fractionation
+
+Approximately one in five RT courses were SFRT, and half were delivered over 2–5 fractions (Table 1). There was higher proportion of SFRT use in patients aged under 60 years (25%) and above 80 years (24%). There was lower proportion of SFRT use for spine (15%) compared to non‐spine (24%) sites of disease (p = .002). There were no significant differences in fractionation use between the different socioeconomic quintiles (p = .2).
+
+TABLE 1 Baseline characteristics and different fractionation for radiation therapy for multiple myeloma
+
+	1	2–5	6–10	>10		
+Number of fractions	175 (18%)	452 (47%)	275 (28%)	65 (6%)	p‐value#	
+Age at RT	
+Mean (SD)	69.2 (13.6)	70.2 (11.3)	69.3 (11.3)	69.8 (11.0)		
+<60	49 (25%)	81 (42%)	54 (28%)	10 (5%)	.001	
+60–69	36 (13%)	137 (50%)	83 (30%)	18 (7%)		
+70–79	41 (14%)	132 (45%)	96 (33%)	26 (9%)		
+≥80	49 (24%)	102 (50%)	42 (21%)	11 (5%)		
+Sex	
+Male	107 (19%)	255 (46%)	148 (27%)	43 (8%)	.2	
+Female	68 (16%)	197 (48%)	127 (31%)	22 (5%)		
+Target site of radiation therapy	
+Non‐spine	83 (24%)	140 (41%)	100 (29%)	22 (6%)	.002	
+Spine	92 (15%)	312 (50%)	175 (28%)	43 (7%)		
+RT techniques	
+3D CRT	172 (19%)	429 (46%)	265 (29%)	63 (7%)	.3	
+Advanced RT a	3 (8%)	23 (61%)	10 (26%)	2 (5%)		
+Socioeconomic status	
+First quintile (most disadvantaged)	41 (19%)	111 (51%)	49 (23%)	15 (7%)	.2	
+Second quintile	30 (21%)	64 (44%)	45 (31%)	5 (3%)		
+Third quintile	21 (13%)	76 (48%)	52 (33%)	11 (7%)		
+Fourth quintile	29 (15%)	85 (43%)	65 (33%)	17 (9%)		
+Fifth quintile (least disadvantaged)	54 (22%)	116 (46%)	64 (26%)	17 (7%)		
+Remoteness of residence	
+Major cities	114 (17%)	311 (46%)	213 (31%)	42 (6%)	.02	
+Regional/remote	61 (21%)	141 (49%)	62 (22%)	23 (8%)		
+Treatment institution type	
+Public	135 (25%)	267 (50%)	104 (19%)	31 (6%)	<.001	
+Private	40 (9%)	185 (43%)	171 (40%)	34 (8%)		
+Treatment institution location	
+Metropolitan	135 (18%)	344 (45%)	234 (31%)	49 (6%)	.03	
+Regional	40 (20%)	108 (53%)	41 (20%)	16 (8%)		
+Year of RT	
+2012	21 (17%)	60 (48%)	32 (26%)	11 (9%)		
+2013	38 (21%)	58 (33%)	65 (37%)	16 (9%)		
+2014	28 (20%)	58 (41%)	49 (35%)	7 (5%)		
+2015	24 (15%)	67 (42%)	53 (34%)	14 (9%)		
+2016	36 (18%)	108 (55%)	42 (21%)	11 (6%)		
+2017	28 (17%)	101 (60%)	34 (20%)	6 (4%)		
+p‐trend*	.5	<.001	.003	.05		
+a Advanced RT techniques include intensity‐modulated radiation therapy, volumetric‐modulated arc therapy, and stereotactic radiation therapy.
+
+# p‐value from Pearson's chi‐squared test.
+
+* p‐trend from Cochran–Armitage test for trend.
+
+John Wiley & Sons, Ltd.
+
+Of the patients treated in private institutions, there were differences in RT fractionation use between patients who lived in major cities versus regional/remote areas—20% of RT delivered in those who lived in regional/remote areas was SFRT compared to 5% of RT delivered in those who lived in major cities (p < .001) (Table 2). Of patients treated in metropolitan centers, SFRT use was lower in those who lived in the major cities (16%) compared to those who live in regional/remote areas (25%) (p = .04) (Table 2).
+
+TABLE 2 Use of different RT fractionation stratified by based on remoteness of residence, stratified by treatment institution type and location
+
+		Number of fractions		
+Treatment institution	Remoteness of residence	1	2–5	6–10	>10	p‐value	
+Public	Major cities	99 (26%)	175 (46%)	81 (21%)	23 (6%)	.09	
+(n = 537)	Regional/remote	36 (23%)	92 (58%)	23 (14%)	8 (5%)		
+Private	Major cities	15 (5%)	136 (45%)	132 (44%)	19 (6%)	<.001	
+(n = 430)	Regional/remote	25 (20%)	49 (38%)	39 (30%)	15 (12%)		
+Metropolitan	Major cities	102 (16%)	281 (45%)	204 (32%)	41 (7%)	.04	
+(n = 762)	Regional/remote	33 (25%)	63 (47%)	30 (22%)	8 (6%)		
+Regional	Major cities	12 (23%)	30 (58%)	9 (17%)	1 (2%)	.3	
+(n = 205)	Regional/remote	28 (18%)	78 (51%)	32 (21%)	15 (10%)		
+John Wiley & Sons, Ltd.
+
+3.2 Trend in practice
+
+Overall, there was no significant change in SFRT use over time (p‐trend = .5) (Table 1). There is, however, a marked increase in the use of 2–5 fraction RT (from 48% in 2012 to 60% in 2017, p‐trend < .001), with corresponding decrease in the use of 6–10 fraction RT (from 26% in 2012 to 20% in 2017; p‐trend = .003) and > 10 fraction RT (from 9% in 2012 to 4% in 2017, p‐trend .05).
+
+This change in fractionation over time was observed when stratified by target site of RT, area of residence, and treatment centers (Figure 1A–H). For RT to non‐spine sites, the most marked changes in fractionation were observed for 6–10 fractions, decreasing from 27% in 2012 to 14% in 2017 (p‐trend = .012) (Figure 1A). For RT to spine, there was marked increase in the use of 2–5 fractions from 50% in 2012 to 62% in 2017 (p < .001) (Figure 1B). When stratified by area of residence, the increase in the use of 2–5 fractions was observed in patients who lived in both major cities (from 28% in 2012 to 54% in 2017; p = .002) (Figure 1C) and regional/remote areas (from 49% in 2012 to 72% in 2017; p = .003) (Figure 1D).
+
+FIGURE 1 Radiation therapy fractionation for multiple myeloma from 2012 to 2017, stratified site of RT (nonspine [A] and spine [B]), area of residence (major cities [C] and regional/remote area [D]), institutional type (public [E] and private [F]), and institutional location (metropolitan [G] and regional [H])
+
+There were no statistically significant changes over time in fractionation in public institutions (Figure 1E). However, in private institutions, there was marked increase in the use of 2–5 fractions (from 38% in 2012 to 62% in 2017; p‐trend < .001), and corresponding decrease in the use of 6–10 fractions (from 42% in 2012 to 29% in 2017, p‐trend < .001) (Figure 1F). In metropolitan centers, there was increase in the use of 2–5 fractions (from 43% in 2012 to 56% in 2017; p‐trend < .001) with corresponding decrease in 6–10 fractions (from 32% in 2012 to 25% in 2017; p‐trend = .017) (Figure 1G). In regional centers, the use of RT fractionation varied over time, but the overall trend for the different RT fractionations over the 6‐year period was not statistically significant (Figure 1H).
+
+3.3 Multivariate analyses
+
+In multivariate analyses, patient age, target site of RT, area of residence, and treatment centers (type and location) were independently associated with the use of multifraction RT compared to SFRT, after adjusting for the year of treatment (Table 3). Compared to patients aged under 60 years, those aged 60–69 were 2.2 times (95%CI = 1.2–4.0; p = .01) more likely to have 2–5 fraction RT (than SFRT), while patients aged above 80 were less likely (OR=0.46; 95%CI = 0.21‐0.99; p=0.05) to have 6–10 fraction RT (than SFRT).
+
+TABLE 3 Factors associated with different fractionation schedule for multiple myeloma‐related bone disease in multivariate analyses (single‐fraction RT was used as reference group)
+
+	2–5 fractions	6–10 fractions	>10 fractions	
+	OR (95% CI)	p‐value	OR (95% CI)	p‐value	OR (95% CI)	p‐value	
+Age at RT	
+<60	Reference		Reference		Reference		
+60–69	2.17 (1.17–3.98)	.01	1.92 (.95–3.85)	.07	2.37 (.79–7.11)	.1	
+70–79	1.54 (.82–2.91)	.2	1.57 (.78–3.16)	.2	2.72 (.98–7.51)	.05	
+≥80	.85 (.45–1.61)	.5	.46 (.21–.99)	.05	.76 (.23–2.48)	.7	
+Sex (male vs. female)	1.33 (.84–2.09)	.2	1.40 (.83–2.36)	.2	.80 (.39–1.66)	.6	
+Target site of radiation therapy (nonspine vs. spine)	2.19 (1.45–3.33)	<.001	1.85 (1.15–2.98)	.01	1.97 (.96–4.06)	.07	
+Socioeconomic status	
+First quintile (most disadvantaged)	Reference		Reference		Reference		
+Second quintile	.66 (.32–1.34)	.2	.96 (.42–2.21)	.9	.33 (.09–1.17)	.09	
+Third quintile	1.29 (.61–2.71)	.5	1.79 (.74–4.30)	.2	1.29 (.35–4.79)	.70	
+Fourth quintile	.91 (.44–1.89)	.8	1.22 (.54–2.80)	.6	1.51 (.55–4.15)	.4	
+Fifth quintile (least disadvantaged)	.70 (.36–1.33)	.3	.67 (.31–1.43)	.3	.86 (.32–2.36)	.8	
+Remoteness of residence (major city vs. regional/remote)	.53 (.29–.98)	.04	.33 (.16–.69)	.003	.83 (.35–1.98)	.7	
+Treatment institution type (public vs. private)	3.29 (2.01–5.38)	<.001	7.82 (4.51–13.57)	<.001	4.98 (2.21–11.24)	<.001	
+Treatment institution location (metropolitan vs. regional)	1.91 (1.00–3.64)	.05	1.76 (.82–3.80)	.1	1.86 (.65–5.32)	.2	
+Year of RT	
+2012	Reference		Reference		Reference		
+2013	.49 (.22–1.12)	.09	.87 (.37–2.09)	.8	.74 (.23–2.40)	.6	
+2014	.74 (.32–1.68)	.5	1.05 (.42–2.63)	.9	.50 (.13–1.87)	.3	
+2015	.96 (.43–2.15)	.9	1.33 (.54–3.30)	.5	1.30 (.39–4.32)	.7	
+2016	1.12 (.52–2.44)	.8	.74 (.30–1.83)	.5	.69 (.18–2.67)	.6	
+2017	1.28 (.60–2.73)	.5	.79 (.32–1.96)	.6	.42 (.10–1.74)	.2	
+John Wiley & Sons, Ltd.
+
+Treatment to the spine was more likely to be multifraction RT than SFRT –2.2 times (95%CI = 1.5–3.3; p < .001) more likely to be 2–5 fractions, and 1.9 times (95%CI = 1.2–3.0; p = .01) more likely to be 6–10 fractions. Compared to patients who lived in major cities, RT delivered to patients who lived in regional or remote centers was less likely to be multifraction RT than SFRT – 47% (95%CI = 2–71%; p = .04) relatively less likely to be 2–5 fractions, and 67% (95%CI = 31–84%; p = .003) less likely to be 6–10 fractions.
+
+Treatment in private institutions was most strongly associated with multifraction RT use, compared to public institutions – 3.3 times (95%CI = 2.0–5.4; p < .001) more likely to be 2–5 fractions, 7.8 times (95%CI = 4.5–13.6; p < .001) more likely to be 6–10 fractions, and 5 times (95%CI = 2.2–11.2; p < .001) more likely to be > 10 fractions.
+
+3.4 EOL cohort
+
+There were 122 courses of RT delivered to 59 patients at the EOL, of which only one‐quarter of the RT courses was SFRT (Table 4). SFRT was more likely to be given closer to death, comprising 18%, 14%, and 33% of RT courses delivered within 2–3 months, 1–2 months, and < 1 months of death, respectively (p = .08). The use of SFRT at the EOL was markedly lower in private institutions (7%) compared to public institutions (41%) (p < .001). In multivariate analyses, treatment in private institutions was the only factor independently associated with SFRT use (OR = .04; 95%CI = .004–.33; p = .003).
+
+TABLE 4 Factors associated with single fraction radiation therapy (SFRT) in the last 3 months of life (N = 122)
+
+	Single fraction N = 30 (25%)	Multifraction N = 92 (75%)	OR (95% CI)	p‐value	
+Age at RT	
+Mean (SD)	68.3 (13.7)	72.0 (11.1)			
+<60	10 (50%)	10 (50%)	Reference		
+60–69	8 (20%)	32 (80%)	.26 (.05–1.33)	.1	
+70–79	4 (14%)	25 (86%)	.86 (.14–5.13)	.9	
+≥80	8 (24%)	25 (76%)	.57 (.10–3.40)	.5	
+Time to death	
+<1 month	20 (33%)	40 (67%)	Reference		
+1–2 months	3 (14%)	19 (86%)	.43 (.05–3.91)	.5	
+2–3 months	7 (18%)	33 (82%)	.56 (.12–2.58)	.5	
+Sex	
+Male	21 (27%)	57 (73%)	Reference		
+Female	9 (20%)	35 (80%)	.83 (.25–2.79)	.8	
+Target site of radiation therapy	
+Nonspine	12 (30%)	28 (70%)	Reference		
+Spine	18 (22%)	64 (78%)	.26 (.06–1.12)	.07	
+Socioeconomic status	
+First quintile (most disadvantaged)	6 (19%)	25 (81%)	Reference		
+Second quintile	5 (26%)	14 (74%)	1.74 (.26–11.5)	.6	
+Third quintile	6 (32%)	13 (68%)	.89 (.17–4.59)	.9	
+Fourth quintile	2 (11%)	16 (89%)	.47 (.04–5.77)	.6	
+Fifth quintile (least disadvantaged)	11 (31%)	24 (69%)	2.54 (.37–17.19)	.3	
+Remoteness of residence	
+Major cities	23 (23%)	75 (77%)	Reference		
+Regional/remote	7 (29%)	17 (71%)	3.73 (.69–20.1)	.1	
+Treatment institution type	
+Public	26 (41%)	38 (59%)	Reference		
+Private	4 (7%)	54 (93%)	.04 (.004–.33)	.003	
+Treatment institution location	
+Metropolitan	27 (25%)	83 (75%)	Reference		
+Regional	3 (25%)	9 (75%)	.66 (.07–5.75)	.7	
+Year of RT	
+2012	1 (9%)	10 (91%)	Reference		
+2013	8 (26%)	23 (74%)	5.39 (.45–65.0)	.2	
+2014	7 (25%)	21 (75%)	2.39 (.17–32.8)	.5	
+2015	9 (33%)	18 (67%)	2.84 (.24–33.2)	.4	
+2016	5 (20%)	20 (80%)	1.04 (.06–16.9	.9	
+John Wiley & Sons, Ltd.
+
+4 DISCUSSION
+
+This is to our knowledge the first Australian population‐based study to evaluate the pattern of RT fractionation for MM‐related bone disease. We found that SFRT remains a minority of RT fractionation regimens, consistent with the findings of RT for bone metastases in solid tumors. 7 , 8 , 9 , 10 , 11 A major strength of this study is the use of population‐based administrative data, which capture all episodes of RT delivered in Victoria, both in public and private institutions. Thus, the data reflect our statewide practice, allowing us to evaluate any sociodemographic and institutional variations in care, which is not possible using single‐institutional studies.
+
+The most common fractionation used in our cohort was 2–5 fractions, and its use has increased over our study period. In contrast, the use of more extended fractionations of 6–10 fractions has decreased. This is in contrast to findings from the only other published population‐based series in the literature, using data from the U.S. National Cancer Database (NCDB) between 2004 and 2014, whereby more than half of the RT fractionations for MM were 6–10 fractions. 12 The use of SFRT in our cohort remained low at 18% over the study period, which is similar in the management of bone metastases in solid tumor in Victoria, 7 , 10 , 11 but was still much higher than the 2% SFRT use for MM reported in NCDB cohort. 12
+
+The number of prescribed RT fractions is often guided by the patients’ MM disease trajectory and overall prognosis. However, one of the major limitations of this study is the lack of information in some of the important patient factors (e.g., ECOG performance status), tumor‐factors (e.g., Revised International Staging System prognostic factors for MM 13 ), and treatment factors (e.g., the use of systemic therapy 14 ) in administrative databases, such as VRMDS. Hence, we are not able to evaluate the appropriateness of RT fractionation use in each individual patient–a young patient with good performance status early in the course of disease with availability of multiple systemic therapy options may warrant higher dose multifraction RT to provide more durable control, and this is different to a frail patient who is refractory to multiple lines of systemic therapy at the EOL. Nonetheless, it is important for radiation oncologists to stay abreast with advancement in systemic therapy options for MM, 15 as new combination systemic therapies (e.g., carfilzomib, daratumumab, and dexamethasone) have been shown to significantly improve outcomes, even in the setting of refractory MM, 16 and this may influence the decision making in RT fractionation prescribed.
+
+There should be less ambiguity in RT fractionation recommendation for patients with limited or poor prognosis at the EOL–ILROG guidelines recommend that single fraction 8 Gy is the preferred RT fraction for patients with poor prognosis who require RT. 2 In the subset of RT courses delivered within 3 months of death (i.e., at the EOL) in our cohort, the overall SFRT use in our cohort still appears reasonably low at 25%. The underutilization of SFRT at the EOL has been previously reported in the management of bone metastases in solid tumor. 17 This could reflect either a general reluctance for the use of SFRT even at the EOL, or clinicians’ overestimation of patients’ likely survival. 18
+
+The RT fractionation used also varied depending on the target site of treatment–with lower use of SFRT for spinal disease. ILROG guidelines recommend the use of multifraction RT of 30 Gy in 10–15 fractions in situations where there is epidural disease with spinal cord compression. 2 One limitation of our study is that we do not have detailed clinical information to determine whether the treated spinal disease was associated with spinal instability, pathological fractures requiring surgical interventions, or spinal cord compression, which may justify the need for multifraction RT. We are also unable to account for reirradiation using VRMDS data, which is especially important in spinal disease, given the RT tolerance dose for spinal cord. Given that VRMDS data were only available from 2012 onward, we were not able to confirm if a patient has had RT to the same site prior to 2012. Even when the same target site was irradiated (e.g., spine) on more than one occasion since 2012, we do not have sufficient information to confirm if it was reirradiation of the same level of vertebra, or radiation of another vertebra level, not previously irradiated.
+
+We also evaluated institutional and demographic factors associated with RT fractionation use. One of the most striking findings is that treatment in private institutions is the strongest predictor of multifraction RT use. The higher proportion of multifraction RT use persisted even at the EOL and after adjusting for patients’ age and target site of RT. A most likely explanation for the differences in RT fractionation use between institutions may be remuneration related. In the current Australian healthcare setting, the Medicare Benefits Schedule (MBS) reimbursement for RT is based on the number of fractions delivered –MBS reimbursement for SFRT, 5‐fraction RT, and 10‐fraction RT delivered using 3D‐conformal technique in Australia was AUD 1320.35–1948.80, AUD 1821.75–2947.35, and AUD 2448.50–4497.60, respectively, depending on the number of organs‐at‐risks and number of RT fields involved. 19 However, we also could not discount other possible explanations for the observed variations in practice, including differences in patient population seen in public versus private institutions, and possibly resources and capacity constraints in public institutions for delivery of multifraction RT.
+
+We observed no differences in RT fractionation use by patient socioeconomic status but there were differences in RT fractionation use depending on patients’ area of residence–those living in regional or remote areas were less likely to be treated with multifraction RT. This may reflect clinicians’ consideration and accommodation of patients’ preference to reduce the number of visits for treatment given the long travel distance to and from RT facilities. While remoteness of residence is an indirect measure of access to RT facilities, there is now increasing number of RT facilities being established in regional areas in Australia. A better measure of access would be the travel distance to the nearest RT facility, but these data were not available in our study. This has been assessed in earlier studies, 20 , 21 which found that increasing distance to the nearest RT facilities was associated with lower likelihood of receiving RT.
+
+Apart from the limitations highlighted above, another inherent limitation with the use of administrative dataset is that it is dependent on accuracy of reporting from each institution, and we cannot discount the possibility of misclassification of variables. This is especially critical in potential miscoding of the diagnosis between MM and solitary plasmacytoma, which will influence the recommended RT fractionation–solitary plasmacytoma is often treated with higher dose and more protracted fractionation. 2
+
+5 CONCLUSION
+
+Using an Australian administrative dataset, we observed increasing use of shorter fractionated RT schedules (2–5 fractions) for MM‐related bone disease between 2012 and 2017 in a population‐based cohort of patients. However, the use of SFRT remained low, even at the EOL. We also observed large variations in RT fractionation use depending on institutional type, with SFRT much more commonly used in public centers. This is an important pattern‐of‐practice study for MM in Australia as it provides us with a baseline benchmark of the contemporary practice pattern for MM to be measured against (which to date, is not available in any published literature) for future quality improvement initiatives to reduce unwarranted variations in practice. 22 With advancement in systemic therapy for MM and as patients with MM are living longer, 15 we anticipate that the pattern of practice of RT for MM‐related bone disease will continue to evolve, not only with respect to RT fractionation, but on the use of advanced RT techniques . 19
+
+ETHICS STATEMENT
+
+The study was approved by Austin Health Human Research Ethics Committee (LNR/18/34).
+
+ACKNOWLEDGMENTS
+
+We acknowledge the Victorian Government Department of Health Centre for Victoria Data Linkage for performing data linkages and providing access to the dataset.
+
+Open access publishing facilitated by Monash University, as part of the Wiley ‐ Monash University agreement via the Council of Australian University Librarians.
+
+DATA AVAILABILITY STATEMENT
+
+Data will be shared upon reasonable request to the corresponding author.
+==== Refs
+REFERENCES
+
+1 Terpos E , Zamagni E , Lentzsch S , et al. Treatment of multiple myeloma‐related bone disease: recommendations from the Bone Working Group of the International Myeloma Working Group. Lancet Oncol. 2021;22 (3 ):e119.33545067
+2 Tsang RW , Campbell BA , Goda JS , et al. Radiation therapy for solitary plasmacytoma and multiple myeloma: guidelines from the International Lymphoma Radiation Oncology Group. Int J Radiat Oncol Biol Phys. 2018;101 (4 ):794‐808.29976492
+3 Featherstone C , Delaney G , Jacob S , Barton M . Estimating the optimal utilization rates of radiotherapy for hematologic malignancies from a review of the evidence: part II‐leukemia and myeloma. Cancer. 2005;103 (2 ):393‐401.15593373
+4 Chow E , Harris K , Fan G , Tsao M , Sze WM . Palliative radiotherapy trials for bone metastases: a systematic review. J Clin Oncol. 2007;25 (11 ):1423‐1436.17416863
+5 Rudzianskiene M , Inciura A , Gerbutavicius R , et al. Single vs. multiple fraction regimens for palliative radiotherapy treatment of multiple myeloma: a prospective randomised study. Strahlenther Onkol. 2017;193 (9 ):742‐749.28573476
+6 Rades D , Hoskin PJ , Stalpers LJ , et al. Short‐course radiotherapy is not optimal for spinal cord compression due to myeloma. Int J Radiat Oncol Biol Phys. 2006;64 (5 ):1452‐1457.16413695
+7 Ong WL , Foroudi F , Milne RL , Millar JL . Variation in the use of single‐ versus multifraction palliative radiation therapy for bone metastases in Australia. Int J Radiat Oncol Biol Phys. 2020;106 (1 ):61‐66.31505246
+8 Batumalai V , Descallar J , Delaney GP , et al. Patterns of use of palliative radiotherapy fractionation for bone metastases and 30‐day mortality. Radiother Oncol. 2021;154 :299‐305.33217497
+9 Ong WL , Foroudi F , Milne RL , Millar JL . Are we choosing wisely in radiation oncology practice‐findings from an Australian population‐based study. Int J Radiat Oncol Biol Phys. 2019;104 (5 ):1012‐1016.30981834
+10 Ong WL , Ball DL , Milne RL , Foroudi F , Millar JL . Evolving practice pattern of palliative radiation therapy for bone metastases from lung cancer in Australia. Clin Oncol (R Coll Radiol). 2021;33 (12 ):e530‐e539.34366206
+11 Ong WL , Milne RL , Foroudi F , Millar JL . Changing pattern of radiation therapy for bone metastases in an Australian population‐based cohort of men with prostate cancer. Clin Genitourin Cancer. 2022;20 (1 ):e7‐e15.34366292
+12 Resende Salgado L , Chang S , Ru M , et al. Utilization patterns of single fraction radiation therapy for multiple myeloma. Clin Lymphoma Myeloma Leuk. 2019;19 (5 ):e238‐e246.30904388
+13 Palumbo A , Avet‐Loiseau H , Oliva S , et al. Revised International Staging System for Multiple Myeloma: a report from International Myeloma Working Group. J Clin Oncol. 2015;33 (26 ):2863‐2869.26240224
+14 Dimopoulos MA , Moreau P , Terpos E , et al. Multiple myeloma: EHA‐ESMO Clinical Practice Guidelines for diagnosis, treatment and follow‐up. Ann Oncol. 2021;32 (3 ):309‐322.33549387
+15 Goel U , Usmani S , Kumar S . Current approaches to management of newly diagnosed multiple myeloma. Am J Hematol. 2022;97 (Suppl 1):S3–S25.
+16 Usmani SZ , Quach H , Mateos MV , et al. Carfilzomib, dexamethasone, and daratumumab versus carfilzomib and dexamethasone for patients with relapsed or refractory multiple myeloma (CANDOR): updated outcomes from a randomised, multicentre, open‐label, phase 3 study. Lancet Oncol. 2022;23 (1 ):65‐76.34871550
+17 Ong WL , Foroudi F , Milne RL , Millar JL . Palliative radiotherapy for bone metastases at the end of life in Victoria. Med J Aust. 2021;214 (5 ):236‐237e1.33610136
+18 Glare P , Virik K , Jones M , et al. A systematic review of physicians' survival predictions in terminally ill cancer patients. BMJ. 2003;327 (7408 ):195‐198.12881260
+19 Fogarty T , Tacey M , McCorkell G , et al. Patterns of the use of advanced radiation therapy techniques for the management of bone metastases and the associated factors in Victoria. J Med Imaging Radiat Oncol. 2022. 10.1111/1754-9485.13381
+20 Gabriel G , Barton M , Delaney GP . The effect of travel distance on radiotherapy utilization in NSW and ACT. Radiother Oncol. 2015;117 (2 ):386‐389.26243679
+21 Punglia RS , Weeks JC , Neville BA , Earle CC . Effect of distance to radiation treatment facility on use of radiation therapy after mastectomy in elderly women. Int J Radiat Oncol Biol Phys. 2006;66 (1 ):56‐63.16814955
+22 Batumalai V , James M . Unwarranted variation in radiation therapy fractionation. J Med Imaging Radiat Oncol. 2022;66 (2 ):233‐241.35243787
+

PMC10084226.txt

@@ -0,0 +1,290 @@
+
+==== Front
+Hematol Oncol
+Hematol Oncol
+10.1002/(ISSN)1099-1069
+HON
+Hematological Oncology
+0278-0232
+1099-1069
+John Wiley and Sons Inc. Hoboken
+
+35794705
+10.1002/hon.3041
+HON3041
+Original Article
+Original Articles
+Network meta‐analysis of randomized trials in multiple myeloma: Efficacy and safety in frontline therapy for patients not eligible for transplant
+Botta Cirino 1 cirino.botta@unipa.it
+
+Gigliotta Emilia 1
+Paiva Bruno 2
+Anselmo Rita 1
+Santoro Marco 1
+Otero Paula Rodriguez 2
+Carlisi Melania 1
+Conticello Concetta 3
+Romano Alessandra 3
+Solimando Antonio Giovanni https://orcid.org/0000-0002-2293-9698
+4
+Cerchione Claudio 5
+Vià Matteo Da 6 7
+Bolli Niccolò 6 7
+Correale Pierpaolo 8
+Di Raimondo Francesco 3
+Gentile Massimo 9
+San Miguel Jesus 2
+Siragusa Sergio 1 sergio.siragusa@unipa.it
+
+1 Department of Health Promotion Mother and Child Care Internal Medicine and Medical Specialties University of Palermo Palermo Italy
+2 Clinica Universidad de Navarra CCUN Centro de Investigacion Medica Aplicada (CIMA) IDISNA, CIBERONC Pamplona Spain
+3 Division of Hematology Azienda Policlinico‐OVE University of Catania Catania Italy
+4 Guido Baccelli Unit of Internal Medicine Department of Biomedical Sciences and Human Oncology (DIMO) School of Medicine Aldo Moro University of Bari Bari Italy
+5 Hematology Unit IRCCS Istituto Romagnolo Per Lo Studio Dei Tumori (IRST) “Dino Amadori” Meldola FC Italy
+6 Department of Oncology and Hematology‐Oncology University of Milan Milan Italy
+7 Hematology Unit Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico Milan Italy
+8 Medical Oncology Unit Grand Metropolitan Hospital “Bianchi‐Melacrino‐Morelli” Reggio Calabria Italy
+9 Hematology Unit Department of Hemato‐Oncology Annunziata Hospital Cosenza Italy
+* Correspondence
+Cirino Botta and Sergio Siragusa, Department of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties, University of Palermo, Palermo 90127, Italy.
+Email: cirino.botta@unipa.it and sergio.siragusa@unipa.it
+
+11 7 2022
+12 2022
+40 5 10.1002/hon.v40.5 987998
+28 6 2022
+26 5 2022
+29 6 2022
+© 2022 The Authors. Hematological Oncology published by John Wiley & Sons Ltd.
+https://creativecommons.org/licenses/by/4.0/ This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
+
+Abstract
+
+The treatment scenario for newly‐diagnosed transplant‐ineligible multiple myeloma patients (NEMM) is quickly evolving. Currently, combinations of proteasome inhibitors and/or immunomodulatory drugs +/− the monoclonal antibody Daratumumab are used for first‐line treatment, even if head‐to‐head comparisons are lacking. To compare efficacy and safety of these regimens, we performed a network meta‐analysis of 27 phase 2/3 randomized trials including a total of 12,935 patients and 23 different schedules. Four efficacy/outcome and one safety indicators were extracted and integrated to obtain (for each treatment) the surface under the cumulative ranking‐curve (SUCRA), a metric used to build a ranking chart. With a mean SUCRA of 83.8 and 80.08 respectively, VMP + Daratumumab (DrVMP) and Rd + Daratumumab (DrRd) reached the top of the chart. However, SUCRA is designed to work for single outcomes. To overcome this limitation, we undertook a dimensionality reduction approach through a principal component analysis, that unbiasedly grouped the 23 regimens into three different subgroups. On the bases of our results, we demonstrated that first line treatment for NEMM should be based on DrRd (most active, but continuous treatment), DrVMP (quite “fixed‐time” treatment), or, alternatively, VRD and that, surprisingly, melphalan as well as Rd doublets still deserve a role in this setting.
+
+I line treatment
+multiple myeloma
+network meta‐analysis
+non‐transplant eligible
+principal component analysis
+Associazione Italiana per la Ricerca sul Cancro 10.13039/501100005010 source-schema-version-number2.0
+cover-dateDecember 2022
+details-of-publishers-convertorConverter:WILEY_ML3GV2_TO_JATSPMC version:6.2.7 mode:remove_FC converted:10.04.2023
+Botta C , Gigliotta E , Paiva B , et al. Network meta‐analysis of randomized trials in multiple myeloma: efficacy and safety in frontline therapy for patients not eligible for transplant. Hematol Oncol. 2022;40 (5 ):987‐998. 10.1002/hon.3041 35794705
+==== Body
+pmc1 INTRODUCTION
+
+Multiple myeloma (MM) is the second most common hematologic malignancy worldwide. 1 , 2 Current milestones of MM therapy include either a quadruple‐, triple‐ or double‐drug combination, based on proteasome inhibitors (PIs) and/or immunomodulatory drugs (IMiDs) plus dexamethasone plus the anti‐CD38 monoclonal antibody (mAb) Daratumumab, with or without chemotherapy. Eligible patients further undergo autologous stem cell transplantation and, eventually, consolidation therapy, while transplant ineligible patients (NEMM) enter follow‐up or maintenance therapy. However, virtually all patients relapse and require further treatments. 1 , 3 , 4 , 5 , 6 A plethora of new agents, including second‐generation PIs, histone deacetylase inhibitors, and monoclonal antibodies (mAbs), have shown consistent activity in prospective phase 2/3 clinical trials in relapsed/refractory MM (RRMM) patients and some of them are currently approaching the frontline setting. 4 In this scenario, current first line treatments for NEMM include the combination of daratumumab + bortezomib, melphalan and prednisone (DrVMP) or lenalidomide and dexamethasone (DrRd) in Europe, while melphalan‐free regimens such as Rd + bortezomib (VRD) or DrRd are the preferred regimens in the USA. 2 However, the lack of direct head‐to‐head comparisons between approved regimens and the recent introduction of monoclonal antibodies, further complicated the decision‐making regarding frontline strategy for NEMM. To overcome these limitations, we adopted an approach based on network meta‐analysis (NMA) (a recently introduced Bayesian statistical methodology that allows combining direct and indirect evidence to rank the different treatments according to their efficacy and safety 1 , 5 ), to identify regimens with the highest probability of being the most efficacious and safest in this setting.
+
+2 METHODS
+
+2.1 Search strategy
+
+Relevant publications have been identified through an electronic search of the main relevant databases including PubMed, Embase, Ovid, Cochrane, and proceedings from the major international meetings in hematology and oncology. The following search terms were used: “multiple myeloma”, “Clinical Trials”, “Phase III”, “Phase II”, “Randomized Controlled Trials”, “untreated”, “transplant ineligible”. All titles were screened and selected abstracts were reviewed. The related‐articles function, article references, and Google Scholar were also screened for other applicable publications and were used for searching related studies, abstracts, and citations. Published articles were considered for the analysis if written in English only. The last date of the search was 25 November 2021. A systematic review was performed according to the guidelines and recommendations from the preferred reporting items for systematic reviews and network meta‐analyses (PRISMA) checklist. 7
+
+2.2 Inclusion criteria
+
+Retrieved studies were included into the final analysis if the following criteria were met: (1) they had to involve NEMM (transplant not‐planned); (2) they should be randomized controlled trials, with or without blinding; (3) they could be abstracts, only if they sufficient information on study design, characteristics of participants, interventions, and outcomes were available; (4) they should include patients who received an unconventional or new regimen in the experimental arm, and a standard regimen in the control arm; (5) all trials should have been performed starting from the introduction of the so called “novel agents”: IMiDs and PI.
+
+2.3 Exclusion criteria
+
+Studies were excluded from the analysis if they were not comparative, if outcomes of interest were not reported, if the methodology was not clearly reported, if included patients eligible for autologous stem cell transplant (without non‐ASCT subgroup analyses) or relapsed after a frontline therapy.
+
+2.4 Data extraction and quality assessment
+
+Three reviewers (C.B., R.A. and E.G.) independently reviewed published literature according to the above predefined strategy and criteria. Each reviewer extracted from each selected study the following data: title and reference details (first author, year), study population characteristics (number of patients in study, number of patients in each treatment), type of interventions, and outcome data. For each trial, we evaluated hazard ratios (HRs) of progression‐free survival (PFS); overall survival (OS); odds ratio (OR) of overall response rate (ORR), complete response (CR); and risk ratio (RR) for safety (evaluation of the most common grade 3–4 toxicity). If the HR of survival curves was not reported, it was derived from the graph by using the method of Tierney et al. 8 All data were recorded independently in separate databases by all 3 reviewers and were compared just before the final analysis to limit selection bias. The final database was also reviewed an additional investigator (M.S.). Duplicates were removed and any disparity clarified.
+
+All the selected studies were assessed for quality according to the Cochrane Handbook for Systematic Reviews of Interventions, as described elsewhere 1 , 9 by computing a score based on the following items (1 point for each of them): method of randomization, allocation concealment, blindness, withdrawal or dropout, and adequacy of follow‐up. Visual inspection of funnel plots were used to assess the presence of publication bias.
+
+2.5 Network meta‐analysis
+
+We performed a NMA by using a Bayesian approach to compare the different therapeutic regimens simultaneously. The analysis was performed in STATA software by using the mvmeta package. Network meta‐analysis synthesizes data from a network of trials that involve multiple interventions and therefore, by integrating direct and indirect comparisons, has the potential to rank the treatments according to the outcome. Within the framework of NMA, we ranked the evaluated regimens based on survival outcomes (PFS and OS), treatment efficacy (ORR, CR), and safety (the most frequent grade 3–4 adverse event in each trial). For each outcome, we performed a NMA with an (RE) model by using a Markov chain Monte Carlo simulation technique with up to 30,000 iterations. Loop inconsistency and heterogeneity were assessed by evaluating the log of the ratio of 2 odds ratios (RoR) from direct and indirect evidence in the loop (ifplot command in STATA). 10 , 11 RoR values close to 0 indicate that both direct and indirect evidence are in agreement. Heterogeneity of the loop was then assessed through the restricted maximum likelihood method. 10 , 11 Relative effects of treatments are reported as HRs for survival outcomes (OS, PFS) and OR or RR for binary outcomes (ORR, CR and safety) along with corresponding 95% credible intervals, the Bayesian equivalent of 95% CIs. Ranking probabilities and surface under the cumulative ranking‐curve (SUCRA) were used to provide hierarchy probabilities. Highest SUCRA values (e.g., closer to 1) corresponded to a better position in the ranking of the treatment schedules. At the end of the analysis each of the treatment analyzed presented 5 different SUCRA scores, one for each endpoint. Beside ranking the treatments according to the mean of the different SUCRAs, we performed a dimensionality reduction through principal component analysis method in R (prcomp command) and grouped the treatments with the cluster package by an unsupervised automatic clustering according to similarities in outcomes results. 12 , 13 This allowed us to identify clusters of regimens with similar profiles of efficacy/safety rather than the “best” treatment.
+
+3 RESULTS
+
+3.1 Study selection and quality assessment
+
+As shown in the PRISMA flow chart in Figure 1, with our search strategy we retrieved a total of 2579 studies. Of them, 27 studies, including a total of 12,935 patients were included in the final analysis (Table 1). 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 Almost all the trials included all the variables necessary to perform the whole analysis, and all the missing information where retrieved from other meta‐analysis, calculated from reported data, or obtained from updated analyses (e.g., OS data were often presented when a longer follow‐up was available). 46 , 47 , 48 , 49 All the trials selected presented data for PFS, OS, ORR, CR and safety analysis and were included in the NMA. In Supplementary Figure 1A are reported the data regarding the quality assessment: most of the study were reported as low risk in the majority of the evaluated criteria according to Cochrane guidelines. Additionally, the funnel plot in Supplementary Figure 1B confirmed the absence of publication biases.
+
+FIGURE 1 PRISMA (preferred reporting items for systematic reviews and meta‐analyses) flow chart reporting the whole work‐flow that lead to final study identification and selection
+
+TABLE 1 This table summarizes the main characteristics of all the studies included in the network meta‐analysis (NMA)
+
+Trial	Year	Treatments	Patients	Most frequent G3‐4 AE	
+Facon/IFM 99‐06	2007	MPT/MP	321	Neutropenia	
+Palumbo	2008	MPTT/MP	331	Cytopenia	
+Hulin/IFM 01/01	2009	MPT/MP	229	Neutropenias	
+Waage	2009	MPTT/MP	357	Neutropenia	
+Ludwig	2009	TD/MP	288	Infections/Leukopenia	
+Beksac	2010	MPTT/MP	115	Cytopenia	
+Wijermans/Hovon49	2010	MPTT/MP	344	Infections	
+Mateos/Vista	2010	VMP/MP	682	Neutropenia	
+Palumbo	2010	VMPT/VMP	511	Neutropenia	
+Morgan/MRC myeloma IX	2011	CTD/MP	849	Cytopenia/Infections	
+Sacchi	2011	MPT/MP	118	Neutropenia	
+Palumbo/MM‐015	2012	MPRR/MPR/MP	459	Neutropenia	
+San Miguel	2013	VMPS/VMP	106	Neutropenia	
+Mateos/GEM2005	2014	VMP/VTP	260	Neutropenia	
+Hungria	2015	MPTT/TD/CTD	82	Neutropenia/Neuropathy	
+Keith Stewart/E1A06	2015	MPRR/MPTT	298	Neutropenia	
+Niesvizky/UPFRONT	2015	VD/VTD/VMP	502	Neuropathy	
+Magarotto	2016	MPR/CPR/RD9	662	Neutropenia	
+Zweegman	2016	MPRR/MPTT	637	Neutropenia	
+Durie/SWOGS0777	2016	VRD/RD	471	Neutropenia	
+Facon/FIRST	2018	MPT/RD/RD18	1623	Neutropenia/Infections	
+Mateos/ALCYONE	2018	VMPDr/VMP	706	Neutropenia	
+Facon/MAIA	2018	DrRD/RD	737	Neutropenia	
+Usmani/Keynote185	2018	PRD/RD	301	Neutropenia	
+Facon/CLARION	2019	KMP/VMP	955	Neutropenia	
+Facon/Tourmaline‐MM2	2021	IRD/RD	705	Neutropenia	
+Puig/CLARIDEX	2021	ClRD/RD	286	Infections	
+Abbreviations: ClRD, Clarithromycin + RD; CPR, Rd, cyclophosphamide/lenalidomide/prednisone; CTD, cyclophosphamide/thalidomide/dexamethasone; DrRd, Rd + Daratumumab; IRD, ixazomib + RD; KMP, Carfilzomib + MP; KMP, MP + carfilzomib; MP, melphalan/prednisone; MPT, MP + thalidomide; MPT‐T, MPT followed by thalidomide maintenance; MPR, MP + lenalidomide; MPR‐R, MPR followed by lenalidomide maintenance; PRd, Rd + pembrolizumab; TD, thalidomide/dexamethasone; VD, bortezomib/dexamethasone; VMP, Rd‐18, Rd for 18 months; Rd‐9, Rd for 9 months followed by R maintenance; VRD, VTD, VD + thalidomide; VRD, DrVMP, VMP + Daratumumab; VMPT, bortezomib‐melphalan‐prednisone‐thalidomide with bortezomib‐thalidomide maintenance; VMPS, VMP + siltuximab.
+
+No significant inconsistency or loop‐specific heterogeneity were found in our NMA (data not shown).
+
+3.2 Quadruplet and mAbs containing‐regimens consistently improve patients' outcome
+
+Figure 2A shows the network of comparisons between all regimens evaluated. We identified a total of 23 different treatment arms/regimens (namely: thalidomide/dexamethasone (TD), melphalan/prednisone (MP), bortezomib/dexamethasone (VD), Rd, cyclophosphamide/lenalidomide/prednisone (CPR), MP + thalidomide (MPT), MPT followed by thalidomide maintenance (MPT‐T), MP + lenalidomide (MPR), MPR followed by lenalidomide maintenance (MPR‐R), VMP, Rd for 18 months (Rd18), Rd for 9 months followed by R maintenance (Rd9), MP + carfilzomib (KMP), VRD, VD + thalidomide (VTD), bortezomib/thalidomide/prednisone (VTP, which being part of the group of VT + steroids we aggregated with VTD) cyclophosphamide/thalidomide/dexamethasone (CTD), VMP + Daratumumab (DrVMP), bortezomib‐melphalan‐prednisone‐thalidomide with bortezomib‐thalidomide maintenance (VMPT), Rd + Daratumumab (DrRd), VMP + siltuximab (VMPS), Rd + pembrolizumab (PRd), Clarithromycin + RD (ClRD), ixazomib + RD (IRD)) to be compared (as reported in Table 1), linked by nine triangular loops.
+
+FIGURE 2 (A) Network plot of all treatment groups evaluated in the network meta‐analysis (NMA) for all the efficacy and safety endpoints. The size is proportional to the numbers of patients included in the analysis for each group and each connection represents the existence of direct comparisons data. (B) Effect estimates of the treatment in terms of progression‐free survival (PFS) and safety by using MP (melphalan prednisone) arm as comparator. Thalidomide/dexamethasone (TD), melphalan/prednisone (MP), bortezomib/dexamethasone (VD), Rd, cyclophosphamide/lenalidomide/prednisone (CPR), MP + thalidomide (MPT), MPT followed by thalidomide maintenance (MPT‐T), MP + lenalidomide (MPR), MPR followed by lenalidomide maintenance (MPR‐R), VMP, Rd for 18 months (Rd‐18), Rd for 9 months followed by R maintenance (Rd‐9), MP + carfilzomib (KMP), VRD, VD + thalidomide (VTD), cyclophosphamide/thalidomide/dexamethasone (CTD), VRD, VMP + Daratumumab (DrVMP), bortezomib‐melphalan‐prednisone‐thalidomide with bortezomib‐thalidomide maintenance (VMPT), Rd + Daratumumab (DrRd), VMP + siltuximab (VMPS), Rd + pembrolizumab (PRd), Carfilzomib + MP (KMP), Clarithromycin + RD (ClRD), ixazomib + RD (IRD)
+
+Each group was subsequently compared against all other groups through a Bayesian NMA, and efficacy results for PFS and safety, using the MP regimen as comparator, are shown in Figure 1B in terms of HRs and credibility intervals (efficacy results in terms of OS, ORR, CR are shown in supplemental Figure 2A). Unsurprisingly, most modern regimens including DrRd, DrVMP and VRD, performed significantly better in terms of PFS as compared to all the other analyzed regimens, while RD(9) and CPR ranged among the worst regimens. Interestingly, DrRD, DrVMP, VRD, IRD, VMPT and RD reached a significant advantage against MP by using the most statistically restrictive “credibility intervals” from NMA. Similar results were obtained for the other efficacy endpoints with quadruplets regimens always reporting the better results (often reaching the statistical significance against MP) (Supplementary Figure 2A).
+
+Regarding safety, regimens combining melphalan and lenalidomide delivered the highest toxicity to patients, while other regimens failed to demonstrate important differences.
+
+3.3 DrRD and DrVMP could guarantee the best outcome for NEMM
+
+Network meta‐analysis has the possibility to calculate the probability of each regimen evaluated of being the best or the worst as well as the probable “position” within a ranking of all regimens. In Figure 3A the probability distribution of being the regimen placed at the “x” position in the PFS rank is showed. DrRd has a 58.6% probability of being the best regimen according to this outcome, immediately followed by DrVMP (25.3%) and VRD (9.7%). Figure 3B, which reports the cumulative probabilities, confirmed these results: indeed, in the “PFS” panel (left) the previously mentioned regimens were the first to reach the 100% cumulative probability, and were strongly separated from the other studied schedules. Regarding the safety panels (on the right), accordingly to what observed in the interval plots, no clear separation could be observed within this graph (all regimens reach the 100% cumulative probability in the late/right part of the graph) with the exception of melphalan/lenalidomide containing regimens, which were the worst schedules as demonstrated by the fact that were the last two reaching the top of the graph.
+
+FIGURE 3 (A) Heatmap reporting the ranking probability of each regimen included in the meta‐analysis. The green color represents the highest probability of being in that position of the ranking chart, while the red represents the lowest probability. (B) Cumulative probability of being the nth in the ranking chart with respect to progression‐free survival (PFS) (left) or safety (right). The soonest the curve reaches the 100%, the highest is the probability of being better according to the endpoint analyzed
+
+Finally, we investigated which regimen, among all regimens included in the NMA, scores as the overall best regimen. To find this answer, we determined the SUCRA values for PFS, OS, ORR, CR, and safety and estimated an average value to rank all the treatments options included in our analysis (Figure 4A). According to average SUCRA values, the DrVMP regimen achieved the highest score (average SUCRA: 83.8) closely followed by DrRd (80.08) (which is better than DrVMP in every field with the exception of safety), VRD (79.94) and IRD (78.94). It should be noted that the top two regimens were Daratumumab based triplets, and that three out of five top regimens are based on Rd backbone.
+
+FIGURE 4 (A) Heatmap reporting the surface under the cumulative ranking‐curve (SUCRA) for each endpoint analyzed for each treatment schedule included in the analysis, ordered according to the mean SUCRA score (from the highest to lowest). (B) Principal component analysis reporting all the regimens analyzed grouped (unsupervised clustering) according to their SUCRA profile (the most similar are the SUCRA scores for each endpoint, the closest are the schedules within the picture). Thalidomide/dexamethasone (TD), melphalan/prednisone (MP), bortezomib/dexamethasone (VD), Rd, cyclophosphamide/lenalidomide/prednisone (CPR), MP + thalidomide (MPT), MPT followed by thalidomide maintenance (MPT‐T), MP + lenalidomide (MPR), MPR followed by lenalidomide maintenance (MPR‐R), VMP, Rd for 18 months (Rd‐18), Rd for 9 months followed by R maintenance (Rd‐9), MP + carfilzomib (KMP), VRD, VD + thalidomide (VTD), cyclophosphamide/thalidomide/dexamethasone (CTD), VRD, VMP + Daratumumab (DrVMP), bortezomib‐melphalan‐prednisone‐thalidomide with bortezomib‐thalidomide maintenance (VMPT), Rd + Daratumumab (DrRd), VMP + siltuximab (VMPS), Rd + pembrolizumab (PRd), Carfilzomib + MP (KMP), Clarithromycin + RD (ClRD), ixazomib + RD (IRD)
+
+3.4 PCA analysis identified the best regimens according to needed outcomes
+
+To overcome the limit of using a simple and not weighted “average” of the SUCRA score, we applied a dimensionality reduction approach known as “principal component analysis,” PCA, to distribute in a plane all the 23 evaluated regimens. The distance between each point depends upon the difference in the “profile” of SUCRA scores. By using this approach we were able to unbiasedly cluster all the evaluated regimens into three different groups (Figure 4B and Supplementary Figure 2B): (1) DrRd, DrVMP, VRD, IRD, VMPT and Rd as the preferred regimens to be used for first line approach (the most important determinants of this group were all the efficacy outcome as reported in Supplementary Figure 2B); of note, DrRd appears to be separated from other regimens (maybe due to the better results obtained in all the efficacy endpoints), while DrVMP and VRD are very close, underscoring the similarity of outcome obtained with both regimens; (2) 10 regimens (VMP, VTD, VD, Rd(18), MPR_R, MPT, VMPS, ClRD, PRD, KMP) to be considered as potentially alternative regimens when the ones of first group are not available; and (3) seven regimens (MPR, MPT‐T, MP, CPR, TD, RD(9), CTD) with the lowest probability of being beneficial in frontline.
+
+3.5 MRD assessment further support NMA results
+
+Currently, the absence of detectable minimal residual disease (MRD), especially if sustained, is considered the best surrogate marker of OS. 50 Along this line we retrieved the rates of MRD negativity in each study that investigated/disclosed this endpoint. Unfortunately, 4 studies only reported these results (Table 2). Interestingly, both DaraRD and DaraVMP reported similar MRD negativity rates, a result that further supports the conclusion of our NMA. No data regarding the SWOG5077, and specifically, the VRD regimen, were reported in any other study on NEMM patients.
+
+TABLE 2 The methodologies and the results of minimal residual disease (MRD) determination are reported in this table
+
+	Treatments	MRD undetectable	Method	
+ALCYONE	DrVMP versus VMP	28% versus 7%	Adaptive Biotechnologies clonoSEQ assay	
+MAIA	DrRD versus RD	24.2% versus 7.3%	Adaptive Biotechnologies clonoSEQ assay	
+CLARION	KMP versus VMP	7.9% versus 7.8%*	NGF	
+CLARIDEX	ClRD versus RD	2.8% versus 3.5%*	NGF	
+Note: Unfortunately for 4 trials only these results are available.
+
+Abbreviations: MRD, minimal residual disease; NGF, next generation flow cytometry.
+
+4 DISCUSSION
+
+The landscape of first line treatment for NEMM has dramatically changed over the past 20 years. 2 , 4 Starting from the introduction of the first PIs and IMiDs, the increase in the knowledge of immunological and biological determinants of myeloma evolution, 3 , 4 , 51 , 52 , 53 enriched the clinical scenario of new schedules and molecules, including the recently approved monoclonal antibodies (the anti‐CD38 daratumumab and isatuximab). Unfortunately, the lack of head‐to‐head comparisons between the regimens considered as standard of care, complicates the therapeutic decision making. On these bases, the aim of our study was to systematically review and compare the activity and safety of new regimens including three or four drugs as well as novel agents such as mAbs, investigated in NEMM since the introduction of PIs or IMiDs. To this end, we believe that Bayesian NMAs are the best tool for exploring the strength of evidence for regimens that have not undergone direct comparison. 10 Indeed, this NMA by ranking treatments according to several activity and safety markers, could facilitate the decision making in the transplant‐ineligible MM setting, taking into account that the “clinical” environment (including patients' willingness) should be carefully considered before treatment selection. Accordingly, we demonstrated, by merging the results of 27 different trials, that regimens including daratumumab perform better in term of every efficacy endpoints, bringing an acceptable safety profile, a result further underscored by ranking regimens according to the “average” SUCRA score. Interestingly, three out of four of the “better” regimens were triplets including the Rd backbone plus a PI or a mAb. Surprisingly, while performing better in each efficacy endpoint, the overall mean SUCRA of DrRd was lower than the one achieved by the quadruplet DrVMP (80.08 vs. 83.8, respectively). This latter point underline a major limitation of NMA: this approach could rank treatments according to one specific end‐point only, and an “average” score, by mixing results obtained in different aspects, could not be able to capture the overall efficacy/safety profile of a regimen. 5 On these bases, we used a dimensional reduction approach (principal component analysis) and the k‐means derived algorithm partitioning around medoids to group the different treatments according to their efficacy and safety profiles. 13 Therefore, we obtained three groups: one efficacy‐driven group, a second “alternative” group and a third “bad” group which includes schedules considered neither the safest nor the most effective. On these bases we considered DrRD, DrVMP and VRD as the preferred regimens to be used in NEMM, with the option to consider VMPT, IRD or even the doublet Rd as reasonable alternatives. Among the alternative regimens, VMP, KMP or even the double VD could be still considered for selected patients. These results have a substantial relevance in the decision‐making algorithm for the treatment of these patients, especially if we take into account that DrRd regimen is not the absolute “winner”. Indeed, the choice between the Dara‐containing regimens or the VRD triplet should take into account different points: (1) according to the registrative clinical trials, the median PFS were about 60, 36 and 41 months for DrRd, DrVMP and VRD respectively 18 , 19 , 20 , 21 , 54 ; of note, the long PFS registered for VRD within the SWOG5077 trial is affected by the high percentage of transplant eligible patients enrolled in the trial, nevertheless, we decided to include it in the whole analysis due to the fact that this schedule is currently approved in the NEMM setting based on the results of this trial. However, the PFS estimation of 35 months, observed in a recent phase 2 study exploring a modified VRD combination for NEMM, potentially represents a more realistic result. 55 (2) No clear differences could still be observed in OS between the 3 regimens 18 , 19 , 20 , 21 , 54 ; this event could be due to the fact that the appearance of lenalidomide resistance reduces the PFS2 of MM patients, 5 negatively affecting OS of these groups, or by the fact that subsequent treatment lines could compensate the initial difference among these regimens. (3) The achievement of an (sustained) undetectable MRD state is considered the best surrogate marker of OS. 50 Accordingly, both DrRd and DrVMP, while reporting notable differences in term of PFS (but still not in os), achieved similar rates of undetectable MRD, a result in line with the conclusion of our NMA. Furthermore, a recent study on pooled patients from MAIA and ALCYONE trials demonstrated that daratumumab significantly increases the probability of achieving a sustained (>12 months) MRD negativity status and that this significantly improves both PFS and PFS2. Interestingly, despite obtaining a higher percentage of MRD negativity at 12 months (14 vs. 10.9%, DrVMP vs. DrRd respectively), MM patients treated with a (quite) fixed duration treatment (DrVMP/VMP) lose the “long time” effect which could be observed with the continuous lenalidomide‐based regimens (DrRd/RD) (at the price of an increased overall toxicity), while retaining the advantage of a better PFS2. 56 It is therefore of utmost importance, to discuss with the patients about schedule‐specific administrations rules. Indeed, lenalidomide and Daratumumab are administered until disease progression while bortezomib is discontinued after nine treatment courses in DrVMP and after 15 cycles in VRD lite (or eight cycles in VRD standard). 55
+
+Currently, no data about the possible best “sequencing” options are available. Additionally, due to the unavoidable increase in the use of Daratumumab‐based regimens, most patients will be daratumumab/lenalidomide double refractory at the beginning of second line of treatment, thus representing an emerging medical need. Taking into account that we have no data on the possibility of continuing the treatment with an anti‐CD38 mAb after progression (we could consider isatuximab‐based combinations after holding the anti‐CD38 for one line, i.e., we should wait a second relapse), pomalidomide/bortezomib/dexamethasone or carfilzomib/dexamethasone combinations are the best therapeutic options for these patients. 1 , 5 On the other side, for patients progressing after a DrVMP regimen, the combination of carfilzomib, lenalidomide and dexamethasone represent a valuable option. 1 On these bases, we could start to imagine a chemo‐free treatment history for myeloma patients, where immunotherapy (IMiDs, bispecific agents, CAR‐T) 57 as well as drugs able to elicit a strong autologous immune response (immunogenic cell death inducers, such as bortezomib or innovative target drugs such as STING agonists, hypomethylating agents or cancer vaccines) 52 will be combined to achieve long and sustained responses with minimal toxicities.
+
+In the last 10 years several NMA in this field have been published, 46 , 47 , 58 , 59 , 60 , 61 , 62 each of them with its own limitations which reflect the fact that this method could not completely replace a randomized clinical trial. Anyway, the most recent ones are in line with the results of our NMA, where the addition of Daratumumab to the previous standard‐of‐care RD and VMP should be considered as the preferred regimen in NEMM, a result further supported by the achievement of similar results in term of MRD negativity. 50
+
+Our work presents some limitations that should be carefully taken into account: first, all data were retrieved or calculated from published studies rather than from individual patients'; second, potential biases can be produced by the heterogeneity of the agents, patient populations as well as the long timeframe included in the analysis: to reduce this factor, we tried to limit the timeframe to the latest 20 years, that is, from the introduction of modern drugs (IMiDs and PIs). Finally, this work should be considered a snapshot of current evidence that could quickly evolves with the introduction of new drugs in the frontline setting.
+
+5 CONCLUSION
+
+Overall, this is, to our knowledge, the first NMA which use a dimensionality reduction approach to group treatments according to their efficacy/safety profiles, thus overcoming the limitation of NMA of being endpoint specific. Finally, our work supports a multiparametric approach in the decision‐making of the first line therapy for NEMM patients: indeed, while the updated results of MAIA trial showed impressive results in term of PFS for the DrRd combination, our results demonstrated a substantial evidence‐based overlap between Daratumumab‐based regimens (DrRd/DaraVMP: no differences in term of OS/MRD) and further support the use of VRD (especially for less fit patients) for the frontline treatment of NEMM patients.
+
+AUTHOR CONTRIBUTIONS
+
+Cirino Botta conceived and designed the study; Cirino Botta, Emilia Gigliotta, Rita Anselmo and Marco Santoro acquired and revised the data; Sergio Siragusa, Jesus San Miguel, Massimo Gentile, Pierpaolo Correale and Bruno Paiva supervised the study; Cirino Botta, Emilia Gigliotta and Marco Santoro did the statistical analysis; Paula Rodriguez Otero, Melania Carlisi, Concetta Conticello, Alessandra Romano, Antonio Giovanni Solimando, Claudio Cerchione, Matteo Da Vià, Francesco Di Raimondo and Niccolò Bolli read different drafts over the development of the whole work giving important analytical suggestions which have been necessary for reaching the final results; Cirino Botta, Emilia Gigliotta and Sergio Siragusa wrote the final draft. All authors read and approved the final draft.
+
+CONFLICT OF INTEREST
+
+The author declares they have no conflict of interest.
+
+TRANSPARENT PEER REVIEW
+
+The peer review history for this article is available at https://publons.com/publon/10.1002/hon.3041.
+
+Supporting information
+
+Supporting Information S1
+
+Click here for additional data file.
+
+ACKNOWLEDGMENTS
+
+This work was supported by grants from the Italian Association for Cancer Research (AIRC) within the My First AIRC Grant 2020 (n. 24534, 2021/2025) PI: CB.
+
+Open Access Funding provided by Universita degli Studi di Palermo within the CRUI‐CARE Agreement.
+
+DATA AVAILABILITY STATEMENT
+
+Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.
+==== Refs
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+54 Durie BGM , Hoering A , Sexton R , et al. Longer term follow‐up of the randomized phase III trial SWOG S0777: bortezomib, lenalidomide and dexamethasone vs. lenalidomide and dexamethasone in patients (Pts) with previously untreated multiple myeloma without an intent for immediate autologous stem cell transplant (ASCT). Blood Cancer J. 2020;10 (5 ):53. 10.1038/s41408-020-0311-8 32393732
+55 Okazuka K , Ishida T , Nashimoto J , et al. The efficacy and safety of modified bortezomib‐lenalidomide‐dexamethasone in transplant‐eligible patients with newly diagnosed multiple myeloma. Eur J Haematol. 2020;104 (2 ):110‐115. 10.1111/ejh.13349 31733155
+56 San‐Miguel J , Avet‐Loiseau H , Paiva B , et al. Sustained minimal residual disease negativity in newly diagnosed multiple myeloma and the impact of daratumumab in MAIA and ALCYONE. Blood. 2022;139 (4 ):492‐501. 10.1182/blood.2020010439 34269818
+57 Botta C , Mendicino F , Martino EA , et al. Mechanisms of immune evasion in multiple myeloma: open questions and therapeutic opportunities. Cancers (Basel). 2021;13 (13 ):3213. 10.3390/cancers13133213 34203150
+58 Kiss S , Gede N , Soos A , et al. Efficacy of first‐line treatment options in transplant‐ineligible multiple myeloma: a network meta‐analysis. Crit Rev Oncol Hematol. 2021;168 :103504. 10.1016/j.critrevonc.2021.103504 34673218
+59 Gil‐Sierra MD , Gimeno‐Ballester V , Fenix‐Caballero S , Alegre‐Del Rey EJ . Network meta‐analysis of first‐line treatments in transplant‐ineligible multiple myeloma patients. Eur J Haematol. 2020;105 (1 ):56‐65. 10.1111/ejh.13407 32145104
+60 Sekine L , Ziegelmann PK , Manica D , et al. Upfront treatment for newly diagnosed transplant‐ineligible multiple myeloma patients: a systematic review and network meta‐analysis of 14, 533 patients over 29 randomized clinical trials. Crit Rev Oncol Hematol. 2019;143 :102‐116. 10.1016/j.critrevonc.2019.07.001 31563077
+61 Kuhr K , Wirth D , Srivastava K , Lehmacher W , Hellmich M . First‐line therapy for patients with multiple myeloma: direct and indirect comparison of treatment regimens on the existing market. Value Health. 2014;17 (7 ):A617. 10.1016/j.jval.2014.08.2179
+62 Giri S , Aryal MR , Yu H , et al. Efficacy and safety of frontline regimens for older transplant‐ineligible patients with multiple myeloma: a systematic review and meta‐analysis. J Geriatr Oncol. 2020;11 (8 ):1285‐1292. 10.1016/j.jgo.2020.05.013 32513568
+

PMC10084276.txt

@@ -0,0 +1,328 @@
+
+==== Front
+Hematol Oncol
+Hematol Oncol
+10.1002/(ISSN)1099-1069
+HON
+Hematological Oncology
+0278-0232
+1099-1069
+John Wiley and Sons Inc. Hoboken
+
+35653225
+10.1002/hon.3038
+HON3038
+Original Article
+Original Articles
+Isatuximab plus carfilzomib and dexamethasone versus carfilzomib and dexamethasone in elderly patients with relapsed multiple myeloma: IKEMA subgroup analysis
+Facon Thierry 1 thierry.facon@chru-lille.fr
+
+Moreau Philippe 2
+Martin Thomas G. 3
+Spicka Ivan 4
+Oriol Albert 5
+Koh Youngil 6
+Lim Andrew 7
+Mikala Gabor 8
+Rosiñol Laura 9
+Yağci Münci 10
+Cavo Michele 11
+Yong Kwee 12
+Risse Marie‐Laure 13
+Asset Gaëlle 14
+Schwab Sandrine 14
+Martinez Gracia 15
+1 Department of Haematology Lille University Hospital Lille France
+2 University of Nantes Nantes France
+3 University of California San Francisco San Francisco California USA
+4 Departments of Medicine and Hematology First Faculty of Medicine Charles University and General Hospital Prague Czech Republic
+5 Hematology Department Institut Català d’Oncologia and Josep Carreras Institute Hospital Germans Trias i Pujol Barcelona Spain
+6 Seoul National University Hospital Seoul South Korea
+7 Austin & Repatriation Medical Center Heidelberg Victoria Australia
+8 Department of Hematology and Stem Cell Transplantation National Institute for Hematology and Infectious Diseases South Pest Central Hospital Budapest Hungary
+9 Hospital Clinic IDIBAPS Barcelona Spain
+10 Gazi University Ankara Turkey
+11 IRCCS Azienda Ospedaliero‐Universitaria di Bologna “Seràgnoli” Institute of Hematology Bologna University School of Medicine Bologna Italy
+12 Department of Haematology University College Hospital London UK
+13 Sanofi R&D Vitry‐Sur‐Seine France
+14 Sanofi R&D Chilly‐Mazarin France
+15 Hospital das Clínicas de São Paulo São Paolo Brazil
+* Correspondence
+Thierry Facon, Department of Haematology, Lille University Hospital, 2 Avenue Oscar Lambret, 59000 Lille, France.
+Email: thierry.facon@chru-lille.fr
+
+08 6 2022
+12 2022
+40 5 10.1002/hon.v40.5 10201029
+27 5 2022
+15 2 2022
+31 5 2022
+© 2022 The Authors. Hematological Oncology published by John Wiley & Sons Ltd.
+https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.
+
+Abstract
+
+In this subgroup analysis of the randomized, Phase 3 IKEMA study (NCT03275285), we evaluated efficacy and safety of the anti‐CD38 monoclonal antibody isatuximab (Isa) in combination with carfilzomib‐dexamethasone (Isa‐Kd) versus Kd in older (≥70 years of age, n = 86) and younger (<70 years, n = 216) patients with relapsed multiple myeloma (MM). Patients received Isa 10 mg/kg intravenously weekly for 4 weeks, then every 2 weeks in the Isa‐Kd arm, and approved schedule of carfilzomib (twice weekly) and dexamethasone in both study arms. Primary endpoint was progression‐free survival (PFS); key secondary efficacy endpoints included rates of overall response (ORR), very good partial response or better (≥VGPR), minimal residual disease negativity (MRD–), and complete response (CR). Addition of Isa to Kd resulted in improved PFS in elderly patients (hazard ratio, 0.36 [95% CI, 0.18–0.75]) consistent with the significant PFS improvement observed in the overall IKEMA population. Treatment with Isa‐Kd improved depth of response versus Kd, with higher rates of ≥VGPR (73.1% vs. 55.9%), MRD– (23.1% vs. 11.8%), and CR (38.5% vs. 23.5%). Although the incidence of grade ≥3 treatment‐emergent adverse events (TEAEs) was higher in Isa‐Kd, the incidence of serious TEAEs was similar between arms. Fewer elderly patients definitively discontinued treatment due to TEAEs in Isa‐Kd than Kd: 11.8% versus 23.5%. In conclusion, Isa‐Kd provides a consistent benefit versus Kd in elderly patients, with a manageable safety profile, and represents a new treatment option for patients with relapsed MM, independent of age.
+
+CD38
+elderly
+isatuximab
+monoclonal antibody
+multiple myeloma
+Sanofi 10.13039/100004339 source-schema-version-number2.0
+cover-dateDecember 2022
+details-of-publishers-convertorConverter:WILEY_ML3GV2_TO_JATSPMC version:6.2.7 mode:remove_FC converted:10.04.2023
+Facon T , Moreau P , Martin TG , et al. Isatuximab plus carfilzomib and dexamethasone versus carfilzomib and dexamethasone in elderly patients with relapsed multiple myeloma: IKEMA subgroup analysis. Hematol Oncol. 2022;40 (5 ):1020‐1029. 10.1002/hon.3038 35653225
+==== Body
+pmc1 INTRODUCTION
+
+Multiple myeloma (MM) often affects older patients, as it is most frequently diagnosed in 65 to 74‐year‐old subjects, with a median age of approximately 69–70 years 1 , 2 The increase in life expectancy currently being achieved in many countries contributes to the observed increase in the global elderly population of patients with MM. 3 Older patients represent a heterogeneous population, which may present with comorbidities, a reduced functional status, and an increased risk of frailty potentially affecting therapeutic outcomes. 4 , 5 , 6
+
+Although treatment with proteasome inhibitors (PIs) and immunomodulatory agents (IMiDs) provides benefit to MM patients, new therapeutic approaches are still needed for older as well as younger MM patients. 5 , 6 Anti‐CD38 antibody therapy with isatuximab (Isa) in combination with an IMiD or a PI and low‐dose dexamethasone represents such an option for patients with relapsed and/or refractory MM (RRMM). 7 , 8 , 9 , 10 , 11 , 12 , 13 Isa (an IgG1 monoclonal antibody) binds to a specific CD38 epitope and exerts anti‐myeloma activity through multiple mechanisms, which is enhanced by combination with IMiD and PI agents. 7 , 8 Based on the Phase 3 ICARIA‐MM study, Isa is approved in a number of countries in combination with pomalidomide and dexamethasone (Pd) for patients with RRMM (≥2 prior treatment lines). 9 , 12 , 13 Furthermore, to date, Isa in combination with carfilzomib and dexamethasone (Kd) is approved in the United States for patients with relapsed MM (≥1–3 prior lines), in the European Union for patients with MM who have received ≥1 prior therapy, and in Japan for patients with RRMM after one prior therapy, based on a prespecified interim analysis of the IKEMA study. 11 , 12 , 13 , 14
+
+IKEMA was a randomized, multi‐national, parallel‐group, Phase 3 study that evaluated treatment with Isa in combination with Kd versus Kd in patients with relapsed MM. 11 Isa‐Kd significantly improved progression‐free survival (PFS) compared with Kd (hazard ratio [HR], 0.53; 99% confidence interval [CI], 0.32–0.89; one‐sided p = 0.0007), with clinically meaningful increases in the rates of very good partial response (VGPR) or better (72.6% vs. 56.1%), minimal residual disease negativity (MRD–, 29.6% vs. 13.0%), and complete response (CR, 39.7% vs. 27.6%), in the intent‐to‐treat (ITT) population, as well as a manageable safety profile. 11
+
+As the median age of patients with MM is ∼70 years and survival with conventional therapies is still limited in the older adult population, 1 , 4 in this subgroup analysis of IKEMA, we evaluated efficacy and safety of treatment with Isa‐Kd versus Kd in elderly MM patients ≥70 years of age as well as in younger MM patients (<70 years of age).
+
+2 METHODS
+
+2.1 Study design, patients, and treatment
+
+IKEMA (NCT03275285) was a prospective, randomized, open‐label, active‐controlled, Phase 3 study conducted in 16 countries at 69 study centers. Detailed inclusion and exclusion criteria were reported previously. 11 Briefly, eligible adults had relapsed MM following 1–3 prior treatment lines, with measurable evidence of disease (serum M‐protein ≥0.5 g/dl and/or urine M‐protein ≥200 mg/24 h). Patients with an estimated glomerular filtration rate ≥15 ml/min/1.73 m2 or prior pulmonary comorbidities (i.e., chronic obstructive pulmonary disease) were eligible. Patients were excluded if they had primary refractory MM or serum free‐light chain measurable disease only, were refractory to anti‐CD38 antibody therapy, had received prior carfilzomib therapy, or had a left ventricular ejection fraction <40%.
+
+Patients were randomized 3:2 to receive Isa‐Kd (n = 179) or Kd (n = 123). In the Isa‐Kd arm, patients received Isa 10 mg/kg intravenously (IV) weekly (QW) for 4 weeks, then every 2 weeks (Q2W). Patients in both arms received approved schedules of Kd with carfilzomib 20/56 mg/m2, as described. 11
+
+The study protocol was approved by the Institutional Ethics Committee or independent review board for each center; the study was conducted following the Declaration of Helsinki and IHC Guidelines for Good Clinical Practice. All patients provided written informed consent.
+
+2.2 Study endpoints and assessments
+
+Primary study endpoint was PFS, as determined by a blinded independent response committee (IRC), which evaluated response and disease progression according to the International Myeloma Working Group (IMWG) response criteria, 15 measured by central radiological evaluation, central laboratory M‐protein quantification, and local bone marrow aspiration when needed.
+
+Key secondary efficacy endpoints included rates of overall response (ORR), ≥VGPR, MRD−, and CR. MRD was assessed in patients with ≥VGPR, by central laboratory next‐generation sequencing (minimum sensitivity: 1/105 nucleated cells) (Adaptive clonoSEQ Assay, Adaptive Biotechnologies, Seattle, WA). M‐protein was assessed each cycle/day 1, at end of treatment, and monthly in follow‐up of patients with no progression at end of treatment. Bone lytic disease was assessed once/year and existing baseline plasmacytoma every 12 weeks.
+
+Adverse events (AEs) and laboratory abnormalities were monitored and graded using the NCI‐CTCAE criteria v4.03. Safety was regularly reviewed by an independent Data Monitoring Committee.
+
+2.3 Statistical analysis
+
+In this subgroup analysis, efficacy and safety of treatment with Isa‐Kd versus Kd were evaluated in patients from the IKEMA study who were ≥70 and <70 years of age. All efficacy analyses were conducted in the ITT population. Median PFS and 95% CIs were calculated using the Kaplan‐Meier method. HRs were estimated by subgroup (≥70 and <70 years) using a non‐stratified Cox proportional‐hazard model with terms for the factor, treatment, and their interaction. The interaction test was performed at the 10% alpha level for descriptive purpose. Treatment‐emergent AEs (TEAEs) were evaluated in the safety population. SAS 9.4 software (SAS, Cary, NC) was used for all the analyses.
+
+3 RESULTS
+
+3.1 Patients and treatment exposure
+
+Baseline characteristics are presented by subgroup in Table 1. Patient age was balanced between treatment arms and it ranged from 33 to 90 years. Among the 302 randomized patients, 28.5% were elderly (≥70 years): 29.1% in Isa‐Kd, 27.6% in Kd and 71.5% were <70 years of age: 70.9% in Isa‐Kd, 72.4% in Kd. Overall, patients had received a median of 2 prior treatment lines (range, 1–4). More elderly patients were refractory to lenalidomide in the Kd (52.9%) than the Isa‐Kd (34.6%) arm, but fewer of them had renal function impairment in Kd (22.6%) than in Isa‐Kd (33.3%). Median treatment duration was longer with Isa‐Kd versus Kd for both elderly and younger patients (75.1 vs. 50.5 and 80.5 vs. 63.1 weeks, respectively) (Table 2). The median relative dose intensities for carfilzomib were comparable across treatment subgroups, and independent of age for both Isa and carfilzomib (Table 2).
+
+TABLE 1 Patient baseline characteristics by age group: ≥70 and <70 years, ITT population
+
+	≥70 Years (n = 86)	<70 Years (n = 216)	
+Isa‐Kd (n = 52)	Kd (n = 34)	Isa‐Kd (n = 127)	Kd (n = 89)	
+Median age, years (range)	73 (70–86)	74 (70–90)	61 (37–69)	61 (33–69)	
+ISS stage at study entry, n (%)					
+Stage I	25 (48.1)	16 (47.1)	64 (50.4)	55 (61.8)	
+Stage II	20 (38.5)	11 (32.4)	43 (33.9)	20 (22.5)	
+Stage III	7 (13.5)	6 (17.6)	19 (15.0)	14 (15.7)	
+Unknown	0	1 (2.9)	1 (0.8)	0	
+R‐ISS stage at study entry, n (%)					
+Stage I	14 (26.9)	8 (23.5)	31 (24.4)	25 (28.1)	
+Stage II	32 (61.5)	22 (64.7)	78 (61.4)	48 (53.9)	
+Stage III	5 (9.6)	2 (5.9)	11 (8.7)	6 (6.7)	
+Not classified	1 (1.9)	2 (5.9)	7 (5.5)	10 (11.2)	
+Cytogenetic risk, a n (%)	
+High risk	11 (21.2)	9 (26.5)	31 (24.4)	22 (24.7)	
+Standard risk	33 (63.5)	22 (64.7)	81 (63.8)	56 (62.9)	
+Unknown	8 (15.4)	3 (8.8)	15 (11.8)	11 (12.4)	
+Prior lines of therapy	
+Median (range)	2 (1–4)	2 (1–4)	2 (1–4)	2 (1–3)	
+1 line, n (%)	24 (46.2)	11 (32.4)	55 (43.3)	44 (49.4)	
+≥2 lines, n (%)	28 (53.8)	23 (67.6)	72 (56.7)	45 (50.6)	
+Patients refractory to, n (%)	
+Lenalidomide	18 (34.6)	18 (52.9)	39 (30.7)	24 (27.0)	
+IMiD and PI	9 (17.3)	10 (29.4)	26 (20.5)	17 (19.1)	
+Baseline eGFR, b n/n (%)	
+≥60 ml/min/1.73 m2	32/48 (66.7)	24/31 (77.4)	88/115 (76.5)	68/79 (86.1)	
+<60 ml/min/1.73 m2	16/48 (33.3%)	7/31 (22.6%)	27/115 (23.5)	11/79 (13.9)	
+Abbreviations: d, dexamethasone; eGFR, estimated glomerular filtration rate; IMiD, immunomodulatory drug; Isa, isatuximab; ISS, International Staging System; ITT, intent‐to‐treat; K, carfilzomib; PI, proteasome inhibitor; R‐ISS, revised International Staging System.
+
+a High risk was defined as del(17p), t(4; 14), or t(14; 16) by fluorescence in situ hybridization. Cytogenetic risk was assessed by a central laboratory with a cut‐off of 50% for del(17p), and 30% for t(4; 14) and t(14; 16).
+
+b Baseline eGFR by the modification of diet in renal disease (MDRD) equation.
+
+TABLE 2 Overall extent of exposure by age group: ≥70 and <70 years, safety population
+
+	≥70 Years (n = 85)	<70 Years (n = 214)	
+Isa‐Kd (n = 51)	Kd (n = 34)	Isa‐Kd (n = 126)	Kd (n = 88)	
+Median number of cycles started (range)	19.0 (1–27)	12.5 (1–22)	19.5 (1–26)	15.0 (1–28)	
+Median treatment duration, weeks (range)	75.1 (4–111)	50.5 (4–94)	80.5 (1–105)	63.1 (1–114)	
+Median relative dose intensity, % (range)	
+Isatuximab	93.41 (66.7–102.2)	N.A.	94.51 (76.6–108.2)	N.A.	
+Carfilzomib	90.29 (18.2–108.7)	91.31 (41.8–103.7)	91.49 (25.6–107.5)	91.44 (48.5–108.6)	
+Dexamethasone	81.25 (37.1–100.2)	81.87 (31.2–101.6)	86.96 (24.5–101.1)	90.21 (27.4–101.1)	
+Abbreviations: d, dexamethasone; Isa, isatuximab; K, carfilzomib; N.A., not applicable.
+
+3.2 Efficacy
+
+The combination of Isa with Kd resulted in improved PFS independently of age, with a HR of 0.36 (95% CI, 0.18–0.75) in elderly patients and 0.61 (95% CI, 0.38–0.97) in younger patients, which were consistent with the significant PFS improvement observed in the overall IKEMA population (Figures 1 and 2). 11
+
+FIGURE 1 Progression‐free survival by IRC in patients ≥70 and <70 years of age, in the intent‐to‐treat population. CI, confidence interval; d, dexamethasone; IRC, Independent Response Committee; Isa, isatuximab; K, carfilzomib
+
+FIGURE 2 Progression‐free survival by IRC—Kaplan‐Meier estimates by age group (≥70 and <70 years), in the intent‐to‐treat population. d, dexamethasone; IRC, Independent Response Committee; Isa, isatuximab; ITT, intent‐to‐treat; K, carfilzomib; PFS, progression‐free survival
+
+At the time of this analysis in elderly patients, median PFS with Isa‐Kd had not been reached (NR) (95% CI, NR–NR), whereas in Kd it was 16.2 months (95% CI, 10.3–NR). Median PFS had not been reached in either study arm in younger patients (95% CI, 15.8–NR in Kd).
+
+Addition of Isa to Kd improved the depth of response, with higher rates of ≥VGPR, MRD–, and CR with Isa‐Kd versus Kd. Within the Isa‐Kd arm, ≥VGPR and CR rates were similar in elderly and younger patients (Figure 3). MRD negativity with Isa‐Kd was reached in 23.1% of older patients (vs. 11.8% with Kd) and 32.3% of younger patients (vs. 13.5% with Kd).
+
+FIGURE 3 Response rates based on IRC assessment by age group: ≥70 and <70 years. CR, complete response; d, dexamethasone; IRC, Independent Response Committee; Isa, isatuximab; K, carfilzomib; MRD–, minimal residual disease negativity; ORR, overall response rate; VGPR, very good partial response
+
+3.3 Safety
+
+Grade ≥3 and serious TEAEs were more frequently reported in the elderly than the younger patients in both arms, as expected (Table 3).
+
+TABLE 3 Safety summary by age group: ≥70 and <70 years, safety population
+
+	≥70 Years (n = 85)	<70 Years (n = 214)	
+n (%)	Isa‐Kd (n = 51)	Kd (n = 34)	Isa‐Kd (n = 126)	Kd (n = 88)	
+Any grade ≥3 TEAE	46 (90.2)	26 (76.5)	90 (71.4)	56 (63.6)	
+Any serious TEAE a	37 (72.5)	24 (70.6)	68 (54.0)	46 (52.3)	
+Any grade 5 TEAE b	3 (5.9)	1 (2.9)	3 (2.4)	3 (3.4)	
+Any TEAE leading to definitive discontinuation c	6 (11.8)	8 (23.5)	9 (7.1)	9 (10.2)	
+Any TEAE leading to premature discontinuation of isatuximab	1 (2.0)	N.A.	0	N.A.	
+Any TEAE leading to premature discontinuation of carfilzomib	8 (15.7)	0	18 (14.3)	1 (1.1)	
+Any TEAE leading to premature discontinuation of dexamethasone	3 (5.9)	1 (2.9)	8 (6.3)	3 (3.4)	
+Abbreviations: d, dexamethasone; Isa, isatuximab; K, carfilzomib; N.A., not applicable; TEAE, treatment‐emergent adverse event.
+
+a TEAEs with a start date before the cut‐off date and becoming serious after the cut‐off date were not counted as serious TEAE in this analysis.
+
+b TEAEs with fatal outcome during the treatment period.
+
+c Definitive discontinuation defined as definitive discontinuation of all study drugs.
+
+For both age groups, although the incidence of grade ≥3 TEAEs was higher in the Isa‐Kd arm, the incidence of serious TEAEs was similar between arms (≥70 years: 72.5% in Isa‐Kd, 70.6% in Kd; <70 years: 54.0% in Isa‐Kd, 52.3% in Kd). In the elderly group, 3 (5.9%) patients in Isa‐Kd and 1 (2.9%) in Kd had TEAEs with fatal outcome during study treatment (Isa‐Kd: pneumonia, Kd: general health deterioration due to progressive disease). In both age groups, fewer patients definitively discontinued study treatment due to TEAEs in the Isa‐Kd than the Kd arm: 11.8% versus 23.5% (≥70 years) and 7.1% versus 10.2% (<70 years). The incidence of TEAEs leading to premature carfilzomib discontinuation in the Isa‐Kd arm was comparable between age groups (15.7% and 14.3%) (Table 3).
+
+Any‐grade TEAEs reported in ≥20% of elderly patients are listed in Table 4, according to age group, for Isa‐Kd and the respective Kd arm. Among older patients, the most common TEAEs were diarrhea (43.1% vs. 29.4%), dyspnea (43.1% vs. 26.5%), upper respiratory tract infection (39.2% vs. 23.5%), fatigue (39.2% vs. 23.5%), and hypertension (37.3% vs. 29.4%); among younger patients, they were hypertension (36.5% vs. 31.8%), upper respiratory tract infection (34.9% vs. 23.9%), and diarrhea (33.3% vs. 28.4%). Infusion reactions occurred in 37.3% versus 5.9% of elderly patients and in 47.6% versus 2.3% of younger patients. The most common grade ≥3 TEAEs reported in ≥10% of patients in Isa‐Kd versus Kd were hypertension and pneumonia in both elderly (25.5% vs. 26.5%, 21.6% vs. 20.6%, respectively) and younger patients (18.3% vs. 17.0%, 14.3% vs. 9.1%, respectively), with similar incidence between study arms.
+
+TABLE 4 TEAEs by age group: ≥70 and <70 years, safety population
+
+	≥70 Years (n = 85)	<70 Years (n = 214)	
+Any‐grade TEAEs preferred term, n (%)	Isa‐Kd (n = 51)	Kd (n = 34)	Isa‐Kd (n = 126)	Kd (n = 88)	
+Most common in at least 20% of patients in one arm, ≥70 years	
+Diarrhea	22 (43.1)	10 (29.4)	42 (33.3)	25 (28.4)	
+Dyspnea	22 (43.1)	9 (26.5)	27 (21.4)	17 (19.3)	
+Upper respiratory tract infection	20 (39.2)	8 (23.5)	44 (34.9)	21 (23.9)	
+Fatigue	20 (39.2)	8 (23.5)	30 (23.8)	15 (17.0)	
+Infusion  reaction	19 (37.3)	2 (5.9)	60 (47.6)	2 (2.3)	
+Hypertension	19 (37.3)	10 (29.4)	46 (36.5)	28 (31.8)	
+Bronchitis	16 (31.4)	2 (5.9)	24 (19.0)	13 (14.8)	
+Pneumonia	15 (29.4)	9 (26.5)	27 (21.4)	15 (17.0)	
+Edema, peripheral	13 (25.5)	10 (29.4)	10 (7.9)	11 (12.5)	
+Insomnia	10 (19.6)	10 (29.4)	32 (25.4)	18 (20.5)	
+Asthenia	9 (17.6)	10 (29.4)	23 (18.3)	10 (11.4)	
+Pyrexia	3 (5.9)	9 (26.5)	13 (10.3)	9 (10.2)	
+Cough	10 (19.6)	7 (20.6)	25 (19.8)	10 (11.4)	
+Selected TEAEs	
+Infections and infestations	48 (94.1)	27 (79.4)	105 (83.3)	71 (80.7)	
+Respiratory infection a	47 (92.2)	25 (73.5)	100 (79.4)	65 (73.9)	
+Thromboembolic events b	9 (17.6)	3 (8.8)	18 (14.3)	17 (19.3)	
+Venous c	8 (15.7)	3 (8.8)	16 (12.7)	15 (17.0)	
+Arterial	1 (2.0)	1 (2.9)	2 (1.6)	3 (3.4)	
+Cardiac failure b	7 (13.7)	5 (14.7)	6 (4.8)	3 (3.4)	
+Ischemic heart disease b	3 (5.9)	3 (8.8)	5 (4.0)	2 (2.3)	
+Second primary malignancy a	8 (15.7)	4 (11.8)	5 (4.0)	2 (2.3)	
+Solid skin malignancy	7 (13.7)	3 (8.8)	2 (1.6)	0	
+Solid non‐skin malignancy	2 (3.9)	2 (5.9)	3 (2.4)	2 (2.3)	
+On‐treatment abnormalities	
+End‐stage renal disease d eGFR <15 ml/min/1.73 m2	1/48 (2.1)	2/31 (6.5)	2/115 (1.7)	1/79 (1.3)	
+Abbreviations: d, dexamethasone; eGFR, estimated glomerular filtration rate; Isa, isatuximab; K, carfilzomib; TEAE, treatment‐emergent adverse event.
+
+a Customized MedDRA query.
+
+b MedDRA SMQ (narrow term).
+
+c In patients with venous thromboembolic events, 3/8 versus 2/3 elderly patients and 4/16 versus 3/15 younger patients, for Isa‐Kd versus Kd, had received antithrombotic prophylaxis, while 5/8 versus 1/3 elderly patients and 12/16 versus 12/15 younger patients, for Isa‐Kd versus Kd, had not received antithrombotic prophylaxis. Most events occurred in patients without a medical history of venous thromboembolism: 7/8 in Isa‐Kd and 3/3 events in Kd in elderly patients; 14/16 in Isa Kd and 14/15 events in Kd in younger patients.
+
+d On‐treatment abnormalities (eGFR <15 ml/min/1.73 m2) by the modification of diet in renal disease (MDRD) equation.
+
+Incidences of selected TEAEs analyzed with grouping terms are presented in Table 4. In elderly patients, respiratory infections and venous thromboembolic events were more frequent in Isa‐Kd than Kd (92.2% vs. 73.5% and 15.7% vs. 8.8%), whereas cardiac failure, ischemic heart disease, and second primary malignancy were similar between arms (13.7% vs. 14.7%, 5.9% vs. 8.8%, and 15.7% vs. 11.8%, respectively). The difference between Isa‐Kd and Kd in respiratory infections was driven by upper respiratory tract infection (39.2% vs. 23.5%) and bronchitis (31.4% vs. 5.9%), with low incidence of grade ≥3 events (2.0% vs. 2.9% and 5.9% vs. 0%, respectively). Incidence of pneumonia was similar between arms (29.4% vs. 26.5%).
+
+Among hematologic laboratory abnormalities, incidence of any‐grade anemia, thrombocytopenia, and neutropenia in elderly patients were similar to those observed in younger patients within the two arms, except for any‐grade neutropenia, which was lower in elderly than in younger patients in the Kd arm (14.7% vs. 54.5%), with comparable carfilzomib exposure (Table 5). Grade 3 neutropenia was higher in Isa‐Kd versus Kd in both age groups (≥70 years: 15.7% vs. 2.9%, <70 years: 18.3% vs. 8.0%). None of the older patients had grade 4 anemia, thrombocytopenia, or neutropenia in the Isa‐Kd arm vs. 0%, 2.9%, and 0%, respectively, in the Kd arm.
+
+TABLE 5 Hematologic laboratory abnormalities by age group: ≥70 and <70 years, safety population
+
+	≥70 Years (n = 85)	<70 Years (n = 214)	
+Isa‐Kd (n = 51)	Kd (n = 34)	Isa‐Kd (n = 126)	Kd (n = 88)	
+Grade (G)	
+Laboratory parameter, n (%)	Any	G3	G4	Any	G3	G4	Any	G3	G4	Any	G3	G4	
+Anemia	51 (100)	9 (17.6)	0	33 (97.1)	7 (20.6)	0	125 (99.2)	30 (23.8)	0	88 (100)	17 (19.3)	0	
+Thrombo‐cytopenia	48 (94.1)	11 (21.6)	0	28 (82.4)	7 (20.6)	1 (2.9)	119 (94.4)	22 (17.5)	20 (15.9)	79 (89.8)	12 (13.6)	9 (10.2)	
+Neutropenia	28 (54.9)	8 (15.7)	0	5 (14.7)	1 (2.9)	0	69 (54.8)	23 (18.3)	3 (2.4)	48 (54.5)	7 (8.0)	1 (1.1)	
+Abbreviations: d, dexamethasone; Isa, isatuximab; K, carfilzomib.
+
+4 DISCUSSION AND CONCLUSION
+
+Results from this subgroup analysis of the Phase 3 IKEMA study have shown that elderly patients ≥70 years of age derived a strong PFS benefit (HR, 0.36; 95% CI, 0.18–0.75) from treatment with Isa‐Kd compared with Kd, similar to the findings previously reported with Isa‐Kd in the overall IKEMA study population. 11 PFS benefit was also observed in this analysis for younger patients (<70 years) with Isa‐Kd versus Kd (HR, 0.61; 95% CI, 0.38–0.97). The rates of ≥VGPR, MRD–, and CR were higher with Isa‐Kd compared with Kd in both elderly and younger patients. The quality of response for both age groups was consistent with the results in the overall population, 11 with high rates of CR (38.5%) and MRD– (23.1%) in elderly patients.
+
+Prior findings reported for treatment with daratumumab in combination with Kd versus Kd in the Phase 3 CANDOR study showed that patients with relapsed MM >65 years of age had an improvement in PFS (HR, 0.76 [95% CI 0.48–1.22]) that appeared less than that observed in all randomized patients (HR, 0.63 [95% CI 0.46–0.85]) and in younger patients (≤65 years; HR, 0.57 [95% CI 0.38–0.86]) 16 , 17 Detailed subgroup analyses of daratumumab‐Kd efficacy and safety in elderly patients have yet to be reported for the CANDOR study.
+
+In IKEMA, 11 patients ≥65 years of age derived a PFS benefit from treatment with Isa‐Kd versus Kd (HR, 0.43 [95% CI 0.25–0.74]) comparable to that observed in all patients (HR, 0.53 [95% CI 0.36–0.79]), in agreement with the efficacy findings of this latest analysis conducted for Isa‐Kd in patients ≥70 years of age. Consistently, results from subgroup analyses in older RRMM patients enrolled in the ICARIA‐MM study demonstrated improved PFS with Isa‐Pd versus Pd (≥75 years: HR, 0.48 [95% CI 0.24–0.95]; 65–74 years: HR, 0.64 [95% CI 0.39–1.06]), indicating that Isa can improve outcomes in elderly patients across different combination therapies. 9 , 10 Benefit from anti‐CD38 combination therapy in elderly RRMM patients was also reported with daratumumab plus bortezomib‐dexamethasone or lenalidomide‐dexamethasone in subgroup analyses of the Phase 3 studies CASTOR and POLLUX, respectively. 18
+
+Elderly patients are generally more fragile patients and it is important that the increased efficacy is not obtained to the detriment of safety. Respiratory infections (by grouping analysis) and diarrhea, dyspnea, and fatigue were more frequent (at least 10% difference) in elderly versus younger patients in Isa‐Kd and higher versus Kd among the elderly. The difference observed with Isa‐Kd versus Kd in incidence of respiratory infections among elderly patients was primarily driven by upper respiratory tract infection and bronchitis, which are manageable infections, with low incidence of grade ≥3 events. Grade ≥3 diarrhea, dyspnea, and fatigue were all below 10%. The most common grade ≥3 TEAEs in both age groups were hypertension and pneumonia, with a higher incidence in elderly patients in both treatment arms, but with a similar incidence between Isa‐Kd and Kd in elderly patients (25.5% vs. 26.5% and 21.6% vs. 20.6%, respectively).
+
+Among selected TEAEs, the incidence of cardiac failure, ischemic heart disease, and second primary malignancy, although numerically higher in older versus younger patients, were similar between Isa‐Kd and Kd in elderly patients. As previously reported in the ENDEAVOR trial, incidence of cardiac TEAEs, in particular cardiac failure, was higher in older versus younger patients treated with Kd. 19 Importantly, in the ENDEAVOR and IKEMA studies, these findings are in the context of clinical trials with specific eligibility criteria that would limit the inclusion of patients with certain, known cardiac comorbidities. Estimates of the incidence of cardiac toxicities related to carfilzomib in a real world setting are variable and depend on the terms included and the data source. 20 , 21 Importantly, age is consistently a risk factor for higher incidence of hypertension and cardiac failure. 20
+
+Thromboembolic events were reported with similar incidence for elderly and younger patients in the Isa‐Kd arm. The majority of thromboembolic events were venous in both treatment arms and age groups. The incidence of venous thromboembolic events in Kd was surprisingly lower in elderly versus younger patients (8.8% vs. 17.0%), leading to a higher incidence in Isa‐Kd versus Kd among elderly patients. Among the patients with venous thromboembolic events, 3/8 vs. 2/3 elderly patients and 4/16 vs. 3/15 younger patients, for Isa‐Kd versus Kd, had received antithrombotic prophylaxis. Most of the observed events occurred in patients that did not receive thromboprophylaxis (5/8 vs. 1/3 elderly patients and 12/16 vs. 12/15 younger patients, for Isa‐Kd vs. Kd) and in patients that had no medical history of venous thromboembolism recorded in the database.
+
+More serious TEAEs were reported in elderly patients, but a similar incidence of serious TEAEs was observed in the Isa‐Kd and Kd arms, within each age group, indicating that addition of Isa to Kd did not increase hospitalization of elderly patients for AE management.
+
+Any‐grade infusion reactions occurred less frequently with Isa‐Kd in the elderly than the younger patients (37.3% and 47.6%, respectively, with similar premedication), as previously observed in RRMM patients ≥75 years (28.1%) and 65–74 years (36.4%) or <65 years (42.6%) treated with Isa‐Pd in the Phase 3 ICARIA‐MM study. 9 In contrast, findings from the CASTOR study showed that older RRMM patients (≥75 years) receiving daratumumab plus bortezomib‐dexamethasone experienced more, any‐grade infusion reactions than younger patients (65–74 years) (65.0% and 45.7%, respectively). 18
+
+Notably, a similar incidence of elderly and younger patients within Isa‐Kd (and a lower incidence of elderly patients in Isa‐Kd vs. Kd) had TEAEs leading to definitive treatment discontinuations, suggesting tolerability of treatment with Isa‐Kd independent of age in most patients. A limitation of this study is the limited number of patients, which prevented further meaningful analyses of efficacy and safety with additional variables associated with aging (i.e., selected comorbidities). An additional limitation of this analysis is that it was conducted in the context of a clinical trial. Eligibility criteria other than age and individual investigator bias (e.g., toward ability of an individual to adhere to trial requirements) may skew the inclusion of older adults that may not represent all the patients in the community of similar age, but with additional comorbidities.
+
+In conclusion, Isa‐Kd provides a consistent benefit versus Kd in elderly patients, with a manageable safety profile. Isa‐Kd represents a new treatment option for patients with relapsed MM, independent of age.
+
+AUTHOR CONTRIBUTION
+
+Thierry Facon, Philippe Moreau, Thomas G. Martin, Ivan Spicka, Albert Oriol, Youngil Koh, Andrew Lim, Michele Cavo, Kwee Yong, Marie‐Laure Risse, Sandrine Schwab, and Gracia Martinez designed the study in collaboration with the study sponsor. Marie‐Laure Risse contributed to the investigations and to the analysis, verification, and interpretation of the data for this study. Gaëlle Asset performed the formal data and statistical analyses. All authors contributed to the investigations, interpretation of the data, and drafting or critically revising the manuscript. All authors approved final version.
+
+CONFLICT OF INTEREST
+
+Thierry Facon: participation on a data safety monitoring board or advisory board for Amgen, Bristol Myers Squibb, Janssen, Karyopharm, Oncopeptides, Roche, and Sanofi; speakers' bureau for Bristol Myers Squibb and Janssen. Philippe Moreau: honoraria and participation on a data safety monitoring board or advisory board for AbbVie, Amgen, Bristol Myers Squibb/Celgene, Janssen, Oncopeptide, Roche, and Sanofi. Thomas G. Martin: research funding (to institution) from Sanofi; participation on a steering committee for Sanofi. Ivan Spicka: research funding, honoraria, and participation on a data safety monitoring board or advisory board for Amgen, Bristol Myers Squibb/Celgene, Janssen‐Cilag, Novartis, PharmaMar, Sanofi, and Takeda. Albert Oriol: honoraria from Amgen and Bristol Myers Squibb/Celgene; participation on a data safety monitoring board or advisory board for Amgen, Bristol Myers Squibb/Celgene, GlaxoSmithKline, Karyopharm, Oncopeptides, and Sanofi. Youngil Koh: nothing to disclose. Andrew Lim: nothing to disclose. Gabor Mikala: honoraria from AbbVie, Amgen, Celgene, Janssen, Krka Pharma, Novartis, Sandoz, and Takeda; travel support from AbbVie, Celgene, Janssen, and Takeda. Laura Rosiñol: honoraria from Amgen, Celgene, GlaxoSmithKline, Janssen, Sanofi, and Takeda; participation on a data safety monitoring board or advisory board for Amgen, Celgene, GlaxoSmithKline, Janssen, Karyopharm, Sanofi, and Takeda. Münci Yağci: nothing to disclose. Michele Cavo: honoraria from AbbVie, Adaptive Biotechnologies, Amgen, Bristol Myers Squibb/Celgene, GlaxoSmithKline, Janssen, Sanofi, and Takeda; speakers bureau for Janssen and Bristol Myers Squibb/Celgene. Kwee Yong: research funding from Bristol Myers Squibb, Janssen, and Sanofi; honoraria and travel support from Amgen, Sanofi, and Takeda; participation on an advisory board or steering committee for Janssen and Sanofi. Marie‐Laure Risse, Gaëlle Asset, and Sandrine Schwab: employed by Sanofi, may hold stock and/or stock options in the company. Gracia Martinez: nothing to disclose.
+
+PEER REVIEW
+
+The peer review history for this article is available at https://publons.com/publon/10.1002/hon.3038.
+
+ETHICS STATEMENT
+
+The study protocol was approved by the Institutional Ethics Committee or independent review board for each center; the study was conducted following the Declaration of Helsinki and IHC Guidelines for Good Clinical Practice. All patients provided written informed consent.
+
+ACKNOWLEDGMENTS
+
+The authors thank the participating patients and their caregivers, and the study centers and investigators for their contributions to the study. Medical writing support was provided by S. Mariani, MD, PhD of Elevate Medical Affairs, contracted by Sanofi for publication support services. This study was funded by Sanofi.
+
+DATA AVAILABILITY STATEMENT
+
+Qualified researchers can request access to patient‐level data and related study documents including the clinical study report, study protocol with any amendments, blank case report forms, statistical analysis plan, and dataset specifications. Patient‐level data will be anonymized, and study documents will be redacted to protect the privacy of trial participants. Further details on Sanofi's data‐sharing criteria, eligible studies, and process for requesting access are at https://vivli.org.
+==== Refs
+REFERENCES
+
+1 Mina R , Bringhen S , Wildes TM , Zweegman S , Rosko AE . Approach to the older adult with multiple myeloma. Am Soc Clin Oncol Educ Book. 2019;39 :500‐518. 10.1200/edbk_239067 31099676
+2 SEER Cancer Statistics Review. 2017. Accessed February 4, 2022. https://seer.cancer.gov/csr/1975_2017/browse_csr.php
+3 Cowan AJ , Allen C , Barac A , et al. Global burden of multiple myeloma: a systematic analysis for the global burden of disease study 2016. JAMA Oncol. 2018;4 (9 ):1221‐1227. 10.1200/jco.2018.36.15_suppl.e20023 29800065
+4 Bringhen S , Mateos MV , Zweegman S , et al. Age and organ damage correlate with poor survival in myeloma patients: meta‐analysis of 1, 435 individual patient data from 4 randomized trials. Haematologica. 2013;98 (6 ):980‐987. 10.3324/haematol.2012.075051 23445873
+5 Madan S , Kumar S . Current treatment options for elderly patients with multiple myeloma: clinical impact of novel agents. Therapy. 2011;8 (4 ):415‐429. 10.2217/thy.11.39
+6 Bonello F , Boccadoro M , Larocca A . Diagnostic and therapeutic challenges in the management of intermediate and frail elderly multiple myeloma patients. Cancers (Basel). 2020;12 (11 ):3106. 10.3390/cancers12113106 33114320
+7 Deckert J , Wetzel MC , Bartle LM , et al. SAR650984, a novel humanized CD38‐targeting antibody, demonstrates potent antitumor activity in models of multiple myeloma and other CD38+ hematologic malignancies. Clin Cancer Res. 2014;20 (17 ):4574‐4578. 10.1158/1078-0432.ccr-14-0695 24987056
+8 Martin TG , Corzo K , Chiron M , et al. Therapeutic opportunities with pharmacological inhibition of CD38 with isatuximab. Cells. 2019;8 (12 ):1522. 10.3390/cells8121522 31779273
+9 Attal M , Richardson PG , Rajkumar SV , et al. ICARIA‐MM study group. Isatuximab plus pomalidomide and low‐dose dexamethasone versus pomalidomide and low‐dose dexamethasone in patients with relapsed and refractory multiple myeloma (ICARIA‐MM): a randomised, multicentre, open‐label, phase 3 study. Lancet. 2019;394 (10214 ):2096‐2107.31735560
+10 Schjesvold FH , Richardson PG , Facon T , et al. Isatuximab plus pomalidomide and dexamethasone in elderly patients with relapsed/refractory multiple myeloma: ICARIA‐MM subgroup analysis. Haematologica. 2021;106 (4 ):1182‐1187.32586908
+11 Moreau P , Dimopoulos M‐A , Mikhael J , et al. Isatuximab, carfilzomib, and dexamethasone in relapsed multiple myeloma (IKEMA): a multicentre, open‐label, randomised phase 3 trial. Lancet. 2021;397 (10292 ):2361‐2371.34097854
+12 SARCLISA® (Isatuximab‐irfc) Injection, for Intravenous Use. Prescribing Information. 2021. Accessed February 4, 2022. https://products.sanofi.us/Sarclisa/sarclisa.pdf
+13 European Medicines Agency . Sarclisa, INN‐Isatuximab. Summary of Product Characteristics; 2021. Accessed February 4, 2022. https://www.ema.europa.eu/en/documents/product‐information/sarclisa‐epar‐product‐information_en.pdf
+14 SARCLISA® (isatuximab) . Prescribing Information Nishi Shinjuku, Tokyo; 2021. Accessed February 4, 2022. https://www.pmda.go.jp/PmdaSearch/iyakuDetail/ResultDataSetPDF/780069_4291454A1021_1_02
+15 Kumar S , Paiva B , Anderson KC , et al. International Myeloma Working Group consensus criteria for response and minimal residual disease assessment in multiple myeloma. Lancet Oncol 2016;17 (8 ):e328‐e346. 10.1016/s1470-2045(16)30206-6 27511158
+16 Dimopoulos M , Quach H , Mateos MV , et al. Carfilzomib, dexamethasone, and daratumumab versus carfilzomib and dexamethasone for patients with relapsed or refractory multiple myeloma (CANDOR): results from a randomised, multicentre, open‐label, phase 3 study. Lancet. 2020;396 (10245 ):186‐197. 10.1016/s0140-6736(20)30734-0 32682484
+17 Silvennoinen R , Heckman CA . A candid view of CANDOR. Lancet. 2020;396 (10245 ):147‐148. 10.1016/s0140-6736(20)30901-6 32682464
+18 Mateos MV , Spencer A , Nooka AK , et al. Daratumumab‐based regimens are highly effective and well tolerated in relapsed or refractory multiple myeloma regardless of patient age: subgroup analysis of the phase 3 CASTOR and POLLUX studies. Haematologica. 2020;105 (2 ):468‐477. 10.3324/haematol.2019.217448 31221782
+19 Ludwig H , Dimopoulos MA , Moreau P , et al. Carfilzomib and dexamethasone vs bortezomib and dexamethasone in patients with relapsed multiple myeloma: results of the phase 3 study ENDEAVOR (NCT01568866) according to age subgroup. Leuk Lymphoma. 2017;58 (10 ):2501‐2504. 10.1080/10428194.2017.1298755 28306371
+20 Bishnoi R , Xie Z , Shah C , et al. Real‐world experience of carfilzomib‐associated cardiovascular adverse events: SEER‐Medicare data set analysis. Cancer Med. 2021;10 (1 ):70‐78. 10.1002/cam4.3568 33169938
+21 Zhai Y , Ye X , Hu F , et al. Cardiovascular toxicity of carfilzomib: the real‐world evidence based on the adverse event reporting system database of the FDA, the United States. Front Cardiovasc Med. 2021;8 :735466. 10.3389/fcvm.2021.735466 34646873
+

PMC10084357.txt

@@ -0,0 +1,250 @@
+
+==== Front
+Hematol Oncol
+Hematol Oncol
+10.1002/(ISSN)1099-1069
+HON
+Hematological Oncology
+0278-0232
+1099-1069
+John Wiley and Sons Inc. Hoboken
+
+35789025
+10.1002/hon.3045
+HON3045
+Original Article
+Original Articles
+Mechanisms underlying synergism between circularized tumor necrosis factor‐related apoptosis inducing ligand and bortezomib in bortezomib‐sensitive or ‐resistant myeloma cells
+Leng Yun 1
+Hu Xiaoyan 2
+Li Lin 2
+Nkwocha Jewel 2
+Satta Toshihisa 2
+Sharma Kanika 2
+Kmeiciak Maciej 2
+Zhou Huixing 1
+Zhang Zhiyao https://orcid.org/0000-0002-6974-373X
+1
+Zhou Liang 2
+Chen Wenming https://orcid.org/0000-0001-6298-8489
+1 13910107759@163.com
+
+Grant Steven https://orcid.org/0000-0003-4452-9320
+2 3 steven.grant@vcuhealth.org
+
+1 Department of Hematology Beijing Chao‐Yang Hospital Capital Medical University Beijing China
+2 Division of Hematology/Oncology Department of Medicine Virginia Commonwealth University Richmond Virginia USA
+3 Massey Cancer Center Virginia Commonwealth University Richmond Virginia USA
+* Correspondence
+Wenming Chen, Department of Hematology, Beijing Chao‐Yang Hospital, Capital Medical University, Beijing 100020, China.
+Email: 13910107759@163.com
+Steven Grant, Division of Hematology/Oncology, Virginia Commonwealth University, P.O. Box 980035, Richmond, VA 23298, USA.
+Email: steven.grant@vcuhealth.org
+
+14 7 2022
+12 2022
+40 5 10.1002/hon.v40.5 9991008
+23 6 2022
+02 5 2022
+27 6 2022
+© 2022 The Authors. Hematological Oncology published by John Wiley & Sons Ltd.
+https://creativecommons.org/licenses/by/4.0/ This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
+
+Abstract
+
+Mechanisms underlying interactions between a novel, clinically relevant circularized tumor necrosis factor‐related apoptosis inducing ligand (TRAIL) agonist, circularly permuted TRAIL (CPT) have been examined in multiple myeloma (MM) cells sensitive or resistant to bortezomib (BTZ). Various MM cell lines for example, U266, including those resistant to bortezomib‐resistant U266 cells were exposed to low nanomolar concentrations of bortezomib ± CPT and apoptosis monitored. Circularly permuted TRAIL and bortezomib synergistically induced apoptosis in both BTZ‐naïve and ‐resistant cells. The regimen up‐regulated DR4 receptor internalization in MM cells, known to modulate both NF‐κB and extrinsic apoptotic pathways. CPT/BTZ disrupted the non‐canonical NF‐κB pathway, reflected by tumor necrosis factor (TNF) receptor associated factors 3 (TRAF3) up‐regulation, NF‐κB inducing kinase down‐regulation, diminished p52 and p50 processing, and B‐cell lymphoma‐extra large (BCL‐XL) down‐regulation, but failed to inactivate the canonical NF‐κB pathway, reflected by unchanged or increased expression of phospho‐p65. The regimen also sharply increased extrinsic apoptotic pathway activation. Cells exhibiting TRAF3 knock‐down, dominant‐negative Fas‐associated protein with death domain, knock‐down of caspase‐8, BCL‐2/BCL‐XL, or exposure to a caspase‐9 inhibitor displayed markedly reduced CPT/BTZ sensitivity. Concordant results were observed in bortezomib‐resistant cells. The regimen was also active in the presence of stromal cells and was relatively sparing toward normal CD34+ hematopoietic cells. Finally, ex vivo results revealed synergism in primary MM primary cells, including those BTZ, and the CPT/BTZ regimen significantly decreased tumor growth in a patient‐derived MM xenograft model. These results indicate that the CPT/BTZ regimen acts via the non‐canonical NF‐κB as well as intrinsic/extrinsic apoptotic pathways to induce cell death in MM cells, and may represent an effective strategy in the setting of bortezomib resistance.
+
+bortezomib
+CPT
+intrinsic/extrinsic apoptotic
+multiple myeloma
+non‐canonical NF‐κB pathway
+TRAIL
+National Institutes of Health 10.13039/100000002 P30CA16059 R01CA205607 UM1CA186644 source-schema-version-number2.0
+cover-dateDecember 2022
+details-of-publishers-convertorConverter:WILEY_ML3GV2_TO_JATSPMC version:6.2.7 mode:remove_FC converted:10.04.2023
+Leng Y , Hu X , Li L , et al. Mechanisms underlying synergism between circularized tumor necrosis factor‐related apoptosis inducing ligand (CPT) and bortezomib in bortezomib‐sensitive or ‐resistant myeloma cells. Hematol Oncol. 2022;40 (5 ):999‐1008. 10.1002/hon.3045 35789025
+
+Yun Leng, Xiaoyan Hu and Lin Li contributed equally to this work.
+
+Wenming Chen and Steven Grant contributed equally to this work.
+==== Body
+pmc1 INTRODUCTION
+
+Multiple myeloma (MM) is a clonal disorder of plasma cells 1 accounting for 13% of hematologic malignancies. 2 Bortezomib (BTZ) is a proteasome inhibitor which promotes MM apoptosis, 3 , 4 possibly by inhibiting the NF‐κB pathway, upon which MM cells depend. 5 The complete remission rate for bortezomib‐based regimens is as high as 30%, and the progression‐free survival of MM patients is significantly prolonged. 6 However, relapse and bortezomib resistance represent significant problems in MM. 7 Notably, BTZ‐resistant cells have shown increased NF‐κB signaling. 8
+
+Tumor necrosis factor‐related apoptosis inducing ligand (TRAIL) induces cell death in diverse malignant cells, but spares normal cells. 9 It has shown pre‐clinical activity in MM, through various mechanisms, including disruption of the canonical and non‐canonical NF‐κB pathways, and activation of the extrinsic apoptotic cascade. 9 It also interacts synergistically with proteasome inhibitors. 10 , 11 TRAIL has limited activity in the clinic, possibly through failure of target engagement. 12 This led to the development of circularly permutated TRAIL (CPT), a cyclization allosteric form of wild‐type TRAI. 13 Compared to wild‐type TRAIL, CPT has greater antitumor activity, and better stability/biological activity in aqueous solution. 14 In a phase II trial of CPT monotherapy, among 27 MM patients with relapsed/refractory multiple myeloma (RRMM), 1 patient achieved a near‐complete response, and 8 patients achieved partial responses. 15 Another phase II clinical trial of CPT combined with thalidomide and dexamethasone showed promise. 16 Here, our goals were to determine whether the CTP/BTZ regimen was effective in BTZ‐sensitive or ‐resistant MM, including in a MM patient‐derived MM xenograft (PDX) model system, and to elucidate mechanisms underlying interactions.
+
+2 MATERIALS AND METHODS
+
+2.1 Cell lines and reagents
+
+Multiple myeloma cell lines U266, bortezomib‐resistant U266 cells (PS‐R), Roswell Park Memorial Institute (RPMI) 8226, H929, U266/Fas‐associated protein with death domain dominant negative (FADD‐DN) cell line, U266/C8‐DN cell line, U266/pcDNA3.1 cell line, U266/shRNA targeting TNF receptor associated factor 3 (shTRAF3) determinant domain silenced cells, U266/BCL‐2, U266/BCL‐XL and HS‐5 cells were used as previously described. 17
+
+Circularly permuted TRAIL was obtained from Beijing Shandong Biotechnology Co., Ltd. and a solution was prepared by diluting CPT100 mg to 1 mg/ml with 0.1% bovine serum albumin in phosphate buffered saline (PBS), which was stored at −80°C. Bortezomib (Spectrum Pharmaceuticals) solution was prepared by diluting bortezomib (PS‐341; molecular weight = 384.24) with dimethyl sulfoxide to 10 mM, and stock solutions diluted in RPMI to achieve the desired final concentration.
+
+3 ISOLATION OF PRIMARY MYELOMA CELLS
+
+All studies were obtained with written informed consent from patients undergoing routine diagnostic aspirations, were conducted in accordance with recognized ethical guidelines (e.g., the Declaration of Helsinki), and were approved by the Virginia Commonwealth Institutional Review Board (#MCC‐8712‐3A; MCC‐02447; MCC‐03340) and the Ethics Committee of Beijing Chaoyang Hospital, Capital Medical University (Reference: 2014‐y‐76). Medical records were de‐identified, and only information relating to pre‐biopsy treatment was reviewed.
+
+CD138+ bone marrow cells from 13 patients with RRMM were purified by CD138 microbeads using a Miltenyi magnetic cell sorting system. The purity of the myeloma cells assessed by CD138/CD45 staining and morphology was ≥95%.
+
+3.1 Animal studies
+
+Animal studies were conducted under an approved protocol by the local Ethics Committee of both participating institutions and complied with the institutional guidelines for the care and use of animals. The establishment of a PDX model is described in Supplemental data. Relapsed/refractory multiple myeloma patient‐derived mononuclear cells were injected subcutaneously into the upper limbs of SPF‐grade nonobese diabetic‐severe combined immunodeficiency female mice (3–4 weeks of age) with 10 × 106 cells/100 μl. When the tumor size was approximately 40–110 mm3, 20 tumor‐bearing mice were randomly divided into four groups (5 mice/group), for example, control group, BTZ single‐drug group, CPT single‐drug group, and the combination group. Mice were injected subcutaneously with BTZ 0.5 mg/kg (at day 1, 4, 8 and 11) ± CPT 10 mg/kg (daily). Control mice were subcutaneously injected with PBS. Tumor size and the general mouse conditions were measured twice/week. After 5 weeks, mice were sacrificed and tumor volumes calculated by the formula V = (width x length 2 )/2.
+
+3.2 Statistical analysis
+
+Values represent the means ± standard deviation for at least three independent experiments performed in triplicate. The significance of differences between experimental variables was determined by using the Student t test or one‐way analysis of variance with the Tukey‐Kramer multiple comparisons test. The significance of p values is indicated: *p < 0.05, **p < 0.01, or ***p < 0.001. The combination index (CI) value was calculated according to Compusyn 1.0 software. CI < 1, = 1 and > 1 indicated that the two drugs had synergistic, additive and antagonistic effects, respectively. 18
+
+4 RESULTS
+
+4.1 CPT interacts synergistically with BTZ in MM cells
+
+Minimally toxic concentrations of BTZ (1.5–3 nM) with CPT (20–50 ng/ml) or TRAIL (20–50 ng/ml) significantly increased lethality, reflected by 7‐AAD uptake, in various MM cells, for example, U266, 8226, and H929 (Figure 1A–C) and highly BTZ‐resistant PS‐R cells, 19 although requiring higher BTZ concentrations (e.g., 7.5–10 nM) (Figure 1 D). Median Dose Effect analysis yielded CI values substantially less than 1.0, indicating synergism (Figure 1E,F lower panels).
+
+FIGURE 1 The circularly permutated TRAIL (CPT)/bortezomib regimen synergistically induces apoptosis in multiple myeloma (MM) cells. (A‐D) U266, 8226, H929 and bortezomib‐resistant U266 cells (PS‐R) cells were exposed (48 h) to indicated doses of CPT and resistant to bortezomib (BTZ) treatment, followed by flow cytometric analysis of cell death after staining with 7‐AAD. The percentages of 7‐AAD (+) cells are presented. (E and F) U266 and PS‐R cells were exposed (24 h) to varying concentrations of CPT ± BTZ at a non‐fixed ratio, after which the percentage of 7‐AAD+ cells was determined. Combination Index (CI) values less than 1.0 denote a synergistic interaction; *p < 0.05; **p < 0.01; ***p < 0.001
+
+4.2 Combined treatment with CPT and BTZ promotes pronounced DR4 receptor internalization
+
+To determine whether CPT/BTZ activity specifically involved DR4/5, U266 and PS‐R cells were incubated with CPT (30 ng/ml) and BTZ (2 nM) for16 h, and stained with DR4 or DR5 antibodies. Immunofluorescence microscopy showed strong internalization of the DR4 receptor in both U266 and PS‐R cells exposed to both agents (Supplemental Figure S1A). In contrast, this was not seen for DR5 (Supplemental Figure S1B), suggesting that the CPT/BTZ regimen activates DR4‐ (but not DR5‐) induced apoptosis.
+
+4.3 The CPT/BTZ regimen activates the extrinsic apoptotic pathway
+
+Exposure (24 h) of U266 cells to low, minimally toxic concentrations of CPT (10–50 nM) or TRAIL and very low BTZ concentrations (1.5 or 2 nM), western blot analysis demonstrated that combined treatment induced marked caspase 8/caspase‐3/poly‐ADP ribose polymerase (PARP) cleavage, Fas‐associated protein with death domain (FADD) up‐regulation, and down‐regulation of cellular FLICE‐inhibitory protein (c‐FLIP), a master anti‐apoptotic regulator (Figure 2A). Similar results were observed in highly BTZ‐resistant PS‐R cells exhibiting high basal c‐FLIP levels, using modestly higher BTZ concentrations (e.g., 10–15 nM) (Supplemental Figure S2A). To assess the functional role of the extrinsic apoptotic pathway in CPT/BTZ responses, U266 cells ectopically expressing dominant‐negative FADD or caspase‐8 (DN‐FADD or DN‐Casp 8) showed dramatically reduced caspase eight and PARP cleavage compared to controls (Figure 2B). Both DN‐FADD and DN‐Casp eight expression significantly diminished CPT/BTZ‐induced cell death (**p < 0.01; Figure 3D), arguing that the extrinsic apoptotic pathway contributes to CPT/BTZ activity in BTZ‐sensitive or ‐resistant MM cells.
+
+FIGURE 2 The circularly permutated TRAIL (CPT)/resistant to bortezomib (BTZ) regimen activates of the extrinsic/intrinsic apoptotic pathway. (A) U266 were incubated with CPT ± BTZ for 48 h. Caspase‐8, Caspase‐3, poly‐ADP ribose polymerase (PARP), Fas‐associated protein with death domain (FADD), and cellular FLICE‐inhibitory protein (c‐FLIP) were monitored by immunoblotting analysis. CF = cleavage fragment. α‐tubulin was assayed to ensure equivalent loading and transfer. (B) U266/EV, U266/DN‐FADD and U266/DN‐Casp 8 were treated with the indicated concentrations of CPT ± BTZ for 48 h. FADD, Caspase 8 and PARP were monitored by immunoblotting analysis. CF = cleavage fragment. α‐tubulin was assayed to ensure equivalent loading and transfer. (C) Cells were treated as mentioned in B, followed by flow cytometric analysis of cell death after staining with 7‐AAD. The percentages of 7‐AAD (+) cells are presented. (D) U266 cells were incubated with indicated doses of CPT ± BTZ for 48 h. Cytochrome C and second mitochondrial activator of caspases (SMAC) were monitored by immunoblotting analysis. (E) U266 cells were incubated with of CPT ± BTZ for 48 h. Caspase 9 monitored by immunoblotting analysis. (F, left panel) U266 cells were pre‐treated for 30 min with caspase nine inhibitor (Z‐LEHD‐FMK, 20 μM) and then incubated with BTZ (2 nM) + CPT (30 ng/ml) for 48 h. After treatment, cells were subjected to flow cytometry to determine the percentage of death (7‐AAD+ cells). (F, right panel) Immunoblotting analysis was then performed to monitor levels of cleaved Caspase‐3. (G) U226/EV, U226/BCL‐XL, and U226/BCL‐2 cells were incubated with CPT (10 ng/ml) ± BTZ (3 nM) for 48 h. BCL‐XL, BCL‐2 and PARP were monitored by immunoblotting analysis. CF = cleavage fragment. (H) The percentages of 7‐AAD (+) cells are presented. Glyceraldehyde‐3‐Phosphate Dehydrogenase, β‐actin, or α‐tubulin was assayed to ensure equivalent loading and transfer. **p < 0.01; ***p < 0.001
+
+FIGURE 3 The circularly permutated TRAIL (CPT)/resistant to bortezomib (BTZ) regimen inhibits non‐canonical NF‐κB signaling pathway (A,B) U266 or PS‐R cells were incubated with CPT ± BTZ for 48 h (A) p‐p65 (S536) was monitored by immunoblotting analysis. (B) DNA binding of NF‐kB (p65 subunit) was determined by using a TransAM assay for NF‐kB. (C) U266 or PS‐R cells were treated with CPT ± BTZ for 40 h. DNA binding of NF‐kB (p52 subunit). (D) Cells were treated as mentioned in A. TNF receptor associated factors 3 (TRAF3) and p‐p100/p52 (S864) were monitored by immunoblotting analysis. (E) U266 or PS‐R cells were treated with indicated doses of CPT ± BTZ for 48 h. NF‐κB inducing kinase (NIK) and p52 were monitored by immunoblotting analysis. (F) U266/shNC and U266/shRNA targeting TNF receptor associated factor 3 (shTRAF3) cells were exposed to the indicated concentrations of CPT ± BTZ for 48 h. Immunoblotting analysis was then performed to monitor levels of TRAF3, B‐cell lymphoma‐extra large (BCL‐XL), Caspase‐8 and poly‐ADP ribose polymerase (PARP). CF = cleavage fragment. α‐tubulin or β‐actin was assayed to ensure equivalent loading and transfer. (G) Cells were treated as mentioned in F, followed by flow cytometric analysis of cell death after staining with 7‐AAD. The percentages of 7‐AAD (+) cells are presented. *p < 0.05
+
+4.4 The CPT/BTZ regimen activates the intrinsic apoptotic pathway
+
+To investigate the role of the intrinsic apoptosis in CPT/BTZ anti‐MM activity, U266 and PS‐R cells were treated with indicated concentrations of CPT or TRAIL ± BTZ for 48 h. Combined exposure to both agents sharply increased cytochrome C and second mitochondrial activator of caspases release, accompanied by caspase‐9 cleavage (Figure 2D,E; Supplemental Figure S2B, 2C). The caspase‐9 inhibitor Z‐LEHD‐FMK(C9i) significantly blocked CPT/BTZ–induced caspase activation and cell death (Figure 2F). To investigate the effect of anti‐apoptotic family members (e.g., BCL‐2 and BCL‐XL), U266 cells ectopically expressing BCL‐XL or BCL‐2 and exposed to CPT/BTZ displayed reduced PARP cleavage (Figure 2G) and significantly diminished cell death compared to controls (Figure 2H), arguing that intrinsic apoptosis contributes functionally to CPT/BTZ activity in MM cells.
+
+4.5 The CPT/BTZ regimen inhibits non‐canonical but not canonical NF‐κB signaling
+
+As MM cell survival is partly dependent upon NF‐κB activation, 20 CPT/BTZ effects on NF‐κB pathways were examined in U266 and PS‐R cells. Exposure to CPT ± BTZ, failed to down‐regulate (or increase) p65 phosphorylation (S536), reflecting canonical NF‐κB activation, 21 in both cell types (Figure 3A). An NF‐κB p65 DNA binding assay confirmed that CPT/BTZ failed to increase p65 binding activity in U266 or PS‐R cells (Figure 3B), arguing that this regimen acts independently of the canonical NF‐κB pathway.
+
+In sharp contrast, CPT/BTZ significantly reduced NF‐κB p52 binding activity determined by a p52 Chemi Act Assay (Figure 3C), an indicator of non‐canonical NF‐κB activation. 22 In both U266 and PS‐R cells, CPT/BTZ clearly increased TNF receptor associated factors 3 (TRAF3) expression, a negative regulator of the non‐canonical pathway, 22 accompanied by increased p100 expression (Figure 3D; S864), presumably reflecting diminished cleavage to the active p52 form. In both U266 and PS‐R cells, CPT/BTZ induced down‐regulation of NF‐κB inducing kinase (NIK) (NF‐κB‐initiating kinase), a non‐canonical NF‐κB pathway activator 23 and downstream inhibitory target of TRAF3 24 (Figure 3E).
+
+To evaluate TRAF3 up‐regulation functionality, U266 cells ectopically expressing TRAF3 knockdown (shTRAF3) were generated. Following CPT/BTZ treatment, shTRAF3 cells exhibited markedly diminished Caspase 8 and PARP cleavage. TNF receptor associated factors 3 knockdown cells exhibited up‐regulation of BCL‐XL, a key down‐stream target of the non‐canonical pathway, 25 and diminished down‐regulation following CPT/BTZ treatment (Figure 3F). They also displayed reduced caspase 8 cleavage/activation (Figure 3F). Finally, TRAF3 knockdown cells were significantly less susceptible to CPT/BTZ than empty‐vector controls (*p < 0.05, Figure 3G). These findings indicate that disruption of the non‐canonical (but not the canonical) NF‐κB pathway plays a significant functional role in CPT/BTZ‐mediated anti‐myeloma activity.
+
+4.6 CPT/BTZ circumvents microenvironment‐driven intrinsic resistance
+
+Co‐culture of GFP‐labeled PS‐R cells with human stromal cells (HS‐5) failed to protect cells following 48 h exposure to CPT/BTZ (Fig. Supplemental Figure S3A). Fluorescence microscopy images revealed a marked increase in 7‐AAD staining (red) after treatment of GFP‐labeled PS‐R cells cultured with HS‐5 cells with the CPT/BTZ regimen (Supplemental Figure S3B), arguing that CPT/BTZ exposure is lethal to BTZ‐resistant MM cells cultured with human stromal cells.
+
+4.7 The CPT/BTZ regimen is active against primary CD138+ MM cells
+
+To examine CPT/BTZ activity against primary CD138+ MM cells, 13 patient specimens were investigated. Detailed clinical/pathologic features are shown in Supplemental Table S1. All but two patients were relapsed/refractory (RR), each of whom had received bortezomib. The median number of prior cycles of bortezomib was 6 (range 2–9), and the median number of cycles was 7 (range 1–24). 5/13 specimens were positive for TP53 deletion by fluorescence in situ hybridization (FISH), 3/13 patients had 1q21 amplification, and 2 had a complex karyotype (Supplemental Figure S4).
+
+Following 24 h exposure of primary MM cells to CPT and BTZ alone or in combination, cell death was determined by annexin/V staining and subjected to Median Dose Effect analysis. 8/12 specimens displayed CI values < 1.0, corresponding to synergistic interactions (Figure 4A). Fluorescence microscopy of cells from a representative specimen illustrates the pronounced increase in green (annexin V) staining of cells exposed to both agents (Figure 4B). Comparable exposure of normal CD34+ cord blood samples (N = 4) failed to reduce viable cell numbers after single or combined drug treatment (Figure 4C).
+
+FIGURE 4 The circularly permutated TRAIL (CPT)/resistant to bortezomib (BTZ) regimen inhibits primary multiple myeloma (MM) cell growth ex vivo and MM cell growth in vivo. (A) Patient‐derived bone marrow mononuclear cells were isolated and treated with indicated doses of CPT ± BTZ for 24 h, after which the cells were stained with CD138‐PE. Flow cytometric analysis was performed to determine the CD138+ population. Combination index (CI) values less than 1.0 denote a synergistic interaction. (B) Representative primary bone marrow cells from a patient with MM (RR, relapse and refractory; prior BTZ) were exposed to 10 ng/ml CPT +/− 1.5 nM BTZ for 24 h, after which the cells were stained with CD138‐PE and annexin V‐fluorescein isothiocyanate (FITC). Images were obtained with an IX71‐Olympus inverted system microscope at × 40 magnification. (C) Experiments were carried out with 4 primary cord blood (CB) CD34+ samples. p > 0.05. (D–G) 20 SPF‐grade NOD‐SCID mice were inoculated via flank s.c. with 10 × 106 patient‐derived MM cells. Mice were randomized to 4 groups (n = 5/group). Treatment was initiated after the tumor size was about 40–100 mm3. Mice were administered subcutaneously with BTZ 0.5 mg/kg (at day 1, 4, 8 and 11) ± CPT 10 mg/kg (daily). Tumor growth and body weight were monitored weekly (D, G). At day 36, tumors were harvested and dissected into small pieces. Immunoblotting analysis was then performed to monitor levels of c‐PARP, cellular FLICE‐inhibitory protein (c‐FLIP), and TNF receptor associated factors 3 (TRAF3). α‐tubulin was assayed to ensure equivalent loading and transfer (E, F)
+
+5 THE CPT/BTZ REGIMEN IS ACTIVE IN A PRIMARY, PATIENT‐DERIVED MM XENOGRAFT MODEL
+
+To assess CPT/BTZ activity in vivo, a PDX model was established using cells obtained from a patient with RRMM (Supplemental Table S1). Primary ascites tumor cell suspensions containing 10 × 106 cells/100 μl were injected subcutaneously into mouse upper limbs. After 4 weeks, transplanted tumors were resected, manipulated into a single‐cell suspensions, and analyzed by FISH, the results of which are shown in Supplemental Figure S4 and Supplemental Table S2. Genetic aberrations included TP53, 1q21, immunoglobulin heavy locus (IGH)/musculoaponeurotic fibrosarcoma, IGH/fibroblast growth factor receptor 3 (FGFR3), and IGH/Cyclin D1 (CCND1) (Supplemental Table S2). Transplanted patient‐derived tumor cells were identified using human‐specific probes/antibodies and enriched in the CD45dim/CD38+/CD56+/CD19‐/CD27+/CD138+or−/cLambda−/cKappa+ myeloma cell population (Supplemental Figure S5). Serum protein electrophoresis revealed highly expressed free κ‐light chains (Supplemental Figure S6).
+
+Relapsed/refractory multiple myeloma patient‐derived mononuclear cells were then injected subcutaneously into upper forelimbs of NOD scid gamma (NSG) mice. When the tumors reached approximately 40–110 mm3, mice were treated with BTZ 0.5 mg/kg (at day 1, 4, 8 and 11) ± CPT 10 mg/kg (daily), after which tumor volumes were measured twice weekly. After 5 weeks, tumor volumes in mice treated with CPT or BTZ alone were significantly reduced compared to untreated controls. However, tumor volumes were significantly diminished following combined CTP/BTZ exposure compared to individual treatment (*p < 0.05 vs. Resistant to bortezomib, **p < 0.01 vs. CPT; Figure 4D). After 5 weeks post‐treatment (Day 36), tumors were excised and imaged (Figure 4E). Western blot analysis performed on tumor specimens revealed that combined CPT/BTZ treatment triggered enhanced PARP cleavage, TRAF3 up‐regulation, and c‐FLIP down‐regulation (Figure 4F), as observed in vitro. Finally, CPT/BTZ treatment induced minimal toxicity and weight loss (p > 0.05, Figure 4G), indicating that the CPT/BTZ regimen is active and tolerable in vivo.
+
+6 DISCUSSION
+
+Resistant to bortezomib represents a staple for MM treatment at all stages of the disease. Circularly permuted TRAIL a recombinant mutant of human Apo2L/TRAIL, is a novel antitumor candidate for MM and other hematologic malignancies. Resistant to bortezomib promotes apoptosis through the mitochondrial pathway, 26 while TRAIL mainly acts through the extrinsic apoptotic pathway. Our group and others have shown that concomitant intrinsic and extrinsic apoptotic pathway activation robustly induces apoptosis in malignant cells. 17 BTZ/TRAIL synergism has been observed in various cell types, including MM. 27 However, whether such interactions could be extended to CPT, particularly in bortezomib‐resistant MM cells, remains unknown.
+
+The present studies demonstrate that CPT/BTZ synergistically induced apoptosis in diverse MM cell lines for example, U266, H929 and 8226, including BTZ‐resistant PS‐R cells. 28 However, modestly higher BTZ concentrations were required in the latter (e.g., 10–15 nM vs. 2–5 nM) to achieve synergism, arguing that BTZ resistance is not completely circumvented. Nevertheless, the former concentrations are still low and readily achievable in the plasma. 3
+
+Death receptors DR4 and DR5 were detected on the surface of both U266 and BTZ‐resistant PS‐R cells after CPT/BTZ treatment. Compared to controls, DR4 endocytosis was markedly increased in cells exposed to both agents, whereas only modest effects on DR5 in both U266 and PS‐R cells, in contrast to findings in solid tumor 29 and myeloma cells 30 exposed to BTZ and TRAIL in which DR5 was implicated in cell death. Our findings implicate endocytosis of DR4, but not DR5, in CPT/BTZ‐mediated apoptosis in MM cells.
+
+The ability of TRAIL to activate the extrinsic apoptosis is well described, 31 and FADD like interleukin‐1‐β converting enzyme inhibitor protein (c‐FLIP), a natural caspase inhibitor protein, regulates extrinsic pathway‐mediated apoptosis. c‐FLIP overexpression inhibits apoptosis mediated by death receptors for example, Fas and TRAIL‐R. 32 , 33 , 34 Increased c‐FLIP expression in PS‐R cells suggests that the alternative apoptotic pathway may be inhibited in BTZ‐resistant cells. Interestingly, CPT/BTZ down‐regulated c‐FLIP, enhanced cleavage/activation of caspase‐8, caspase‐3 and PARP, and up‐regulated FADD. Significantly, CPT/BTZ induced cell death was markedly reduced in dominant‐negative FADD or caspase‐8 cells, indicating that extrinsic cascade activation plays an important functional role in CPT/BTZ‐induced cell death.
+
+The CPT/BTZ regimen activated the intrinsic apoptotic pathway, indicated by caspase‐3 and 9 cleavage, contributing significantly to regimen activity. This pathway has been implicated in bortezomib lethality, 35 and potentiation by CPT may reflect cooperation between intrinsic and extrinsic apoptosis. 36 Intrinsic apoptosis is regulated by anti‐apoptotic proteins such as BCL‐2 and BCL‐XL) 37 and the observation that both BCL‐2 and BCL‐XL overexpression protected cells from CPT/BTZ implicates the intrinsic apoptosis in the regimen's activity.
+
+In view of evidence that bortezomib kills malignant cells by interrupting the canonical NF‐κB, 38 the finding that CPT/BTZ did not inhibit canonical NF‐κB signaling was unanticipated, Instead, combining CPT with BTZ increased phosphorylation and nuclear accumulation of p65, arguing against a role for canonical NF‐κB pathway interruption. Notably, in some malignant hematopoietic cells, BTZ activates this pathway by inducing autophagic degradation of IκBα. 39
+
+In marked contrast to canonical NF‐κB signaling, the CPT/BTZ regimen diminished non‐canonical pathway activation. TNF receptor associated factors 3 is an inhibitor of non‐classical NF‐κB pathway 40 and it negatively regulates the NF‐κB inducing kinase (NIK), which represents a key component of the non‐canonical pathway of NF‐κB activation. 41 Cells treated with CPT and BTZ exhibited pronounced up‐regulation of TRAF3 and down‐regulation of NIK, associated with increased expression of phospho‐p100, reflecting impaired processing of p100 to p52, and diminished expression/nuclear accumulation of p52, hallmarks of non‐canonical NF‐kB activation. Notably, TRAF3 knockdown significantly reduced CPT/BTZ toxicity, collectively arguing that the inhibition of the non‐canonical, but not the canonical NF‐κB pathway, plays a critical functional role in CPT/BTZ activity.
+
+Multiple myeloma cell survival is highly dependent on the bone marrow microenvironment for survival and drug resistance. 42 Notably, the CPT/BTZ regimen effectively induced cell death in MM cells co‐cultured with human stromal. Non‐canonical pathway activation is implicated in microenvironmental forms of resistance, 17 , 43 suggesting that pathway inactivation by CPT/BTZ contributes to stromal cell resistance circumvention.
+
+The CPT/BTZ regimen induced cell death in primary CD138+ MM cells, including cells from proteasome inhibitor‐resistant patients. In six of 10 RR MM specimens and two of two newly diagnosed MM samples, primary cells displayed synergistic CPT/BTZ interactions. Unfortunately, the small number of CD138+ cells available from these specimens made it impractical to determine whether events responsible for CPT/BTZ synergism in cell lines were operative in primary cells. Future studies may address this question.
+
+The present findings demonstrate that CPT/BTZ co‐administration was well tolerated and effective in a PDX mouse model. Unlike other hematologic malignancies for example, adult acute myeloid leukemia, PDX myeloma models are difficult to generate. 44 In some cases, genetically humanized mice are required for primary MM cell growth. 44 Here, primary MM cells were identified that propagated in standard NSG mice. Cell identity was validated using human antibodies and probes consistent with a myeloma origin for example, CD45dim/CD38+/CD56+/CD19‐/CD28+/CD138+or−/cLambda+/cKaappa−. Importantly, combined CPT/BTZ treatment in vivo induced significantly greater tumor growth inhibition compared with single‐agent administration associated with minimal toxicity. That tumor cells extracted from post‐treatment mice displayed several of the in vitro findings (e.g., PARP cleavage, TRAF3 up‐regulation, and down‐regulation of c‐FLIP) suggests that analogous mechanisms for example, involvement of the extrinsic apoptotic and non‐canonical NF‐κB pathways operate in vivo.
+
+In summary, the present studies indicate that CPT interacts synergistically with BTZ in MM cells, including those resistant to BTZ, through a mechanism involving activation of both the intrinsic and extrinsic apoptotic pathways and inactivation of the non‐canonical NF‐κB cascade. Significantly, similar interactions were observed in primary MM cells, and the CPT/BTZ regimen was active in a MM PDX model with minimal toxicity. Given the introduction of CPT into the clinical arena in MM, these findings provide a theoretical foundation for considering a regimen combining CPT with BTZ in relapsed/refractory MM patients, particularly those resistant to proteasome inhibitors. They also provide mechanistic insights that could guide the rational design of correlative pharmacodynamic assays accompanying future trials.
+
+AUTHOR CONTRIBUTIONS
+
+Yun Leng, Xiaoyan Hu and Lin Li performed in vitro and in vivo studies, carried out statistical analyses, designed the figures, and wrote the manuscript; Jewel Nkwocha and Kanika Sharma contributed to experimental procedures and checked the original data; Toshihisa Satta collected and analyzed patient samples; Huixing Zhou and Zhiyao Zhang contributed to experimental procedures; Liang Zhou designed the figures and wrote the manuscript; Wenming Chen and Steven Grant conceived and supervised the study and edited the figures and the manuscript.
+
+CONFLICT OF INTEREST
+
+The authors declare that they have no competing interests.
+
+PEER REVIEW
+
+The peer review history for this article is available at https://publons.com/publon/10.1002/hon.3045.
+
+ETHICS STATEMENT
+
+Informed consent was obtained from all patients participating in this study. All animal studies were conducted according to the Ethics Committee of Beijing Chaoyang Hospital and complied with the institutional guidelines for the care and use of animals.
+
+Supporting information
+
+Supporting Information S1
+
+Click here for additional data file.
+
+ACKNOWLEDGMENTS
+
+This work was supported by awards R01CA205607, P30CA16059, and UM1CA186644.
+
+DATA AVAILABILITY STATEMENT
+
+All original source data (chiefly Western blot data) linked to the figures in the manuscript are shared on the website OSFHOME. https://osf.io/yu4vs/?view_only=f7f365c2b610497eb3bd9e8799057bc8.
+==== Refs
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+27 Koschny R , Ganten TM , Sykora J , et al. TRAIL/bortezomib cotreatment is potentially hepatotoxic but induces cancer‐specific apoptosis within a therapeutic window. Hepatology. 2007;45 (3 ):649‐658. 10.1002/hep.21555 17326159
+28 Pei XY , Dai Y , Felthousen J , et al. Circumvention of Mcl‐1‐dependent drug resistance by simultaneous Chk1 and MEK1/2 inhibition in human multiple myeloma cells. PLoS One. 2014;9 (3 ):e89064. 10.1371/journal.pone.0089064 24594907
+29 Guidelines on the investigation and management of thrombophilia. The British committee for standards in haematology. J Clin Pathol. 1990;43 (9 ):703‐709.2212062
+30 Lee S , Yagita H , Sayers TJ , Celis E . Optimized combination therapy using bortezomib, TRAIL and TLR agonists in established breast tumors. Cancer Immunol Immunother. 2010;59 (7 ):1073‐1081. 10.1007/s00262-010-0834-0 20213120
+31 Woo SM , Kwon TK . E3 ubiquitin ligases and deubiquitinases as modulators of TRAIL‐mediated extrinsic apoptotic signaling pathway. BMB Rep. 2019;52 (2 ):119‐126. 10.5483/bmbrep.2019.52.2.011 30638181
+32 Santo L , Hideshima T , Kung AL , et al. Preclinical activity, pharmacodynamic, and pharmacokinetic properties of a selective HDAC6 inhibitor, ACY‐1215, in combination with bortezomib in multiple myeloma. Blood. 2012;119 (11 ):2579‐2589. 10.1182/blood-2011-10-387365 22262760
+33 Andreu‐Vieyra CV , Berenson JR . The potential of panobinostat as a treatment option in patients with relapsed and refractory multiple myeloma. Ther Adv Hematol. 2014;5 (6 ):197‐210. 10.1177/2040620714552614 25469210
+34 Riley JS , Hutchinson R , McArt DG , et al. Prognostic and therapeutic relevance of FLIP and procaspase‐8 overexpression in non‐small cell lung cancer. Cell Death Dis. 2013;4 (12 ):e951. 10.1038/cddis.2013.481 24309938
+35 Iskandarani A , Bhat AA , Siveen KS , et al. Bortezomib‐mediated downregulation of S‐phase kinase protein‐2 (SKP2) causes apoptotic cell death in chronic myelogenous leukemia cells. J Transl Med. 2016;14 (1 ):69. 10.1186/s12967-016-0823-y 26956626
+36 Wong SHM , Kong WY , Fang CM , et al. The TRAIL to cancer therapy: hindrances and potential solutions. Crit Rev Oncol Hematol. 2019;143 :81‐94. 10.1016/j.critrevonc.2019.08.008 31561055
+37 Campbell KJ , Tait SWG . Targeting BCL‐2 regulated apoptosis in cancer. Open Biol. 2018;8 (5 ):180002. 10.1098/rsob.180002 29769323
+38 Hideshima T , Ikeda H , Chauhan D , et al. Bortezomib induces canonical nuclear factor‐kappaB activation in multiple myeloma cells. Blood. 2009;114 (5 ):1046‐1052. 10.1182/blood-2009-01-199604 19436050
+39 Jia L , Gopinathan G , Sukumar JT , Gribben JG . Blocking autophagy prevents bortezomib‐induced NF‐kappaB activation by reducing I‐kappaBalpha degradation in lymphoma cells. PLoS One. 2012;7 (2 ):e32584. 10.1371/journal.pone.0032584 22393418
+40 Bishop GA . TRAF3 as a powerful and multitalented regulator of lymphocyte functions. J Leukoc Biol. 2016;100 (5 ):919‐926. 10.1189/jlb.2mr0216-063r 27154354
+41 Liao G , Zhang M , Harhaj EW , Sun SC . Regulation of the NF‐kappaB‐inducing kinase by tumor necrosis factor receptor‐associated factor 3‐induced degradation. J Biol Chem. 2004;279 (25 ):26243‐26250. 10.1074/jbc.m403286200 15084608
+42 Gupta VA , Matulis SM , Conage‐Pough JE , et al. Bone marrow microenvironment‐derived signals induce Mcl‐1 dependence in multiple myeloma. Blood. 2017;129 (14 ):1969‐1979. 10.1182/blood-2016-10-745059 28151428
+43 Moreaux J , Legouffe E , Jourdan E , et al. BAFF and APRIL protect myeloma cells from apoptosis induced by interleukin 6 deprivation and dexamethasone. Blood. 2004;103 (8 ):3148‐3157. 10.1182/blood-2003-06-1984 15070697
+44 Das R , Strowig T , Verma R , et al. Microenvironment‐dependent growth of preneoplastic and malignant plasma cells in humanized mice. Nat Med. 2016;22 (11 ):1351‐1357. 10.1038/nm.4202 27723723
+

PMC10084376.txt

@@ -0,0 +1,140 @@
+
+==== Front
+Am J Hematol
+Am J Hematol
+10.1002/(ISSN)1096-8652
+AJH
+American Journal of Hematology
+0361-8609
+1096-8652
+John Wiley & Sons, Inc. Hoboken, USA
+
+10.1002/ajh.26602
+AJH26602
+Correspondence
+Correspondences
+Isatuximab plus carfilzomib and dexamethasone in patients with relapsed multiple myeloma based on prior lines of treatment and refractory status: IKEMA subgroup analysis
+CORRESPONDENCE
+Dimopoulos Meletios A. https://orcid.org/0000-0001-8990-3254
+1 mdimop@med.uoa.gr
+
+Moreau Philippe https://orcid.org/0000-0003-1780-8746
+2
+Augustson Bradley 3
+Castro Nelson 4
+Pika Tomas 5
+Delimpasi Sosana https://orcid.org/0000-0001-7523-7510
+6
+De la Rubia Javier 7
+Maiolino Angelo 8
+Reiman Tony 9
+Martinez‐Lopez Joaquin 10
+Martin Thomas 11
+Mikhael Joseph https://orcid.org/0000-0001-9670-2864
+12
+Yong Kwee 13
+Risse Marie‐Laure 14
+Asset Gaelle 15
+Marion Sylvia 16
+Hajek Roman 17 18
+1 The National and Kapodistrian University of Athens Athens Greece
+2 Department of Hematology University Hospital Hôtel‐Dieu Nantes France
+3 Sir Charles Gairdner Hospital Perth Western Australia Australia
+4 Hospital de Cancer de Barretos São Paulo Brazil
+5 Department of Hemato‐Oncology University Hospital Olomouc Olomouc Czech Republic
+6 Department of Haematology General Hospital of Athens Athens Greece
+7 Hematology Department University Hospital La Fe Valencia Spain
+8 Instituto COI de Ensino e Pesquisa and Faculdade de Medicina Universidade Federal do Rio de Janeiro Rio de Janeiro Brazil
+9 Department of Oncology, Saint John Regional Hospital Dalhousie University and University of New Brunswick Saint John New Brunswick Canada
+10 Departamento de Hematología Hospital 12 de Octubre, Complutense University Madrid Spain
+11 UCSF Helen Diller Family Comprehensive Cancer Center San Francisco California USA
+12 Translational Genomics Research Institute, City of Hope Cancer Center Phoenix Arizona USA
+13 Department of Haematology University College Hospital London UK
+14 Sanofi Vitry‐sur‐Seine France
+15 Sanofi Chilly‐Mazarin France
+16 Sanofi Cambridge Massachusetts USA
+17 Department of Hemato‐Oncology University Hospital Ostrava Ostrava Czech Republic
+18 Department of Hemato‐Oncology, Faculty of Medicine University of Ostrava Ostrava Czech Republic
+* Correspondence
+Meletios A. Dimopoulos, National and Kapodistrian University of Athens, School of Medicine, 80 Vasilisis Sofias, Athens 11528, Greece.
+Email: mdimop@med.uoa.gr
+
+04 6 2022
+1 2023
+98 1 10.1002/ajh.v98.1 E15E19
+05 5 2022
+03 3 2022
+09 5 2022
+© 2022 The Authors. American Journal of Hematology published by Wiley Periodicals LLC.
+https://creativecommons.org/licenses/by/4.0/ This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
+
+Sanofi 10.13039/100004339 source-schema-version-number2.0
+cover-dateJanuary 2023
+details-of-publishers-convertorConverter:WILEY_ML3GV2_TO_JATSPMC version:6.2.7 mode:remove_FC converted:10.04.2023
+Funding information Sanofi
+==== Body
+pmc To the Editor:
+
+Patients with multiple myeloma (MM) often relapse or become refractory to successive lines of therapy (LOT), warranting more effective treatments. Novel treatments have improved outcomes; however, MM is associated with a significant patient burden. Patients who are refractory to immunomodulatory drugs and proteasome inhibitors (PIs) have poor prognosis. Many patients with MM are exposed to lenalidomide or bortezomib in early LOT; those refractory to these agents are challenging to treat and represent a high unmet medical need. 1
+
+Based on the Phase 3 ICARIA‐MM study (NCT02990338), 2 isatuximab (Sarclisa), a CD38 monoclonal antibody, is approved in combination with pomalidomide and dexamethasone (Isa‐Pd) for adult patients with relapsed and refractory MM (RRMM) who have received ≥2 prior therapies, including lenalidomide and a PI. Based on the IKEMA study (NCT03275285), 3 to date, isatuximab in combination with carfilzomib and dexamethasone (Isa‐Kd) is approved in the United States for adult patients with relapsed or refractory MM with 1–3 prior LOT, in the European Union for adult patients with MM with ≥1 prior therapy, and in Japan for adult patients with relapsed or refractory MM with one prior treatment.
+
+IKEMA demonstrated that, in patients with relapsed MM, Isa‐Kd significantly improved progression‐free survival (PFS) compared with Kd (hazard ratio [HR] 0.53; 99% confidence interval [CI] 0.32–0.89; p = .0007), with a clinically meaningful increase in minimal residual disease (MRD) negativity and complete response (CR) rates in the intent‐to‐treat population, and a manageable safety profile. 3 We conducted a prespecified subgroup analysis of IKEMA to evaluate the efficacy and safety of Isa‐Kd versus Kd according to number of prior LOT (1 vs. >1), and an exploratory subgroup analysis based on refractoriness to two frequently used front‐line agents, lenalidomide and bortezomib.
+
+Randomized patients (N = 302) received Isa‐Kd (n = 179) or Kd (n = 123). Subgroup analyses were conducted by number of prior LOT (1 vs. >1) as entered by the investigator at randomization and by refractory status (defined as: (i) reason for discontinuation was progression, or (ii) progression ≤60 days posttreatment, or (iii) best response was stable disease or progressive disease). The study design and procedures are described in Supporting Information.
+
+In the overall population, patients received a median (range) of 2 (1–4) prior LOT in both treatment arms; 44.4% of patients received 1 prior line, 32.8% were lenalidomide‐refractory, and 30.1% were bortezomib‐refractory. Table S1 shows patient baseline characteristics in each subgroup. Compared with Kd, more patients with Isa‐Kd were aged ≥75 years in the 1 prior line subgroup, fewer patients were International Staging System Stage I in the >1 prior line subgroup, and more were aged <65 years in the lenalidomide‐refractory subgroup.
+
+Exposure to study treatment was longer with Isa‐Kd than Kd. The median (range) number of treatment cycles with Isa‐Kd versus Kd was: 20.0 (1–25) versus 16.5 (1–28), 1 prior line; 18.0 (1–27) versus 12.5 (1–26), >1 prior line; 14.0 (1–27) versus 11.5 (1–28), lenalidomide‐refractory; 13.5 (1–26) versus 13.0 (1–28), bortezomib‐refractory. More patients with Isa‐Kd than Kd received ≥18 cycles in all subgroups: 65.8% versus 48.1%, 1 prior line; 51.0% versus 32.4%, >1 prior line; 43.9% versus 33.3%, lenalidomide‐refractory; 38.5% versus 25.6%, bortezomib‐refractory. These results are consistent with IKEMA overall population where a longer treatment duration was reported with Isa‐Kd (median [range] number of cycles 19.0 [1–27] and 57.6% patients with ≥18 cycles) than Kd (14.5 [1–28] and 39.3% patients with ≥18 cycles).
+
+Consistent with IKEMA overall population, PFS improvement was observed with Isa‐Kd versus Kd across all subgroups analyzed, regardless of number of prior LOT (HR 0.59 [95% CI, 0.31–1.12], 1 prior line; HR 0.48 [95% CI, 0.29–0.78], >1 prior line) or refractory status (HR 0.60 [95% CI, 0.34–1.1], lenalidomide‐refractory; HR 0.69 [95% CI, 0.35–1.39], lenalidomide‐refractory at last regimen; HR 0.62 [95% CI, 0.33–1.16], bortezomib‐refractory; HR 0.38 [95% CI, 0.16–0.92], bortezomib‐refractory at last regimen; Figure 1A). The p values for interaction suggest no interaction with any of the parameters evaluated. The PFS‐event‐free probability at 18 months for Isa‐Kd versus Kd was: 77% versus 64%, 1 prior line; 68% versus 45%, >1 prior line; 53% versus 31%, lenalidomide‐refractory patients; and 63% versus 43%, bortezomib‐refractory (Table S2).
+
+FIGURE 1 Efficacy with Isa‐Kd versus Kd. (A) PFS by number of prior lines of therapy and refractory status; depth of response by (B) number of prior lines of therapy, (C) lenalidomide‐refractory status, or (D) bortezomib‐refractory status. Bor, bortezomib; CI, confidence interval; CR, complete response; d, dexamethasone; IMiD, immunomodulatory drug; Isa, isatuximab; K, carfilzomib; Len, lenalidomide; mPFS, median progression‐free survival; MRD−, minimal residual disease negativity; NC, not calculated; ORR, overall response rate; PFS, progression‐free survival; PI, proteasome inhibitor; VGPR, very good partial response
+
+Overall response rate (ORR) in IKEMA overall population was high in both treatment groups with no statistically significant difference (87%, Isa‐Kd vs. 83%, Kd; one‐sided p = 0.19); thus, p values of subsequent key secondary endpoints (≥very good partial response [VGPR] and MRD negativity rates) were provided for descriptive purposes only. 3 Similar results were observed irrespective of number of prior LOT (Figure 1B; 87.5% vs. 85.5%, 1 prior line; 85.9% vs. 80.9%, >1 prior line), but a trend toward higher ORR with Isa‐Kd versus Kd was seen in refractory subgroups (Figure 1C,D; 82.5% vs. 71.4%, lenalidomide‐refractory; 88.9% vs. 74.2%, lenalidomide‐refractory at last regimen; 75.0% vs. 71.8%, bortezomib‐refractory; 84.4% vs. 73.9%, bortezomib‐refractory at last regimen).
+
+Consistent with overall population (≥VGPR 73% vs. 56%, p = 0.0011; MRD negativity 29.6% vs. 13.0%, p = 0.0004), 3 numerically and clinically meaningful higher ≥VGPR and MRD negativity rates, respectively, with Isa‐Kd versus Kd were observed across all subgroups: 1 prior line (75.0% vs. 61.8% and 33.8% vs. 18.2%), >1 prior line (70.7% vs. 51.5% and 26.3% vs. 8.8%), lenalidomide‐refractory (66.7% vs. 35.7% and 24.6% vs. 9.5%), lenalidomide‐refractory at last regimen (72.2% vs. 38.7% and 27.8% vs. 9.7%), bortezomib‐refractory (55.8% vs. 51.3% and 17.3% vs. 10.3%), and bortezomib‐refractory at last regimen (62.5% vs. 47.8% and 25.0% vs. 8.7%; Figure 1). A clinically meaningful difference in CR rates with Isa‐Kd versus Kd was observed for >1 prior line subgroup (38.4% vs. 20.6%) and 1 prior line subgroup (41.3% vs. 36.4%; Figure 1). Similarly, a clinically meaningful difference in CR rates with Isa‐Kd versus Kd was also observed in refractory subgroups: lenalidomide‐refractory (38.6% vs. 11.9%), lenalidomide‐refractory at last regimen (47.2% vs. 12.9%), bortezomib‐refractory (28.8% vs. 17.9%), and bortezomib‐refractory at last regimen (31.3% vs. 17.4%).
+
+The incidence of patients with all‐grade treatment‐emergent adverse events (TEAEs) in all subgroups was similar to IKEMA safety population 3 (97.2%, Isa‐Kd vs. 95.9%, Kd; Table S3), with infusion‐related reactions being the most frequent (Tables S4 and S5). Other most common TEAEs reported more frequently (≥10% patients) with Isa‐Kd versus Kd included pneumonia and bronchitis in 1 prior line; upper respiratory infection, fatigue, and vomiting in >1 prior line (Table S4); diarrhea, cough, hypertension, fatigue, dyspnea, upper respiratory tract infection, constipation, bronchitis, arthralgia, and nausea in lenalidomide‐refractory; and cough, fatigue, and bronchitis in bortezomib‐refractory subgroups (Table S5).
+
+The incidence of patients with Grade ≥3 TEAEs was higher with Isa‐Kd versus Kd across all subgroups (77.2% vs. 64.8%, 1 prior line; 76.5% vs. 69.1%, >1 prior line; 73.7% vs. 61.9%, lenalidomide‐refractory; 76.9% vs. 66.7%, bortezomib‐refractory), and consistent with overall safety population 3 (76.8% vs. 67.2%; Table S3). 3 The most frequent Grade ≥3 TEAEs were hypertension and pneumonia, with similar incidences between treatment arms in all subgroups (Tables S4 and S5).
+
+The incidence of patients with serious TEAEs with Isa‐Kd versus Kd was similar to that in the overall population 3 (59.3% vs. 57.4%) in all subgroups, except in 1 prior line (62.0% vs. 48.1%) and lenalidomide‐refractory (59.6% vs. 50.0%) subgroups (Table S3). Grade 5 TEAEs occurred in 3.8% versus 0% in 1 prior line, 3.1% versus 5.9% in >1 prior line, 3.5% versus 4.8% in lenalidomide‐refractory, and 1.9% versus 7.7% in bortezomib‐refractory patients.
+
+The incidence of patients with TEAEs leading to discontinuations was lower or similar with Isa‐Kd versus Kd in all subgroups (8.9% vs. 11.1%, 1 prior line; 8.2% vs. 16.2%, >1 prior line; 7.0% vs. 11.9%, lenalidomide‐refractory; 3.8% vs. 17.9%, bortezomib‐refractory; Table S3).
+
+The current analysis strongly supports similar treatment benefit of Isa‐Kd versus Kd on PFS and depth of response regardless of number of prior lines or lenalidomide‐ or bortezomib‐refractory status versus the control arm Kd, which has shown in ENDEAVOR subgroup analysis to be an efficient treatment in lenalidomide‐ or bortezomib‐exposed patients, irrespective of number or type of prior LOT, with improved outcomes versus bortezomib‐dexamethasone. 4 CANDOR reported favorable benefit‐to‐risk profile of another CD38 antibody, daratumumab, plus Kd versus Kd in patients with RRMM, regardless of number of prior lines (1 vs. ≥2) or refractoriness to bortezomib/ixazomib or lenalidomide. 5 One key difference between these studies is the lack of M‐protein interference assay for isatuximab; CR was assessed without correction for M‐protein interference and is likely underestimated in IKEMA. The clinical significance of numerical differences observed between IKEMA and CANDOR has not been elucidated. Notably, a similar ICARIA‐MM subgroup analysis showed that Isa‐Pd improved PFS and ORR regardless of number of prior LOT and in patients who were lenalidomide‐refractory, lenalidomide‐refractory at last line, and double‐refractory to lenalidomide and PIs. 2 , 6
+
+Limitations of the current study are exclusion of daratumumab‐treated patients and a relatively small number of patients owing to subgroup analysis (limiting the statistical analysis power). However, the efficacy and safety benefits of Isa‐Kd in patients with relapsed MM were seen irrespective of number of prior LOT, or lenalidomide‐ or bortezomib‐refractory status and were consistent with IKEMA overall population. Isa‐Kd is a new treatment option for patients with relapsed MM, particularly in the difficult‐to‐treat lenalidomide‐ and bortezomib‐refractory patients.
+
+AUTHOR CONTRIBUTIONS
+
+Marie‐Laure Risse was responsible for study oversight. Philippe Moreau and Thomas Martin were coprincipal investigators of the study. Meletios A. Dimopoulos, Bradley Augustson, Nelson Castro, Tomas Pika, Sosana Delimpasi, Javier De la Rubia, Angelo Maiolino, Tony Reiman, Joaquin Martinez‐Lopez, Joseph Mikhael, Kwee Yong, and Roman Hajek were investigators in the study and contributed to data acquisition and analysis. Philippe Moreau, Thomas Martin, Marie‐Laure Risse, and Gaelle Asset designed the study. Marie‐Laure Risse, Gaelle Asset, and Sylvia Marion contributed to analysis and interpretation of data for the work. All authors revised the work for important intellectual content and assume responsibility for data integrity and the decision to submit this manuscript for publication, had full access to the study data, edited and reviewed manuscript drafts, and approved the final version for submission.
+
+CONFLICT OF INTEREST
+
+Roman Hajek: Consulting or Advisory role—Takeda, Amgen, Celgene, Abbvie, Bristol‐Myers Squibb, PharmaMar, Janssen‐Cilag, Novartis; Speakers' Bureau—Takeda, Amgen; research funding—Novartis, Bristol‐Myers Squibb, Amgen, Celgene, Takeda. Philippe Moreau: Consulting or Advisory role—Celgene, Janssen, Amgen, GlaxoSmithKline, Sanofi, Abbvie; Honoraria—Celgene, Janssen‐Cilag, Amgen, GlaxoSmithKline, Sanofi, Abbvie. Sosana Delimpasi: Consulting or Advisor role—Janssen, Takeda, Amgen; Speakers' Bureau—Janssen, Takeda, Amgen. Javier De la Rubia: Speakers' Bureau—Amgen, Bristol‐Myers Squibb; Honoraria—Amgen, Celgene, Takeda, Janssen, Sanofi, Bristol‐Myers Squibb. Tony Reiman: Research funding—Sanofi, Terry Fox Research Institute, Canadian Cancer Society, New Brunswick Health Research Foundation, New Brunswick Foundation for Innovation, Canadian Institutes of Health Research; Hematology Disease Site Co‐Chair–Canadian Cancer Trials Group. Joaquin Martinez‐Lopez: Honoraria—Sanofi, Janssen, Novartis, Bristol‐Myers Squibb, Incyte, Roche; research funding—Janssen, Novartis, Bristol‐Myers Squibb, Incyte, Roche; nonfinancial support—Janssen, Novartis, Bristol‐Myers Squibb. Thomas Martin: Consulting or advisory role—Juno Therapeutics, GlaxoSmithKline; research funding—Sanofi, Amgen, Janssen. Joseph Mikhael: Honoraria—Amgen, Karyopharm Therapeutics, Sanofi, Janssen, Celgene, GlaxoSmithKline, Takeda. Marie‐Laure Risse, Gaelle Asset, and Sylvia Marion are employed by Sanofi and may have stock and/or stock options in the company. Meletios A. Dimopoulos: Consulting or advisory role—Amgen, Janssen‐Cilag, Takeda, Bristol‐Myers Squibb, Beigene; honoraria—Amgen, Takeda, Janssen‐Cilag, Bristol‐Myers Squibb, Beigene.
+
+Supporting information
+
+Appendix S1 Supporting Information
+
+Click here for additional data file.
+
+ACKNOWLEDGMENTS
+
+The IKEMA study was sponsored and funded by Sanofi. The authors thank the study centers and investigators, for their contributions to the study. Medical writing support was provided by Smitha Reddy, PhD of Elevate Medical Affairs, contracted by Sanofi for publication support services.
+
+DATA AVAILABILITY STATEMENT
+
+Qualified researchers can request access to patient‐level data and related study documents including the clinical study report, study protocol with any amendments, blank case report forms, statistical analysis plan, and dataset specifications. Patient‐level data will be anonymized, and study documents will be redacted to protect the privacy of trial participants. Further details on Sanofi's data‐sharing criteria, eligible studies, and process for requesting access are at: https://www.vivli.org.
+==== Refs
+REFERENCES
+
+1 Raab MS , Fink L , Schoen P , et al. Evolution of multiple myeloma treatment practices in Europe from 2014 to 2016. Br J Haematol. 2019;185 (5 ):981‐984.30467828
+2 Attal M , Richardson PG , Rajkumar SV , et al. Isatuximab plus pomalidomide and low‐dose dexamethasone versus pomalidomide and low‐dose dexamethasone in patients with relapsed and refractory multiple myeloma (ICARIA‐MM): a randomised, multicentre, open‐label, phase 3 study. Lancet. 2019;394 (10214 ):2096‐2107.31735560
+3 Moreau P , Dimopoulos M , Mikhael J , et al. Isatuximab, carfilzomib, and dexamethasone in relapsed multiple myeloma (IKEMA): a multicentre, open‐label, randomised phase 3 trial. Lancet. 2021;397 (10292 ):2361‐2371.34097854
+4 Moreau P , Joshua D , Chng WJ , et al. Impact of prior treatment on patients with relapsed multiple myeloma treated with carfilzomib and dexamethasone vs bortezomib and dexamethasone in the phase 3 ENDEAVOR study. Leukemia. 2017;31 (1 ):115‐122.27491641
+5 Quach H , Nooka A , Samoylova O , et al. Carfilzomib, dexamethasone and daratumumab in relapsed or refractory multiple myeloma: results of the phase III study CANDOR by prior lines of therapy. Br J Haematol. 2021;194 (4 ):784‐788.34046887
+6 Bringhen S , Pour L , Vorobyev V , et al. Isatuximab plus pomalidomide and dexamethasone in patients with relapsed/refractory multiple myeloma according to prior lines of treatment and refractory status: ICARIA‐MM subgroup analysis. Leuk Res. 2021;104 :106576.33839618
+

PMC10087368.txt

@@ -0,0 +1,64 @@
+
+==== Front
+Am J Hematol
+Am J Hematol
+10.1002/(ISSN)1096-8652
+AJH
+American Journal of Hematology
+0361-8609
+1096-8652
+John Wiley & Sons, Inc. Hoboken, USA
+
+36030404
+10.1002/ajh.26704
+AJH26704
+Clinical Pearls in Blood Diseases
+Clinical Pearls in Blood Diseases
+Morphology Update
+G6PD deficiency in patients identified as female
+Bain et al.
+Bain Barbara J. https://orcid.org/0000-0003-3077-4579
+1 2 b.bain@imperial.ac.uk
+
+Myburgh Jane 1
+Lund Kirstin 3
+Chaidos Aristeidis 4
+1 Blood Sciences, Imperial College Healthcare NHS Trust St Mary's Hospital London UK
+2 Centre for Haematology, St Mary's Hospital Campus of Imperial College Faculty of Medicine St Mary's Hospital London UK
+3 Department of Paediatric Haematology St Mary's Hospital London UK
+4 Hugh & Josseline Langmuir Centre for Myeloma Research, Centre for Haematology, Department of Immunology and Inflammation, Imperial College London Hammersmith Hospital London UK
+* Correspondence
+Barbara J. Bain, Blood Sciences, St Mary's Hospital, Praed Street, London, W2 1NY, United Kingdom.
+Email: b.bain@imperial.ac.uk
+
+12 9 2022
+2 2023
+98 2 10.1002/ajh.v98.2 359360
+22 8 2022
+23 8 2022
+© 2022 The Authors. American Journal of Hematology published by Wiley Periodicals LLC.
+https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.
+
+source-schema-version-number2.0
+cover-dateFebruary 2023
+details-of-publishers-convertorConverter:WILEY_ML3GV2_TO_JATSPMC version:6.2.7 mode:remove_FC converted:11.04.2023
+Bain BJ , Myburgh J , Lund K , Chaidos A . G6PD deficiency in patients identified as female. Am J Hematol. 2023;98 (2 ):359‐360. doi:10.1002/ajh.26704 36030404
+==== Body
+pmc
+
+Symptomatic glucose‐6‐phosphate dehydrogenase (G6PD) deficiency, being X‐linked, is seen particularly in males. Cases in female homozygotes are uncommon but well recognized. Symptomatic hemolysis can also occur in female heterozygotes since cells that express the defective gene are prone to lysis. Since lyonization can be unbalanced, hemolysis is sometimes severe. There are also other uncommon circumstances when G6PD deficiency leads to clinically apparent hemolysis in females or patients identified as female. Recognition of this possibility requires close liaison between clinical and laboratory staff.
+
+The left image (both images ×100 objective) is the blood film of a 3‐year‐old Syrian girl who presented with acute hemolysis 3 days after eating falafel. Her blood count showed a hemoglobin concentration (Hb) of 74 g/L and an MCHC of 375 g/L. The image shows irregularly contracted cells and numerous “blister cells” or “hemighosts.” In addition, there is one cell that is virtually devoid of hemoglobin, a “ghost cell.” G6PD was 2.2 U/gHb (normal range 6.3–11.2). The increased MCHC reflects the presence of numerous irregularly contracted cells. Because the hemolysis was unusually severe for a female, DNA analysis was performed. This showed homozygosity for G6PD c.653C>T; p.Ser218Phe, also known as G6PD Mediterranean. The patient's 5‐year‐old brother presented simultaneously with an Hb of 70 g/L and a G6PD assay of 2 U/gHb. The patient's father, who was known to be G6PD deficient, had milder haemolysis. Falafel are traditional made with chick peas but are sometimes made with fava beans (broad beans) or with a mixture of the two. Patients and physicians may not be aware of this possibility.
+
+The right image is of the blood film of an adult South Asian patient identified in hospital records as “female,” who presented with fever and symptomatic anemia. It shows similar features to the first patient with numerous irregularly contracted cells and blister cells. In addition, Heinz bodies are apparent, precipitated within the otherwise empty area of cytoplasm within blister cells. Hb was 70 g/L. A G6PD assay confirmed deficiency. Again, the hemolysis was unusually severe for a female and further enquiries were made. It was discovered that the patient was a trans female, genetically male but choosing to identify in medical records as female.
+
+Other circumstances in which G6PD deficiency leading to symptomatic hemolysis of unexpected severity in a female that have been reported include Turner syndrome (female patients with a single X chromosome) and females who have been transplanted with bone marrow from a G6PD‐deficient male. As it becomes possible in various jurisdictions for trans persons to legally change their gender in official records, diagnostic conundrums are likely to arise. Close liaison between clinical and laboratory staff is essential for these patients and also for those who have had a bone marrow transplant that is unknown to the laboratory as it was performed in another hospital.
+
+CONFLICT OF INTEREST
+
+The authors declare no conflict of interest.
+
+DATA AVAILABILITY STATEMENT
+
+Data availability statement is not applicable.
+

PMC10091809.txt

@@ -0,0 +1,395 @@
+
+==== Front
+Br J Haematol
+Br J Haematol
+10.1111/(ISSN)1365-2141
+BJH
+British Journal of Haematology
+0007-1048
+1365-2141
+John Wiley and Sons Inc. Hoboken
+
+36205375
+10.1111/bjh.18479
+BJH18479
+BJH-2022-01337.R1
+Original Paper
+Haematological Malignancy–Clinical
+Real‐world experience with belantamab mafodotin therapy for relapsed/refractory multiple myeloma: A multicentre retrospective study
+Shragai Tamir https://orcid.org/0000-0002-3741-689X
+1 2 tamir1shragai@gmail.com
+
+Magen Hila https://orcid.org/0000-0002-3990-394X
+2 3
+Lavi Noa https://orcid.org/0000-0002-2511-8940
+4 5
+Gatt Moshe https://orcid.org/0000-0002-9426-4482
+6 7
+Trestman Svetlana 1
+Zektser Miri 8
+Ganzel Chezi https://orcid.org/0000-0002-1722-4807
+7 9
+Jarchowsky Osnat 2 10
+Berger Tamar https://orcid.org/0000-0001-5547-6936
+2 11
+Tadmor Tamar https://orcid.org/0000-0002-3435-8612
+5 12
+Leiba Merav 13 14
+Hertzog‐Tzarfaty Katrin 15
+Horowitz Netanel 4 5
+Shapira Michael 16
+Varssano David 2 17
+Berger Yoav 18
+Frenkel Shahar 7 19
+Krauthammer Mark 2 17
+Avivi Irit 1 2
+Luttwak Efrat 1 2
+Cohen Yael C. https://orcid.org/0000-0002-9061-7287
+1 2
+for the Israeli myeloma study group
+1 Department of Haematology Tel‐Aviv Sourasky Medical Center Tel‐Aviv Israel
+2 Sackler Faculty of Medicine Tel‐Aviv University Tel‐Aviv Israel
+3 Department of Haematology Chaim Sheba Medical Center Ramat‐Gan Israel
+4 Department of Haematology Rambam Health Care Campus Haifa Israel
+5 Faculty of Medicine Technion‐Israel Institute of Technology Haifa Israel
+6 Department of Haematology Hadassah Medical Center Jerusalem Israel
+7 Faculty of Medicine Hebrew University of Jerusalem Jerusalem Israel
+8 Department of Haematology Soroka University Medical Center Beer‐Sheva Israel
+9 Department of Haematology Shaare Zedek Medical Center Jerusalem Israel
+10 Department of Haematology Meir Medical Center Kfar‐Saba Israel
+11 Institute of Haematology Davidoff Cancer Center, Rabin Medical Center Petah‐Tikva Israel
+12 Department of Haematology Bnai‐Zion Medical Center Haifa Israel
+13 Department of Haematology Assuta University Hospital Ashdod Israel
+14 Faculty of Health Science Ben‐Gurion University of the Negev Beer‐Sheva Israel
+15 Department of Haematology Shamir Medical Center Rishon‐Lezion Israel
+16 Department of Haematology Assuta Ramat‐HaHayal Tel‐Aviv Israel
+17 Department of Ophthalmology Tel‐Aviv Sourasky Medical Center Tel‐Aviv Israel
+18 Department of Ophthalmology Chaim Sheba Medical Center Ramat‐Gan Israel
+19 Department of Ophthalmology Hadassah Medical Center Jerusalem Israel
+* Correspondence
+Tamir Shragai, Department of Haematology, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel.
+Email: tamir1shragai@gmail.com
+
+07 10 2022
+1 2023
+200 1 10.1111/bjh.v200.1 4553
+23 8 2022
+04 7 2022
+14 9 2022
+© 2022 The Authors. British Journal of Haematology published by British Society for Haematology and John Wiley & Sons Ltd.
+https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.
+
+Summary
+
+Belantamab mafodotin, an immuno‐conjugate targeting B‐cell maturation antigen, showed single‐agent activity in phase 1 and 2 studies, and was recently approved for heavily pretreated relapsed/refractory multiple myeloma (RRMM) patients. Real‐world data and long‐term follow‐up are scarce. We conducted a multisite retrospective study aimed to assess safety and efficacy of belantamab mafodotin monotherapy administered via the GSK expanded access compassionate care programme. One‐hundred and six RRMM patients were treated with belantamab mafodotin between July 2019 and March 2021. The median age was 69.4 years. Patients were heavily pretreated with a median of six (range 2–11) prior therapy lines. Major adverse effects included ocular toxicity (keratopathy 68.4%, grade ≥3: 40.5%; blurred vision 36.8%, grade ≥3: 6.3%), thrombocytopenia (27.4%, grade ≥3: 17.9%) and infections (11.3%, grade ≥3: 7.5%). Median follow‐up time was 11.9 [95% confidence interval (CI) 10.0–13.8] months. Overall response rate was 45.5%. Median progression‐free survival was 4.7 (95% CI 3.5–5.9) months in the entire cohort and 8.8 (95% CI 6.6–10.9) months among responders. Median overall survival was 14.5 (95% CI 9.5–19.6) months, and not reached for responders. To conclude, in a real‐world setting, belantamab mafodotin monotherapy showed efficacy comparable with the prospective clinical trials, with a tolerable toxicity profile.
+
+immunotherapy
+multiple myeloma
+myeloma therapy
+GSK 10.13039/100004330 source-schema-version-number2.0
+cover-dateJanuary 2023
+details-of-publishers-convertorConverter:WILEY_ML3GV2_TO_JATSPMC version:6.2.7 mode:remove_FC converted:12.04.2023
+Shragai T , Magen H , Lavi N , Gatt M , Trestman S , Zektser M , et al. Real‐world experience with belantamab mafodotin therapy for relapsed/refractory multiple myeloma: A multicentre retrospective study. Br J Haematol. 2023;200 (1):45–53. 10.1111/bjh.18479 36205375
+
+Tamir Shragai and Hila Magen share equal contribution.
+
+Local EC number: 0362‐18.
+==== Body
+pmcAbbreviations
+
+AE adverse event
+
+AKI acute kidney injury
+
+ASCT autologous stem‐cell transplantation
+
+BCMA B‐cell maturation antigen
+
+CI confidence interval
+
+CMV cytomegalovirus
+
+DOR duration of response
+
+EMD extramedullary disease
+
+FISH fluorescence in‐situ hybridization
+
+HDM high‐dose melphalan
+
+IMiD immunomodulatory drug
+
+ISS international staging system
+
+MM multiple myeloma
+
+MoAb monoclonal antibody
+
+ORR overall response rate
+
+OS overall survival
+
+PFS progression‐free survival
+
+PI proteasome inhibitor
+
+PR partial response
+
+rISS revised international staging system
+
+RRMM relapsed/refractory multiple myeloma
+
+TLS tumour lysis syndrome
+
+TTNT time to next treatment
+
+INTRODUCTION
+
+Despite the advances in management of multiple myeloma (MM), outcome remains poor for triple‐class‐refractory patients, i.e., patients who are refractory to immunomodulatory agents (IMiDs; lenalidomide or pomalidomide), proteasome inhibitors (PI; bortezomib, carfilzomib or ixazomib) and anti‐CD38 monoclonal antibodies (MoAbs; daratumumab or isatuximab). In a large retrospective study, the median overall survival (OS) rate of such patients was 9.2 months. 1 Prognosis is even worse for penta‐refractory patients (patients refractory to two PIs, three IMiDs and an anti‐CD 38 MoAb), with a median OS of less than 6 months. 1 This population represents an unmet need, and a search for new targeted therapy is ongoing. B‐cell maturation antigen (BCMA) expression was previously shown to be associated with longer survival time of plasma cells. 2 Patients with relapsed/refractory MM (RRMM) have higher levels of BCMA expression. 3 Therefore, BCMA is being extensively studied as a target for anti‐myeloma therapy in various modalities, including chimaeric antibody‐receptor T cells, 4 , 5 , 6 , 7 T‐cell redirecting bispecific antibodies 8 , 9 , 10 and antibody–drug conjugates. 11 , 12 Belantamab mafodotin is a first‐in‐class anti‐BCMA immunoconjugate, recently approved for the treatment of advanced RRMM after four treatment lines in the United States (for triple‐exposed patients) and Europe (triple‐refractory patients). In the first‐in‐human DREAMM1 study, 11 35 heavily pretreated (five median prior therapy lines) RRMM patients who received belantamab mafodotin monotherapy had an overall response rate (ORR) of 60% with a median progression‐free survival (PFS) of 12.0 months and median duration of response (DOR) of 14.3 months in the entire cohort, but only 6.2 month in patients refractory to IMiDs and PIs, and exposed to anti‐CD 38 MoABs. Treatment was well tolerated with thrombocytopenia and corneal toxicity being the major adverse events (AEs). 11 The DREAMM2 phase 2 study randomized 196 heavily pretreated RRMM patients (median number of prior lines: six) to receive belantamab mafodotin either in 3.4 or 2.5 mg/kg dose. 12 The ORR was 31% and 34% for the 2.5 and 3.4 mg/kg doses, respectively. Median PFS was 2.8 months. In a recent update, DOR for the 2.5 mg/kg dosing was reported to be 13.1 months for patients achieving at least partial response (PR) and 11.7 months for patients achieving at least minimal response. 13 Keratopathy was observed in 72% of the patients (24% of the grade ≥3), resulting in treatment discontinuation in four (2%) patients. 12
+
+Prospective trials show encouraging outcomes, yet, clinical trials, in particular registration pivotal trials, tend to apply highly selective eligibility criteria, excluding patients with significant comorbidities such as recent active coronary disease, advanced heart failure, renal failure or any serious or unstable medical condition or lab abnormality or poor performance function. Specific populations tend to be under‐represented in clinical trials, namely frail patients, and patients with aggressive and rapidly progressing disease. 14 Thus, while trial data are essential for establishing the safety and efficacy of drug combinations in a rigorous and unbiased methodology, there is increasing recognition of the complimentary role of real‐world evidence, in understanding the effectiveness of treatment regimens in broader settings, and guiding treatment selection among the multiple alternatives. This is of particular importance with a novel therapy such as belantamab, which presents a new challenge of managing ocular toxicity in collaboration with ophthalmologists. So far, three real‐world experience series were published, describing 39, 15 36 16 and 28 17 very heavily pretreated patients. Response rates were 27%, 33% and 46%, respectively. Median PFS was 1.8, 2.0 and 4.7 months respectively; median OS was 9.2, 6.5 and 7.4 months, respectively. Patients were treated with belantamab mafodotin as monotherapy (95% of the patients in Becnel et al. 15 and 83% in Vaxman et al. 16 ), or in combination with corticosteroids. 17
+
+In this study, we aimed to analyse real‐world outcomes of belantamab mafodotin therapy among a multisite Israeli cohort treated with belantamab mafodotin via the GSK compassionate access programme, and to assess whether clinical trial results are compatible with outcomes in the real‐world setting.
+
+METHODS
+
+This was a retrospective, multisite study, conducted in 12 hospitals throughout Israel. All consecutive RRMM patients aged 18 years or older who received more than a single dose of belantamab mafodotin as monotherapy or in combination with corticosteroids under GSK expanded access compassionate care, from 1 May 2019 through 1 March 2021, were included. Exclusion criteria are mentioned in Appendix S1.
+
+The study was approved by institutional review boards (IRBs) of the participating centres. Data were extracted by review of electronic medical charts and abstracted using the REDCAP electronic data capture tool. 18
+
+High‐risk cytogenetics were defined as the presence of any of the following aberrations: t(4, 14), t(14; 16), del(17p), or 1q21 gain or amplification.
+
+Patients received belantamab mafodotin at an initial dose of 3.4 mg/kg, which was reduced to 2.5 mg/kg in September 2019, according to GSK guidance following DREAMM2 trial 12 results. No premedication was given routinely. Patients received corticosteroids eye‐drops until November 2019, when they were withheld according to GSK instructions. Artificial tears and eye cooling during belantamab mafodotin administration were applied according to the treating physician's decision. Belantamab mafodotin was administered every 21 days unless deemed ineligible due to AEs, in which case dosing was delayed until recovery of toxicity to grade 1 or better.
+
+Patients were considered refractory to a drug in the prior anti‐myeloma regimens if a documented relapse or progression according to the international myeloma working group (IMWG) criteria occurred during, or within 60 days of, drug administration. 19
+
+The primary end‐point was ORR according to IMWG criteria, 19 as reported by the investigator. Secondary outcomes included PFS, OS, DOR, and time to next treatment (TTNT).
+
+Dose delay was defined as doses given beyond 25 days following the preceding dose. Length of delay was defined as number of days from last dose minus 25. Proportion of delayed doses was calculated as the cumulative number of days of each delay, divided by total number of doses beyond first dose, for each patient.
+
+Non‐ocular AEs were assessed using Common Terminology Criteria for AEs version 5.0 (CTCAE v5.0) 20 Ocular side effects were assessed at the beginning of each cycle, and as needed, by an ophthalmologist, and were recorded on a designated form as part of the access programme requirements.
+
+Statistical analysis
+
+Categorical variables were compared with the use of Fisher's exact test or the chi‐squared test. Continuous variables were analysed using the Mann–Whitney test for independent samples. Survival probabilities were estimated by the Kaplan–Meier method. All tests were two‐sided, and p < 0.05 was considered statistically significant. Multivariate analysis was carried out using a logistic regression model and included variables reaching statistical significance (p < 0.05). All analyses were obtained using the statistical software IBM SPSS Statistics for Windows, version 25 (IBM Corporation, 2017).
+
+RESULTS
+
+A total of 106 patients (60 males, 56.6%) who received more than one dose of belantamab mafodotin between May 2019 and March 2021 were included. Three patients were excluded as they received a single dose (none of them discontinued therapy due to AEs). Patient characteristics are presented in Table 1. The median age was 69.4 (range 36.3–88.0) years. Patients were heavily pretreated with a median of six (range 2–11) previous treatment lines.
+
+TABLE 1 Patient characteristics.
+
+		No. of patients with available data	
+Age: median (range)	69.4 (36.3–88.0)	106	
+Age >70, n (%)	52 (49)		
+Male sex n, (%)	60 (56.6)	106	
+ISS at diagnosis, % 1/2/3	43/30/26	76	
+rISS at diagnosis	33/51/15	54	
+Cytogenetics, n %		63	
+High‐risk a	27 (42.8)		
+Double‐hit b	5 (7.9)		
+del17p	12 (19.0)		
+t 4;14	1 (1.6)		
+t 14;16	1 (1.6)		
++1q21	18 (28.5)		
+t 11;14	15 (25.3)		
+EMD n (%)	12 (21.4)	57	
+Previous lines of therapy median, n (range)	6 (2–11)	106	
+Previous exposure, n (%)		106	
+PIs	
+Bortezomib	103 (97.1)		
+Carfilzomib	77 (72.6)		
+IMiDs	
+Lenalidomide	97 (91.5)		
+Pomalidomide	82 (77.3)		
+Daratumumab	101 (95.2)		
+HDM/ASCT	62 (58.5)		
+Refractoriness, n (%)	
+PIs	
+Bortezomib	58 (58.0)		
+Carfilzomib	68 (64.5)		
+IMiDs	
+Lenalidomide	79 (74.5)		
+Pomalidomide	67 (63.2)		
+Daratumumab	85 (80.1)		
+IMiD + PI refractory	85 (80.2)	106	
+Triple‐refractory	77 (72.6)	106	
+Penta‐refractory	34 (32.0)	106	
+Refractory to last line of therapy	82 (91.1)	90	
+Abbreviations: EMD, extramedullary disease; HDM/ASCT, high‐dose melphalan/autologous stem cell transplantation; IMiDs, immunomudolators; ISS, international staging system; PIs, proteasome inhibitors; rISS, revised international staging system.
+
+a High‐risk cytogenetics defined as: t(4; 14), t(14; 16), del(17p), or +1q21.
+
+b Double‐hit: two high‐risk cytogenetic aberrations.
+
+Exposure rates to bortezomib, lenalidomide and daratumumab were 97.1%, 91.5% and 95.2%, respectively. Seventy‐seven patients (72.6%) were triple‐refractory, and 34 patients (32.0%) were penta‐refractory. Sixty‐two (58.5%) patients were post autologous transplant. Twenty‐seven patients (42.8% of patients with available cytogenetic data) had high‐risk fluorescence in‐situ hybridization (FISH) cytogenetic aberrations. Extramedullary disease (EMD) was present in 21.4% of evaluable patients, mostly paraskeletal (15.8%) and skin (3.5%).
+
+The initial belantamab mafodotin dose was 2.5 mg/kg for 82 (80%) patients and 3.4 mg/kg for 20 (20%) patients. The median number of cycles administered was four (range 2–17) and five (range 2–17) cycles for the entire cohort and responding patients (patients achieving PR or better) respectively.
+
+Efficacy
+
+Response
+
+The ORR was 45.5% (46/101); five patients could not be evaluated for response due to non‐secretory disease (bone marrow biopsy and/or imaging were unavailable). Rates of complete response, very good partial response and PR were 4.0%, 13.9% and 27.7% respectively. ORR rates were similar regardless of initial dose (47.4% for 2.5 mg/kg and 42.1% for 3.4 mg/kg, p = 0.1). Triple‐refractory and penta‐refractory patients responded similarly to the ORR of the entire cohort (Table 2). By univariate analysis, no significant association was found between age, sex, triple‐/penta‐refractoriness, international staging system (ISS), revised ISS, high‐risk cytogenetics and EMD to ORR. The proportional length of delayed cycles did not correlate with treatment outcomes (as detailed below). Ocular toxicity after the first dose did not affect response rate (ORR 8/20, 40%). The median time to first and best response was 23 (range 23–119) and 42 (range 7–152) days respectively.
+
+TABLE 2 Response rates, PFS and OS.
+
+	N	ORR, %	Median PFS, months (95% CI)	Median OS, months (95% CI)	
+Entire cohort	106	45.5 a	4.7 (3.5–5.9)	14.5 (9.5–19.6)	
+Responders	46		8.8 (6.6–10.9)	NR b	
+Triple‐refractory	77	43.0 c	5.3 (3.6–6.9)	14.5 (8.8–20.2)	
+Penta‐refractory	34	45.4 d	4.7 (3.2–6.2)	13.8 (9.2–18.3)	
+Initial BELA dose 2.5 mg/kg	82 e	47.4 f	5.1 (3.9–6.4)	14.5 (11.3–17.7)	
+Initial BELA dose 3.4 mg/kg	20	42.1	1.6 (0–4.2)	9.5 (3.4–15.7)	
+Abbreviations: BELA, belantamab mafodotin; CI, confidence interval; NR, not reached; ORR, overall response rate; OS, overall survival; PFS, progression‐free survival.
+
+a Response could be assessed in 101 patients.
+
+b There were not enough events to estimate a standard error for the median survival time.
+
+c Response could be assessed in 72 patients.
+
+d Response could be assessed in 33 patients.
+
+e Four patients had missing data regarding their initial dose.
+
+f Response could be assessed in 78 patients.
+
+PFS, TTNT, DOR, and OS
+
+The median follow‐up was 11.9 months [95% confidence interval (CI) 10.0–13.8]. Median PFS was 4.7 months (95% CI 3.5–5.9) for the entire cohort and 8.8 months (95% CI 6.6–10.9) for responders (Figure 1). TTNT was 5.4 months (95% CI 4–6.8). Median DOR was 8.1 months (95% CI 5.7–10.5). The median PFS was not different in the triple‐refractory patients [5.3 months (95% CI 3.6–6.9), p = 0.382] (Figure 2) and penta‐refractory patients [4.7 months (95% CI 3.2–6.2), p = 0.977]. Similarly, no difference in PFS was found between patient with and without high‐risk cytogenetics (p = 0.46). No statistically significant difference in PFS was found between patients starting at the 3.4 mg/kg dose [median PFS: 1.6 months (95% CI 0–4.2)] and 2.5 mg/kg dose [median PFS 5.1 months (95% CI 3.9–6.4)], p = 0.328 (Table 2). Median DOR for responders was also not different between patients receiving an initial dose of 3.4 mg/kg [6.4 months (95% CI 3.9–10.3)] and 2.5 mg/kg [8.1 months (95% CI 6.7–10.3)], p = 0.53.
+
+FIGURE 1 (A) Progression‐free survival and overall survival. (B) Overall survival was significantly longer among patients achieving partial response or better. OS, overall survival; PFS, progression‐free survival. [Colour figure can be viewed at wileyonlinelibrary.com]
+
+FIGURE 2 Progression‐free survival (A) and overall survival (B) were not statistically different among triple‐refractory and non‐triple‐refractory patients. [Colour figure can be viewed at wileyonlinelibrary.com]
+
+The median OS was 14.5 months (95% CI 9.5–19.6). Patients achieving PR or better had a statistically significant longer OS [not reached (NR) for responders vs. 7.1 for non‐responders]. At 12 months the OS was 81.9% ± 6.3% versus 35.0% ± 7.5% in responders versus non‐responders (Figure 1) (p = 0.000016). This finding was consistent within the triple‐refractory patients [median OS NR for responders vs. 7.3 months (95% CI 6–8.6) for non‐responders, p = 0.0003]. The median OS was similar in the triple‐refractory patients and the non‐triple‐refractory cohort (14.5 months for triple‐refractory vs. 13.1 months for non‐triple‐refractory patients, respectively; p = 0.80) (Figure 2). At 12‐months, the OS for triple‐refractory was 57.8% ± 6.5%. Median OS for penta‐refractory patients was 13.8 months (95% CI 9.2–18.8) and was similar to that for the non‐penta‐refractory cohort (14.5 months, p = 0.768). The 12‐months OS for penta‐refractory was 61.7 ± 9.7% (Table 2). No difference was found in the OS of patients with and without high‐risk cytogenetics (p = 0.31).
+
+Safety
+
+Ocular toxicity
+
+Data regarding ocular toxicity were available for 95 (89.6%) patients. Sixty‐five patients (68.4%) experienced keratopathy. Maximal grade of keratopathy reached grade 1, 2, 3 and 4 in 11.6%, 16.8%, 38.9% and 1.1% of patients, respectively. Of the 52 patients who experienced grade 2–4 corneal toxicity with available data, 33 (63.4%) had resolution to grade 1 or less during the follow‐up period. Thirteen (25.0%) and six (11.5%) patients remained with grade 2 and 3 keratopathy respectively. None of the patients had grade 4 keratopathy at the end of the follow‐up period. Blurred vision was reported in 36.8% (35/95) of patients with available data. Grade of blurred vision was 1, 2, 3, 4 in 13.7%, 16.9%, 5.3% and 1.0%, respectively. Of the 20 patients with grade 2 or worse blurred vision with available data, resolution to grade 1 or less was observed in 16 (75%) and four (25%) remained with grade 2, at time of data cut‐off. Four patients (3.8%) discontinued treatment due to ocular toxicity. These patients had a median PFS of 7.5 (range 5.7–21.7) months and median OS of 12.6 (range 7.5–21.7) months, respectively. Blurred vision significantly correlated with slit‐lamp findings: keratopathy grade 2 or more was associated with blurred vision (any grade); hazard ratio 14.5 (95% CI 4.0–53.2). No association was found between starting dose and keratopathy (p = 0.42) nor blurred vision (p = 0.49).
+
+Non‐ocular toxicity
+
+Safety data for non‐ocular AEs were reported for all patients. Most AEs were haematological. Thrombocytopenia occurred in 27.4% (grade ≥3: 17.9%; one major bleeding) of the patients, anaemia in 11.3% (grade ≥3: 3.8%) and neutropenia in 7.5% (grade ≥3: 4.7%). Other frequent (≥5%) AEs were infection (11.3%, grade ≥3: 3.8%) and hypersensitivity/infusion reaction (7.5%; grade ≥3: 2.8%). Two patients (1.9%) experienced hepatitis B virus reactivation. Two patients (1.9%) experienced tumour lysis syndrome; both had a high disease burden prior to belantamab mafodotin treatment initiation. Treatment‐related mortality was 1.9% (2/106). Both of these patients died from infections (pneumonia and sepsis). Reported non‐ocular AEs are shown in Table 3.
+
+TABLE 3 Treatment‐emergent adverse events (non‐ocular).
+
+	All grades n (%)	Grade 3–5 a n (%)	
+Thrombocytopenia	29 (27.4)	19 (17.9)	
+Infection	12 (11.3)	8 (7.5)	
+Anaemia	12 (11.3)	4 (3.8)	
+Hypersensitivity/infusion reaction	8 (7.5)	3 (2.8)	
+Neutropenia	8 (7.5)	5 (4.7)	
+Transaminitis	5 (4.7)	1 (0.9)	
+Dry eyes	5 (4.7)	0	
+Fever	4 (3.8)	1 (0.9)	
+TLS	2 (1.9)	1 (0.9)	
+Cholangitis/elevated bilirubin	2 (1.9)	2 (1.9)	
+CMV reactivation	2 (1.9)	2 (1.9)	
+AKI	2 (1.9)	1 (0.9)	
+Nausea/vomiting	2 (1.9)	1 (0.9)	
+Diarrhoea	2 (1.9)	1 (0.9)	
+Confusion	2 (1.9)	0	
+Hepatitis B reactivation	2 (1.9)	0	
+Dermatitis	1 (0.9)	0	
+Other b	11 (10.3)	6 (5.6)	
+Abbreviations: AKI, acute kidney injury; CMV, cytomegalovirus; TLS, tumour lysis syndrome.
+
+a Two grade 5 adverse events were reported (pneumonia and sepsis).
+
+b Other adverse effects included (one event each): cough, fatigue, gastritis, general deterioration, gamma glutamyl transferase increase, hypotension, impaired hearing, listeria cerebritis, peripheral neuropathy, pneumonitis, sialadenitis.
+
+Dose delays
+
+In all, 524 doses were recorded. Of 97 patients with available data, 54 (55.6%) experienced dose delays. Of the 418 doses given beyond the first dose, 116 doses were delayed (27.8%). Dose were delayed due to ocular toxicity in 82 cases (70.7%), haematological toxicity in 11 cases (9.5%) and infections in four (3.5%) cases. Nineteen cases of delay had other reason. The median duration of delay (per dose) was 31 days (range: 1–153 days). First delay occurred in the second, third, fourth or fifth cycle in 10.3%, 29.3%, 15.2% and 14.5%, respectively (percentages are calculated from the number of patients receiving this cycle). Five patients (out of 36, 13.8%) had their first delay beyond the fifth cycle. Out of 33 patients who had dose delay because of ocular toxicity and received subsequent doses, 26 (78.8%) had at least 1 more dose delay secondary to ocular toxicity. Proportion of dose delays, calculated as cumulative number of days of delay divided by number of cycles (see the “Methods” section) did not correlate significantly with response rates, as mentioned earlier.
+
+DISCUSSION
+
+We present real‐world data on 106 RRMM patients treated in Israel between 2019 and 2021with belantamab mafodotin with or without corticosteroids under the GSK compassionate programme. To the best of our knowledge, this is the largest belantamab mafodotin real‐world series reported to date. This was a heavily pretreated population of patients, who has a dismal prognosis and represent an urgent unmet need for new therapeutic options. In our cohort, belantamab mafodotin monotherapy resulted in an encouraging response rate, as well as OS and PFS, considerably higher than expected in this patient population. 1 These findings were obtained in a real‐world compassionate programme setting, with a broad patient population, excluding mostly patients with severe renal failure and very low cytopenia. A comparison between clinical trials cohorts and our cohort is presented in Table 4. Notably, our cohort population was older (with almost one quarter of patients older than 75 years) and had similar rates of multidrug‐refractory patients to the DREAMM2 cohort and higher compared to DREAMM1. Yet, response rates were 45% (exceeding response rates in DREAMM2). The PFS was 4.7 months, and DOR 8.1 months. OS was relatively favourable with a median of 14.5 ± 2.5 months. A highly significant difference was noticed in PFS and OS among responders versus non‐responders, suggesting the improved OS is likely attributable to belantamab mafodotin therapy. The PFS and OS of responders in our study are comparable to the results published for the 2.5 mg/kg cohort of the DREAMM2 study, with a median estimated PFS and OS of 6.2 months and 13.7 months for responders respectively. 13 Another encouraging finding was that response rates, PFS and OS were not inferior for triple‐ and penta‐refractory patients. Efficacy and safety outcomes did not differ in patients receiving an initial dose of 3.4 or 2.5 mg/kg.
+
+TABLE 4 Comparison between phase 1 and phase 2 clinical trial and study cohort
+
+	DREAMM1 11	DREAMM2 12	Real‐world a	
+	2.5 mg/kg	3.4 mg/kg	
+Patients, n	35	97	96	106	
+Age, years; median (range)	60 (46–75)	65 (60–70)	67 (61–72)	69 (36–88)	
+>75 years, n (%)	NR	13 (13)	17 (17)	24 (23)	
+ISS 1/2/3, %	54/17/11	22/34/43	18/51/30	43/30/26	
+High‐risk cytogenetics, n(%)	
+del17p	6 (17)	16 (16)	22 (22)	12 (19)	
+t 4;14	3 (9)	11 (11)	11 (11)	1 (1.6)	
+t 14;16	1 (3)	7 (7)	2 (2)	1 (1.6)	
++1q gain/amp	3 (9)	25 (26)	30 (30)	18 (28)	
+Extra‐medullary disease	NR	22 (23)	18 (18)	12 (21)	
+Number of previous lines, n (range)	5 (1–10+)	7 (3–21)	6 (3–21)	6 (2–11)	
+Exposure/refractoriness to anti‐myeloma drugs; (%/%)	
+PIs	
+Bortezomib	100/97	98/76	98/75	97/58	
+Carfilzomib	NR	76/65	65/58	72/64	
+IMiDs	
+Lenalidomide	83/77	100/90	100/89	92/74	
+Pomalidomide	100/94	92/87	85/78	77/63	
+Daratumumab	NR	100/100	97/92	95/80	
+Triple refractory, %	63/63	100	100	73	
+Penta‐refractory, n (%)	40/40			32	
+Abbreviations: IMiD, immunomodulatory drug; ISS, international staging system; NR, not reached; PI, proteasome inhibitor.
+
+a Percentages in real‐world cohort are computed from number of patients with available data.
+
+Toxicity, mainly ocular toxicity, is associated with belantamab mafodotin therapy in the real‐world setting, comparable to the findings observed in clinical trials. Among patients, 69% experienced keratopathy, 41% of them grade ≥3, very similar to the DREAMM2 13‐months follow‐up update. 13 High rates (36.8%) of blurred vision were recorded, consistent with the results of the clinical trials (25%–34%), and similarly, only a minority (6.3%) of these cases was high‐grade (grade 3–4). 11 , 13 Keratopathy and blurred vision were reversible in the majority of patients. Although a significant portion remained with some degree of ocular toxicity at the time of data cut‐off, follow‐up time was short and thus, improvement could have occurred later. All of the above‐mentioned findings regarding ocular toxicity are in concordance with the clinical trials results 11 , 13 and no new safety signals were noted. Not surprisingly, keratopathy grade 2 or more significantly correlated with blurred vision. Abeykoon et al. recently published their analysis of a real‐world RRMM cohort treated with belantamab mafodotin, focusing on ocular toxicity and its consequences. 21 They found a similar rate of ocular toxicity (75%) but a higher rate of treatment discontinuation secondary to this toxicity (14%). Interestingly, they found ocular toxicity after the first dose to be a significant predictor of response. The authors concluded that keratopathy significantly complicates belantamab mafodotin therapy and mitigates its full potential. 21 Compromised efficacy due to treatment discontinuation and/or delays, and association between toxicity after the first cycle and response rates, were not found in our current study. Further analysis of real‐world cohorts may contribute to further clarification of this important issue.
+
+Haematological toxicity was manageable, although concern for thrombocytopenic bleeds remains a challenge in the ambulatory setting in some patients. Infectious complications were not uncommon, highlighting the need for close surveillance and early intervention as needed. The two cases of hepatitis B reactivation are worrisome, and repeated testing prior to initiation of treatment should be considered. We report two cases of tumour lysis syndrome, not previously reported in clinical trials but reported by Vaxman et al. 16 in the real‐world setting, highlighting the need for risk assessment and appropriate prophylactic and supportive measures in high‐risk patients.
+
+Our study has several limitations. First, due to its retrospective nature, not all data were available. Second, exclusion of three patients receiving only one dose may have caused overestimation of response rates, PFS and OS; however, the small number could not skew the results significantly. Importantly, none of these patients discontinued therapy beyond first dose because of AEs. Third, the compassionate access programme did have exclusion criteria (see the “Methods” section) and may not fully represent all real‐life patients.
+
+To conclude, belantamab mafodotin efficacy was confirmed in a real‐world setting, in patients with advanced RRMM. Response rate, duration of response and toxicity profile appear to be comparable to those observed in prospective trial settings. Ocular toxicity remains a major challenge due to the high percentage of keratopathy, dose reduction and delays. Nevertheless, these findings support the role of belantamab mafodotin as a benificial treatment option for heavily pretreated RRMM patients.
+
+AUTHOR CONTRIBUTIONS
+
+Tamir Shragai and Yael C. Cohen designed the study, collected the data and wrote the manuscript. Efrat Luttwak performed the statistical analyses and also wrote the manuscript. Tamir Shragai, Hila Magen, Noa Lavi, Moshe Gatt, Svetlana Trestman, Miri Zektser, Chezi Ganzel, Osnat Jarchowsky, Tamar Berger, Tamar Tadmor, Merav Leiba, Katrin Hertzog‐Tzarfaty, Netanel Horowitz, Michael Shapira, Irit Avivi, Efrat Luttwak and Yael C. Cohen contributed patients. David Varssano, Yoav Berger, Shahar Frenkel and Mark Krauthammer performed the ophthalmologic testing. All authors had access to the study data, proofread the manuscript, agreed with the content, and approved its submission.
+
+CONFLICT OF INTEREST
+
+The authors have no competing interests. Yael C. Cohen received honoraria from GSK, unrelated to this research. All other authors have no conflict of interests to declare.
+
+Supporting information
+
+Appendix S1
+
+Click here for additional data file.
+
+ACKNOWLEDGEMENTS
+
+The Israeli Belantamab Mafodotin compassionate care programme was supported by GSK. The authors wish to thank Claire Wardel D.Phil and the GSK Israel Belantamab Mafodotin expanded compassionate programme for their collaboration. The authors wish to thank Mr Nathan Melamed from the haematology institute, Tel‐Aviv Sourasky Medical Center, for his technical support preparing the figures.
+
+DATA AVAILABILITY STATEMENT
+
+For the original data, please contact the corresponding author at tamirsh@tlvmc.gov.il.
+==== Refs
+REFERENCES
+
+1 Gandhi UH , Cornell RF , Lakshman A , Gahvari ZJ , McGehee E , Jagosky MH , et al. Outcomes of patients with multiple myeloma refractory to CD38‐targeted monoclonal antibody therapy. Leukemia. 2019 Sep 1;33 (9 ):2266–75.30858549
+2 O'Connor BP , Raman VS , Erickson LD , Cook WJ , Weaver LK , Ahonen C , et al. BCMA is essential for the survival of long‐lived bone marrow plasma cells. J Exp Med. 2004 Jan 5;199 (1 ):91–7.14707116
+3 Sanchez E , Li M , Kitto A , Li J , Wang CS , Kirk DT , et al. Serum B‐cell maturation antigen is elevated in multiple myeloma and correlates with disease status and survival. Br J Haematol. 2012 Sep;158 (6 ):727–38.22804669
+4 Cohen YC , Cohen AD , Delforge M , Hillengass J , Goldschmidt H , Weisel K , et al. Efficacy and safety of Ciltacabtagene Autoleucel (Cilta‐cel), a B‐cell maturation antigen (BCMA)‐directed chimeric antigen receptor (CAR) T‐cell therapy, in lenalidomide‐refractory patients with progressive multiple myeloma after 1‐3 prior lines of therapy: updated results from CARTITUDE‐2. Blood. 2021 Nov 23;138 (Suppl 1 ):3866–6.
+5 Berdeja JG , Madduri D , Usmani SZ , Jakubowiak A , Agha M , Cohen AD , et al. Ciltacabtagene autoleucel, a B‐cell maturation antigen‐directed chimeric antigen receptor T‐cell therapy in patients with relapsed or refractory multiple myeloma (CARTITUDE‐1): a phase 1b/2 open‐label study. Lancet. 2021 Jul 24;398 (10297 ):314–24.34175021
+6 Raje N , Berdeja J , Lin Y , Siegel D , Jagannath S , Madduri D , et al. Anti‐BCMA CAR T‐cell therapy bb2121 in relapsed or refractory multiple myeloma. N Engl J Med. 2019 May 2;380 (18 ):1726–37.31042825
+7 Munshi NC , Anderson LD , Shah N , Madduri D , Berdeja J , Lonial S , et al. Idecabtagene vicleucel in relapsed and refractory multiple myeloma. N Engl J Med. 2021 Feb 25;384 (8 ):705–16. 10.1056/NEJMoa2024850 33626253
+8 Topp MS , Duell J , Zugmaier G , Attal M , Moreau P , Langer C , et al. Anti‐B‐cell maturation antigen BiTE molecule AMG 420 induces responses in multiple myeloma. J Clin Oncol. 2020 Mar 10;38 (8 ):775–83.31895611
+9 Moreau P . Updated results from MajesTEC‐1: phase 1/2 study of teclistamab, a B‐cell maturation antigen × CD3 bispecific antibody, in relapsed/refractory multiple myeloma. ASH. 2021;138 :896.
+10 Usmani SZ , Garfall AL , van de Donk NWCJ , Nahi H , San‐Miguel JF , Oriol A , et al. Teclistamab, a B‐cell maturation antigen × CD3 bispecific antibody, in patients with relapsed or refractory multiple myeloma (MajesTEC‐1): a multicentre, open‐label, single‐arm, phase 1 study. Lancet (London, England). 2021 Aug 21;398 (10301 ):665–74.34388396
+11 Trudel S , Lendvai N , Popat R , Voorhees PM , Reeves B , Libby EN , et al. Targeting B‐cell maturation antigen with GSK2857916 antibody‐drug conjugate in relapsed or refractory multiple myeloma (BMA117159): a dose escalation and expansion phase 1 trial. Lancet Oncol. 2018 Dec 1;19 (12 ):1641–53.30442502
+12 Lonial S , Lee HC , Badros A , Trudel S , Nooka AK , Chari A , et al. Belantamab mafodotin for relapsed or refractory multiple myeloma (DREAMM‐2): a two‐arm, randomised, open‐label, phase 2 study. Lancet Oncol. 2019 Dec 2019 Dec [cited 2020 Jan 6]; Available from: https://linkinghub.elsevier.com/retrieve/pii/S1470204519307880.
+13 Lonial S , Lee HC , Badros A , Trudel S , Nooka AK , Chari A , et al. Longer term outcomes with single‐agent belantamab mafodotin in patients with relapsed or refractory multiple myeloma: 13‐month follow‐up from the pivotal DREAMM‐2 study. Cancer. 2021 Nov 15;127 (22 ):4198–212.34314018
+14 Chari A , Romanus D , Palumbo A , Blazer M , Farrelly E , Raju A , et al. Randomized clinical trial representativeness and outcomes in real‐world patients: comparison of 6 Hallmark randomized clinical trials of relapsed/refractory multiple myeloma. Clin Lymphoma Myeloma Leuk. 2020 Jan 1;20 (1 ):8–17.e16.31722839
+15 Becnel M , Ferreri CJ , Feng L , Richards TA , Horowitz SB , Patel N , et al. Retrospective, single‐center, real‐world experience of belantamab mafodotin in relapsed/refractory multiple myeloma. J Clin Oncol. 2022 Jun 2;40 (16_suppl ):8060. 10.1200/JCO20224016_suppl8060
+16 Vaxman I , Abeykoon J , Dispenzieri A , Kumar SK , Buadi F , Lacy MQ , et al. “Real‐life” data of the efficacy and safety of belantamab mafodotin in relapsed multiple myeloma—the Mayo Clinic experience. Blood Cancer J. 2021 Dec 7;11 (12 ):196.34876555
+17 Atieh T , Atrash S , Mohan M , Shune L , Mahmoudjafari Z , Quick J , et al. Belantamab in combination with dexamethasone in patients with triple‐class relapsed/refractory multiple myeloma. Blood. 2021 Nov 23;138 (Suppl 1 ):1642.34709379
+18 Harris PA , Taylor R , Thielke R , Payne J , Gonzalez N , Conde JG . Research electronic data capture (REDCap)‐a metadata‐driven methodology and workflow process for providing translational research informatics support. J Biomed Inform. 2009 Apr 1;42 (2 ):377–81.18929686
+19 Kumar S , Paiva B , Anderson KC , Durie B , Landgren O , Moreau P , et al. International myeloma working group consensus criteria for response and minimal residual disease assessment in multiple myeloma. Lancet Oncol. 2016;17 :e328–e346.27511158
+20 National Cancer Institute . Common Terminology Criteria for Adverse Events (CTCAE) Common Terminology Criteria for Adverse Events (CTCAE) v5.0 [Internet]. 2017 [cited 2022 Mar 22]. Available from: https://www.meddra.org/
+21 Abeykoon JP , Vaxman J , Patel SV , Kumar S , Malave GC , Young KS , et al. Impact of belantamab mafodotin‐induced ocular toxicity on outcomes of patients with advanced multiple myeloma. Br J Haematol. 2022. 10.1111/bjh.18298
+

PMC10092069.txt

@@ -0,0 +1,282 @@
+
+==== Front
+Br J Haematol
+Br J Haematol
+10.1111/(ISSN)1365-2141
+BJH
+British Journal of Haematology
+0007-1048
+1365-2141
+John Wiley and Sons Inc. Hoboken
+
+36210485
+10.1111/bjh.18502
+BJH18502
+BJH-2022-01372.R1
+Original Paper
+Haematological Malignancy–Clinical
+Prognostic impact of MYD88 and CXCR4 mutations assessed by droplet digital polymerase chain reaction in IgM monoclonal gammopathy of undetermined significance and smouldering Waldenström macroglobulinaemia
+Moreno David F. https://orcid.org/0000-0002-1752-3081
+1 2 3 1
+López‐Guerra Mónica 2 3 4 5 1
+Paz Sara 4
+Oliver‐Caldés Aina https://orcid.org/0000-0002-7921-5420
+1 2 3
+Mena Mari‐Pau 1 2
+Correa Juan G. 1 2 3
+Battram Anthony M. 1 2 3
+Osuna Miguel 2
+Rivas‐Delgado Alfredo https://orcid.org/0000-0003-0385-3415
+2
+Rodríguez‐Lobato Luis Gerardo 1 2 3
+Cardús Oriol 1 2 3
+Tovar Natalia 1 2 3
+Cibeira María Teresa 1 2 3
+Jiménez‐Segura Raquel https://orcid.org/0000-0003-1333-0343
+1 2 3
+Bladé Joan 1 2 3
+Rosiñol Laura https://orcid.org/0000-0002-2534-9239
+1 2 3
+Colomer Dolors 2 3 4 5 dcolomer@clinic.cat
+
+Fernández de Larrea Carlos https://orcid.org/0000-0003-4930-9255
+1 2 3 cfernan1@clinic.cat
+
+1 Present address: Amyloidosis and Myeloma Unit, Department of Hematology Hospital Clínic de Barcelona Barcelona Spain
+2 Institut d'Investigacions Biomèdiques August Pi I Sunyer (IDIBAPS) Barcelona Spain
+3 Facultat de Medicina i Ciències de la Salut Universitat de Barcelona (UB) Barcelona Spain
+4 Hematopathology Unit, Department of Pathology Hospital Clínic de Barcelona Barcelona Spain
+5 Centro de Investigación Biomédica en Red de Cáncer (CIBERONC) Madrid Spain
+* Correspondence
+Carlos Fernández de Larrea, MD, PhD, Amyloidosis and Myeloma Unit, Department of Hematology, Hospital Clínic of Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), University of Barcelona, 08036, Barcelona, Spain.
+Email: cfernan1@clinic.cat
+Dolors Colomer, PhD, Experimental Therapeutics in Lymphoid Malignancies Group, Institut d’ Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Centro de Investigación Biomédica en Red de Cáncer (CIBERONC). Hematopathology Unit, Department of Pathology, Hospital Clinic of Barcelona, University of Barcelona, 08036, Barcelona, Spain.
+Email: dcolomer@clinic.cat
+
+09 10 2022
+1 2023
+200 2 10.1111/bjh.v200.2 187196
+09 9 2022
+08 7 2022
+25 9 2022
+© 2022 The Authors. British Journal of Haematology published by British Society for Haematology and John Wiley & Sons Ltd.
+https://creativecommons.org/licenses/by-nc/4.0/ This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.
+
+Summary
+
+Waldenström macroglobulinaemia (WM) is characterized by recurrent somatic mutations in MYD88 and CXCR4 genes. However, limitations arise when analysing these mutations in IgM monoclonal gammopathy of undetermined significance (MGUS) or smouldering WM (SWM) given the lower tumour load. Here, we used droplet digital polymerase chain reaction (ddPCR) to analyse MYD88 L265P and CXCR4 S338* mutations (C1013G and C1013A) in unsorted bone marrow (BM) or cell‐free DNA (cfDNA) samples from 101 IgM MGUS and 69 SWM patients. ddPCR was more sensitive to assess MYD88 L265P compared to allele‐specific PCR, especially in IgM MGUS (64% vs 39%). MYD88 mutation burden correlated with other laboratory biomarkers, particularly BM infiltration (r = 0.8; p < 0.001). CXCR4 C1013G was analysed in MYD88‐mutated samples with available genomic DNA and was detected in 19/54 (35%) and 18/42 (43%) IgM MGUS and SWM cases respectively, also showing correlation with BM involvement (r = 0.9; p < 0.001). ddPCR also detected 8 (38%) and 10 (63%) MYD88‐mutated cfDNA samples in IgM MGUS and SWM respectively. Moreover, high BM mutation burden (≥8% MYD88 and ≥2% CXCR4) was associated with an increased risk of progression to symptomatic WM. We show the clinical applicability of ddPCR to assess MYD88 and CXCR4 in IgM MGUS and SWM and provide a molecular‐based risk classification.
+
+CXCR4
+droplet digital PCR
+IgM MGUS
+MYD88
+Waldenström macroglobulinaemia
+Fundación Científica Asociación Española Contra el Cáncer 10.13039/501100002704 Lab_AECC2021 Hospital Clínic de BarcelonaEmili Letang Grant Janssen Research and Development 10.13039/100005205 CP043186 Instituto de Salud Carlos III 10.13039/501100004587 PI19/00669 European Union 10.13039/501100000780 source-schema-version-number2.0
+cover-dateJanuary 2023
+details-of-publishers-convertorConverter:WILEY_ML3GV2_TO_JATSPMC version:6.2.7 mode:remove_FC converted:12.04.2023
+Moreno DF , López‐Guerra M , Paz S , Oliver‐Caldés A , Mena M‐P , Correa JG , et al. Prognostic impact of MYD88 and CXCR4 mutations assessed by droplet digital polymerase chain reaction in IgM monoclonal gammopathy of undetermined significance and smouldering Waldenström macroglobulinaemia Br J Haematol. 2023;200(2) :187–196. 10.1111/bjh.18502 36210485
+
+David F. Moreno and Mónica López‐Guerra these authors contributed equally in this work.
+
+Dolors Colomer and Carlos Fernández de Larrea these authors jointly supervised this work.
+==== Body
+pmcINTRODUCTION
+
+Waldenström macroglobulinaemia (WM) is a B‐cell neoplasm characterized by a lymphoplasmacytic infiltration in the bone marrow (BM) and the presence of a serum IgM monoclonal protein (M‐protein). 1 WM is preceded by two asymptomatic stages named IgM monoclonal gammopathy of undetermined significance (MGUS) and smouldering WM (SWM). The presence of highly recurrent somatic mutations is another key feature of WM. For instance, whole‐genome sequencing has identified mutations in MYD88 and CXCR4 genes in up to 90% and 27% of WM patients respectively. 2 , 3 Another recurrent alteration well described is del(6q), which was identified in up to 50% of WM patients using fluorescent in situ hybridization. 4 , 5 The identification of abnormal B cells in IgM MGUS with a mutational and phenotypical background similar to those found in later stages of the disease strongly supports this evolutionary model. 6 , 7 , 8
+
+Allele‐specific polymerase chain reaction (AS‐PCR)‐based methods to detect MYD88 L265P using unsorted or CD19+ selected BM samples showed that the mutation prevalence varied from 54% to 87% in IgM MGUS and 86% to 93% in WM respectively. 9 , 10 On the other hand, AS‐PCR and Sanger sequencing in CD19+ selected BM samples have identified 17% and 43% of CXCR4 mutations in IgM MGUS and untreated WM respectively. 11 Similarly, another study reported a prevalence of up to 33% of CXCR4 mutations in IgM MGUS using Sanger sequencing. 12 Regarding targeted next‐generation sequencing (NGS), CXCR4 mutations were identified in 9% and 23% in IgM MGUS and WM respectively. 13
+
+This knowledge of the genomic landscape has given insights to better elucidate the impact on disease progression from asymptomatic stages. For instance, a higher risk of progression was observed in MYD88‐mutated IgM MGUS 14 and wild‐type (wt) MYD88 SWM patients. 15 Regarding CXCR4 mutations, a worse outcome was described in CXCR4 mutated SWM patients, 13 while another study reported no significant difference. 16
+
+Although data in WM have shown highly reproducible results using different technologies, data in IgM MGUS and SWM are more variable due to the low tumour burden, the heterogeneous BM infiltration, or the availability of BM samples in asymptomatic patients. 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 Thus, there is a need to find more precise techniques to measure disease burden in this group of patients. In addition, considering that the reproducibility of the prognostic studies might be affected by the technology used to assess MYD88 and CXCR4 mutations, the impact of the mutational status of early disease stages on the risk of progression to symptomatic WM remains an ongoing field of investigation.
+
+Therefore, we wondered whether using more precise methods to detect somatic mutations in asymptomatic IgM monoclonal gammopathies could overcome technical diagnostic issues in samples with low tumour burden, thereby improving prognostic risk models. In this sense, droplet digital PCR (ddPCR) technology provides an absolute quantification of nucleic acid target sequences, thus being useful to detect small clones. ddPCR can achieve higher sensitivity, precision and reproducibility compared to the standard AS‐PCR. 20 ddPCR has been used for MYD88 L265P detection using genomic DNA from CD19+ selected, unsorted BM samples, and cell‐free DNA (cfDNA) of WM patients. 21 , 22 More recently, ddPCR has also been applied to assess CXCR4 S338* mutations in symptomatic WM patients. 23 However, MYD88 and CXCR4 mutations in cfDNA samples from asymptomatic patients have not yet been systematically evaluated.
+
+We thus analysed MYD88 and CXCR4 mutations leveraging ddPCR technology in a cohort of patients with IgM MGUS and SWM, and identified more accurate markers of disease progression. The findings of this study provide further insights into the genomic landscape of IgM MGUS and SWM.
+
+METHODS
+
+Patients
+
+From 1985 to 2020, 201 patients that met the search criteria for ‘IgM monoclonal gammopathy’ were identified in the monoclonal gammopathies database of the Hospital Clínic of Barcelona. Diagnosis was based on the Second International Workshop on Waldenström Macroglobulinaemia and the Mayo Clinic criteria. 1 , 24 Patients with less than 10% BM involvement and the presence of immunophenotypical findings of lymphoplasmacytic lymphoma were categorized as SWM. Patients diagnosed with ‘IgM‐related disorders’ were not included in this study. The patients provided informed consent for sample collection in the biological samples bank of the Hospital Clínic of Barcelona in accordance with the Declaration of Helsinki. The study was approved by the institutional review board.
+
+Sample collection
+
+In this study, 240 BM and plasma samples from 170 patients categorized as IgM MGUS (N = 101) and SWM (N = 69) were included. They were the main cohort of analysis. Additional samples from patients with symptomatic WM (N = 31) were used as positive controls for experimental analysis. BM samples were available in the entire cohort (101 IgM MGUS, 69 SWM, and 31 symptomatic WM patients), while plasma samples were only available in 39 patients (21 IgM MGUS, 16 SWM and two symptomatic WM patients). BM and plasma samples were collected at diagnosis (IgM MGUS and SWM), and before treatment initiation (symptomatic WM). The distribution of paired samples according to each diagnosis is graphically depicted in Figure 1. Processing of the samples is detailed in the supporting information (File S1).
+
+FIGURE 1 Sample processing methodology. AS‐PCR, allele‐specific polymerase chain reaction; BM, bone marrow; ddPCR, droplet digital polymerase chain reaction; MGUS, monoclonal gammopathy of undetermined significance; MFC, multiparameter flow cytometry; SWM, smouldering Waldenström macroglobulinaemia; WM, Waldenström macroglobulinaemia.
+
+Flow cytometry analysis
+
+BM samples treated with ethylenediaminetetraacetic acid (EDTA) were evaluated by multiparametric flow cytometry. Briefly, three eight‐colour monoclonal antibody (mAb) combinations were used for lymphocyte immunophenotyping (Table S1). Data acquisition was performed with a BD FACSCanto II flow cytometer and analysed using FACSDiva software (BD Biosciences, San Jose, CA, USA).
+
+Allele‐specific polymerase chain reaction assay for MYD88 L265P
+
+MYD88 L265P mutation analysis was performed on DNA from unsorted BM samples using AS‐PCR technology (qBiomarker Somatic Mutation PCR Assay, MYD88_85940; Qiagen, Germany). Amplification Refractory Mutation System technology was used for allele‐specific amplification. Diffuse large B‐cell lymphoma cell line OCI‐LY3 DNA was used as positive control, as described previously. 9
+
+Droplet digital polymerase chain reaction assays for MYD88 L265P and CXCR4 S338* mutations
+
+ddPCR assays for MYD88 L265P and CXCR4 mutations were commercially available (Bio‐Rad, Hercules, CA, USA). The two CXCR4 mutations analysed were: c.1013C > G p.Ser338Ter (CXCR4 C1013G) and c.1013C > A p.Ser338Ter (CXCR4 C1013A). Briefly, DNA samples were tested in duplicate. We used the QX200 Droplet Digital PCR System (Bio‐Rad) to generate and individually analyse each droplet. Data were then analysed in the QuantaSoft Software 1.0 (Bio‐Rad). We assessed CXCR4 mutations in MYD88‐mutated cases, as nearly all CXCR4 mutations occur in MYD88‐mutated patients. 3 When analysing CXCR4 mutations, we first assessed the C1013G mutation. Then, we analysed the C1013A mutation only in samples that were negative for the C1013G. This approach was chosen to take advantage of the availability of genomic DNA or cfDNA, the exceptional cooccurrence of both CXCR4 mutations in the same sample, and the clinical‐oriented applicability of ddPCR. Detailed description is available in the supplementary information (S1).
+
+Statistical analysis
+
+Pearson correlation or Spearman tests were used to analyse correlation between tumour burden and laboratory biomarkers. The Fisher exact test was used to analyse categorical data. Mutation burden (MYD88 and CXCR4) was fitted as a continuous variable into a Fine and Grey regression model to analyse the impact on the risk of progression to symptomatic WM. Progression to symptomatic disease criteria was defined according to previous consensus recommendations. 25 , 26 To construct a practical model, we later categorized the mutation burden according to an X‐tile approach obtaining cut‐point subsets. Plots were calculated based on the cumulative incidence function (CIF) (Figure S1). All statistical analyses were performed using Stata version 16 (StataCorp LLC, College Station, TX, USA). Detailed description is available in the supplementary information (S1).
+
+RESULTS
+
+Baseline patient characteristics
+
+One‐hundred seventy patients (88 male/82 female; median age, 75 years) with available sample material, diagnosed with IgM MGUS (101 patients) and SWM (69 patients) were selected for the current study. Thirty‐eight (22%) patients were diagnosed before the year 2000, 31 (18%) patients between 2000 and 2010, and 101 (59%) after 2010. The main clinical and biological patient characteristics are summarized in Table 1.
+
+TABLE 1 Baseline characteristics of the patients with asymptomatic IgM monoclonal gammopathies
+
+Baseline characteristics	N = 170	
+Median age, years (IQR)	75 (65–84)	
+Sex, female (%)	82 (48)	
+Diagnosis (%)		
+IgM MGUS	101 (59)	
+SWM	69 (41)	
+M‐protein size (g/l), median (IQR)	12.1 (6–15)	
+Bone marrow involvement (% total celullarity)	17 (11–28)	
+Albumin (g/l), median (IQR)	43 (41–45)	
+Haemoglobin (g/l), median (IQR)	134 (121–145)	
+Platelet count (103/μl), median (IQR)	236 (184–287)	
+β2‐microglobulin (mg/dl), median (IQR) a	2.3 (1.8–2.9)	
+Abbreviations: IQR, interquartile range; M‐protein, serum monoclonal protein; MGUS, monoclonal gammopathy of undetermined significance; SWM, smouldering Waldenström macroglobulinaemia.
+
+a Available in 145 patients.
+
+MYD88 L265P detection by allele‐specific versus droplet digital polymerase chain reaction in bone marrow
+
+We first analysed the MYD88 L265P mutation prevalence in genomic DNA from unsorted BM samples using both AS‐PCR and ddPCR techniques. We did both AS‐PCR and ddPCR analyses in 84 IgM MGUS and 55 SWM patients at diagnosis. In IgM MGUS, the MYD88 L265P mutation was detected in 33 (39%) patients using AS‐PCR, but ddPCR was more sensitive, detecting the mutation in 54 (64%) patients (p < 0.001). Similarly, in SWM, MYD88 L265P was detected in 40 (73%) patients using AS‐PCR, while ddPCR identified the mutation in 45 (82%) patients (p < 0.001) (Figure 2A). Analysing MYD88 mutation burden distribution, we observed that the MYD88 L265P mutation was not detected in 50% of cases by AS‐PCR when the variant allelic frequency of MYD88 L265P assessed by ddPCR was lower than 1%. This finding was supported by the 0.0259% limit of detection of ddPCR after serial dilutions of a MYD88 L265P sample in wt DNA (Table S2). To evaluate the higher sensitivity of ddPCR compared to AS‐PCR, we analysed 50 out of the 84 IgM MGUS cases who had their immunophenotype analysis available at the time of DNA collection. In this group, 30 out of 50 (60%) and 19 out of 37 (51%) patients were positive for the MYD88 mutation by ddPCR and AS‐PCR respectively. In samples without detectable clonal B cells by flow cytometry, MYD88 L265P was detected in 15 (50%) and 7 (33%) cases using ddPCR and AS‐PCR respectively. In the case of SWM, 27 out of 32 (84%) and 23 out of 28 (82%) patients were positive for the MYD88 mutation by ddPCR and AS‐PCR respectively. All 27 (100%) SWM patients who had clonal B cells were also positive for the mutation by ddPCR, and 22 (96%) by AS‐PCR. Thus, the agreement of clonal populations observed by ddPCR and flow cytometry was 62% (Cohen's k 0.3; p = 0.016) in IgM MGUS and 91% (Cohen's k 0.6; p < 0.001) in SWM.
+
+FIGURE 2 MYD88 L265P analysis in the bone marrow of IgM monoclonal gammopathy patients. (A) Comparison between AS‐PCR and droplet digital PCR (ddPCR) to detect MYD88 L265P in IgM monoclonal gammopathy of undetermined significance (MGUS) and smouldering Waldenström macroglobulinaemia (SWM). (B) MYD88 mutation burden distribution in IgM MGUS, SWM and symptomatic Waldenström macroglobulinaemia (WM). (C) Heatmap correlation plot of MYD88 mutation burden and other common laboratory biomarkers in IgM monoclonal gammopathies. Each number shows the Pearson correlation coefficient. AS‐PCR, allele‐specific polymerase chain reaction; BM, Bone marrow; ddPCR, droplet digital polymerase chain reaction; M‐protein, serum monoclonal protein; MGUS, monoclonal gammopathy of undetermined significance; SWM, smouldering Waldenström macroglobulinaemia; WM, Waldenström macroglobulinaemia.
+
+MYD88 L265P mutation burden in bone marrow
+
+The median MYD88 mutation burden obtained by ddPCR was 1.13% [interquartile range (IQR) 0.42–2.78] in IgM MGUS (n = 62) and 5.36% (IQR 2.49–11.00) in SWM (n = 54). We also analysed a group of symptomatic WM cases as positive controls (n = 31) and the median MYD88 mutation burden was 11.00% (IQR 5.61–18.49). MYD88 mutation burden was significantly higher in successive disease stages (IgM MGUS versus SWM, p < 0.001; IgM MGUS, SWM versus symptomatic WM, p < 0.001). The distribution plots are shown graphically in Figure 2B. We then compared MYD88 mutation burden assessed by ddPCR to standard laboratory biomarkers. We observed that the mutation burden as a continuous variable correlated with the serum M‐protein size (r = 0.3; p = 0.001), the serum IgM concentration (r = 0.4; p < 0.001), the infiltration of the BM by morphology (r = 0.7; p < 0.001), and the percentage of BM clonal B cells by flow cytometry (r = 0.8; p < 0.001) (Figure 2C).
+
+Analysis of CXCR4 C1013G and C1013A mutations by droplet digital polymerase chain reaction in bone marrow
+
+In addition, we analysed the two most prevalent CXCR4 mutations in WM (C1013G and C1013A) using ddPCR. The limit of detection of 0.0399% was established after a serial dilution of a CXCR4 S338* C1013G sample in wt DNA (Table S3). Four symptomatic WM BM samples previously known to harbour the MYD88 L265P mutation were used as controls. Amongst them, three cases harboured the C1013G variant with a mutation burden of 1.5%, 4.8% and 7.6%, while the MYD88 L265P mutation burden was 20.5%, 8.1% and 5.1% for each case respectively. The fourth case had the C1013A variant with a mutation burden of 17.2%, while the MYD88 L265P was 18.5%. We then analysed the presence of CXCR4 mutations in asymptomatic IgM monoclonal gammopathy patients who were MYD88‐positive and had available genomic BM DNA (54 IgM MGUS and 42 SWM patients). By ddPCR, CXCR4 C1013G was positive in 19 (35%) and 18 (43%) patients with IgM MGUS and SWM respectively. The median CXCR4 C1013G mutation burden distribution in IgM MGUS was 0.4% (IQR 0.3–1.4), which was similar to that in SWM (0.4%, IQR 0.2–12). Overall, the distribution of the mutation burden suggested a subclonal pattern for CXCR4 mutations. As a continuous variable, CXCR4 C1013G showed positive correlations with BM infiltration assessed by morphology (r = 0.4; p < 0.001), MYD88 mutation burden assessed by ddPCR (r = 0.6; p < 0.001), and BM clonal B cells assessed by flow cytometry (r = 0.9; p < 0.001). CXCR4 C1013A was identified in only five MYD88‐mutated patients not harbouring the C1013G mutation, two corresponding to IgM MGUS cases and three with SWM, all of them with less than 2% mutation burden. Figure 3 shows the mutation burden distribution along samples that had both MYD88 and CXCR4 mutations.
+
+FIGURE 3 CXCR4 mutations burden (%) distribution in MYD88 L265P‐positive bone marrow samples. (A) CXCR4 S338* C1013G and C1013A in IgM MGUS. (B) CXCR4 S338* C1013G and C1013A in smouldering Waldenström macroglobulinaemia (SWM).
+
+Evaluation of MYD88 L265P by droplet digital polymerase chain reaction in cell‐free DNA
+
+ddPCR has been reported to be a reliable technology to detect MYD88 L265P in cfDNA samples with high tumour burden. 21 Here, we attempted to demonstrate that ddPCR could additionally be used to detect MYD88 mutation in the cfDNA of patients with a low tumour burden. In cfDNA samples from IgM MGUS patients, MYD88 mutation was detected in eight out of 21 (38%) with a mutation burden median distribution of 0.54% (IQR 0.20–1.32). In the case of SWM patients, ddPCR detected the mutation in 10 out of 16 (63%) patients with a median distribution of 1.78% (IQR 0.24–7.26). In two cases of symptomatic WM, MYD88 mutation was detected in both cases (1.84% and 3.12% of mutation burden). Overall, the minimum MYD88 mutation burden in cfDNA was 0.20%, and the maximum was 7.26% (Figure 4A). As a biomarker, MYD88 in cfDNA positively correlated with the serum M‐protein size (n = 39; r = 0.3; p = 0.047), the serum IgM concentration (n = 37; r = 0.4; p = 0.016), the BM infiltration assessed by morphology (n = 39; r = 0.4; p = 0.015), and the MYD88 BM mutation burden as assessed by ddPCR (n = 35; r = 0.5; p = 0.001) (Figure 4B–D). Regarding CXCR4 mutations assessed in cfDNA, we were only able to detect the C1013G variant in one sample from a SWM patient (1.16% mutation burden), which was also positive for that mutation in BM (13.1%) and for MYD88 L265P in both cfDNA (0.84%) and BM (13.36%).
+
+FIGURE 4 Analysis of MYD88 L265P cell‐free DNA (cfDNA) by ddPCR in plasma from IgM MGUS (n = 21), SWM (n = 16), and symptomatic WM (n = 2). (A) Distribution of MYD88 L265P burden in cfDNA in samples of patients with IgM MGUS, SWM and symptomatic WM. (B) Correlation plot of MYD88 L265P cfDNA (%) and M‐protein size (n = 39; r = 0.3; p = 0.047). (C) Correlation plot of MYD88 L265P cfDNA (%) and the BM infiltration by morphology (n = 39; r = 0.4; p = 0.015). (D) Correlation plot of MYD88 L265P cfDNA (%) and the MYD88 BM mutation burden (values transformed to fit in the scatterplot) by ddPCR (n = 35; r = 0.5; p = 0.001). BM, bone marrow; ddPCR, droplet digital polymerase chain reaction; MGUS, monoclonal gammopathy of undetermined significance; SWM, smouldering Waldenström macroglobulinaemia; WM, Waldenström macroglobulinaemia.
+
+Prognostic impact of MYD88 and CXCR4 mutations assessed by droplet digital polymerase chain reaction in bone marrow
+
+With a median follow‐up of six years (IQR 3–9 years), progression to symptomatic WM was observed in 23 (14%) patients (7 MGUS and 16 SWM patients). Death without progression accounted for up to 37 (22%) patients. All patients progressed due to disease complications related to tumour burden: significant peripheral blood cytopenia (anaemia and/or thrombocytopenia) and symptomatic lymphadenopathy and/or splenomegaly. There were no patients lost to follow‐up. At five and 10 years, the cumulative incidence of progression of patients with IgM MGUS to symptomatic WM was 5% (95% CI 2–12) and 7% (95% CI 3–19) respectively. In the case of SWM, the cumulative incidence of progression was 22% (95% CI 12–36) at five years, and 30% (95% CI 17–47) at 10 years. Median overall survival of all patients was 13 years (95% CI 11–20) (Figure S4).
+
+To assess the impact of BM ddPCR‐detected MYD88 and CXCR4 mutations on the risk of progression, we fitted a competing‐risk framework as described previously. Using the Fine and Grey regression model, we first analysed the mutation burden of each of the mutations as continuous variables. In the univariate analysis, the subhazard ratios (SHR) of the MYD88 mutation burden were 1.2 (95% CI 1.04–1.36; p = 0.012) and 1.04 (95% CI 1.01–1.08; p = 0.040) in IgM MGUS and SWM respectively. The SHRs of CXCR4 C1013G were 1.8 (95% CI 1.33–2.41; p < 0.001) and 1.02 (95% CI 0.9–1.04; p = 0.065) in IgM MGUS and SWM respectively (Table S4).
+
+We then established cut‐off points for the MYD88 and CXCR4 mutation burden to identify risk categories of progression to symptomatic WM. For the whole series, patients who had a MYD88 mutation burden higher than 8% [SHR 4.8, 95% confidence interval (CI) 2–11.2; p < 0.001] or a CXCR4 mutation burden higher than 2% (SHR 4.2, 95% CI 1.7–10.7; p = 0.003) had a cumulative incidence of progression of 30% and 25% at five years respectively. In our series, 19 patients who progressed were previously tested for MYD88 and CXCR4 mutations. Two out of six IgM MGUS patients and nine out of 13 SWM patients having a MYD88 or CXCR4 high mutation burden (>8% and >2% respectively) progressed (SHR 3.5, 95% CI 1.4–9.3; p = 0.01) (Figure 5). Based only on the MYD88 L265P mutation, we identified a small group of patients with high risk at each stage. Thus, IgM MGUS patients with a MYD88 mutation burden higher than 4% (SHR 7.8, 95% CI 1–32; p = 0.005) had a cumulative incidence of progression of 20% at five years. In the case of SWM, patients with a MYD88 mutation burden higher than 25% (SHR 3.4, 95% CI 1–8; p = 0.012) had a cumulative incidence of progression of 45% at five years (Figure S5). Regarding patients who were MYD88 L265P wt using ddPCR, only three out of 51 (6%) patients progressed to symptomatic disease. On the other hand, 21 out of 117 (18%) MYD88‐mutated patients by ddPCR progressed. So, MYD88 L265P wt did not impact the progression to symptomatic disease (SHR 0.4, 95% CI 0.1–1.2; p = 0.112).
+
+FIGURE 5 Risk of progression to symptomatic Waldenström macroglobulinaemia. Cut‐off points were calculated using an X‐tile approach. Shown is the cumulative incidence of progression for both IgM monoclonal gammopathy of undetermined significance and smouldering Waldenström macroglobulinaemia considering MYD88 L265P and CXCR4 S338* C1013G tumour burden when assessed by droplet digital polymerase chain reaction in the bone marrow.
+
+DISCUSSION
+
+Our study highlighted the usefulness of ddPCR to detect MYD88 and CXCR4 mutations in the clinical setting of asymptomatic IgM monoclonal gammopathies. We showed the feasibility of MYD88 L265P detection and quantification using ddPCR in unsorted BM samples with low tumour burden. In matched samples, we found that ddPCR was able to detect more MYD88‐mutated cases than conventional AS‐PCR, especially in IgM MGUS. Moreover, ddPCR identified the mutation even in samples for which flow cytometry could not detect B‐cell clonality. This in fact could be explained by either the sensitivity of multiparameter flow cytometry or, as recently reported, the presence of the MYD88 mutation in precursor lymphocytes. 7 , 8 Although our manuscript is not able to answer this question, we can infer from our data that the MYD88 mutation is again a very early event. Further studies comparing next‐generation flow cytometry to detect B‐cell clonality along with ddPCR could help to solve this issue.
+
+Using MYD88 mutation burden as a continuous variable, we demonstrated that the mutation distribution was higher with successive disease stages. Moreover, MYD88 mutation burden by ddPCR positively correlated with well‐known biomarkers, such as involvement of the BM either evaluated by flow cytometry or morphology; therefore, it could be considered a specific biomarker that accurately reflects disease burden. In addition, inclusion of this novel biomarker along with the classical laboratory features in early disease stages of WM might increase the predictive power of risk models.
+
+Previous studies have found that nonsense CXCR4 mutations were also recurrently prevalent in WM. 3 Among all variants, nonsense CXCR4 S338* mutations were the most prevalent. Up to 50% are transversions C > G and C > A at nucleotide position 1013. 11 , 13 , 27 , 28 These variants have been associated with hyperviscosity and worst outcome regarding progression‐free survival in WM patients. 3 , 11 , 27 , 29 , 30 , 31 However, most of these studies have been done in purified tumour BM samples. Due to the high sensitivity of ddPCR, we could detect the two most recurrent CXCR4 mutations (C1013G and C1013A) in unsorted BM samples. To our knowledge, this is the first report of testing CXCR4 mutations by ddPCR in IgM MGUS and SWM. We detected the CXCR4 C1013G mutation in 35% of IgM MGUS cases and in 43% of SWM carrying also the MYD88 mutation. The variant CXCR4 C1013A was identified in fewer cases, all of them with low mutation burden. Most of the samples that harboured CXCR4 mutations had a mutation burden lower than that of MYD88. The only three SWM cases that harboured higher CXCR4 mutation burden did not differ in any other clinical or laboratory characteristic from the whole series, including MYD88 mutation burden itself, which was quite similar. Moreover, together with two recent single‐cell studies suggesting that CXCR4 mutations behave as second clonal hits, 7 , 8 these three cases could be better explained by technical sample‐processing issues. In addition, CXCR4 C1013G positively correlated with the BM involvement, as was reported previously in CD19+ selected BM cells in WM. 28 Here, we showed that ddPCR can reliably detect CXCR4 mutations in unsorted BM cells, which, considering cell sorting is not feasible in most laboratories, makes it more easily transferable to the clinic.
+
+Considering that MYD88 testing is based on single BM samples for each patient and that BM disease distribution can be somehow heterogeneous and patchy, it could affect mutation detection. Therefore, we also analysed cfDNA in a small set of cases, following the promising results observed in WM. 21 , 32 , 33 , 34 We found that ddPCR was able to detect and quantify MYD88 mutation in cfDNA in IgM MGUS and SWM, allowing us to infer correlation with other biomarkers. Regarding CXCR4 mutations in cfDNA, previous reports have been mostly focused on WM samples 21 , 33 ; while one study using a different sequencing approach reported that two out of nine IgM MGUS patients harboured CXCR4 mutations. 32 In our study, it has been more difficult to show solid cfDNA data in asymptomatic IgM patients. This may be explained by the fact that CXCR4 mutations are subclonal and that we have only assessed the two most common mutations. We consider that performing both ddPCR as well as deep next‐generation sequencing in cfDNA could allow us to draw more conclusions on CXCR4 mutation detection in samples with very low tumour burden. Taken together, we have demonstrated that cfDNA is a promising source of material for biomarker detection in IgM MGUS and SWM and thus can overcome diagnostic challenges such as performing BM biopsy especially in an ageing population.
+
+Given the previous results, we analysed the impact of MYD88 and CXCR4 mutations on risk of progression to symptomatic WM. We established cut‐off points for IgM MGUS and SWM, both individually and combined, in a competing‐risk framework. 35 We found that the cumulative incidence of progression was higher with greater MYD88 and CXCR4 BM mutation burden, either analysed as continuous or categorical variables. Larger studies could demonstrate the independent impact of the mutation burden on the risk of progression compared to other well‐known biomarkers related to tumour burden. Nevertheless, our data suggested that both the presence and the mutation BM tumour burden confer a greater risk of progression. Previous studies have shown non‐concordant results regarding the clinical impact of MYD88 and CXCR4 mutations in IgM MGUS and SWM. 10 , 14 , 15 , 18 The low number of patients tested, the technical issues regarding sample preparation in IgM MGUS and SWM, along with the heterogeneous histological features of wt MYD88 cases, 36 , 37 might explain the variability of previous findings. Of note, we only observed disease progression to symptomatic WM and no other lymphoproliferative disorder or amyloid light‐chain (AL) amyloidosis. Another important driver of disease progression is the presence of del(6q). Although we did not have data to draw conclusions about the interaction between MYD88, CXCR4 and del(6q) in our series, we consider it a potential field of future research, partially solved recently by single‐cell technology. 8 Regarding cfDNA mutation burden, we were unable to predict the risk of progression given that plasma samples were only collected in the last five years, and most of these patients have not yet progressed during the follow‐up. Longer follow‐up will help to elucidate if cfDNA could also predict the risk of progression.
+
+Overall, our study establishes that MYD88 and CXCR4 mutations can be analysed by ddPCR with high sensitivity, making them excellent disease biomarkers for asymptomatic IgM monoclonal gammopathies in the clinic. The main advantages of ddPCR are that it can be applied in almost any academic centre, due to its easy setup, and that it avoids the need of sample sorting, making it an excellent candidate to replace standard AS‐PCR for MYD88 mutation analysis. We also showed that MYD88 testing in cfDNA is a promising tool that might overcome diagnostic challenges. In addition, we propose the first genomic risk classification of asymptomatic IgM monoclonal gammopathies using novel techniques.
+
+AUTHOR CONTRIBUTIONS
+
+David F. Moreno, Mónica López‐Guerra, Dolors Colomer, and Carlos Fernández de Larrea designed the research project, analysed and interpreted data, and wrote the manuscript. David F. Moreno, Carlos Fernández de Larrea, Laura Rosiñol, Joan Bladé, and María Teresa Cibeira recruited and followed the patients, and collected the clinical data from the patients' registry. Sara Paz, David F. Moreno, Mari‐Pau Mena, Miguel Osuna, Alfredo Rivas‐Delgado, and Oriol Cardús conducted the experiments. Aina Oliver‐Caldés, Juan G. Correa, David F. Moreno, Luis Gerardo Rodríguez‐Lobato, Oriol Cardús, Anthony M. Battram, and Carlos Fernández de Larrea analysed the data. All authors reviewed and approved the final manuscript.
+
+FUNDING INFORMATION
+
+This study was supported in part by grants PI19/00669 from Instituto de Salud Carlos III (ISCIII) and co‐funded by the European Union, the Emili Letang Grant 2020 (from Hospital Clínic de Barcelona for D.F.M.), Grant Lab_AECC2021 from Asociación Española contra el Cáncer (AECC), and an unrestricted grant from Janssen.
+
+CONFLICT OF INTERESTS
+
+Joan Bladé: honoraria for lectures from Janssen, Celgene, Amgen, Takeda, and Oncopeptides. Laura Rosiñol: consulting fees from Amgen, Celgene, Sanofi, Janssen, and Takeda. Carlos Fernández de Larrea: advisory boards from Amgen, Janssen, and BMS; research grants from Janssen, BMS, Takeda, and Amgen; honoraria for lectures: BMS, Takeda, Sanofi, Amgen, Janssen, GSK, and Beigene. María Teresa Cibeira: honoraria from Amgen and Janssen. Luis Gerardo Rodríguez‐Lobato: honoraria from Janssen and travel grants from Janssen and Amgen. David F. Moreno and Aina Oliver‐Caldés: travel grants from Janssen. Mónica López‐Guerra, Juan G. Correa, Dolors Colomer, Anthony M. Battram, Sara Paz, Mari‐Pau Mena, Oriol Cardús, Raquel Jiménez‐Segura, Natalia Tovar, Miguel Osuna, and Alfredo Rivas‐Delgado have nothing to disclose.
+
+The results from this study were presented as an oral presentation at the 63rd American Society of Haematology Annual Meeting (December, 2021).
+
+Supporting information
+
+Figure S1
+
+Click here for additional data file.
+
+Figure S2
+
+Click here for additional data file.
+
+Figure S3
+
+Click here for additional data file.
+
+Figure S4
+
+Click here for additional data file.
+
+Figure S5
+
+Click here for additional data file.
+
+Appendix S1
+
+Click here for additional data file.
+
+ACKNOWLEDGEMENTS
+
+We are indebted to the Genomics core facility of the Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS).
+
+DATA AVAILABILITY STATEMENT
+
+Data are available on request to the corresponding authors, Dolors Colomer (dcolomer@clinic.cat) and Carlos Fernández de Larrea (cfernan1@clinic.cat).
+==== Refs
+REFERENCES
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+22 Ferrante M , Furlan D , Zibellini S , Borriero M , Candido C , Sahnane N , et al. MYD88L265P detection in IgM monoclonal gammopathies: methodological considerations for routine implementation. Diagnostics. 2021;11 (5 ):779.33926007
+23 Tomowiak C , Poulain S , Herbaux C , Perrot A , Mahé B , Morel P , et al. Obinutuzumab and idelalisib in symptomatic patients with relapsed/refractory Waldenström macroglobulinemia. Blood Adv. 2021;5 (9 ):2438–46.33961019
+24 Gertz MA . Waldenström macroglobulinemia: 2021 update on diagnosis, risk stratification, and management. Am J Hematol. 2021;96 (2 ):258–69.33368476
+25 Kyle RA , Treon SP , Alexanian R , Barlogie B , Björkholm M , Dhodapkar M , et al. Prognostic markers and criteria to initiate therapy in Waldenstrom's macroglobulinemia: consensus Panel recommendations from the Second International Workshop on Waldenstrom's macroglobulinemia. Semin Oncol. 2003;30 (2 ):116–20.12720119
+26 Castillo JJ , Garcia‐Sanz R , Hatjiharissi E , Kyle RA , Leleu X , McMaster M , et al. Recommendations for the diagnosis and initial evaluation of patients with Waldenström Macroglobulinaemia: a task force from the 8th International Workshop on Waldenström Macroglobulinaemia. Br J Haematol. 2016;175 (1 ):77–86.27378193
+27 Treon SP , Cao Y , Xu L , Yang G , Liu X , Hunter ZR . Somatic mutations in MYD88 and CXCR4 are determinants of clinical presentation and overall survival in Waldenström macroglobulinemia. Blood. 2014;123 (18 ):2791–6.24553177
+28 Poulain S , Roumier C , Venet‐Caillault A , Figeac M , Herbaux C , Marot G , et al. Genomic landscape of CXCR4 mutations in waldenström macroglobulinemia. Clin Cancer Res. 2016;22 (6 ):1480–8.26490317
+29 Treon SP , Xu L , Guerrera ML , Jimenez C , Hunter ZR , Liu X , et al. Genomic landscape of waldenström macroglobulinemia and its impact on treatment strategies. JCO. 2020;38 (11 ):1198–208.
+30 Kaiser LM , Hunter ZR , Treon SP , Buske C . CXCR4 in Waldenström's Macroglobulinema: chances and challenges. Leukemia. 2021;35 (2 ):333–45.33273682
+31 Roccaro AM , Sacco A , Jimenez C , Maiso P , Moschetta M , Mishima Y , et al. C1013G/CXCR4 acts as a driver mutation of tumor progression and modulator of drug resistance in lymphoplasmacytic lymphoma. Blood. 2014;123 (26 ):4120–31.24711662
+32 Bagratuni T , Ntanasis‐Stathopoulos I , Gavriatopoulou M , Mavrianou‐Koutsoukou N , Liacos C , Patseas D , et al. Detection of MYD88 and CXCR4 mutations in cell‐free DNA of patients with IgM monoclonal gammopathies. Leukemia. 2018;32 (12 ):2617–25.30026568
+33 Demos MG , Hunter ZR , Xu L , Tsakmaklis N , Kofides A , Munshi M , et al. Cell‐free DNA analysis for detection of MYD88 L265P and CXCR4 S338X mutations in Waldenström macroglobulinemia. Am J Hematol [Internet]. 2021;96 :E250–3.33819355
+34 Wu YY , Jia MN , Cai H , Qiu Y , Zhou DB , Li J , et al. Detection of the MYD88L265P and CXCR4S338X mutations by cell‐free DNA in Waldenström macroglobulinemia. Ann Hematol. 2020;99 (8 ):1763–9.32577844
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+36 Treon SP , Gustine J , Xu L , Manning RJ , Tsakmaklis N , Demos M , et al. MYD88 wild‐type Waldenstrom Macroglobulinaemia: differential diagnosis, risk of histological transformation, and overall survival. Br J Haematol. 2018;180 (3 ):374–80.29181840
+37 Hunter ZR , Xu L , Tsakmaklis N , Demos MG , Kofides A , Jimenez C , et al. Insights into the genomic landscape of MYD88 wild‐type Waldenström macroglobulinemia. Blood Adv. 2018;2 (21 ):2937–46.30401751
+

PMC10104776.txt

@@ -0,0 +1,232 @@
+
+==== Front
+Cancer Gene Ther
+Cancer Gene Ther
+Cancer Gene Therapy
+0929-1903
+1476-5500
+Nature Publishing Group US New York
+
+35338347
+450
+10.1038/s41417-022-00450-9
+Article
+SUMOylation inhibition enhances multiple myeloma sensitivity to lenalidomide
+http://orcid.org/0000-0002-4215-3801
+Du Li lidu@coh.org
+
+123
+Liu Wei 14
+Pichiorri Flavia 23
+http://orcid.org/0000-0002-8818-7724
+Rosen Steven T. srosen@coh.org
+
+1235
+1 grid.410425.6 0000 0004 0421 8357 Toni Stephenson Lymphoma Center, Beckman Research Institute of City of Hope, Duarte, CA USA
+2 grid.410425.6 0000 0004 0421 8357 Judy and Bernard Briskin Center for Multiple Myeloma Research, Beckman Research Institute of City of Hope, Duarte, CA USA
+3 grid.410425.6 0000 0004 0421 8357 Department of Hematology and Stem Cell Transplant, Beckman Research Institute of City of Hope, Duarte, CA USA
+4 grid.452223.0 0000 0004 1757 7615 Department of Hematology, Xiangya Hospital, Central South University, Changsha, China
+5 grid.410425.6 0000 0004 0421 8357 City of Hope Comprehensive Cancer Center, City of Hope National Medical Center, Duarte, CA USA
+25 3 2022
+25 3 2022
+2023
+30 4 567574
+3 8 2021
+18 1 2022
+24 2 2022
+© The Author(s) 2022
+https://creativecommons.org/licenses/by/4.0/ Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
+Despite the potent effect of lenalidomide (Len) in multiple myeloma (MM) treatment, patients develop Len resistance leading to progressive disease, demanding an urgent need to investigate the mechanisms mediating Len resistance. Our study identified SUMOylation as a potential mechanism regulating Len resistance in MM. Len-resistant MM cell line MMR10R presented much higher SUMO E1 (SAE2) expression and more global SUMOylation than Len-sensitive MM1S cell line. SUMOylation inhibition by using TAK-981, a novel and specific SUMO E1 inhibitor, significantly enhances myeloma sensitivity to Len in MM cell lines. Moreover, the enhanced anti-MM activity by TAK-981 and Len combination has been validated using primary relapsing MM patient samples. Overexpression of IRF4 and c-Myc is a major mechanism of Len resistance. Len showed limited effect on IRF4 and c-Myc level in Len-resistance cell line, but TAK-981 treatment reduced IRF4 and c-Myc expression in Len-resistant line and caused further decrease when combined with Len. We found SUMOylation inhibition decreases IRF4 at transcriptional and post-translational level. SUMOylation inhibition reduced DOT1L with decreased methylation of histone H3 lysine 79, to suppress IRF4 gene transcription. SUMOylation inhibition also reduced IRF4 protein level by enhancing degradation. Overall, our data revealed SUMOylation inhibition enhances Len sensitivity through downregulating IRF4.
+
+Subject terms
+
+Cancer therapeutic resistance
+Myeloma
+issue-copyright-statement© The Author(s), under exclusive licence to Springer Nature America, Inc. 2023
+==== Body
+pmcIntroduction
+
+Multiple myeloma is an incurable hematological malignancy, emerging from plasma cells. As the second most common blood cancer, MM accounts for 1% of all cancers and 10% of hematologic malignancies in the United States. Worldwide there are ~100,000 deaths each year caused by MM [1]. Despite several new drugs that have improved the survival of myeloma patients in the past decade, patients typically develop relapsed and/or refractory MM and long-term disease-free survival remains low [2]. The immunomodulatory drugs IMiDs play a pivotal role in the treatment of MM. Lenalidomide (Len) is one of the most widely used IMiD drug in combination with Dexamethasone and antibody-based MM therapy. Interferon regulatory factor 4 (IRF4) and pro-survival myelocytomatosis viral oncogene (c-Myc) are a critical pathway for MM cell growth and survival [3–5]. IKZF1 (Ikaros) and IKZF3 (Aiolos), two zinic finger transcriptional factors, bind and activate the IRF4 promoter, which in turn enhance the transcription of c-Myc. Len directly binds to an E3 ubiquitin ligase Cereblon (CRBN), which rapidly triggers the ubiquitination of IKZF1/3, leading to degradation by proteasome [6–9]. Len mediated IKZF1/3 degradation leads to reduced IRF4 and MYC expression in MM cells and to loss of their viability.
+
+Although majority newly diagnosed patients respond to Len therapy, most eventually develop resistance [10, 11]. Low CRBN expression was the first described mechanism associated with Len resistance in MM [12–14]. Other Len resistance mechanisms could bypass CRBN-IKZF1/3 axis to promote MM cell survival through upregulating pro-survival factors- IRF4 and c-Myc. The overexpression of IRF4 and c-Myc has been reported mediating Len resistance in MM [3–5]. Besides IKZF1/3, there are other transcriptional factors of IRF4 have been identified. DOT1 Like Histone Lysine Methyltransferase (DOT1L), which catalyzes methylation of histone H3 lysine 79, has been reported to be required for myeloma cell survival through enhancing IRF4-Myc signaling [15]. Transcription factor PU.1, encoded by gene SPI1, acts as tumor suppressor for myeloma cells through direct transcriptional repression of IRF4 [16, 17]. Despite all these findings, there is still urgent need to elucidate novel pathways involved Len resistance to develop new agents to enhance Len sensitivity [18–20].
+
+One potential mechanism to address this need might be SUMOylation, a post-translational modification characterized by covalent attachment of small ubiquitin-like modifier (SUMO) proteins to a lysine (Lys) residue on target proteins [21–23]. It is carried out via an enzymatic cascade involving the sequential action of an activating enzyme E1 (a heterodimer of SAE1 and SAE2), a conjugating enzyme E2 (UBC9), and a ligating enzyme E3 (one of ~10). SUMOylation enzymes are expressed at higher levels in cancer cells than in normal cells; their elevated expression is required for tumor progression, cancer metastasis, and cancer stem cell maintenance and self-renewal, and is usually associated with poor survival in various human cancers, including MM, colorectal (CRC), and breast cancers [24–27].
+
+In the present study, we found that the expression level of SUMO E1 (SAE2) and global SUMOylation were significantly higher in Len-resistant MM cell line compared to the parental Len-sensitive MM cell line MM1S. SUMOylation inhibition by using a novel selective SUMO E1 inhibitor, TAK-981, was effective against Len-sensitive and Len-resistant MM cell lines and primary relapsing MM samples. More importantly, the effect of TAK-981 was further enhanced in combination with Len. Further experiments indicated that TAK-981 treatment decreased key pro-survival factors IRF4 and c-Myc level. Therefore, we investigated how SUMOylation regulates IRF4-Myc pathway in MM.
+
+Materials and methods
+
+Reagents
+
+TAK-981 was purchased from ChemieTek (IN, USA). Lenalidomide (SML2283) was purchased from Sigma (MO, USA).
+
+Multiple myeloma cell lines and primary samples
+
+Primary MM cells were isolated from bone marrow aspirates of MM patients, using Ficoll-Hypaque density gradient sedimentation followed by CD138 microbeads separation (Miltenyi Biotec), with informed patient consent and the Research Ethics Board approval at City of Hope (IRB 15150). Normal B lymphocytes from healthy donor PBMCs were enriched by Mojosort human CD19+ cell selection kit (Biolegend) according to the manufacture manual. Purify was validated by flow cytometry of CD19 staining. Human myeloma cell lines MM1S, H929, KMS11, and RPMI8226 were obtained from ATCC. Lenalidomide resistant cell line MMR10R was a kind gift from Dr. R Z Orlowski (M.D. Anderson cancer cell, TX, USA) [28]. All myeloma cell lines and primary CD138+ MM cells were cultured in RPMI1640 medium (Corning) with 10% heat-inactivated FCS (Omega Scientific, Inc.), 2 mmol/L l-glutamine, and 1% antibiotic-antimycotic (Life Technologies). Mycoplasma was routinely tested using Mycoplasma PCR detection kit (G238, Abcam).
+
+MM cell line transfection
+
+Plasmid MSCB-hDot1Lwt was a gift from Dr. Yi Zhang (Addgene plasmid # 74173; http://n2t.net/addgene:74173; RRID: Addgene_74173) [29]. MM1S cells were transfected by electroporation using Nucleofector 4D system and SE Cell Line 4D-NucleofectorTM X Kit L(Lonza). Briefly, 1×106 cells were resuspended in 100 μl of the nucleofector solution SF, 3 μg of plasmid MSCB-hDot1Lwt, or empty control vector were added and transferred to a cuvette. Program CA-137 was used for MM1S cells. After electroporation, cells were immediately plated out in pre-warmed medium onto 12-well plate. Compound treatments were performed after 24 h.
+
+Cell viability assay and drug-synergy calculations
+
+Cells (0.5–2 × 104/200 μl/well) were cultured in 96-well plates and treated with the indicated reagents for 48 h or 72 h. Cell viability assays were performed using Cell Titer-Glo Luminescent Cell Viability (G7572, Promega) according to the manufacturer’s instruction. Median inhibitor concentration (IC50) was determined using GraphpadPrism 8.0. Combination indices (CIs) were calculated using CompuSyn software (Biosoft, Cambridge, UK). Simulating calculated CI values and experimental CI values based on combination data points are plotted as a function of the fraction affected (Fa). Fraction affected indicates percentage inhibition of cell, growth/100. Synergism, additive effect and antagonism of combine treatment assays are defined as CI < 1, CI = 1 and CI > 1 respectively, utilizing the Chou-Talalay Method [30, 31]. For MM1S and MMR10R cell line drug-synergy analysis, SynergyFinder software was used to calculate synergy scores using effect-based strategy, Highest Single Agent (HSA) model or dose-effect-based strategies, Loewe additivity model. Synergy scores > 0 indicate synergism (red regions) and scores < 0 indicate antagonism (green regions) [32].
+
+Flow cytometry-based apoptosis assay
+
+Cell apoptosis was measured after Annexin V FITC and PI staining (# 556419, BD Bioscience) according to the manufacturer’s instructions using a BD Fortessa LSR II and FlowJo Version V10.6.2.
+
+Western blot
+
+Cells were harvested and lysed in Laemmli sample buffer (5% SDS, 25% glycerol, 150 mmol/L Tris-HCl pH 6.8, 0.01% bromophenol blue). After protein concentration was measured using BCA protein assay, 0.7 mol/L β-mercaptoethanol was added and protein samples were boiled for 10 minutes. Samples were separated by SDS-PAGE, and protein was transferred onto a polyvinylidene fluoride membrane (Immobilon-P membrane, Millipore). Following antibodies were used: SAE2 (ab58451, abcam), SUMO-2,3 (M114-3, MBL), c-Myc (ab32072, Abcam), GAPDH (sc-20357, Santa Cruz Biotechnology), SUMO-1 (#4930), IRF4 (#4964), CRBN (#71810), cleaved PARP (#5625), Aiolos (#15103), Ikalos (#14859), DOT1L (#77087), H3K79me2 (#5427), PU.1 (#2266), UBC9(#4918) and SAE1(#13585) were from Cell Signaling Technology. Western blot results were visualized using an Odyssey detection system (Licor) or Pierce ECL Western Blotting Substrate (Thermo Fisher Scientific).
+
+Reverse Transcription and qPCR
+
+Total cellular RNA was extracted using miRNeasy Mini Kit (Qiagen). Total RNA (2 μg) was reverse-transcribed using an Omniscript RT Kit (Qiagen) and oligo dT primer. Real-time qPCR of gene expression was performed using the SYBR-Green Master Mix (Applied Biosystems). All quantitative PCR reactions were performed using ViiA 7 real-time PCR system (Applied Biosystem). Relative expression was calculated using the comparative Ct method normalized to GAPDH. The following primers were used for PCR: IRF4 sense, 5′-GCTGATCGACCAGATCGACAG-3′; IRF4 antisense, 5′-CGGTTGTAGTCCTGCTTGC-3′; DOT1L sense, 5′-GAGACCTCCTTCGACCTGGT-3′; DOT1L antisense, 5′-CGACGCCATAGTGATGTTTGC-3′; GAPDH sense, 5′-AGGTCGGAGTCAACGGATTTG-3′; and GAPDH antisense, 5′-GTGATGGCATGGACTGTGGT-3′.
+
+Chromatin immunoprecipitation assay
+
+To detect the binding occupancy of DOT1L or H3K79me2 on the human IRF4 promoter, chromatin immunoprecipitation (ChIP) analysis was conducted using SimpleChIP Enzymatic Chromatin IP kit (Magnetic Beads) (#9003, Cell Signaling Technology). A total of 2 × 107 MM1S or MMR10R cells were incubated in culture medium containing 1% formaldehyde for 10 min at room temperature, after which, the cross-linking reaction was quenched with addition of glycine to a final concentration of 0.125 mol/L. Cells were washed with cold PBS and harvested, followed by sonication to produce chromatin of primarily mononucleosome size. Fragmented chromatin was then incubated with DOT1L or H3K79me2 antibody (#5427, Cell Signaling Tech) at 4 °C overnight. Protein–DNA complexes were recovered using protein G dynabeads, washed, and eluted with elution buffer. Crosslinks were reversed at 65 °C in 0.25 mol/L NaCl overnight; then, the DNA was digested with proteinase K for 2 h at 50 °C. The immunoprecipitated DNAs were subsequently isolated and used for qPCR. Primer for ChIP: forward, 5’-TTCGCATGCCATCTGTCATG-3’, reverse, 5’-TTTTCAGCAACTCCCTTGGG-3’
+
+Protein degradation assay
+
+IRF4 protein stability was measured on treatment with protein synthesis inhibitor cycloheximide (CHX). Cells treated with 100 μg ml−1 CHX (#2112, Cell Signaling Technology, Inc.) were collected at different time points and cell lysate was used for western blot to determine the protein level at different CHX treatment time. Western blot results were quantified by the ImageJ Software (NIH).
+
+Gene expression analysis from public datasets
+
+UBA2 and DOT1L expression extracted from GEO databases was plotted and analyzed.
+
+Statistical analyses
+
+No samples were excluded from analysis. For all experiments, P values were derived using a two-tailed Student’s t-test or ANOVA. Data presented as mean ± SD. Estimated variation is indicated as SD in each figure. For all graphs, *p < 0.05, **p < 0.01 and ***p < 0.001.
+
+Results
+
+Expression of SUMO E1 is upregulated in Lenalidomide resistant MM cells
+
+MM cell line with acquired resistance to Len (MMR10R) was established by culturing MM1S with addition of Len to the medium for an extended period of time as previously described [28]. IC50 values of Len in MM1S and MMR10R were determined by cell viability assay. MMR10R cell line presented resistance to Len compared to MM1S, with IC50 at 15 vs 0.1 µM (Fig. 1A). Western blot indicated MMR10R cells showed a significant decrease of CRBN and a huge induction of IRF4 protein, which is consistent with previous reports that loss of CRBN and overexpression of IRF4 contribute to Len-resistance in MM. More importantly, MMR10R cells expressed greater levels of SUMO E1 SAE2, SAE1, and global SUMOylation (SUMO-1 and SUMO-2,3) than MM1S cells. SUMO E2 enzyme, UBC9 level showed no difference between these two cell lines (Fig. 1B). These results suggest SUMOylation, especially SUMO E1, might be involved in Len resistance mechanism.Fig. 1 Lenalidomide resistant MM cells have higher SAE2 and global SUMOylation than Lenalidomide sensitive MM cells.
+
+A Cell viability assay showing Len-sensitive MM1S and Len-resistant MMR10R cell line treated with Lenalidomide at indicated concentrations. IC50 values were calculated by GraphPad Prism 8. B Western blot showing CRBN, IRF4, SAE2, SAE1, UBC9, and global SUMOylation (SUMO-2,3 and SUMO-1) level of MM1S and MMR10R cell lines; GAPDH, loading control. Relative protein level was quantified using Image J, normalized to GAPDH, and labeled below each blotting band.
+
+SUMOylation inhibition enhances Len anti-MM activity in cell lines and primary patient samples
+
+To test if inhibition of SUMOylation can sensitize the effects of Len in MM, we utilize TAK-981, a novel, selective small molecule inhibitor of SUMO E1 enzyme, which is currently in Phase 1 trials in adult patients with metastatic solid tumors and lymphomas [33]. We conducted cytotoxicity assay in MM1S and MMR10R cell lines, TAK-981 synergized with Len at decreasing cell viability in both Len-sensitive and Len-resistant cell lines with CI values calculated by CompuSyn (Fig. 2A). We also performed synergy matrix with TAK-981 and Len in MM1S and MMR10R cells. SynergyFinder software was used to calculate synergy scores using effect-based strategy, Highest Single Agent (HSA) model or dose-effect-based strategies, Loewe additivity model. Both models confirmed the synergistic effects of TAK-981 and Len in both cell lines MM1S and MMR10R (Supplementary Fig. S1). Len showed limited effects on inducing apoptosis or inhibiting cell growth in MMR10R. Annexin-V staining indicated TAK-981 treatment led to apoptosis in both cell line, and the effects were further enhanced in combination with Len (Fig. 2B, C). The synergistic effects of combination TAK-981 and Len were observed in other MM cell lines RPMI8226 and H929 (Supplemental Fig. S2).Fig. 2 SUMOylation inhibition synergizes with Len in decreasing cell viability in both Len-sensitive and Len-resistant MM cell lines.
+
+A TAK-981 synergizes with Len cytotoxicity in sensitive MM line (MM1S) and resistant MM line (MMR10R). MM1S and MMR10R cells were treated with indicated concentration of TAK-981 or Len or both (TAK + Len) with indicated concentration for 48 h and cell viability was determined by Cell-Titer-Glo. Drug synergy was analyzed using CompuSym program. Simulating calculated CI values (open circle) and experimental combination indice (CI) values (solid circle) based on combination data points are plotted as a function of the fractional affected (Fa) derived from analysis report. Fraction affected indicates percentage inhibition of cell growth/100. Drug synergism is defined as CI < 1. B TAK-981 enhances cytotoxicity of Len in sensitive and resistant MM. MM1S and MMR10R cells were treated with Vehicle (Veh), 0.1 µM TAK-981 (TAK), 2.5 µM Len (Len), or 0.1 µM TAK-981 with 2.5 µM Len (TAK + Len). Apoptosis was measured by flow cytometry using Annexin V/PI staining. C Quantified apoptosis from three experimental repeats. Data were analyzed using one-way ANOWA test: Data presented as mean ± SD. *p < 0.05; **p < 0.01; ****p < 0.0001.
+
+We then evaluated the effect of TAK-981 and Len combination using primary relapsing MM patient samples. CD138+ primary MM cells were isolated from relapsing myeloma patients and treated with TAK-981 alone, Len alone or both for 72 h, then cell viability was measured. Among 5 out of 6 primary MM samples, combination of TAK-981 with Len showed significantly enhanced cytotoxicity compared to the single agents alone in MM patient samples (Fig. 3A and Supplementary Fig. S3). The effects of TAK-981 on enhancing Len effects in MM cell lines and primary samples suggests SUMOylation inhibition might enhance MM sensitivity to Len.Fig. 3 SUMOylation inhibition synergizes with Len in decreasing cell viability in primary multiple myeloma cells but doesn’t affect normal primary B lymphocytes viability.
+
+A Cell viability assay showing 1 out of 6 primary CD138+ cells from bone marrow aspirates of relapsing MM patients treated with TAK-981 or Len or both (TAK + Len) with indicated concentration. Cell viability was assessed by Cell-Titer-Glo after 48 h of treatment. Drug synergy was analyzed using CompuSym program. Combination Indice (CI) values are plotted. Drug synergism is defined as CI < 1. B Cell viability assay showing 1 out of 3 primary CD19+ cells from PBMCs of healthy donors. Normal B lymphocytes were purified by Mojosort human CD19+ cell selection kit. CD19+ cells were treated with TAK-981 or Len or both (TAK + Len) with indicated concentration. Cell viability was assessed by Cell-Titer-Glo after 48 hours of treatment. C TAK-981 or Len or both showed no effect on normal CD19+ cell viability by apoptosis assay. Healthy donor PBMC cells were treated with Vehicle (Veh), 0.1 µM TAK-981 (TAK), 2.5 µM Len (Len), or 0.1 µM TAK-981 with 2.5 µM Len (TAK + Len). Apoptosis was measured by flow cytometry using Annexin V staining gated on CD19+ population. Quantified apoptosis from three individual healthy donors was plotted. Data presented as mean ± SD. Data were analyzed using ANOVA test. Ns, not significant, *p < 0.05.
+
+To evaluate the effect of SUMOylation inhibition on non-transformed primary B lymphocytes, CD19+ cells were isolated from healthy donor PBMCs then treated with TAK-981 or Len for cell viability assay. There is no obvious cytotoxicity observed upon either TAK-981, Len or combination treatment in all three tested healthy donor PBMCs (Fig. 3B and Supplementary Figure S4AB). In PBMCs treated with TAK-981, Len or both, Annexin V staining in CD19+ gated cells were used to measure the apoptotic cell percentage. Consistent with Fig. 3B, no difference of apoptosis of CD19+ cells was observed in TAK-981, Len or combination treatment compared to vehicle control (Fig. 3C and Supplementary Fig. S4C). Cell viability assay of PBMCs indicated that TAK-981 or Len or both showed no effect on cell viability of health donor PBMCs (Supplementary Fig. S4D). Taken together, TAK-981 showed cytotoxicity on MM cells without affecting normal primary B lymphocytes cell viability.
+
+SUMOylation inhibition decreased IRF4 level independent of CRBN-IKF1/3 regulation
+
+To explore how SUMOylation inhibition mediates Len resistance, we evaluated the impact of TAK-981 on IRF4 and c-Myc, two key regulators mediating MM cell growth and Len resistance. 4 MM cell lines were treated with TAK-981 alone, Len alone or both for 48 h, cell lysates were collected for western blot. Len treatment caused downregulation of IRF4 and MYC with increased apoptosis marker cleaved PARP level, but the effects were limited in MMR10R cells (Fig. 4A). However, TAK-981 treatment significantly decreased IRF4 and c-Myc levels along with increased cleaved PARP expression in both MM1S and MMR10R cell lines. The decrease of TAK-981 on IRF4 and c-Myc level was further enhanced when combined with Len. Consistent effects were observed in other two MM cell lines H929 and KMS11(Fig. 4A). The data indicated SUMOylation inhibition enhanced Len effect at suppressing MM cell growth by downregulation of IRF4 and c-Myc expression. Then, CRBN-IKZF1/3 pathway was investigated. Len treatment significantly induced CRBN level followed by dimished expression of Aiolos and Ikaros in MM1S cells. But CRBN-mediated degradation of Aiolos and Ikaros upon Len treatment was substantially reduced due to loss of CRBN in MMR10R cells. TAK-981 treatment, although slightly decreased CRBN level, showed little effect on downstream protein levels of Aiolos and Ikaros. Consistent results were observed in H929 and KMS11 as well (Fig. 4B). These findings indicated SUMOylation inhibition decreased IRF4 level through a different regulation mechanism.Fig. 4 SUMOylation inhibition decreased IRF4 level independent of CRBN-IKF1/3 regulation.
+
+A TAK-981 synergizes with Len at increasing apoptosis marker cleaved PARP and decreasing IRF4 and c-Myc levels in MM. Western blot showing global SUMOylation (SUMO-2,3), cleaved PAPR, IRF4, and c-Myc level of MM1S and MMR10R (left) and H929 and KMS11 (right) cell lines. B Western blot showing CRBN, Aiolos (IKZF3) and Ikaros (IKZF1) level of MM1S and MMR10R (left) and H929 and KMS11 (right) cell lines. MM1S, MMR10R and H929 cells were treated with TAK-981 (0.1 µM) or Len 2.5 µM or both for 48 h; KMS11 cells were treated with TAK-981 (1 µM) or Len (25 µM) or both for 48 h. GAPDH was used as loading control. Relative protein level was quantified using Image J, normalized to GAPDH, and labeled below each blotting band.
+
+SUMOylation inhibition decreased IRF4 level through downregulating DOT1L and H3K79me2 at IRF4 promoter region
+
+In order to evaluate the regulation of IRF4 at transcriptional level, IRF4 mRNA level was determined in MM1S and MMR10R cells by real-time qPCR. MMR10R cells exhibit higher IRF4 mRNA level than MM1S. Len treatment greatly decreased IRF4 mRNA in MM1S but showed less effect in MMR10R, but TAK-981 treatment decreased IRF4 mRNA level in both MM1S and MMR10R cell lines and showed further reduction in combination with Len (Fig. 5A). DOT1L has been reported to promote MM cell proliferation through activating IRF4 transcription via methylation of histone H3 lysine 79 at the promoter region. We found Len treatment reduced DOT1L level in MM1S, H929, and KMS11 cell lines but showed no effect in MMR10R cell line. TAK-981 treatment significantly reduced DOT1L level in all 4 MM cell lines and the reduction was further enhanced in combination with Len, even in Len-resistant MMR10R cell line (Fig. 5B). DOT1L mRNA level showed consistent change as protein level in MM1S and MMR10R cells upon TAK-981 and Len treatment (Fig. 5C).Fig. 5 SUMOylation inhibition decreased IRF4 level through downregulating DOT1L and H3K79me2 at IRF4 promoter region.
+
+A TAK-981 synergizes with Len at decreasing IRF4 mRNA level in MM1S and MMR10R cell lines determined by quantitative PCR (qPCR). B Western blot showing decreased DOT1L level upon TAK-981 treatment in MM1S and MMR10R (Top) and H929 and KMS11 (Bottom) cell lines. Cells were treated as described in (Fig. 4). C TAK-981 treatment decreases DOT1L mRNA levels in both MM1S and MMR10R cell lines and Len has no effect on DOT1L level in MMR10R cell line measured by qPCR. D TAK-981 treatment decreased the occupancy of DOT1L and H3K79me2 on the IRF4 promoter region as measured by ChIP assay. ChIP was performed using an anti-DOT1L and H3K79me2 antibody in MM1S and MMR10R treated with TAK-981 (0.1 µM) or Len 2.5 µM for 48 h. The occupancy was normalized to DNA input and calculated relative to IgG control. E Overexpression of DOT1L compensated the decrease of IRF4 mRNA level caused by TAK-981 treatment. MM1S cells were transduced with plasmid MSCB-hDot1Lwt expressing DOT1L(DOT1L) or empty vector (EV) by electroporation then treated with TAK-981(0.1 µM) for 48 h. IRF4 mRNA level was determined by qPCR. Data presented as mean ± SD. ns, not significant; ***p < 0.001. F Overexpression of DOT1L compensated the decrease of IRF4 protein level caused by TAK-981 treatment. Western blot presenting IRF4 and DOT1L protein level in same treatment of (E). GAPDH, loading control. Quantified protein level was labeled below each blot. G UBA2 level correlates with DOT1L expression in patient specimens. Analysis of cohort (GSE2658) of 559 MM patients. Patients with high SAE2 (UBA2; UBA2high group) showed higher DOT1L level than patients with low SAE2 (UBA2; UBA2low group). Data were analyzed using unpaired Student t tests: Data presented as mean ± SD. ****p < 0.0001.
+
+We performed ChIP assay to determine the binding occupancy of DOT1L and H3K79me2 level at the IRF4 promoter region in MM1S and MMR10R cells treated with TAK-981 or Len. TAK-981 significantly decreased DOT1L occupancy and H3K79me2 level at IRF4 promoter at similar extent levels in both MM1S and MMR10R cell lines. Len showed less effect than TAK-981 in MM1S cells and barely no effect in MMR10R (Fig. 5D). We then investigated if the decrease of IRF4 level caused by TAK-981 treatment could be compensated by overexpression of DOT1L. We transfected plasmids expressing DOT1L or empty vector in MM1S cells by electroporation then treated MM1S cells with or without TAK-981 for 48 h, then measured IRF4 mRNA level by qPCR and protein level by western blot. TAK-981 treatment significantly decreased IRF4 mRNA level (1:0.5 fold) in empty vector transfected cells, but only slightly decreased IRF4 level (1:0.9fold) in cells with overexpression of DOT1L, indicating overexpression of DOT1L compensated the IRF4 level (Fig. 5E). Western bolt showed consistent results (Fig. 5F). The results suggest SUMOylation inhibition caused decrease of IRF4 is through mediating DOT1L.
+
+We analyzed a MM patients data set GSE2658 (n = 559), among which SAE2(UBA2) level is associated with poor outcome [34]. Patients with high SAE2 (UBA2; UBA2high group) showed higher DOT1L level than patients with low SAE2 (UBA2; UBA2low group) (Fig. 5G), indicating the regulation of DOT1L expression by SAE2 is not restricted to cell lines. These results indicated SUMOylation inhibition decreases IRF4 level via downregulating the transcription activator DOT1L.
+
+PU.1, an E-twenty-six family transcription factor, has been reported as a transcriptional factor suppressing IRF4 expression, acting as a tumor suppressor in MM [16]. We observed TAK-981 treatment increased PU.1 protein and mRNA levels in MM1S and MMR10R cell lines (Supplementary Fig. S5A, B). Analyzing MM patient cohort GSE2658 indicated SAE2 level was negatively associated with PU.1 (gene name SPI1) expression (Supplementary Fig. S5C), suggesting SUMOylation inhibition might increase PU.1 to decrease IRF4 expression. However, TAK-981 treatment didn’t show any change on PU.1 level in H929 and KMS11 cell lines (Supplementary Fig. S5D). Although TAK-981 and Len both increased PU.1 level in MM1S and MMR10R, the combination treatment led to no change or even less PU.1 level compared to vehicle treatment. The results are not consistent with our observation that combining TAK-981 and Len showed lower IRF4 level than single agent treatment, suggestion the regulation of PU.1 by SUMOylation may not contribute to the synergistic effect in TAK-981 and Len combination.
+
+SUMOylation inhibition affects IRF4 protein stability
+
+IRF4 has been identified as SUMO target protein, which can be SUMO-modified at Lysine 349 (K349) by SUMO-2 [35]. SUMOylation can promotes IRF4 protein stability. We then evaluated the impact of TAK-981 on the protein stability using a cycloheximide (CHX) chase assay. MMR10R cells were treated with or without TAK-981 followed by addition of CHX to block protein synthesis, which allows monitoring protein degradation. Western blot showed IRF4 degraded faster in myeloma cells treated with TAK-981 compared to vehicle (Fig. 6 and Supplementary Fig. S6), indicating SUMOylation inhibition decreased IRF4 protein level through enhancing degradation.Fig. 6 SUMOylation inhibition accelerates IRF4 protein degradation.
+
+A Representative western blot of IRF4 level over time in MMR10R cells treated with or without TAK-981(0.1 µM) for 4 h, followed by 100 μg ml−1 CHX treatment for indicated time; GAPDH, loading control. B IRF4 decay curve was determined by quantifying protein level normalized to GAPDH from three independent experiments.
+
+Discussion
+
+Overcoming Lenalidomide resistance is a critical medical need in MM therapy. Identification of novel mechanism of Lenalidomide resistance and developing new agents to sensitize the effect of Lenalidomide is of the utmost importance. Our study uncovered a mechanism in which SUMOylation inhibition reduces IRF4 and c-Myc level, leading to enhancement of Len sensitivity in MM. SUMO E1 inhibitor TAK-981 was effective against Len-resistant cell line and primary relapsing MM samples. Moreover, combination TAK-981 with Len showed potent synergistic anti-MM effects, supporting translation of this SUMO E1 inhibitor into myeloma trials.
+
+Our study uncovered a mechanism in which SUMOylation inhibition sensitizes Lenalidomide effects at decreasing IRF4 expression at transcriptional regulation of DOT1L and H3K79me2 and protein stability (Fig. 7). Unlike Len, which decreases IRF4 via CRBN-IKZF1/3 axis, TAK-981 decreases IRF4 transcriptional through epigenetic modulation at IRF4 promoter via downregulating DOT1L level. SUMOylation inhibition can decrease IRF4 level through enhancing protein degradation. Taken together, SUMOylation inhibition downregulated IRF4 at transcriptional and protein level, suppressed MM growth with overcoming Len resistance effect.Fig. 7 Schematic diagram showing the mechanism of how SUMOylation inhibition sensitizes Lenalidomide effects in MM cells.
+
+Upper panel (Control): Two transcriptional factors, IKZF1 (Ikaros) and IKZF3 (Aiolos), bind and activate the IRF4 promoter. Methyltransferase DOT1L can activate IRF4 transcription via methylation of histone H3 lysine 79 at the promoter region. Both IKZF1/3 and DOT1L enhance IRF4 expression, thus promote MM cell growth. Lower panel (Lenalidomide+TAK-981): Len directly binds to an E3 ubiquitin ligase Cereblon (CRBN), which rapidly triggers the ubiquitination of IKZF1/3, leading to degradation by proteasome. TAK-981 decreases DOT1L level and causes less DOT1L and H3K79me2 binding at IRF4 promoter region. Both drugs treatment leads to decreased transcription of IRF4 mRNA level. Further, TAK-981 treatment inhibits the SUMOylation of IRF4, which accelerates IRF4 protein degradation by proteasome. All these result in decrease of IRF4 level, leading to suppression of MM cell growth.
+
+Epigenetic alterations including aberrant DNA methylation and histone modification play important roles in the pathogenesis of MM and are considered as potential therapeutic targets [36, 37]. Our previous work has reported SUMOylation regulates Enhancer of zeste homolog 2 (EZH2), the enzymatic component of polycomb repressive complex 2 (PRC2), which catalyzes trimethylation of histone H3 on lysine 27 (i.e., H3K27me3). SUMOylation inhibition caused decreased EZH2 and H3K27me3 level in colorectal cancer and breast cancer [27]. In this study, we demonstrated SUMOylation mediates another histone modification H3K79me2 via downregulating DOT1L. TAK-981 caused lower level of H3K79me2 on the promoter of IRF4 despite no change on H3K79me2 level in whole cell lysates, suggesting the regulation might be genome location specific. Interestingly, Len treatment also reduced DOT1L level but the effect was not abolished in Len-resistant cell line MMR10R, presenting a possibility that DOT1L might be involved in Len resistance mechanism.
+
+We observed TAK-981 decreased EZH2 level in MM cells, consistent with our findings in colorectal cancer and breast cancer [27]. PRC2 activation and broad H3K27me3 formation was reported to promote MM tumorigenicity [38]. Taken together, the cytotoxicity effect of TAK-981 might be partially contributed by the downregulation on PRC2 and H3K27me3 level.
+
+IRF4 activate c-Myc expression, and IRF4 was itself a direct target of MYC transactivation, generating an autoregulatory circuit in myeloma cells. In our previous work, we revealed SUMOylation regulates c-Myc mRNA level through regulation its targeting microRNA miR-34b/c [39]. We observed SUMOylation inhibition decreased c-Myc level, which might subsequently lower IRF4 level, leading to suppressed MM growth.
+
+Together, our study revealed that SUMOylation inhibition enhances Len sensitivity in MM by decreasing IRF4 transcription level via downregulating DOT1L and IRF4 protein level via promoting degradation. Combination SUMO E1 inhibitor TAK-981 with Len showed potent synergistic anti-MM effects. We have also found SUMOylation inhibition enhanced MM sensitivity to dexamethasone, a synthetic glucocorticoid, which is most widely used in MM combination regimen. TAK-981 showed potent synergistic efficacy with dexamethasone against MM ex vivo and in vivo [34]. Since Lenalidomide plus dexamethasone is a standard of care for MM patients and resistance to the therapy remains a main challenge, our study revealed SUMOylation inhibition could be a novel strategy to address this need. Overall, our findings strongly support translation of TAK-981 into clinical trials for MM patients and possibly other hematologic malignancies with potential to improve outcome of the existing therapies.
+
+Supplementary information
+
+supplementary-0114
+
+Supplementary information
+
+The online version contains supplementary material available at 10.1038/s41417-022-00450-9.
+
+Acknowledgements
+
+We thank the City of Hope Core facilities for excellent technical support. Research reported in this publication included work performed in the Analytical Cytometry Core supported by the National Cancer Institute of the National Institutes of Health under grant number P30CA033572. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
+
+Author contributions
+
+Contribution: LD designed the study, performed experiments, analyzed the data and wrote the manuscript; WL performed experiments and analyzed the data; FP provided cell line and advice on study design; STR supervised the study and edited the manuscript.
+
+Data availability
+
+All data generated or analyzed during this study are included in this published article and its Supplementary Data files.
+
+Competing interests
+
+The authors declare no competing interests.
+
+Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
+
+These authors contributed equally: Li Du, Wei Liu.
+==== Refs
+References
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+

PMC10107198.txt

@@ -0,0 +1,243 @@
+
+==== Front
+Eur J Haematol
+Eur J Haematol
+10.1111/(ISSN)1600-0609
+EJH
+European Journal of Haematology
+0902-4441
+1600-0609
+John Wiley and Sons Inc. Hoboken
+
+36433728
+10.1111/ejh.13905
+EJH13905
+Original Article
+Original Articles
+Amp(1q) and tetraploidy are commonly acquired chromosomal abnormalities in relapsed multiple myeloma
+Locher et al.
+Locher Maurus https://orcid.org/0000-0002-9299-3454
+1
+Jukic Emina https://orcid.org/0000-0001-6909-7005
+1
+Vogi Verena 1
+Keller Markus A. https://orcid.org/0000-0002-8654-9920
+1
+Kröll Teresa 1
+Schwendinger Simon https://orcid.org/0000-0002-7542-222X
+1
+Oberhuber Klaus 1
+Verdorfer Irmgard 1
+Mühlegger Beatrix E. 1
+Witsch‐Baumgartner Martina https://orcid.org/0000-0002-9379-5345
+1
+Nachbaur David 2
+Willenbacher Wolfgang 2 3
+Gunsilius Eberhard https://orcid.org/0000-0003-1327-2921
+2
+Wolf Dominik 2 4
+Zschocke Johannes 1 johannes.zschocke@i-med.ac.at
+
+Steiner Normann 2 normann.steiner@i-med.ac.at
+
+1 Institute of Human Genetics, Medical University of Innsbruck Innsbruck Austria
+2 Internal Medicine V (Hematology & Oncology), Medical University of Innsbruck Innsbruck Austria
+3 syndena GmbH, connect to cure Innsbruck Austria
+4 Medical Clinic 3, Oncology, Hematology, Immunoncology and Rheumatology, University Hospital Bonn Bonn Germany
+* Correspondence
+Johannes Zschocke, Institute of Human Genetics, Medical University of Innsbruck, Peter‐Mayr‐Str. 1, A‐6020 Innsbruck, Austria.
+Email: johannes.zschocke@i-med.ac.at
+Normann Steiner, Department of Hematology and Oncology, Medical University of Innsbruck, Anichstrasse 35, A‐6020 Innsbruck, Austria.
+Email: normann.steiner@i-med.ac.at
+
+04 12 2022
+3 2023
+110 3 10.1111/ejh.v110.3 296304
+14 11 2022
+20 8 2022
+17 11 2022
+© 2022 The Authors. European Journal of Haematology published by John Wiley & Sons Ltd.
+https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.
+
+Abstract
+
+Long‐term disease control in multiple myeloma (MM) is typically an unmet medical need, and most patients experience multiple relapses. Fluorescence in situ hybridization (FISH) is the standard technique to detect chromosomal abnormalities (CAs), which are important to estimate the prognosis of MM and the allocation of risk adapted therapies. In advanced stages, the importance of CAs needs further investigation. From 148 MM patients, two or more paired samples, at least one of which was collected at relapse, were analyzed by FISH. Using targeted next‐generation sequencing, we molecularly investigated samples harboring relapse‐associated CAs. Sixty‐one percent of the patients showed a change in the cytogenetic profile during the disease course, including 10% who acquired high‐risk cytogenetics. Amp(1q) (≥4 copies of 1q21), driven by an additional increase in copy number in patients who already had 3 copies of 1q21, was the most common acquired CA with 16% affected patients. Tetraploidy, found in 10% of the samples collected at the last time‐point, was unstable over the course of the disease and was associated with TP53 lesions. Our results indicate that cytogenetic progression is common in relapsed patients. The relatively high frequency of amp(1q) suggests an active role for this CA in disease progression.
+
+chromosome aberrations
+multiple myeloma
+recurrence
+tetraploidy
+source-schema-version-number2.0
+cover-dateMarch 2023
+details-of-publishers-convertorConverter:WILEY_ML3GV2_TO_JATSPMC version:6.2.7 mode:remove_FC converted:17.04.2023
+Locher M , Jukic E , Vogi V , et al. Amp(1q) and tetraploidy are commonly acquired chromosomal abnormalities in relapsed multiple myeloma. Eur J Haematol. 2023;110 (3 ):296‐304. doi:10.1111/ejh.13905 36433728
+==== Body
+pmc Novelty statements
+
+What is the new aspect of your work?
+
+In a longitudinal multiple myeloma study, we examined the occurrence of chromosomal amplifications such as tetraploidy.
+
+What is the central finding of your work?
+
+Amp(1q) and tetraploidy are acquired relatively frequently in relapsed multiple myeloma.
+
+What is (or could be) the specific clinical relevance of your work?
+
+Therapeutically targeting chromosomal features such as amp(1q) and tetraploidy could potentially help overcome resistance mechanisms in the relapse setting.
+
+1 INTRODUCTION
+
+Multiple myeloma (MM) is caused by monoclonal proliferation of malignant plasma cells. 1 The disease shows marked heterogeneity, which is reflected in the survival range from few months to over 10 years. 2 It was shown that disease progression is linked to genetic events. 3 Due to primary oncogenic events of either an immunoglobulin heavy chain gene (IGH) translocation or gains of odd‐numbered chromosomes (hyperdiploidy), MM can be divided into two nearly equal sized groups with little overlap. 4 , 5 , 6 Secondary events, including gain of 1q, del(17p), del(1p) and MYC translocations, accumulate along the disease course. Recently, large datasets and computational approaches have been used to refine the genomic classification of MM by accounting for numerous features, resulting in a granular view of the disease. 7 , 8 , 9 In the context of genetic risk stratification of newly diagnosed MM (NDMM), the IGH translocations t(4;14), t(14;16) and t(14;20) as well as del(17p), all established unfavorable markers, are usually analyzed using fluorescence in situ hybridization (FISH). 2 , 10 , 11 Additional copies of 1q, detected in approximately 40% of NDMM, are also considered as prognostically unfavorable. 12 Amp(1q) (≥4 copies of 1q) appear to be correlated with shorter survival than gain(1q) (3 copies of 1q). 13 , 14 In longitudinal analyses, samples from the same patient obtained at different stages of disease (e.g., paired samples from diagnosis and relapse) are analyzed to study clonal evolution and disease progression. 3 , 15 , 16 This approach identified different evolution patterns after therapy: linear increase of abnormalities, losses and gains of abnormalities indicative of branching evolution, and clonal stability. 15 , 17 Longitudinal analyses by FISH provided evidence that clonal instability may be associated with an adverse outcome. 18 , 19 In particular, gain/amp(1q) and del(17p), which are among the most commonly acquired chromosomal abnormalities (CAs) at relapse, have been associated with an adverse prognosis, 19 , 20 , 21 , 22 underscoring the utility of repeated FISH testing during the disease course. 23 Limited data are available on near‐tetraploidy/tetraploidy, which refers to 4 copies of (almost) every genomic region and results from genome doubling. 24
+
+In our cohort of 148 patients with at least two longitudinal samples we aimed to investigate the evolution of CAs such as amp(1q) and near‐tetraploidy/tetraploidy. In addition, we investigated in which cytogenetic subgroup and therapy context, cytogenetic evolution or stable progression took place.
+
+2 METHODS
+
+2.1 Patients
+
+Our database was reviewed to identify 148 MM patients for whom two or more samples were analyzed by Interphase FISH. The FISH analyses were performed between January 2010 and June 2021. In 120 patients, the first sample was analyzed at diagnosis and in 28 patients at relapse. The following samples were obtained at subsequent relapses or refractory stages. The number of therapy lines was determined according to standard guidelines. 25 The study was approved by the local ethics committee of the Medical University of Innsbruck (protocol #1348/2020).
+
+2.2 Interphase FISH
+
+Interphase FISH analysis was carried out either on unsorted samples or on CD138+ enriched plasma cells after purification by magnetic‐activated cell sorting (Miltenyi Biotec, Bergisch Gladbach, Germany) or RoboSep (STEMCELL Technologies, Vancouver, Canada). Probes for the chromosomal regions 1q21 (CKS1B), 11q22 (ATM), 13q14 (DLEU1) and 17p13 (TP53) as well as a break‐apart probe for the region 14q32 (IGH) were used. If the results showed an IGH split, reflex testing was performed with the translocation probes t(4;14) (FGFR3/IGH), t(11;14) (CCND1/IGH), and t(14;16) (IGH/MAF). The 1p probe was changed in August 2016 from 1p36 (D1S2795, D1S253) to 1p32 (CDKN2C). Hybridization was carried out according to the manufacturer's instructions (Kreatech, Amsterdam, Netherlands; MetaSystems, Altlussheim, Germany; Vysis/Abbott, Downers Grove, IL). The thresholds for anomaly detection were set at 5% for gains and translocations and 10% for deletions. ≥3 and ≥4 copies of a particular region were defined as gain and amplification (amp), respectively. Near‐tetraploidy/tetraploidy (denoted here as tetraploidy or 4N) was predicted when 3 or more chromosomal regions indicated a doubled genome. A region was counted as indicative if either 4 copies were present or a previous deletion was lost. Cytogenetic subgroup designation was performed as previously published. 14
+
+2.3 Next‐generation sequencing
+
+Samples from 53 patients (10 with tetraploidy, 25 with amp(1q) and 18 control MM as determined by FISH) were analyzed by custom hybridization‐based sequencing panel of 28 genes known to be mutated in MM (Table S1). Of these patients, 39 were treatment‐naive and 14 were relapsed/refractory. To be considered, the unsorted samples or the isolated CD138+ plasma cells had to show CAs in at least 40% of the cells as determined by prior FISH analysis. Unsorted samples were prepared from methanol:acetic acid‐fixed cells as previously described. 26 Libraries were sequenced on a NextSeq 500 using 150‐bp paired‐end reads (Illumina, San Diego, CA). Using DRAGEN Somatic app (v3.8.4) with default parameters (tumor‐only mode) on BaseSpace (Illumina), reads were mapped to the human reference genome (hg38) and single‐nucleotide variants (SNVs) and indels were called. Sequencing artifacts were flagged using a panel of normals and filtered out. Variant Interpreter (Illumina) was used to annotate passed variants with coding consequences, a variant allele frequency of ≥5% and a frequency of less than 0.05% in the Genome Aggregation Database. The passed variants were visually inspected in the Integrative Genomics Viewer.
+
+2.4 Data analysis
+
+Data analysis was performed in R version 4.1.1 (www.r-project.org/). The McNemar test was used to examine the changes in CAs from the first to the last cytogenetic evaluation. Fisher's exact test was used to test association between categorical parameters. P values of ≤.05 were considered statistically significant.
+
+3 RESULTS
+
+3.1 Patient characteristics
+
+Our cohort included 148 patients of whom two to five samples were analyzed. Median time from first cytogenetic evaluation to last sampling was 729.5 days. At baseline, 35 patients (24%) had high risk cytogenetics. 2 During follow‐up the number of patients with high risk abnormalities increased to 50 (34%). Similarly, the number of patients with co‐occurrence of two and three adverse abnormalities (double hit and triple hit) 27 increased from 21 (14%) to 34 (23%) and 0 to 4 (3%), respectively. Patient characteristics are shown in Table S2.
+
+3.2 Stability of CAs during the disease course
+
+Seventy‐eight of 148 cases (53%) had acquired and/or lost CAs and were considered as cytogenetically unstable. The most frequently gained CA was del(17p) in 13% (19/148), gain/amp(1q) (≥3 copies of 1q21) in 12% (18/148), gain/amp(11q) in 9% (14/148), and del(13q) in 9% (13/148) of cases (Table S3). We found an increased risk of gain/amp(1q) (p = .02, McNemar's test) and del(17p) (p < .001, McNemar's test) occurring in the samples collected at the last time‐point compared to the baseline samples. In six patients, del(13q) was acquired together with del(17p) in the same sample (Table S4), which suggests that these two abnormalities may function cooperatively in tumor progression. By considering amplifications such as amp(1q) as distinct from gains, the percentage of cytogenetically unstable cases increased to 61% (90 of 148 cases). Moreover, with this counting approach, amp(1q) (≥4 copies of 1q21) was significantly enriched in the group of samples obtained last (p = .01, McNemar's test) and, with 16% (23/148) affected cases, the most frequently gained CA in our cohort (Tables S3 and S4). In 26% of these cases (6/23), amp(1q) was gained in the context of an acquired tetraploidy. In 7% of cases (10/148) ≥5 copies of 1q21 were acquired during the disease course. Nine percent of patients (13/148) acquired a tetraploidy, a condition, which happened to be particularly unstable during the disease course (Figure 1A,B). In the majority of cases with tetraploidy (13/21), tetraploidy was acquired and this mostly occurred in the last samples analyzed. Patients who showed tetraploidy in their first sample almost always lost it, usually between the first and second sampling. Only patient MMP‐46581 showed tetraploidy over the whole analyzed disease course (137 days) (Table S4). Patient MMP‐69541 had a temporary loss of tetraploidy before it was detected again in the last sample collected. Interestingly, 7 of 13 patients with acquired tetraploidy had an amp(1q) in a previous sample. In two other cases with lost tetraploidy, amp(1q) persisted, indicating that amp(1q) preceded the tetraploid clone also in these patients.
+
+FIGURE 1 (A) Summary of the cases with a stable (represented by light blue), a lost (red), a gained and lost (dark blue), and a gained (white color) CA during the follow‐up. Gain/amp(1q) and gain/amp(11q) indicate ≥3 copies of 1q21 and 11q22, respectively, and amp(1q) indicates ≥4 copies of 1q21. The percentages relate to the total number of the respective chromosome abnormalities detected in the cohort. (B) Profile of each patient with regard to frequently gained CAs (i.e., del(17p), amp(1q) and tetraploidy). Red color indicates the presence and gray color the absence of a CA. The patients are ordered from left to right according to the subgroups t(11;14), t(4;14), t(14;16), and CG11q and other. Asterisk indicates presence of ≥5 copies of 1q21.
+
+As previously reported 21 , 28 , 29 and in contrast to the unstable tetraploidy, the status of the primary IGH translocations t(4;14), t(11;14), and t(14;16) did not change over the disease course. The most frequent losses of CAs were gain/amp(17p), del(13q) and gain/amp(1q) in 7%, 5%, and 4% of cases, respectively (Table S3). Four of the seven supposed del(13q) losses and the only del(17p) loss occurred in connection with an acquired tetraploid karyotype and therefore presumably do not represent true losses, but rather masked deletions in a doubled genome (Table S4).
+
+3.3 Cytogenetic subgroups
+
+We and others have previously reported on the role of subgroups in MM in acquiring CAs. 7 , 14 , 21 , 30 Tumors with t(4;14) were characterized by stable del(13q) (74% [14/19] of cases) and gain(1q) (68% [13/19]) (Table S5). One t(4;14) case (MMP‐61227), initially tetraploid with 3 copies of 13q, showed the del(13q) only after loss of tetraploidy. A second t(4;14) case (MMP‐80274), lacking del(13q) in the first sample, was also accompanied by tetraploidy; the 2 copies of 13q in this patient, indicated a relative loss of 13q. In another t(4;14) tumor (MMP‐53996), gain(1q) was acquired later. Furthermore, 40% (4/10) of the cases in which ≥5 copies of 1q21 were acquired belonged to the t(4;14) subgroup. Tumors with t(11;14) acquired relatively often a gain/amp(11q) (50% of cases [7/14] with acquired gain/amp(11q)) and a tetraploidy (46% of cases [6/13] with acquired tetraploidy). Seven of 11 (64%) stable del(17p) were detected in the clonal gain(11q) (CG11q) subgroup.
+
+3.4 Patients with detailed clinical information
+
+We had knowledge of treatment regimens and clinical outcomes from 41 of the 148 patients (Table S6). Fish plots of eight patients who showed tetraploidy at some point during their disease are depicted in Figure 2 and the plots of the other patients with detailed clinical data are shown in Supplemental Figure 1. Three patients (MMP‐47355, MMP‐73677, and MMP‐98449) with acquired tetraploidy had a preceding amp(1q). Tetraploidy was accompanied by fatal outcome: patients MMP‐10707, MMP‐35601, MMP‐73677, MMP‐94267, and MMP‐98449 died 49, 174, 28, 188, and 5 days after the detection of tetraploidy. An exception was patient MMP‐47355 who was alive 643 days after detection of acquired tetraploidy. In three of the patients (MMP‐10707, MMP‐35601, MMP‐73677), del(17p) and extramedullary disease were co‐acquired with tetraploidy. Patient MMP‐35601 showed a relative loss of 17p in the tetraploid clone before a second clone with an ordinary del(17p) appeared. Two patients (MMP‐52190 and MMP‐61227) showed tetraploidy already at diagnosis and lost it during follow‐up. Patients with clonal gain of 11q without presence or acquisition of high risk features such as gain/amp(1q) or del(17p) showed a long survival (MMP‐02871, MMP‐10766, and MMP‐52638; Supplemental Figure 1).
+
+FIGURE 2 Cytogenetic evolution of patients with tetraploidy. Fish plots are drawn by the fishplot R package. 31 Days from treatment start are shown above and applied treatment schemes below. Last follow‐up (LF), death (DTH), extramedullary multiple myeloma (EMM), and plasma cell leukemia (PCL) are indicated in brackets above. Vertical lines indicate performed cytogenetic analyses and/or treatment changes. Time‐points with cytogenetic analyses are indicated in bold. Tetraploidy (4N) and amp(1q) are indicated by specific colors. A, anthracycline; alloSCT, allogeneic stem cell transplantation; autoSCT, autologous SCT; B, bendamustine; C, cyclophosphamide; D, daratumumab; D‐PACE, dexamethasone, continuous‐infusion cisplatin, doxorubicin, cyclophosphamide, and etoposide; I, ixazomib; K, carfilzomib (Kyprolis); P, pomalidomide; Pembro, pembrolizumab; R, lenalidomide (Revlimid); R2, Revlimid and rituximab; T, thalidomide; V, bortezomib (Velcade).
+
+3.5 Mutational profile of samples with amp(1q) and tetraploidy
+
+We analyzed 53 MM cases by targeted next‐generation sequencing (NGS) and found (putative) driver mutations in 44 of the cases (Table S7). Based on cytogenetic characterization, we divided the cases into three groups: tetraploid cases (n = 10), cases with amp(1q) (n = 25), and a control group of patients without tetraploidy or amp(1q) (n = 18). The mutational profile of these patients is shown in Figure 3. In the patients with amp(1q) and tetraploidy, the most frequently mutated gene was NRAS (29%), followed by DIS3 (20%), and KRAS, KMT2C, CREBBP, and ATM (all 15%). TP53 mutations were enriched in tetraploid cases compared to amp(1q) cases (30% [3/10] versus 0% [0/25]; p = .02, Fisher's exact test) and control cases (30% [3/10] versus 0% [0/18]; p = .04, Fisher's exact test) (Table S8). In two of the three tetraploid cases with TP53 mutation also a del(17p) was detected by FISH, indicating a bi‐allelic inactivation of TP53. An additional tetraploid case showed only a del(17p); thus, in 40% of tetraploid cases TP53 was disrupted. We also found significantly more mutations in CREBBP in tetraploid cases compared to control cases (30% [3/10] vs. 0% [0/18]; p = .04, Fisher's exact test). Mutational profiles of treated (n = 14) and untreated patients (n = 39) were not significantly different (Table S9). Due to the small study population, the results should be interpreted with caution.
+
+FIGURE 3 Mutational profiles. The oncoplot was created based on the GenVisR package. 32 Copy number, subgroup and amplification type were determined by FISH.
+
+4 DISCUSSION
+
+Although the drugs used to treat MM have advanced over the past 10 years, long‐term control of the disease or cure is only achieved in very few patients. 33 Therapy resistance and disease progression driven by genetic abnormalities are common features of the disease. 3 , 34 Understanding resistance mechanisms, including the emergence of fitter cancer clones, may ultimately help to design treatments that are more appropriate to the patient's need. While cytogenetic assessment at diagnosis is obligatory according to recommendations, 10 only limited cytogenetic data are available at relapse. Nonetheless, recent studies showed evidence that high‐risk abnormalities acquired during the course of the disease have a significant negative impact on patient outcomes. 20 , 22 By comparing with a control group, Lakshman et al. found reduced progression‐free survival and overall survival (OS) for MM patients who acquired del(17p) during follow‐up, 22 and Audil and colleagues showed poorer OS for patients who acquired a 1q22 gain. 20
+
+In our cohort, similar to other reports, 35 , 36 we observed an increase in high‐risk CAs from presentation to relapse. The most commonly gained CAs at relapse were del(17p) and gain/amp(1q) in 13% and 12% of cases, respectively, in agreement with the literature (8–23% and 13–19% of cases, respectively). 19 , 21 , 28 , 37 However, if only the first relapse is considered, the percentages might be lower. For example, del(17p) was acquired in 7% of cases in a homogeneously treated patient cohort (bortezomib, thalidomide, and dexamethasone induction, ASCT and consolidation therapy) of the Intergroupe Francophone du Myélome. 29 If we consider amp(1q) to be different from gain(1q), amp(1q) was the most common acquired CA (16% of cases). Consistent with previous findings, 19 , 21 we found an increase in 1q copy number from diagnosis to relapse in a significant proportion of gain(1q)‐positive tumors. A progression from 3 copies to ≥4 copies of 1q21, which has been recently associated with poor outcome, 19 was observed in 16/148 (11%) cases (Table S4). Additionally, an amp(1q) clone was acquired in seven patients who had 2 copies of 1q at first presentation. The relatively high frequency of amp(1q) at relapses of up to ~45% 38 indicates a specific role for amp(1q) in disease progression. This is supported by reports showing that amp(1q) has an even more pronounced adverse impact than gain(1q) at diagnosis, 7 , 39 and relapse, 40 although an negative impact of amp(1q) at diagnosis was not consistently found across all cohorts. 41
+
+Several candidate genes in the commonly gained/amplified region of chromosome 1 have been implicated in drug resistance, MM cell survival, and also genomic instability. 42 In line with that, we found evidence of an antecedent amp(1q) clone in 54% of patients who developed a tetraploidy, a feature associated with genomic instability. 43 To identify potential predisposing molecular factors for tetraploidy, we sequenced tetraploid MM cases with a custom panel. In 4 out of 10 patients with tetraploidy, we detected TP53 lesions previously described as being significantly mutated in genome doubling of various cancers. 43 , 44 In addition, mutations in the histone acetyltransferase gene CREBBP were overrepresented in tetraploid cases, a gene found to be frequently mutated in relapsed MM. 45 As seen before, 8 tetraploidy was observed more frequently in the late stage of the disease. It was present in 10% of the samples from the last time‐point and was, particularly when acquired, often followed by a rapid fatal outcome; five out of six patients died within seven months after its discovery. Treatments were similar in the patients who acquired del(17p), amp(1q), and tetraploidy as well as in patients who do not acquire any high risk cytogenetics. With the FISH panel used, ~40% of patients showed a cytogenetically stable disease course. Binder et al. found that developing new CAs over a 3‐year period after diagnosis was associated with increased mortality in MM. 18 The adverse effect of new abnormalities was independent of the presence of high‐risk abnormalities at the time of diagnosis. In a more recent study, 19 cytogenetic stability was associated with improved overall survival and detected in one‐third of the cases. The lower proportion of cytogenetic stable cases compared to our study despite similar tested chromosomal regions could be, besides of technical differences, due to the fact that in this study shifts in subclones in follow‐up samples were considered (differential evolution in ~20% of the cases). Taking into account a large number of copy number alterations and/or gene mutations may further decrease the percentage of cases with stable progression. 21 , 37 In the past decade, bulk tumor NGS and single‐cell sequencing have revealed extensive inter‐ and intrapatient genetic heterogeneity and enabled characterization of patterns of clonal evolution and identification of ~80 driver mutations. 46 Connecting genomics to clinical data, as pursued in the large, observational CoMMpass study of the Multiple Myeloma Research Foundation, 47 contributes to the understanding of outcome heterogeneity and ultimately holds promise for exploring precision medicine approaches. Due to the longitudinal nature of CoMMpass, this study provides insights into relapses. At progression events, samples will be analyzed comprehensively using whole genome sequencing (WGS), whole exome sequencing and RNA sequencing (RNA‐seq). Recently, a novel resistance signature present in a large subset of highly therapy‐resistant MM patients was identified in a single‐cell RNA‐seq study and validated in the CoMMpass dataset. 48 Since this signature was highly correlated with poor outcome, it outperformed a current risk stratification strategy by FISH. In the context of new methods such as WGS and RNA‐seq, which allow detection of gene mutations, CAs and transcriptional changes, the role of (serial) FISH is questioned. 49 , 50 In addition, patient material in relapsed MM may be scarce and occasionally insufficient for all examinations. However, apart from being a valid routine method for minimal residual disease detection, NGS has mainly been used in research. 46 After overcoming challenges such as cost, lack of standardization, emergence of new predictive models, it is expected that the importance of the new sequencing methods in the clinical routine of MM will increase. To date, FISH is still a widely used and accepted tool to stratify MM patients. 2 Although only a small proportion of drivers are usually tested with FISH, some of the tested abnormalities are clinically highly significant, as also shown by the sequencing of a large data set from the Myeloma Genome Project. 13 Specifically, amp(1q) and del(17p) along with TP53 mutations are associated with an adverse outcome in this study. Compared to bulk analysis, FISH has the advantage to detect CAs at the single‐cell level. 19 Moreover, depending on the method and bioinformatic tools used, tetraploidy might be missed with NGS. 50 As we show in this work, a common and potentially clinically important feature of relapse can be thereby overlooked.
+
+This retrospective study has several limitations. Clinical information was missing for the majority of patients who were treated at other centers and referred only for cytogenetic analysis to our center. The treatment schemes were heterogeneous, which makes it difficult to draw conclusions about possible drug‐related abnormalities. The samples were partially unsorted, which reduced the sensitivity for detecting subclonal abnormalities. This may have led to underreporting of changes in patients with unsorted samples. In patients with an unsorted sample at baseline and a subsequent sample with CD138+‐enriched cells, changes may have been overestimated. No distinction was made between clonal and subclonal CAs in our study. Molecular lesions and some recurrent CAs (such as relapse‐associated MYC rearrangements) were not systematically analyzed and therefore not included in the study. Our cohort may exhibit sampling biases. Standard‐risk patients with a favorable course of disease who have not suffered a relapse in follow‐up and high‐risk patients with early death are likely to be underrepresented in our cohort. Moreover, frail patients are also likely to have fewer bone marrow biopsies than younger, fitter patients. We did not correct for multiple comparisons, because of the retrospective nature of the study. Any results need confirmation in larger data sets.
+
+In summary, our study confirmed that cytogenetic evolution is common in relapses. Since amp(1q) is frequently acquired and associated with an adverse prognosis, it may be important to evaluate this marker in follow‐up. As shown in this study, a common feature of relapse is the gain of tetraploidy. In the future, deregulated genes of the 1q21 amplicon 42 and tetraploidy‐associated vulnerabilities 44 may be therapeutically addressed after relapse.
+
+CONFLICT OF INTEREST
+
+None.
+
+Supporting information
+
+Supplemental Figure 1. Cytogenetic evolution of MM patients. Fish plot shows the cytogenetic evolution in patients with plasma cell diseases. Days from treatment start are shown above and applied treatment schemes below. Last follow‐up (LF), death (DTH), extramedullary MM (EMM), and plasma cell leukemia (PCL) are indicated in brackets above. Vertical lines indicate performed cytogenetic analyses and/or treatment changes. Time‐points with cytogenetic analyses are indicated in bold. Amp(1q) and del(17p) are indicated by specific colors. A, anthracycline; alloSCT, allogeneic stem cell transplantation; autoSCT, autologous SCT; B, bendamustine; C, cyclophosphamide; D, daratumumab; D‐PACE, dexamethasone, continuous‐infusion cisplatin, doxorubicin, cyclophosphamide, and etoposide; E, elotuzumab; I, ixazomib; K, carfilzomib (Kyprolis); M, melphalan; P, pomalidomide; Pan, panobinostat; PB, peripheral blood; R, lenalidomide (Revlimid); R2, Revlimid and rituximab; T, thalidomide; V, bortezomib (Velcade); w & w, watch and wait
+
+Table S1. Gene list
+
+Table S2. Patient characteristics
+
+Table S3. Comparison of cytogenetic profiles at first analysis and last analysis in 148 MM patients
+
+Table S4. Cytogenetic profile at first FISH analysis and subsequent relapses
+
+Table S5. Relationship between chromosomal changes at relapse and MM subgroup
+
+Table S6. Clinical characteristics of patients with acquired amp(1q), del(17p), and tetraploidy
+
+Table S7. Mutational profiles of samples in the amp(1q), tetraploidy and control group
+
+Table S8. Comparison of mutational profiles between amp(1q), tetraploidy and control group
+
+Table S9. Comparison of mutational profiles between untreated and treated samples
+
+Click here for additional data file.
+
+ACKNOWLEDGMENTS
+
+The authors thank the involved technicians of the Institute of Human Genetics for their skilled work.
+
+DATA AVAILABILITY STATEMENT
+
+The data that support the findings of this study are available from the corresponding authors upon reasonable request.
+==== Refs
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+42 Burroughs Garcìa J , Eufemiese RA , Storti P , et al. Role of 1q21 in multiple myeloma: from pathogenesis to possible therapeutic targets. Cells. 2021;10 (6 ):1360.
+43 Bielski CM , Zehir A , Penson AV , et al. Genome doubling shapes the evolution and prognosis of advanced cancers. Nat Genet. 2018;50 (8 ):1189‐1195.30013179
+44 Quinton RJ , DiDomizio A , Vittoria MA , et al. Whole‐genome doubling confers unique genetic vulnerabilities on tumour cells. Nature. 2021;590 (7846 ):492‐497.33505027
+45 Dupéré‐Richer D , Licht JD . Epigenetic regulatory mutations and epigenetic therapy for multiple myeloma. Curr Opin Hematol. 2017;24 (4 ):336‐344.28441149
+46 Dutta AK , Alberge JB , Sklavenitis‐Pistofidis R , Lightbody ED , Getz G , Ghobrial IM . Single‐cell profiling of tumour evolution in multiple myeloma—opportunities for precision medicine. Nat Rev Clin Oncol. 2022;19 (4 ):223‐236.35017721
+47 Laganà A , Perumal D , Melnekoff D , et al. Integrative network analysis identifies novel drivers of pathogenesis and progression in newly diagnosed multiple myeloma. Leukemia. 2018;32 (1 ):120‐130.28642592
+48 Cohen YC , Zada M , Wang SY , et al. Identification of resistance pathways and therapeutic targets in relapsed multiple myeloma patients through single‐cell sequencing. Nat Med. 2021;27 (3 ):491‐503.33619369
+49 Rusch M , Nakitandwe J , Shurtleff S , et al. Clinical cancer genomic profiling by three‐platform sequencing of whole genome, whole exome and transcriptome. Nat Commun. 2018;9 (1 ):3962.30262806
+50 Höllein A , Twardziok SO , Walter W , et al. The combination of WGS and RNA‐Seq is superior to conventional diagnostic tests in multiple myeloma: ready for prime time? Cancer Genet. 2020;242 :15‐24.31980417
+

PMC10107668.txt

@@ -0,0 +1,276 @@
+
+==== Front
+Eur J Haematol
+Eur J Haematol
+10.1111/(ISSN)1600-0609
+EJH
+European Journal of Haematology
+0902-4441
+1600-0609
+John Wiley and Sons Inc. Hoboken
+
+36413106
+10.1111/ejh.13904
+EJH13904
+Original Article
+Original Articles
+Geographical and ecological analyses of multiple myeloma in Denmark: Identification of potential hotspot areas and impact of urbanisation
+Bertelsen et al.
+Bertelsen Lise Dueholm https://orcid.org/0000-0002-9493-962X
+1 lise.bertelsen@rn.dk
+
+Børty Nielsen Lars https://orcid.org/0000-0002-3715-8528
+1 2
+Christensen Heidi Søgaard https://orcid.org/0000-0002-2692-168X
+3
+Bøgsted Martin https://orcid.org/0000-0001-9192-1814
+1 2 4
+Gregersen Henrik 1
+Pedersen Robert Schou 5
+Klostergaard Anja https://orcid.org/0000-0002-5477-746X
+6
+Schnack Brian Iversen 7
+Pedersen Per Trøllund https://orcid.org/0000-0002-8821-7807
+8
+Abildgaard Niels https://orcid.org/0000-0001-6852-2014
+9
+Hermansen Emil https://orcid.org/0000-0002-1754-5336
+10
+Vangsted Annette Juul https://orcid.org/0000-0002-2131-731X
+11
+Severinsen Marianne Tang https://orcid.org/0000-0003-0996-1812
+1 2
+1 Department of Haematology Clinical Cancer Research Centre, Aalborg University Hospital Aalborg Denmark
+2 Department of Clinical Medicine Aalborg University Aalborg Denmark
+3 Department of Clinical Medicine, Center for Molecular Prediction of Inflammatory Bowel Disease (PREDICT) Aalborg University Aalborg Denmark
+4 Department of Clinical Medicine, Center for Clinical Data Science (CLINDA) Aalborg University, and Research, Education and Innovation, Aalborg University Hospital Aalborg Denmark
+5 Department of Haematology Gødstrup Hospital Herning Denmark
+6 Department of Haematology Aarhus University Hospital Aarhus Denmark
+7 Department of Haematology Vejle Hospital Vejle Denmark
+8 Department of Haematology Esbjerg Central Hospital Esbjerg Denmark
+9 Haematology Research Unit, Department of Haematology Odense University Hospital, and Department of Clinical Research, University of Southern Denmark Odense Denmark
+10 Department of Haematology Zealand University Hospital Roskilde Denmark
+11 Department of Haematology Rigshospitalet Copenhagen Denmark
+* Correspondence
+Lise Dueholm Bertelsen, Department of Haematology, Clinical Cancer Research Centre, Aalborg University Hospital, Aalborg, Denmark.
+Email: lise.bertelsen@rn.dk
+
+04 12 2022
+3 2023
+110 3 10.1111/ejh.v110.3 289295
+14 11 2022
+15 8 2022
+17 11 2022
+© 2022 The Authors. European Journal of Haematology published by John Wiley & Sons Ltd.
+https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.
+
+Abstract
+
+Background
+
+The aetiology of multiple myeloma (MM) is unknown but various environmental exposures are suspected as risk factors. We present the first paper analysing the geographical distribution of MM in Denmark at the municipal level to investigate variations that could be explained by environmental exposures.
+
+Methods
+
+Patients diagnosed with MM in Denmark during 2005–2020 were identified from nationwide registries and grouped into the 98 Danish municipalities based on residence. The age‐ and sex‐standardised incidence rate (SIR) of each municipality was compared to the national incidence in a funnel plot with 95% control limits. Differences in SIRs of rural, suburban, and urban areas were evaluated with incidence rate ratios.
+
+Results
+
+In total, 5243 MM patients were included. Overall, we found a heterogeneous geographical distribution of MM and a potential hotspot in southern Denmark. This hotspot contains three municipalities with SIRs above the 95% control limit assuming considerably higher rate of MM compared to the national incidence rate. A significant higher SIR was found in rural areas compared to urban areas.
+
+Conclusion
+
+The geographical distribution of MM in Denmark indicates that the risk of developing MM depends on place of residence probably due to environmental factors.
+
+disease hotspot
+epidemiology
+incidence
+multiple myeloma
+spatial analysis
+Svend Andersen Fonden 10.13039/501100007371 source-schema-version-number2.0
+cover-dateMarch 2023
+details-of-publishers-convertorConverter:WILEY_ML3GV2_TO_JATSPMC version:6.2.7 mode:remove_FC converted:17.04.2023
+Bertelsen LD , Børty Nielsen L , Christensen HS , et al. Geographical and ecological analyses of multiple myeloma in Denmark: Identification of potential hotspot areas and impact of urbanisation. Eur J Haematol. 2023;110 (3 ):289‐295. doi:10.1111/ejh.13904 36413106
+
+Funding information Svend Andersen Fonden
+==== Body
+pmc Novelty statements
+
+What is the NEW aspect of your work? To our knowledge, this is the most detailed study of the geographical distribution of multiple myeloma in an entire country due to small geographical groupings with adjustments for both age and sex.
+
+What is the CENTRAL finding of your work? We found a heterogeneous geographical distribution with a hotspot area and higher incidence in rural areas compared to urban indicating that the risk of multiple myeloma depends on place of residence probably due to environmental factors.
+
+What is (or could be) the SPECIFIC clinical relevance of your work? “Why me and could it have been prevented?” is a common question in the consultation room; nevertheless, our findings encourage future studies to clarify the aetiology focusing on risk factors in the environment, particularly in rural areas and the identified hotspot area.
+
+1 INTRODUCTION
+
+Multiple myeloma (MM) is a clonal plasma cell proliferative disorder in the bone marrow and the second most common haematological malignancy. Knowledge of aetiology may pave the way for prevention and thereby reducing the incidence of MM in the future. 1 , 2
+
+The aetiology of MM is largely unknown, but risk factors as older age, positive family history, male sex, Afro‐American ethnicity, and some polygenetic factors are acknowledged; however, the exact causal mechanisms are undetermined. 1 , 3 , 4 Various environmental exposures such as pesticides and benzene have been suspected as risk factors but have mostly been investigated in an occupational context. 3 , 4 Previous studies have examined the geographical distribution of MM in selected countries or areas. Some propose that the spatial heterogeneity in incidence rate may be explained by the degree of urbanisation due to environmental factors, but reports are conflicting and often limited because of large geographical groupings or lack of adjustment for the demographic in the background population. 5 , 6 , 7 , 8 , 9
+
+Denmark has a population of about six million inhabitants and is organised into five regions and 98 municipalities. Taking the small area of the country into account, 98 zones allow a detailed investigation of the spatial distribution of MM. Furthermore, comprehensive data on population demographics can be accessed through the Danish Civil Registration System. 10 Few studies have investigated the geographical distribution of MM in an entire country and none in such detailed level as the Danish municipalities allow. The overall aim of this study was to present how MM is geographically distributed in Denmark. Furthermore, we evaluated the incidence rate of MM by degree of urbanisation.
+
+2 MATERIALS AND METHODS
+
+2.1 Data source
+
+All citizens in Denmark are registered in the Danish Civil Registration System (CRS) and assigned with a personal identification number (CPR number). 10 The CPR number enables linkage between various registers, specifically allowing us to link a patient's place of residence with health conditions from clinical registries such as the population‐based Danish National Multiple Myeloma Registry (DMMR). The data in DMMR have been described in detail and validated by Gimsing et al. 11 In brief, hospitals and haematological centres in Denmark are obligated to register patients with plasma cell dyscrasia to DMMR and the completeness is almost 100%. The registry contains clinical information including date of diagnosis, disease characteristics, and date of treatment. From DMMR we identified Danish residents diagnosed with MM, while the municipality of residence was extracted from CRS. We chose the municipal residence of patients to be the residence 6 months before the date of diagnosis. Population demographics from 2014 of the entire country and each municipality were obtained from Statistics Denmark. 12
+
+Municipalities were categorised by Statistics Denmark in 2012 using Eurostat's Degree of Urbanisation classification (DEGURBA). 13 , 14 DEGURBA differentiates between ‘Cities’ (densely populated areas), ‘Towns and suburbs’ (intermediate density areas), and ‘Rural’ (thinly populated areas) areas, which in the present study are termed urban, suburban, and rural, respectively.
+
+Coordinates describing municipal boundaries for choropleth mapping were extracted from the Danish Administrative Geographical Division register which is available online. 15
+
+2.2 Study population
+
+The study cohort included patients fulfilling the following criteria: (1) diagnosed with symptomatic MM in Denmark during January 1st, 2005, and December 31st, 2020. (2) Permanent residence in Denmark at 6 months before diagnosis. Patients diagnosed with smouldering MM (SMM), monoclonal gammopathy of undetermined significance, amyloid light‐chain amyloidosis, POEMS syndrome, solitary plasmacytoma, primary plasma cell leukaemia, and other plasma cell dyscrasias were not included in this study. As patients diagnosed with SMM and symptomatic MM were registered under the same code in DMMR between 2005 and 2018, we distinguished between SMM and symptomatic MM using the following criteria: Patients without treatment or progression (death within 90 days from date of diagnosis) throughout the study period were identified as SMM. Patients who had deselected treatment were categorised as symptomatic MM. In the following, symptomatic MM is termed MM. 16
+
+2.3 Statistical methods
+
+Baseline characteristics of MM patients were presented as percentage for categorical variables (sex, age group, M‐protein type, stage, and year of diagnosis), while age as a continuous variable was summarised by median and interquartile range (IQR). All incidence rates were estimated as the number of MM cases in the study period divided by the 2014 Danish population multiplied by 15 years (to approximate person‐years). All incidence rates were presented as per 100 000 person‐years. Crude incidence rates were supplied with 95% confidence intervals (CIs) based on Byar's method. 17 To account for different demographics in the municipalities, standardised incidence rates (SIRs) were calculated by direct standardisation with respect to sex and age using the age groups <60, 60–64, 65–69, 70–74, 75–79, ≥80 years and the 2014 Danish population as reference population. 18 , 19 The estimated SIRs were equipped with 95% CIs based on the method by Tiwari et al. 19 Confidentiality rules in Denmark prescribe exclusion of municipalities with <5 cases in tables and figures; however, the direct method required exclusion of municipalities with <10 cases. The SIRs were visualised in funnel plots using the national incidence of MM as benchmark with 95% and 99.95% control limits. 20 If the SIR was above the 95% control limit it was considerably higher than the national incidence. The second control limit (99.95%) was based on Bonferroni correction to limit the challenge of multiple testing, as we conducted the test across all municipalities. The spatial distribution of SIRs was visualised in choropleth maps.
+
+The SIRs were calculated for rural, suburban, and urban areas according to the above principles of standardisation. To evaluate the impact of the degree of urbanisation, the differences of SIRs were analysed with incidence rate ratios (IRRs) with 95% CIs based on the method by Tiwari et al. 19 The difference were considered significant if the CIs did not contain the value of one.
+
+In secondary analyses, we explored the bias due to relocations in residence as the data quality allowed examination at least 25 years prior to date of diagnosis for all the included patients. This was presented as percentage of MM patients who have lived in the same municipality throughout the period.
+
+All data were pseudo‐anonymised and stored on a secured server governed by Statistics Denmark. Statistical analyses were carried out in R version 4.0.3 (2020‐10‐10). 21 In Denmark, register‐based studies conducted for the sole purpose of statistics and scientific research do not require ethical approval or informed consent by law. However, the study was approved by the data responsible institute (North Denmark Region—Approval number: 2021‐034 and 2021‐056) in accordance with the General Data Protection Regulation (GDPR).
+
+3 RESULTS
+
+3.1 Baseline characteristics
+
+In total, 6176 patients were diagnosed with MM in Denmark during the 2005–2020 period. We excluded 21 MM patients without Danish residence and 912 patients categorised as SMM leaving 5243 patients for the study population. The median age at diagnosis for males was 71 years (IQR: 63–78) and 72 years (IQR: 64–79) for females. Baseline characteristics of the study population are shown in Table 1.
+
+TABLE 1 Baseline characteristics of multiple myeloma patients and the national crude incidence rates between 2005 and 2020.
+
+Characteristics	Patients N (%)	Population a N (%)	Incidence rate b (95% CI)	
+Total	5243 (100)	5 627 000 (100)	6.2 (6.0–6.4)	
+Age group (years)				
+<60	853 (16.3)	4 263 000 (75.8)	1.3 (1.2–1.4)	
+60–64	634 (12.1)	338 000 (6.0)	12.5 (11.6–13.5)	
+65–69	824 (15.7)	356 000 (6.3)	15.4 (14.4–16.5)	
+70–74	957 (18.3)	255 000 (4.5)	25.0 (23.4–26.6)	
+75–79	860 (16.4)	180 000 (3.2)	31.8 (29.7–34.0)	
+≥80	1115 (21.3)	235 000 (4.2)	31.6 (29.8–33.6)	
+Sex				
+Female	2307 (44.0)	2 835 000 (50.4)	5.4 (5.2–5.7)	
+Male	2936 (56.0)	2 792 000 (49.6)	7.0 (6.8–7.3)	
+Type of M‐protein c				
+FLC	496 (9.5)	‐	‐	
+IgA	1076 (20.5)	‐	‐	
+IgD	31 (0.6)	‐	‐	
+IgG	2847 (54.3)	‐	‐	
+IgM	31 (0.6)	‐	‐	
+More than one type	140 (2.7)	‐	‐	
+Non‐specific	15 (0.3)	‐	‐	
+ISS stage d				
+1	1130 (21.6)	‐	‐	
+2	1679 (32.0)	‐	‐	
+3	1654 (31.5)	‐	‐	
+Year of diagnosis				
+2005–2010	1755 (33.5)	‐	‐	
+2011–2015	1611 (30.7)	‐	‐	
+2016–2020	1877 (35.8)	‐	‐	
+Abbreviations: CI, confidence intervals; FLC, Free light chain; ISS stage, the International Staging System.
+
+a Population refers to the Danish population in 2014 and is shown as thousands.
+
+b The national incidence rate per 100 000 person‐years.
+
+c Due to confidentiality rules the exact number of patients with IgE type (<5 patients) could not be shown in the table. Patients without recordings of M‐protein type and patients with IgE accounted for 607 (11.6%) patients.
+
+d ISS stage could not be categorized for 780 (14.9%) patients.
+
+3.2 Geographical distribution of MM in Denmark
+
+The SIRs of MM cases were analysed in 95 municipalities as two municipalities were excluded due to confidentiality rules and direct standardisation excluded one municipality due to less than 10 cases. The national incidence rate of MM was 6.2 per 100 000 person‐years in Denmark. As can be seen in the funnel plot and the map in Figure 1, the SIRs of four municipalities were above the control limit (95%) assuming substantial higher incidence rate of MM compared to the national incidence rate. These four municipalities were Vejen, Vejle, Horsens, and Albertslund and had a SIR per 100 000 person‐years of 9.6 (95% CI: 7.4–12.2), 7.5 (95% CI: 6.2–9.0), 7.7 (95% CI: 6.2–9.4), and 8.7 (95% CI: 5.8–12.6), respectively. Three out of the four municipalities (Vejen, Vejle, and Horsens) were located almost next to each other in southern Denmark. The lowest SIRs were found in eastern Denmark, except for Albertslund which is a suburb of the capital Copenhagen (København). The SIRs of Vejen, Vejle, Horsens, and Albertslund were above the 95% control limit with 1.41, 0.09, 0.12, and 0.14 per 100 000 person‐years, respectively. The most southern of the four municipalities, Vejen, had the highest SIR and was almost above the very restrictive control limit of 99.95% with a difference of only 0.07 per 100 000 person‐years.
+
+FIGURE 1 The distribution of age‐ and sex‐standardised incidence rates (SIRs) of multiple myeloma among Danish municipalities. (A) Funnel plot of SIR of each municipality expressed as point estimate. The x‐axis expresses the population size within each municipality. The horizontal line is the national incidence (6.2). The dashed lines represent the 99.95% (Bonferroni‐corrected limits) and the 95% control limits, respectively. (B) The geographical distribution of multiple myeloma based on SIRs. The shaded areas indicate municipalities with higher SIR than the upper limit of the 95% control limit. Numbers in the colour range are determined as the smallest incidence, 1st quartile, the median incidence, 3rd quartile, and the highest incidence of all SIRs.
+
+As can be seen in Table 2, the SIR of urban, suburban, and rural areas in Denmark was 5.9 (95% CI: 5.6–6.2), 6.3 (95% CI: 6.0–6.6), and 6.4 (95% CI: 6.1–6.7) per 100 000 person‐years, respectively. We found rural areas to have a slight but statistically significant higher SIR of 8% (95% CI: 1.01–1.16) compared to urban areas. The SIR in suburban areas was 7% (95% CI: 1.00–1.15) higher than urban areas but not statistically significant. Two out of the four high‐incidence municipalities (Vejen and Vejle) were categorised as rural, while Horsens and Albertslund were categorised as suburban and urban, respectively.
+
+TABLE 2 Distribution of the Danish municipalities and SIRs of multiple myeloma patients by degree of urbanisation (urban, suburban, and rural areas).
+
+Degree of municipality	Municipalities N	Population a N (%)	SIR b (95% CI)	IRR (95% CI)	
+Urban	18	1 921 000 (34.1)	5.9 (5.6–6.2)	Ref	
+Suburban	35	1 821 000 (32.4)	6.3 (6.0–6.6)	1.07 (1.00–1.15)	
+Rural	45	1 886 000 (33.5)	6.4 (6.1–6.7)	1.08* (1.01–1.16)	
+Abbreviations: CI, confidence intervals; IRR, incidence rate ratio; SIR, age‐ and sex‐standardised incidence rate.
+
+* Significant (CI does not contain the value of one).
+
+a Population refers to the Danish population in 2014 and is shown as thousands.
+
+b per 100.000 person‐years.
+
+Characteristics (name, crude incidence rate, SIR, degree of urbanisation) of each municipality are accessible in Table S1. Figure S1 gives a geographical overview of the distribution of rural, suburban, and urban municipalities. Location and name of each municipality are accessible when comparing Table S1 and Figure S1 by the assigned number. Figure S2 shows the spatial distribution of the municipalities based on crude incidence rates. Figure S3 shows that only 20% of MM patients have changed municipal residence up to 25 years before diagnosis.
+
+4 DISCUSSION
+
+In this nationwide study, we evaluated the SIRs of MM in 95 municipalities in Denmark to explore environmental risk factors for MM. We found four municipalities with SIRs above the control limit of 95%, assuming a considerably higher incidence rate of MM in these areas compared to the national incidence rate in Denmark. The highest municipal SIR of MM was marginals from being above the control limit of 99.95%. Furthermore, we found approximately 8% higher SIR of MM in rural areas compared to urban areas. Few studies have investigated the geographical distribution of MM in an entire country. To our knowledge, this is the most detailed study considering the relatively small sizes of Danish municipalities and at the same time taking the demographics into account. 6 , 7 , 9
+
+The geographical distribution of MM revealed a substantial heterogeneous pattern, as the lowest SIRs were found in eastern Denmark and the highest SIRs mainly in southern Denmark (Figure 1). Three municipalities with substantial higher SIRs were located almost next to each other supporting the idea of a hotspot or clustering area. Further studies are needed to investigate potential environmental factors related to this hotspot.
+
+Several suggestions have been raised in the literature and some assume that agricultural employment and pesticides are a risk factor for MM. 3 , 4 This contributes to a theory of higher incidence in rural areas. We analysed the difference between rural, suburban, and urban areas and found a higher SIR of MM in rural areas compared to urban areas. Our finding agrees with Tsang et al. who investigated the MM distribution in Canada. The Canadian study is comprehensive, but no adjustments for background demographics were made. 9 Sneyd et al. investigated the distribution of MM patients in New Zealand and found both highest and lowest incidence rates in rural areas, but the geographic areas in their study were large compared to Danish municipalities. 7 By contrast, Rajabli et al. found that MM was most common in urban areas, but the analysis was only based on 124 cases of MM from the Golestan province of Iran. 8 Some suggests air pollution as a risk factor in urban areas; however, a significantly negative correlation between MM incidence rates and areas with high air concentration of pollutants as black carbon and fine particles were found by Kamath et al. based on analysis of the 34 neighbourhoods in New York City. 5
+
+In a preventive context, type 1 errors are preferred to type 2 errors; therefore, municipalities were assessed by a 95% control limit, despite the risk of false discoveries due to multiple testing. Nevertheless, Bonferroni correction was made in addition for comparison but must be considered an overly restrictive limit as the SIR are most likely correlated due to the neighbouring municipalities made with man‐made boundaries. Afro‐American ethnicity is stated as an established risk factor in literature based on the US population. 1 , 2 However, no adjustment for race were made in this study due to the small number of immigrants and relatives from Africa in Denmark. 12 , 22 This study has neither adjusted for possible inheritance because it is not immediately possible to distinguish between genetic factors and the fact that relatives probably have been exposed to the same environment throughout life. Although a combination of both is not inconceivable. As in other studies on this topic, the potential bias due to different diagnostic practices may occur. We attempted to eliminate this by exclusion of SMM patients; however, there may always appear a small but mentionable difference in how to diagnostically obtain the basis for assessing the myeloma‐defining events. 1 , 16 If a nationwide screening program will be implemented in the future as a result of the iStopMM study, this type of study will be able to include SMM patients due to more homogeneity in diagnostic practice. 23 Possible environmental exposure need time to evolve onset of a malignancy, therefore, change of residence may dilute our findings. We found that only 20% of MM patients have changed municipal residence up to 25 years prior to MM diagnosis. We chose a cross‐sectional date 6 months prior to diagnosis to accommodate that people may move due to poorer health at the time of diagnosis. In addition, environmental factors may vary over time, and we investigated the cumulative incidence of MM during the 15 years. Thereby, we may miss possible environmental risk factors that only occurred part‐time of the study period. To overcome this limitation, shorter time periods are needed; however, this will introduce lack of power when investigating a relatively rare cancer as MM. The strengths of our study are the nationwide study design with complete information regarding place of residence thanks to the CRS. The health system in Denmark is based on taxation giving free access to health services, thereby decreasing the impact of socioeconomic factors. We analysed SIRs taking both sex and age into account to eliminate the demographic difference between municipalities. We estimated the SIRs of MM in relatively small geographic areas making our results more precise regarding potential permanent environmental risk factors.
+
+In conclusion, we found a MM hotspot area of neighbouring municipalities in southern Denmark containing three out of four municipalities with a substantial higher SIR of symptomatic MM compared to the national incidence rate. One of these municipalities is particularly suspicious as its SIR was almost above the overly restrictive control limit of 99.95%. Presumably, environmental factors related to the residence are not solely to blame, considering the many variables that could interact with people over time. However, these findings support the possibility that the residential environment may have a role to play in the development of myeloma and demands further research on environmental risk factors. The slight but statistically significant higher SIR in rural areas asks for further research on risk factors associated with rurality, while Danish municipalities may cover too large an area.
+
+AUTHOR CONTRIBUTIONS
+
+Marianne Tang Severinsen, Lars Børty Nielsen, Henrik Gregersen, Heidi Søgaard Christensen, Martin Bøgsted, and Lise Dueholm Bertelsen conceptualized, and designed the study. Henrik Gregersen, Robert Schou Pedersen, Anja Klostergaard, Brian Iversen Schnack, Per Trøllund Pedersen, Niels Abildgaard, Emil Hermansen, and Annette Juul Vangsted facilitated the data collection. Lise Dueholm Bertelsen, Lars Børty Nielsen, Heidi Søgaard Christensen, and Martin Bøgsted conducted the statistical analysis. Lise Dueholm Bertelsen, Lars Børty Nielsen, Heidi Søgaard Christensen, Martin Bøgsted, Henrik Gregersen, and Marianne Tang Severinsen analysed and interpreted the data. Lise Dueholm Bertelsen wrote the manuscript. All authors made a critical revision and approved the final manuscript.
+
+FUNDING INFORMATION
+
+With great gratitude, we would like to thank the Svend Andersen Foundation for supporting this project.
+
+CONFLICT OF INTEREST
+
+Annette Juul Vangsted has received honoraria from Celgene. All the other authors have none to declare.
+
+Supporting information
+
+Table S1: Distribution of multiple myeloma patients by each Danish municipality with the assigned number, which can be compared with the geographical location in Figure S1.
+
+Figure S1. Maps illustrating the location of each municipality. The assigned number refers to the specific municipality in Table S1. The colour range indicates type of municipality.
+
+Figure S2. The distribution of crude incidence rates of multiple myeloma among Danish municipalities. (A) Funnel plot of crude incidence rate of each municipality expressed as point estimate. The x‐axis expresses the population size within each municipality. The horizontal line is the national incidence (6.2). The dashed lines represent the 99.95% (Bonferroni limits) and the 95% control limits, respectively. (B) The geographical distribution of multiple myeloma based on crude incidence rates. The shaded areas indicate municipalities with higher crude incidence rate than the upper limit of the 95% control limit. Numbers in the colour range are determined as the smallest incidence, 1st quartile, the median incidence, 3rd quartile and the highest incidence of all crude incidence rates.
+
+Figure S3. Relocations in residents of multiple myeloma patients in the period from 25 years before diagnosis to six months before date of diagnosis.
+
+Click here for additional data file.
+
+ACKNOWLEDGMENTS
+
+We acknowledge the Danish National Multiple Myeloma Registry for making high‐quality data available.
+
+DATA AVAILABILITY STATEMENT
+
+Research data from Statistics Denmark cannot be shared according to Danish law.
+==== Refs
+REFERENCES
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+8 Rajabli N , Naeimi‐Tabeie M , Jahangirrad A , Sedaghat SM , Semnani S , Roshandel G . Epidemiology of leukemia and multiple myeloma in Golestan. Iran Asian Pac J Cancer Prev. 2013;14 :2333‐2336.23725136
+9 Tsang M , Le M , Ghazawi FM , et al. Multiple myeloma epidemiology and patient geographic distribution in Canada: a population study. Cancer. 2019;125 :2435‐2444.30951209
+10 Mainz J , Hess MH , Johnsen SP . The Danish unique personal identifier and the Danish civil registration system as a tool for research and quality improvement. International J Qual Health Care. 2019;31 :717‐720.
+11 Gimsing P , Holmström MO , Klausen TW , et al. The Danish National Multiple Myeloma Registry. Clin Epidemiol. 2016;8 :583‐587.27822103
+12 Statistics Denmark . FOLK1A: Folketal den 1. i kvartalet efter område, køn, alder og civilstand—Statistikbanken—data og tal [Internet]. Accessed May 5, 2022. Available from: https://www.statbank.dk/FOLK1A
+13 Statistics Denmark . Degree of Urbanisation (DEGURBA)—Eurostat, v1:2012—Statistics Denmark [Internet]. 2012 Accessed Apr 21, 2022. Available from: https://www.dst.dk/en/Statistik/dokumentation/nomenklaturer/degurba-eu
+14 Eurostat . Methodology—Degree of urbanisation—Eurostat [Internet]. Accessed April 21, 2022. Available from: https://ec.europa.eu/eurostat/web/degree-of-urbanisation/methodology
+15 Styrelsen for Dataforsyning og Effektivisering . 2022. DAWA (Danmarks Adressers Web API) [Internet]. Accessed May 6, 2022. Available from: https://dawadocs.dataforsyningen.dk/
+16 Rajkumar SV , Dimopoulos MA , Palumbo A , et al. International myeloma working group updated criteria for the diagnosis of multiple myeloma. Lancet Oncol. 2014;15 :e538‐e548.25439696
+17 Georgina A. PHEindicatormethods: Common Public Health Statistics and their Confidence Intervals. R package version 1.3.2. 2022. https://CRAN.R-project.org/package=PHEindicatormethods
+18 Inskip H , Beral V , Fraser P , Haskey J . Methods for age‐adjustment of rates. Stat Med. 1983;2 :455‐466.6672943
+19 Tiwari RC , Clegg LX , Zou Z . Efficient interval estimation for age‐adjusted cancer rates. Stat Methods Med Res. 2006;15 :547‐569.17260923
+20 Dover DC , Schopflocher DP . Using funnel plots in public health surveillance. Popul Health Metr. 2011;9 :1‐11.21219615
+21 R Core Team . R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria; 2020. https://www.R-project.org/.
+22 Statistics Denmark . FOLK1C: Folketal den 1. i kvartalet efter område, køn, alder (5‐års intervaller), herkomst og oprindelsesland —Statistikbanken—data og tal [Internet]. 2022. Accessed October 28, 2022. Available from: https://www.statistikbanken.dk/FOLK1C
+23 Rögnvaldsson S , Love TJ , Thorsteinsdottir S , et al. Iceland screens, treats, or prevents multiple myeloma (iStopMM): a population‐based screening study for monoclonal gammopathy of undetermined significance and randomized controlled trial of follow‐up strategies. Blood Cancer J. 2021;11 :94.34001889
+

PMC10110653.txt

@@ -0,0 +1,331 @@
+
+==== Front
+Cancer Immunol Immunother
+Cancer Immunol Immunother
+Cancer Immunology, Immunotherapy
+0340-7004
+1432-0851
+Springer Berlin Heidelberg Berlin/Heidelberg
+
+36355079
+3317
+10.1007/s00262-022-03317-y
+Research
+Generation of NK cells with chimeric-switch receptors to overcome PD1-mediated inhibition in cancer immunotherapy
+Susek Katharina H. 1
+Schwietzer Ysabel A. 1
+Karvouni Maria 1
+Gilljam Mari 1
+Keszei Marton 2
+Hussain Alamdar 1
+Lund Johan 1
+Kashif Muhammad 1
+Lundqvist Andreas 3
+Ljunggren Hans-Gustaf 4
+Nahi Hareth 1
+Wagner Arnika K. 1
+Alici Evren evren.alici@ki.se
+
+1
+1 grid.4714.6 0000 0004 1937 0626 Department of Medicine Huddinge, Center for Hematology and Regenerative Medicine, Karolinska Institutet, Karolinska University Hospital Huddinge, Stockholm, Sweden
+2 grid.4714.6 0000 0004 1937 0626 Department of Microbiology, Tumor and Cell Biology, Karolinska Institutet, Stockholm, Sweden
+3 grid.4714.6 0000 0004 1937 0626 Department of Oncology-Pathology, Karolinska Institutet, Solna, Stockholm, Sweden
+4 grid.4714.6 0000 0004 1937 0626 Department of Medicine, Center for Infectious Medicine, Karolinska Institutet, Karolinska University Hospital Huddinge, Stockholm, Sweden
+10 11 2022
+10 11 2022
+2023
+72 5 11531167
+11 4 2022
+25 10 2022
+© The Author(s) 2022
+https://creativecommons.org/licenses/by/4.0/ Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
+Multiple myeloma (MM) is an incurable hematological cancer, in which immune checkpoint inhibition (ICI) with monoclonal antibodies (mAbs) has failed due to uncontrollable immune responses in combination therapies and lack of efficacy in monotherapies. Although NK cell-specific checkpoint targets such as NKG2A and KIRs are currently being evaluated in clinical trials, the clinical impact of NK cells on the PD1 cascade is less well understood compared to T cells. Furthermore, while NK cells have effector activity within the TME, under continuous ligand exposure, NK cell dysfunctionality may occur due to interaction of PD1 and its ligand PD-L1. Due to above-mentioned factors, we designed novel NK cell specific PD1-based chimeric switch receptors (PD1-CSR) by employing signaling domains of DAP10, DAP12 and CD3ζ to revert NK cell inhibition and retarget ICI. PD1-CSR modified NK cells showed increased degranulation, cytokine secretion and cytotoxicity upon recognition of PD-L1+ target cells. Additionally, PD1-CSR+ NK cells infiltrated and killed tumor spheroids. While primary NK cells (pNK), expressing native PD1, showed decreased degranulation and cytokine production against PD-L1+ target cells by twofold, PD1-CSR+ pNK cells demonstrated increased activity upon PD-L1+ target cell recognition and enhanced antibody-dependent cellular cytotoxicity. PD1-CSR+ pNK cells from patients with MM increased degranulation and cytokine expression against autologous CD138+PD-L1+ malignant plasma cells. Taken together, the present results demonstrate that PD1-CSR+ NK cells enhance and sustain potent anti-tumor activity in a PD-L1+ microenvironment and thus represent a promising strategy to advance adoptive NK cell-based immunotherapies toward PD-L1+ cancers.
+
+Supplementary Information
+
+The online version contains supplementary material available at 10.1007/s00262-022-03317-y.
+
+Keywords
+
+Antibody-dependent cellular cytotoxicity
+Chimeric switch receptor
+Hematologic neoplasms
+Immunotherapy
+Natural killer cells
+Programmed cell death 1 receptor
+http://dx.doi.org/10.13039/501100001858 VINNOVA 2019-00056 2019-00056 2019-00056 2019-00056 2019-00056 2019-00056 2019-00056 2019-00056 2019-00056 2019-00056 2019-00056 2019-00056 2019-00056 Susek Katharina H. Schwietzer Ysabel A. Karvouni Maria Gilljam Mari Keszei Marton Hussain Alamdar Lund Johan Kashif Muhammad Lundqvist Andreas Ljunggren Hans-Gustaf Nahi Hareth Wagner Arnika K. Alici Evren http://dx.doi.org/10.13039/100018857 International Myeloma Society http://dx.doi.org/10.13039/501100007232 Radiumhemmets Forskningsfonder 191063 Alici Evren Castenbäcks Stiftelse för cancer researchKarolinska InstituteOpen access funding provided by Karolinska Institute.
+
+issue-copyright-statement© Springer-Verlag GmbH Germany, part of Springer Nature 2023
+==== Body
+pmcBackground
+
+The field of cancer immunotherapy has shown breakthrough advances due to the success of immune checkpoint inhibition (ICI) and chimeric antigen receptor (CAR)-T cell therapy. As resistance toward ICI and adoptive cell therapies occurs, combination therapies are explored [1, 2]. One approach is to genetically modify effector cells to make them less prone to PD-L1/PD-L2-mediated inhibition. Currently, several registered clinical trials employ PD1 knockout (PD1-KO) or PD1 disrupted chimeric antigen receptor (CAR) T cells for various malignancies [3–5]. Although this approach has been proven successful in some tumor models, emerging data indicate that PD1-KO might also impair T cell functionality [6]. Therefore, another novel approach is the utilization of chimeric switch receptors (CSR) that link PD-L1 engagement to an activating signal.
+
+Natural killer (NK) cells are innate lymphoid cells that recognize and kill infected, stressed or malignant cells without prior antigen exposure [7]. They exert direct cytotoxicity against target cells and enhance immune responses via cytokine and chemokine secretion [8]. NK cell activation depends on the balance of several germline-encoded inhibitory and activating receptors [9]. One of the strongest activating receptors is CD16 that binds to the constant region (Fc) of immunoglobulins and induces antibody-dependent cellular cytotoxicity (ADCC). Many activating receptors lack a signaling domain and rather depend on adaptor proteins for a functional response. The most prominent of these are the immunoreceptor tyrosine-based activating motif (ITAM)-bearing adaptor proteins CD3ζ and DAP12 as well as DAP10 which signals via a YINM motif [9–11].
+
+In a recent clinical trial, CD19-CAR-NK cells displayed a good clinical response with seven out of eleven patients reaching a complete remission with only minimal toxicity [12]. Adoptive cell therapies, employing NK cells, are thus increasingly becoming important due to several reasons such as a beneficial risk profile [13]. However, there are still many open questions to ensure the success of NK cell-based immunotherapies in the clinical setting. In this study, we addressed the concern of NK cell hypofunctionality due to immune-checkpoint receptor engagement in the tumor microenvironment (TME). Although the role of PD1 on NK cells from healthy individuals is not fully understood, it has been shown that tumor-infiltrating NK cells often show increased PD1 expression with reduced effector cell functionality that can be reverted by PD1-PD-L1 blockade with mAb [14–18]. Therefore, we set out to assess the ability of PD1-based CSR to sustain the functionality of NK-92 and primary NK (pNK) cells against different PD-L1+ tumor targets. Here, we demonstrate that PD1-CSR expressing NK-92 and primary NK (pNK) cells increase degranulation, cytokine secretion, and tumor cell killing upon recognition of PD-L1.
+
+Methods
+
+Cells
+
+All cell lines were purchased from ATCC. The B cell lymphoma cell line Raji (ATCC® CCL-86™) and the renal cell carcinoma cell line 786-O (ATCC® CRL-1932™) were maintained in RPMI medium (Gibco), supplemented with 10% FBS (Gibco). NK-92 cells (ATCC® CRL-2407™) were maintained in SCGM (CellGenix), supplemented with 20% FBS (Gibco). Cell lines were split every 2–3 days. Interleukin-2 (R&D) was added at a final concentration of 500 U/ml to the cell culture medium of NK-92 cells.
+
+PBMC and primary NK cell isolation and culture
+
+Peripheral blood mononuclear cells (PBMCs) were obtained from buffy coats. According to institutional guidelines ethical permits were not required for healthy donors due to de-identification of donors. Ethical permits were granted for work with patient derived PBMCs and bone marrow samples (Permit Numbers: 2019-04973 and 2020-02119). PBMC isolation was performed with LymphoPrep™ (Fresenius Kabi) according to the manufacturer’s recommendations. Isolated PBMCs were cultured in SCGM medium (CellGenix), supplemented with 5% human serum (Biowittaker). CD3 Ab (Miltenyi, clone OKT3) was added to the culture at a final concentration of 10 ng/ml on the day of isolation. Interleukin (IL)-2 (R&D) was added to the culture at a final concentration of 500 U/ml on days 1 to 4 (daily), and then five times/week. pNK cells were isolated from PBMCs by negative selection and magnetic separation according to the manufacturer’s recommendations (Miltenyi, 130-092-657). pNK cells were cultured in SCGM medium (CellGenix), supplemented with 10% human serum. IL-21 (ImmunoTools) was added on the day of isolation at a concentration of 20 ng/ml. IL-2 (R&D) was added daily to the culture at a final concentration of 1,000 U/ml.
+
+Isolation of bone marrow mononuclear cells (BM MNC)
+
+Bone marrow aspirates were obtained from patients with MM after obtaining informed consent and according to our ethical permit (Permit Numbers: 2019-04973 and 2020-02119). BM MNC were isolated using Ficoll-Paque (Sigma-Aldrich) according to the manufacturer’s recommendations. Isolated BM MNC were passaged in RPMI medium (Gibco), supplemented with 10% FBS (Gibco).
+
+Generation of chimeric switch receptors
+
+For the generation of PD1-based CSR, the canonical human cDNA sequence was used without further modification or codon optimization. A truncated version of PD1 (amino acid (AA) 1–211), which lacks the intracellular signaling domains, was designed and is hereafter referred to as PD1EC-TM CSR. The PD1-CD28-CD3ζ CSR consists of AA 1–170 of PD1, an 85AA long hinge region, the AA sequence 153–220 of CD28 and AA 52–164 of the CD3ζ protein. For the PD1-NKp46 CSR, PD1 AA 1–170 was fused together with AA 239–304 of the NKp46 protein. NKp46 lacks an intracellular signaling domain but can associate with ITAM-bearing CD3ζ homodimers or CD3ζ/FcεRIγ heterodimers through oppositely charged residues within the transmembrane region [19]. In the PD1EcDAP10TM-IC and PD1EcDAP12TM-IC constructs, PD1 protein from AA 1 to 170 was fused to the full length DAP10 (AA 19–93) or DAP12 (AA 22–113) protein. In the PD1EC-TMDAP10IC and PD1EC-TMDAP12IC CSR the PD1 AA sequence 1 to 212 was fused together with AA 77–93 of DAP10 or AA 73–113 of DAP12. The designed constructs were cloned into the LeGoiG2 or LeGo_T2A-eGFP vector, upstream of the IRES or T2A under the control of the SFFV promoter. The LeGo_T2A-eGFP vector was designed by replacing the IRES of the LeGoG2 vector with a T2A sequence (LeGO-iG2 and LeGo-G2 were a kind gift from Dr B. Fehse) [20]. These plasmids were used to produce VSV-G-pseudotyped lentiviral vectors.
+
+Generation of PD-L1+ and PD-L1− target cell lines
+
+Raji cells were transfected with PD-L1 plasmid (GenScript, OHu22144), using the Amaxa Cell Line Nucleofector kit V (Lonza, VCA-1003) according to the manufacturer’s recommendations. PD-L1 protein was knocked out in 786-0 cells with CRISPR-Cas9 technology according to previously published protocols [21]. Two different guide RNAs were used to generate KO1 (guide RNA sequence: TACCGCTGCATGATCAGCTATGG) and KO2 (guide RNA sequence: TACCATACTCTACCACATATAGG) to ensure obtained results were due to PD-L1 KO and not unwanted off-target alterations associated with CRISPR technology.
+
+Production of lentiviral vectors
+
+Lentiviruses were generated by calcium-phosphate based transfection (Sigma, CAPHOS-1KT) of either 1 × 106 HEK293FT cells (CRISPR plasmids) or 14 × 106 HEK293FT cells (PD1-CSR plasmids) according to the manufacturer’s recommendations. Briefly, the plasmids of interest were co-transfected with the envelope plasmid pCMV-VSV-G [22] and two packaging plasmids, pDMLg/pPRE [23] and pRSV-Rev [23] to produce VSV-G-pseudotyped lentiviruses. For the transduction of NK cells, lentiviruses were concentrated prior to freezing with the Lenti-X™ concentrator according to the manufacturer’s recommendations (Takara Bio, 631,232). All lentiviruses were titrated on HEK293FT cells. Briefly, 5 × 104 cells per well of a 24-well plate were seeded in medium, containing different amounts of concentrated virus, in the presence of 8 μg/μl protamine sulfate (Sigma-Aldrich P3369-10G). Cells were spinoculated for one hour at 1000 × g and 32 °C after which incubation for 6 h at 37 °C and 5% CO2 followed. Protamine-sulfate containing medium was then replaced with fresh medium. Green fluorescent protein (GFP) percentage was analyzed by flow cytometry three days post-transduction. Lentiviral titer was calculated with the formula %of GFPpositive cells×50.000viral supernatantin ml
+
+Lentiviral transduction of NK-92, pNK cells and 786-O cells
+
+NK cells were transduced as described previously [24]. pNK cells were isolated from healthy donor PBMCs. Briefly, cells were seeded at 5 × 105 cells/ml in viral supernatant at an MOI of 4 (NK-92) or 15 (pNK) in the presence of (5Z)-7-Oxozeanol (Biotechne​) and 8 μg/μl protamine sulfate (Sigma-Aldrich P3369-10G). Cells were spinoculated for one hour at 1,000 × g and 32 °C after which incubation for five hours at 37 °C and 5% CO2 followed. Protamine-sulfate containing medium was then replaced with fresh medium, containing IL-2 at a final concentration of 500 U/ml (NK-92) or 1000 U/ml (pNK) (R&D 202-IL-500). 786-0 cells were plated at a density of 3,300 cells/cm2 in the presence of viral supernatant and 8 μg/μl protamine sulfate (Sigma-Aldrich P3369-10G). Cells were spinoculated for one hour at 800× g and 32 °C after which incubation for five hours at 37 °C and 5% CO2 followed. Medium was then replaced with fresh medium. GFP, PD1 and PD-L1 expression were analyzed three days after transduction. Cells were sorted using BD FACS AriaFusion.
+
+Flow cytometry
+
+The following mAb were used for flow cytometry analysis: CD56 (clone NCAM1), CD16 (clone 3G8) CD3 (clone SK3), CD11b (clone ICRF44), CD14 (clone MOP9) PD1 (clone EH12.1), PD-L1 (clone MIH1), PD-L2 (clone MIH18), CD138 (clone MI15) MICA/B (clone 6D4), CD155 (clone TX24), NKG2A (clone 131,411), NKp44 (clone p44-8), TIM3 (clone 7D3), TIGIT (clone 741,182), DNAM1 (clone DX11) from BD Biosciences; HLA-ABC (clone W6/32), CD38 (clone HIT2), NKp30 (clone p30-15), NKp46 (9E2/NKp46), LAG3 (clone 11C3C65), NKG2D (clone 1D11), CD112 (clone TX31) from BioLegend; CD158a/h/g (clone HP-MA4), CD158e (clone DX9) from Thermofisher, CD158B (clone GL183) from Invitrogen, ULBP256 (clone 165,903) from R&D. All antibodies were titrated prior to usage. Briefly, cells were collected and washed once in PBS. Cells were stained with Aqua live dead cell staining (ThermoFisher) for 20 min at 4 °C, in the dark. Cells were washed once with PBS, containing 2% FBS. Surface staining was performed for 25 min at 4 °C, in the dark. Cells were washed with PBS, containing 2% FBS, centrifuged and fixed with 1% paraformaldehyde for 10 min. Acquisition was performed the following day with Beckman Coulter Cytoflex flow cytometers. Analysis was performed with FlowJo analysis software version 10. Gates were — unless otherwise specified – placed based on the unstained control or FMO (fluorescence minus one).
+
+NK cell degranulation assay and evaluation of IFNγ and TNF intracellular staining
+
+0.03 × 106 786-O cells were seeded in a flat 96-well plate 24 h before the assay to allow cells to attach. 0.15 × 106 NK-92 or pNK cells were co-incubated with either 0.15 × 106 Raji cells or 786-O cells in a final volume of 200 μl at 37 °C and 5% CO2 for four hours in the presence of CD107a antibody (BioLegend, clone H4A3). Where indicated, Rituximab was added to the co-culture at a final concentration of 2.5 μg/ml. As controls, 0.15 × 106 NK-92 or pNK cells were incubated alone or with phorbol 12-myristate 13-acetate (PMA) and ionomycin (0.5 μg/mL, Sigma-Aldrich), together with CD107a antibody for 4 h. After one hour of incubation, monensin (GolgiStop, BD Biosciences) was added to cultures to inhibit protein transportation. Subsequently, surface staining with CD56 (clone NCAM16.2), CD16 (clone 3G8), CD3 (clone UCHT1) and PD1 (clone EH12.1) was performed for 25 min at 4 °C, in the dark. For intracellular staining of IFNγ (clone B27) and TNF (clone MAb11) (all from BD Bioscience) cells were washed with PBS followed by fixation and permeabilization with cytofix/cytoperm (BD Biosciences). Cell were incubated for 30 min at RT with intracellular antibodies. Cells were washed and resuspended in PBS. Acquisition was performed with Beckman Coulter Cytoflex or BD Symphony flow cytometers. Analysis was performed with FlowJo analysis software version 10. Gates were placed on the unstimulated samples for the readout of CD107a, IFNγ and TNF.
+
+Chromium release assay
+
+NK cell cytotoxicity was measured in a standard 51Cr-release assay against tumor target cells. Briefly, target cells were labeled with 100 uL sodium chromate (PerkinElmer) for one hour at 37 °C, after which they were washed three times with PBS. NK cells were mixed with the labeled target cells at different effector to target ratios and incubated for four hours. 20 μL of the supernatant was transferred to LumaPlate-96 and subsequently analyzed with a MicroBeta2 counter (PerkinElmer).
+
+Live cell imaging assays
+
+Live cell imaging was performed as recently described [25]. Briefly, 3 × 104 PD-L1+ 786-O WT or PD-L1− 786-O KO cells that were previously transduced to express the fluorescent protein tdTomato were seeded per well in a low-attachment 96-well plate and incubated at 37 °C, 5% CO2 for 72 h to allow spheroids to form spontaneously. Prior to analysis, 3 × 103 NK-92 or pNK cells were added to the culture. The number of killed target cells was monitored by imaging every four hours over 48 h (NK-92) to seven days (pNK) using an IncuCyte S3 Live Cell Analysis System (Sartorius). Percent of killing was quantified as decrease in red intensity and normalized to the red fluorescence intensity at the beginning of the assay with the formula red cellcount attimepoint xred cellcount attimepoint1.
+
+Proliferation assays
+
+NK-92 and pNK cells were labeled with Cell Trace Violet (ThermoFisher) according to the manufacturer’s recommendations. Cells were cultured alone or co-cultured in the presence of PD-L1+ 786-O WT or PD-L1− 786-O KO cells at an effector to target ratio of 1:1. Acquisition was performed with Beckman coulter Cytoflex flow cytometers. Analysis was performed with FlowJo analysis software version 10.
+
+Statistical analysis
+
+The Student’s t test was used to compare the means of two groups. Two-way ANOVA test was used to compare the means between several groups. p < 0.05 was determined as statistically significant (*), p < 0.01 (**), p < 0.001 (***), p < 0.0001 (****) as statistically highly significant. Statistical analysis was performed with GraphPad Prism software version 9 (GraphPad, La Jolla, USA).
+
+Results
+
+Expression of PD1-based chimeric switch receptors in NK-92 cells
+
+Initially, six different CSR constructs were generated with the purpose of determining optimal signaling in NK cells. All CSR expressed the unmodified human PD1 extracellular domain fused with various activating intracellular domains. Specifically, DAP10, DAP12, CD3ζ and NKp46 were utilized. Furthermore, a control coding for a truncated, signaling-deficient PD1 construct was generated (Fig. 1A, B). PD1 surface expression among the untransduced or empty vector transduced NK-92 cell lines remained below 2% of the total population. After sorting, all other transduced cell lines stably expressed the transgenes as confirmed by positive PD1 staining. As expected, expression levels differed between the constructs (Fig. 1C). Importantly, neither PD-L1 nor PD-L2 expression was detected in NK-92 wildtype (WT) cells (Fig. 1D).Fig. 1 PD1-based chimeric switch receptors are stably expressed in NK-92 cells. A, B Table and vector maps depicting the design of the truncated PD1 receptor (PD1EcTM) and six chimeric switch receptors (CSR) with different signaling domains C NK-92 cells containing different PD1-CSR and sorted for positive PD1 surface staining. D PD-L1 and PD-L2 expression on NK-92 cells. E PD1, PD-L1 and PD-L2 expression on PD-L1+ Raji and PD-L1− Raji WT cells. F PD1, PD-L1 and PD-L2 expression on PD-L1+ 786-O WT and PD-L1− 786-O KO cells
+
+Generation of target cell lines
+
+To study the function of the PD1-CSR+ NK-92 cell lines, PD-L1+ and PD-L1− target cell lines were generated. As target cell lines 786-O and Raji cells were chosen, with the first expressing PD-L1 and the latter being devoid of PD-L1. The target cell lines were chosen due to their different potential to activate NK cells. Based on these cell lines, PD-L1 knock-out 786-O (786-O KO) cell lines and a PD-L1+ Raji cell line were generated. Hereafter, the target cell lines are referred to as PD-L1+ 786-O WT, PD-L1− 786-O KO1, PD-L− 786-O KO2, PD-L1+ Raji and PD-L1− Raji WT. Neither of the target cell lines expressed PD1 or PD-L2 (Fig. 1E, F). The genetic modification of the target cell lines did not alter the expression of other ligands for activating NK cell receptors (Figure S1A, B).
+
+PD1-CSR transduced NK-92 cell lines show superior degranulation and cytokine secretion
+
+To evaluate the induction of degranulation and cytokine expression, CD107a, IFNγ and TNF expression by all generated PD1-CSR+ NK-92 cell lines and controls was assessed against PD-L1+ 786-O WT and PD-L1− 786-O KO cell lines (Fig. 2A, Figure S2A). Generally, 786-O cells are resistant to NK cell-mediated cytotoxicity and the PD1-CSR+ NK-92 cells were tested for their ability to circumvent the resistance. In line with this, CD107a, IFNγ and TNF expression by NK-92 WT and PD1EcTM+ NK-92 remained below 10% against both PD-L1+ 786-O WT and PD-L1− 786-O KO cell lines, with no significant differences between the three target cell lines.Fig. 2 PD1-CSR+ NK-92 cells increase degranulation, cytokine production and killing of PD-L1+ target cells. A Percentage of CD107a, IFNγ and TNF by different PD1-CSR+ NK-92 cells against PD-L1+ 786-O WT and two PD-L1− 786-O KO cell lines. Each data point represents the mean (± SD) of three independent experiments performed in triplicates. B Killing of PD-L1+ 786-O WT versus PD-L1− 786-O KO1 and PD-L1− 786-O KO2 cells by NK-92 WT, PD1EcTM+, PD1EcTMDAP10IC+ or PD1EcTMDAP12IC+ NK-92 cells. Each data point represents the mean (± SD) of three independent experiments performed in quadruplets. Statistical significance (* p < 0.05; ** p < 0.01) was determined with a two-way ANOVA test
+
+The PD1-CD28-CD3ζ and PD1EcTMDAP12IC transduced NK-92 cell lines showed a three-to four-fold increase in CD107a expression and corresponding increase in IFNγ and TNF expression against PD-L1+ 786-O WT compared to both PD-L1− 786-O KO cell lines. CD107a, IFNγ and TNF production was 1.5-fold higher by PD1ECDAP10TM-IC+ NK-92 against PD-L1+ 786-O WT cells. CD107a, IFNγ and TNF expression by PD1ECDAP12TM-IC+ NK-92 cells showed the highest degranulation against PD-L1+ 786-O WT cells among the PD1-CSR+ NK-92 cell lines, approaching 50% (± 12%), 30% (± 8.5%) and 30% (± 8.5%), respectively. Notably, PD1-NKp46 and PD1EcTMDAP10IC expressing NK-92 cell lines did not show increased CD107a nor IFNγ or TNF expression against PD-L1+ 786-O WT compared to PD-L1− 786-O KO cell lines. To conclude, PD1-CD28-CD3ζ+, PD1EcDAP10TM-IC+, PD1EcDAP12TM-IC+ and PD1EcTMDAP12IC+ NK-92 cells increased degranulation and cytokine expression against PD-L1+ 786-O WT cells which are inherently resistant to NK cell-mediated cytotoxicity.
+
+PD1-CSR transduced NK-92 cell lines show superior degranulation and cytokine secretion against PD-L1+ Raji cells
+
+To extend these results to a cell line that is already highly susceptible to NK cell killing, all generated PD1-CSR+ NK-92 cell lines were assessed against PD-L1+ Raji cells and PD-L1− Raji WT cells (Figure S2 B–D). NK92 WT and PD1EcTM+ NK92 cells showed a high CD107a, IFNγ and TNF expression against both PD-L1− Raji WT cells and PD-L1+ Raji cells with values reaching up to 80% (± 7%), 70% (± 6%) and 70% (± 1.2%), respectively. Despite the high baseline values, the expression of CD107a, IFNγ and TNF by PD1-CD28-CD3ζ+, PD1EcTMDAP10IC+, PD1EcDAP12TM-IC+ and PD1EcTMDAP12IC+ NK-92 cells was significantly higher against PD-L1+ Raji cells compared to PD-L1− Raji WT cells. PD1-NKp46 and PD1EcDAP10TM-IC expressing NK-92 cell lines did not increase CD107a, IFNγ or TNF expression against PD-L1+ Raji cells compared to PD-L1− Raji WT cells. Taken together, PD1-CD28-CD3ζ+, PD1EcTMDAP10IC+, PD1EcDAP12TM-IC+ and PD1EcTMDAP12IC+ NK-92 cells increased degranulation and cytokine expression against PD-L1+ Raji cells.
+
+Receptor-independent degranulation and cytokine production by PD1-CSR+ NK-92 cells
+
+To confirm that the induction of CD107a, IFNγ and TNF expression is based on PD1-PD-L1 interaction, degranulation and cytokine expression by unstimulated or maximal chemically (PMA/Iono) stimulated PD1-CSR+ NK-92 cell lines was measured (Figure S3A–C). No significant differences in CD107a, IFNγ or TNF expression by unstimulated PD1-CSR+ NK-92 cell lines was observed. However, chemical stimulation of PD1-CD28-CD3ζ+ and PD1-NKp46+ NK-92 cells resulted in reduced CD107a, but not IFNγ or TNF expression, compared to NK-92 WT cells. The other PD1-CSR+ NK-92 cells did not show differences in CD107a, IFNγ or TNF expression after chemical stimulation compared to NK-92 WT cells. PD1 positivity decreased in the PD1-NKp46+ NK-92 cell line in the absence of stimulation as well as after chemical stimulation or target cell recognition. After chemical stimulation or recognition of PD-L1+ target cells, PD1 surface expression also decreased in the PD1EcDAP12TM-IC expressing cell line and remained stable for the other PD1-CSR+ NK-92 cell lines (Figure S3D, E). Taken together, PD1EcTMDAP10IC+ and PD1EcTMDAP12IC+ NK-92 cells showed a stable PD1 surface staining, a higher CD107a, IFNγ and TNF expression upon PD-L1+ target cell recognition and no alteration in degranulation or cytokine secretion in the unstimulated or maximal chemically stimulated controls compared to NK-92 WT cells. Genetic modification did not alter the expression of other activating and inhibitory NK cell receptors on mock-transduced, PD1EcTM, PD1EcTMDAP10IC and PD1EcTMDAP12IC transduced NK92 cells (Figure S4A) Therefore, PD1EcTMDAP10IC and PD1EcTMDAP12IC CSR were chosen for the following assays.
+
+PD1EcTMDAP10IC+ and PD1EcTMDAP12IC+NK-92 cells show higher killing of PD-L1+ 786-O WT cells
+
+Since PD1EcTMDAP10IC+ and PD1EcTMDAP12IC+ NK-92 cells showed higher efficacy, we explored whether these cells would also directly kill tumor target cells. Killing of 51Cr labeled PD-L1+ 786-O WT or PD-L1− 786-O KO cells by either NK-92 WT, PD1EcTM+, PD1EcTMDAP10IC+ or PD1EcTMDAP12IC+ NK-92 cell lines was assessed at different effector to target (E:T) ratios (Fig. 2B). There was no significant difference in killing of PD-L1+ 786-O WT cells compared to both PD-L1− 786-O KO cell lines by either NK-92 WT or PD1EcTM+ NK92 cells. PD1EcTMDAP10IC+ NK-92 cells increased killing of PD-L1+ 786-O WT cells twofold compared to both PD-L1− 786-O KO cell lines. Similarly, PD1EcTMDAP12IC+ NK-92 cells increased killing of PD-L1+ 786-O WT cells by twofold at higher E:T ratios and 3.5-fold at lower E:T ratios compared to PD-L1− 786-O KO cells. No significant difference in killing of PD-L1+ Raji compared to PD-L1− Raji WT cells was observed by either of the PD1-CSR+ NK-92 cells (Figure S4 B-E).
+
+PD1EcTMDAP10IC+ and PD1EcTMDAP12IC+NK-92 show increased killing of large PD-L1+ 786-O WT tumor spheroids
+
+Since 786-O cells can form large tumor spheroids with diameters reaching up to one millimeter, the ability of PD1EcTMDAP10IC+ and PD1EcTMDAP12IC+ NK-92 cells to kill PD-L1+ 786-O WT cells was tested in the 3D co-culture model as it more accurately resembles the TME than a 2D co-culture model. For this purpose, PD-L1+ 786-O WT and both PD-L1− 786-O KO cell lines were further transduced to express the red fluorescence protein tdTomato. Killing of 786-O spheroids was assessed based on the decrease of red fluorescence intensity as captured with the IncuCyte live cell imager (Fig. 3A–D; supplemental Video V1-V4) and by flow cytometry (Fig. 3E–F). Red fluorescence intensity decreased from 100 to 70% (± 11%) in PD-L1+ 786-O WT and PD-L1− 786-O KO cell lines over a 48 h period when NK-92 WT or PD1EcTM+ NK-92 cells were added to the tumor spheroids, with no significant differences between the three target cell lines. PD1EcTMDAP10IC+ NK-92 cells resulted in a reduction of red fluorescence intensity from 100 to 35% (± 10%) in the PD-L1+ 786-O WT cells and from 100 to 60% (± 9%) in the PD-L1− 786-O KO cell lines, with statistical significance between the PD-L1+ and PD-L1− target cell lines. Similarly, there was a significant decrease in red fluorescence intensity in PD-L1+ 786-O WT cells (100% to 45% (± 7%)) compared to PD-L1− 786-O KO cell lines (100% to 65% (± 8%)) when PD1EcTMDAP12IC+ NK-92 cells were added. The tumor spheroids were therafter harvested, washed and dissociated to assess spheroid killing by flow cytometry (Fig. 3E–F; Figure S5 A). Live 786-O cells showed a high expression of tdTomato and were classified as 786-Obright while dying 786-O cells gradually lost tdTomato expression and were classified as 786-Odim cells. As expected, both PD1EcTMDAP10IC+ and PD1EcTMDAP12IC+ NK-92 cells, but not NK-92 WT or PD1EcTM+ NK-92 cells, led to a significant increase in the 786-Odim population and corresponding decrease in the 786-Obright population against the PD-L1+ 786-O WT but not PD-L1− 786-O KO cell lines. Furthermore, the percentage and total amount of CD45+ cells within the 786-O tumor spheroids was assessed (Fig. 3G, Figure S5 B). There was a statistically significant difference in the percentage of CD45+ cells within the PD-L1+ 786-O WT spheroid compared to the PD-L1− 786-O KO spheroids when PD1EcTMDAP10IC+ and PD1EcTMDAP12IC+ NK-92 cells were added. However, no statistical significant difference was observed for the absolute numbers of CD45+ cells. Lastly, proliferation of PD1-CSR+ NK-92 cells was measured. No significant differences in proliferation of PD1EC-TMDAP10IC+ or PD1EC-TMDAP12IC+ NK-92 cells compared to WT or PD1EC-TM+ NK-92 cells were observed when cultured alone (Figure S5C), exposed one-time to PD-L1+ 786-O WT cells (Figure S5D) or repetitively exposed to PD-L1+ 786-O WT cells (Figure S5E). In summary, both PD1EC-TMDAP10IC+ and PD1EC-TMDAP12IC+ NK-92 cells increased killing of large PD-L1+ 786-O WT tumor spheroids over a 48 h period.Fig. 3 PD1-CSR+ NK-92 cells increase cytotoxicity against PD-L1+ tumor spheroids. A–D Killing of PD-L1+ 786-O WT versus PD-L1− 786-O KO1 and PD-L1− 786-O KO2 tumor spheroids by NK-92 WT, PD1EcTM+, PD1EcTMDAP10IC+ or PD1EcTMDAP12IC+ NK-92 cells. Each data point represents the mean (± SD) of six independent experiments performed in duplicates. E–G Tumor spheroids were collected, washed, dissociated and analyzed by flow cytometry. Displayed is the gating strategy (E), the percentage of 786-Odim cells per spheroid (F) and percentage of CD45+ NK-92 cells per spheroid (G). Each data point represents the mean (± SD) of three independent experiments performed in duplicates. Statistical significance (* p < 0.05; ** p < 0.01; *** p < 0.001) was determined with a two-way ANOVA test
+
+PD1EcTMDAP10IC+ and PD1EcTMDAP12IC+ pNK cells increase degranulation and cytokine expression against PD-L1+ Raji cells
+
+With the aim to implement PD1-CSR for adoptive cell therapies, the function of PD1EcTMDAP10IC and PD1EcTMDAP12IC constructs in pNK cells, isolated from healthy donor PBMCs, was tested. While PD1 surface expression in untransduced pNK cells or mock-transduced (empty vector) pNK cells remained below 5%, its expression increased on average to 42% (± 22%), 52% (± 17%) and 45% (± 16%) in the pNK cells transduced with either PD1EcTM, PD1EcTMDAP10IC or PD1EcTMDAP12IC CSR, respectively (Fig. 4A). Two separate levels of PD1 surface expression were observed and CD56+ CD16+ pNK cells were classified as either PD1dim or PD1bright (Fig. 4B). The expression of the main activating and inhibitory NK cell receptors on PD1+ PD1-CSR+ pNK cells compared to PD1+ WT or mock-transduced pNK cells from three different donors was measured by flow cytometry (Figure S6A). Although inter-individual differences in receptor expression were observed, the genetic modification of pNK cells with PD1-CSR did not cause any consistent intra-individual phenotypic changes of pNK cells. CD107a, IFNγ and TNF expression were measured in a degranulation assay against PD-L1+ Raji cells and PD-L1− Raji WT cells (Fig. 4C–E, Figure S7, Figure S8A–C). To facilitate direct comparison, the fold ratio of CD107a, IFNγ and TNF expression against PD-L1+ Raji cells compared to PD-L1− Raji WT cells is displayed, with numbers below one indicating a reduction and numbers above one an increase upon PD-L1 engagement in the respective parameter (Fig. 4C–E). PD1dim pNK cells from WT or mock-transduced pNK cells showed a lower CD107a expression against PD-L1+ Raji cells, significantly reducing the ratio below one. On the contrary, PD1bright PD1EcTM+ pNK cells increased degranulation against PD-L1+ Raji cells compared to WT or mock-transduced pNK cells, raising the ratio to one. PD1bright PD1EcTMDAP10IC+ and PD1bright PD1EcTMDAP12IC+ pNK cells significantly increased CD107a expression against PD-L1+ Raji cells compared to PD-L1− Raji WT cells, raising the ratio to 1.5. Similarly, the ratio of IFNγ and TNF expression against PD-L1+ Raji cells compared to PD-L1− Raji WT cells was higher than one for the PD1bright PD1EcTMDAP10IC+ and PD1bright PD1EcTMDAP12IC+ pNK cells but not PD1bright PD1EcTM+ pNK cells or PD1dim WT or PD1dim mock-transduced pNK cells. In conclusion, PD1EcTM+ pNK cells blocked native PD1-PD-L1 mediated pNK cell inhibition, while both PD1EcTMDAP10IC+ and PD1EcTMDAP12IC+ pNK cells reverted inhibition into an increased degranulation and cytokine expression against PD-L1+ Raji cells.Fig. 4 PD1-CSR+ pNK cells increase degranulation and cytokine production against PD-L1+ target cells. A PD1 surface expression on untransduced or transduced CD56+ CD3− pNK cells. Each dot represents PD1 surface expression on one individual donor (n = 12). The mean of all 12 donors is displayed. B Gating strategy for degranulation assays. WT and empty vector transduced cells were gated on PD1dim population while PD1-CSR+ cells were gated on the PD1high population. C–E Ratio of CD107a (C), IFNγ (D) and TNF (E) expression by different PD-CSR+ pNK cells against PD-L1+ Raji cells versus PD-L1− Raji WT cells. Each dot represents the mean of one individual donor, performed in duplicates (n = 8) The mean ± SD of all 8 donors is displayed. F–H Percentage of CD107a (F), IFNγ (G) and TNF (H) expression by different PD-CSR+ pNK cells against PD-L1+ Raji cells. Each dot represents the mean of one individual donor, performed in duplicates (n = 8) The mean ± SD of all 8 donors is displayed. I Killing of PD-L1+ Raji cells versus PD-L1− Raji WT cells by different PD-CSR+ pNK cells at an E:T of 1:1. Displayed are data from 3 independent donors with each data point representing the mean (± SD) of one experiment performed in triplicates. Statistical significance was determined with a Students t test (* p < 0.05, ** p < 0.01, ***p < 0.001, ****p < 0.0001)
+
+PD1EcTMDAP10IC+ and PD1EcTMDAP12IC+ pNK cells increase degranulation and cytokine expression against PD-L1+ Raji cells together with ADCC
+
+After demonstrating functionality of PD1EcTMDAP10IC and PD1EcTMDAP12IC CSR in pNK cells, their ability to synergize with CD16 mediated ADCC was tested to evaluate their potential in combinatorial treatment approaches. The percentages of CD107a, IFNγ and TNF expression against PD-L1+ Raji cells and PD-L1− Raji WT cells with or without the addition of the anti-CD20 mAb Rituximab were measured (Fig. 4F–H, Figure S7). Rituximab increased CD107a, IFNγ and TNF expression against PD-L1+ Raji cells in both PD1dim WT and mock-transduced pNK cells compared to no ADCC. Likewise, PD1bright PDEcTM+, PD1EcTMDAP10IC+ and PD1EcTMDAP12IC+ pNK cells showed increased CD107a, IFNγ and TNF expression with the addition of Rituximab compared to no ADCC. Overall, PD1bright PD1EcTMDAP10IC+ and PD1EcTMDAP12IC+ pNK showed the highest increase in CD107a, IFNγ and TNF expression with values reaching up to 72% (± 12%), 63% (± 12%) and 51% (± 19%), respectively, compared to WT or mock-transduced pNK cells with values approaching 42% (± 28), 26% (± 12) and 11% (± 8%), respectively. Compared to PD1bright PD1-CSR+ pNK cells, PD1dim PD1EcTMDAP10IC+ and PD1EcTMDAP12IC+ pNK cells did not increase degranulation and cytokine production against PD-L1+ Raji cells but equalized it to the CD107a, IFNγ and TNF expression levels against PD-L1− Raji cells (Figure S8A–C). In conclusion, both PD1EcTMDAP10IC+ and PD1EcTMDAP12IC+ pNK cells increased degranulation and cytokine expression against PD-L1+ Raji cells with or without the addition of Rituximab and thus reverted native PD1 mediated NK cell inhibition.
+
+PD1EcTMDAP12IC+ pNK cells increase degranulation and cytokine expression against PD-L1+ 786-O WT cells
+
+To extend these results, degranulation and cytokine expression of PD1-CSR+ pNK cells was also measured against PD-L1+ 786-O WT and PD-L1− 786-O KO cells (Figure S8D–F). Similar to the data from NK92 cell lines, WT or mock-transduced pNK cells showed a low expression of CD107a, IFNγ and TNF against both PD-L1+ 786-O WT and PD-L1− 786-O KO cells, with no significant differences between the target cell lines. Compared to that PD1bright PD1EcTMDAP12IC+ pNK cells, but not PD1dim PD1EcTMDAP12IC+ pNK cells, significantly increased CD107a, IFNγ and TNF expression against PD-L1+ 786-O WT cells compared to PD-L1− 786-O KO cells. While PD1EcTM+ and PD1EcTMDAP10IC+ pNK cells did not correlate with a higher CD107a expression, they showed a higher IFNγ and TNF expression against both PD-L1+ 786-O WT and PD-L1− 786-O KO cells. All in all, PD1bright PD1EcTMDAP12IC+ pNK cells increased degranulation and cytokine expression against PD-L1+ 786-O WT cells.
+
+PD1EcTMDAP10IC+ and PD1EcTMDAP12IC+ pNK cells do not alter killing of PD-L1+ target cells
+
+Next, the ability of the PD1-CSR enriched pNK cells to kill PD-L1+ and PD-L1− target cells in a 2D and 3D co-culture model was assessed. No difference in killing of PD-L1+ Raji cells compared to PD-L1− Raji WT cells by PD1EcTMDAP10IC+ or PD1EcTMDAP12IC+ pNK cells from three different donors compared to WT, mock-transduced or PD1EcTM+ pNK cells was observed (Fig. 4I, Figure S9A). Similarly, neither PD1EcTMDAP10IC+ nor PD1EcTMDAP12IC+ pNK cells increased killing of PD-L1+ 786-O WT tumor spheroids compared to PD-L1− 786-O KO tumor spheroids (Figure S9B). However, this lack of killing ability might be due to the fact that pNK cells were not sorted for high expression of PD1-CSR prior to use. Finally, the proliferative capacity of PD1-CSR+ pNK cells was measured (Figure S9C). PD1dim WT pNK cells showed a lower proliferation rate compared to PD1negative WT pNK cells. In contrast, PD1bright PD1-CSR+ pNK cells increased the proliferation rate above the value observed in PD1dim WT pNK cells closer to the value observed in PD1negative pNK cells. All in all, pNK cells, enriched with PD1EcTMDAP10IC+ and PD1EcTMDAP12IC+ pNK cells, did not show an increased killing of PD-L1+ tumor target cells.
+
+PD1EcTMDAP10IC+ and PD1EcTMDAP12IC+ pNK cells from patients with newly diagnosed MM increase degranulation and cytokine production against autologous PD-L1+ tumor samples
+
+After establishing that both PD1EcTMDAP10IC+ and PD1EcTMDAP12IC+ pNK cells from healthy donors increased degranulation and cytokine expression against PD-L1+ tumor cell lines, their function in pNK cells from patients with MM against autologous bone marrow mononuclear cells (BM MNC) was evaluated. For this, PBMCs from three patients with newly diagnosed MM were expanded for 13 days prior to transduction with lentiviral vectors, encoding PD1EcTM, PD1EcTMDAP10IC and PD1EcTMDAP12IC CSR. Degranulation was performed on day 4 after transduction with approximately 40% CD56+CD3− pNK cells among the expanded PBMCs (Fig. 5A). CD138 expression on BM MNC, indicative of malignant plasma cells, was measured by flow cytometry and only detected in BM MNC from donor 1 (MM1 BM MNC), but not donor 2 (MM2 BM MNC) or donor 3 (MM3 BM MNC). A more detailed phenotypic analysis of BM MNC from donor 1 and donor 2 is provided in Figure S10. PD-L1 and PD-L2 expression was detected on CD138+ cells, but only at very low levels on CD138− BM MNCs (Fig. 5A, Figure S10A, B). The percentage of PD1bright cells on CD56+CD3− pNK cells ranged between 7 to 20% depending on the CSR construct and donor (Fig. 5B, F, J). Both PD1bright PD1EcTMDAP10IC+ and PD1EcTMDAP12IC+ pNK cells from donor 1 increased CD107a, IFNγ and TNF expression significantly against autologous BM MNC two-to three-fold compared to PD1negative pNK cells (Fig. 5C–E). PD1bright PD1EcTMDAP10IC+ and PD1EcTMDAP12IC+ pNK cells from donor 2 did not increase CD107a expression, but showed an increased IFNγ and TNF expression compared to PD1negative pNK cells (Fig. 5G–I). PD1bright PD1EcTMDAP10IC+ and PD1EcTMDAP12IC+ pNK cells from donor 3 did not increase CD107a, IFNγ or TNF expression, but showed a slight decreased IFNγ response (Fig. 5K–M). These data confirm that PD1EcTMDAP10IC+ and PD1EcTMDAP12IC+ pNK augment degranulation and cytokine expression against autologous CD138+ PD-L1+ malignant bone marrow cells.Fig. 5 PD1-CSR+ pNK cells increase degranulation and cytokine production against PD-L1+ autologous tumor samples. A Flow cytometry plots show the gating strategy of BM MNC (upper panel) and PBMCs (lower panel) from newly diagnosed MM patients B Percentage of PD1bright cells among CD56+CD3− pNK cells from MM donor 1 C–E Percentage of CD107a (C), IFNγ (D) and TNF (E) by different PD1negative versus PD1bright PD1-CSR+ pNK cells from donor 1 against autologous BM MNC. F Percentage of PD1bright cells among CD56+CD3− pNK cells from MM donor 2 G–I Percentage of CD107a (G), IFNγ (H) and TNF (I) by different PD1negative versus PD1bright PD-CSR+ pNK cells from donor 2 against autologous BM MNC. J Percentage of PD1bright cells among CD56+CD3− pNK cells from MM donor 3K–M) Percentage of CD107a (K), IFNγ (L) and TNF (M) by different PD1negative versus PD1bright PD-CSR+ pNK cells from donor 3 against autologous BM MNC. Displayed are data from each donor with each data point representing the mean (± SD) of one experiment performed in duplicates. Statistical significance was determined with a Students t test (* p < 0.05, ** p < 0.01)
+
+Discussion
+
+In this paper, we demonstrate that PD1-based CSR revert NK cell inhibition imposed by PD1-PD-L1 engagement and, hence, are able to skew the response toward NK cell activation. The results emphasize that replacement of the ITIM and ITSM domain of PD1 by either an ITAM or YINM motif confers a higher degranulation and cytokine production by both NK-92 and pNK cells toward PD-L1 expressing target cells in 2D and 3D tumor co-culture models. Most importantly, pNK cells from patients with MM were successfully transduced to express PD1EcTMDAP10IC or PD1EcTMDAP12IC CSR and showed higher degranulation and cytokine expression against autologous CD138+PD-L1+ tumor samples.
+
+Different to CAR, the present CSR shall enhance NK cell cytotoxicity in concert with general target cell recognition and tip the balance toward activation in an immunosuppressive TME. The aim in the present study was to design CSR that are not activating NK cells toward healthy tissue where PD1 ligands are abundantly expressed and can explain common side-effects of immune-checkpoint blockade with mAb [26]. Therefore, the human canonical sequence of PD1 without any further modification was employed. Furthermore, only one signaling domain was used compared to the second and third generation CARs that are designed with different co-stimulatory domains. Whether PD-L1 targeting CAR NK cells would, however, cause severe side-effects is yet not elucidated. PD-L1 targeting high-affinity NK-92 cells (PD-L1-t-haNK) showed promising preclinical results and are currently in early phase clinical trials (NCT04050709, NCT04847466, NCT04927884) [27]. The results and toxicity profile of these PD-L1 CAR expressing NK-92 cells are eagerly awaited. However, employing the extracellular domain of PD1, instead of the single-chain fragment targeting PD-L1, poses the advantage of recognizing both PD-L1 and PD-L2. In humans, PD-L2 is mainly expressed on professional antigen-presenting cells and over-expressed in cancer cells as well as stromal and epithelial cells of several tumor types [28]. Targeting both PD1 ligands was associated with a better clinical outcome in lung cancer [29]. Besides PD1, NK cells express a plethora of canonical checkpoints that are important both for control of activation as well as retention of educated state upon adoptive transfer [30, 31]. The surface retained checkpoints include NKG2A, T cell immunoreceptor with Ig and ITIM domains (TIGIT), Lymphocyte Activating Gene 3 (LAG3), T cell immunoglobulin domain and mucin domain 3 (TIM3) as well as inhibitory KIRs. It is conceivable that some of these receptors could also be engineered in a similar fashion as described here for PD1.
+
+PD1-CSR+ pNK cells from MM patient number 3 showed a decrease in IFNγ production against autologous BM MNCs. Unfortunately, we were not able to determine the factors leading to this small but significant reduction in cytokine expression. Taken the complexity of the immunosuppressive TME into account, it is conceivable that PD1-CSR expressing NK cells might be inhibited by other soluble or receptor-mediated factors. A solution could be the combination of PD1-CSR+ pNK cells with other immune checkpoint targeting therapies. Monalizumab, a monoclonal antibody against NKG2A that is widely used in clinical trials, promoted both NK and CD8+ T cell anti-cancer functions, especially in combination with PD1-PD-L1 blockade [30]. In line with this, disruption of NKG2A in primary NK cells improved NK cell cytotoxicity against primary MM cells [32].
+
+Blockade of immune checkpoint receptors such as PD1 or TIGIT with mAb was shown to restore NK cell effector functions against tumor cells [15, 33]. However, the majority of available antibodies merely blocks the PD1-PD-L1 interaction and does not induce ADCC to enhance NK cell functions. So far, avelumab is the only PD-L1-targeting antibody available with ADCC function [34]. To date, no clinical study evaluated the combination of avelumab with adoptive NK cell therapy. Here, we show that both PD1EcTMDAP10IC and PD1EcTMDAP12IC revert PD1 based NK cell inhibition, with PD1EcTMDAP12IC+ cells eliciting a higher increase. In line with our present findings, a PD1-NKG2D CSR with 4-1BB costimulatory domain enhanced killing of PD-L1+ target cells, but did not increase cytokine release [35]. NKG2D is a type-II transmembrane protein that dimerizes and forms a hexameric structure with four DAP10 molecules [36]. Moreover, a DAP12 based CAR increased both target cell killing and IFNγ production by pNK cells [37]. The effector cell functionality of CAR-DAP12 transduced NK cells was higher than CAR-CD3ζ transduced cells. Both PD1-CSR constructs were able to revert NK cell hypofunctionality induced by native PD1-PD-L1 signaling. However, this increase was only observed in transduced PD1bright pNK cells. In contrast, PD1dim cells blocked native PD1-PD-L1 engagement and restored degranulation and cytokine secretion. With 5–10% of PD1bright cells within the NK cell product, we have not observed an overall higher target cell killing.
+
+The present findings indicate that PD1-CSR+ pNK cells could be employed in combinatorial treatment approaches such as in combination with mAb. Therefore, the ability of PD1-CSR+ pNK to engage in ADCC was studied and demonstrated that both PD1EcTMDAP10IC+ and PD1EcTMDAP12IC+ synergistically increased degranulation against PD-L1+ Raji cells in combination with Rituximab. Another mAb that is known to work mainly via ADCC is Daratumumab that targets CD38 expressed on malignant plasma cells [38]. Daratumumab is approved as a frontline therapy in patients with newly diagnosed MM [39]. Furthermore, NK cell based therapies are currently in early-phase clinical trials for MM (NCT04558853, EudraCT: 2020-000994-26) [40, 41]. The results of these trials are eagerly awaited. We envision an indication for PD1-CSR+ pNK cells in patients with MM, a disease in which immune checkpoint blockade with mAb has failed. Monotherapy with the monoclonal PD1 antibody nivolumab in heavily pretreated MM patients only led to a stable disease without significant disease regression [42]. Two phase III clinical trials, studying the combinatorial application of PD1 receptor blockade by pembrolizumab with an immunomodulatory drug (IMiD) and dexamethasone (Keynote-183, Keynote-185), had to be suspended in 2017 due to dissatisfactory interim results, revealing increased death rates among patients that were enrolled in the experimental arm [43, 44]. Specifically, severe cardiac events, myocarditis and pneumonia were higher in the group that received pembrolizumab, causing increased death rates. Studies are ongoing to determine patient cohorts, combination regimens and treatment agents to efficiently target the PD1-PD-L1 axis in MM and improve patient outcome. Recently, avelumab showed a good toxicity profile but unfortunately no clinical benefit in combination with radiotherapy for relapsed or refractory MM [45]. Promisingly, our preclinical data show that both PD1EcTMDAP10IC+ and PD1EcTMDAP12IC+ pNK cells from newly diagnosed MM patients increase degranulation and cytokine production against autologous PD-L1+ CD138+ BM MNC while sparing PD-L1− CD138− samples. However, PD1-CSR+ pNK cells could potentially also target PD-L1 expressed on other cells of the TME such as myeloid-derived suppressor cells (MDSC) or tumor-associated macrophages (TAM) and thus re-shape the TME via increased cytokine expression or reduction of pro-tumorigenic cell numbers. Further studies to advance PD1-CSR+ pNK cells for the treatment of MM; e.g., in combination with Daratumumab, are warranted. Specifically, PD1-CSR should be tested in pNK cells from a larger cohort of patients with MM to confirm our observations reported here.
+
+In conclusion, we have here demonstrated that PD1EcTMDAP10IC+ and PD1EcTMDAP12IC+ CSR revert PD1-PD-L1 induced NK cell inhibition. PD1-CSR+ NK cells hence represent a feasible approach for future adoptive NK cell-based immunotherapy platforms in human cancer treatment.
+
+Supplementary Information
+
+Below is the link to the electronic supplementary material.Supplementary file1 (EPS 1871 KB)
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+Supplementary file2 (EPS 3592 KB)
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+Supplementary file3 (EPS 2850 KB)
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+Supplementary file4 (EPS 2469 KB)
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+Supplementary file5 (EPS 2505 KB)
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+Supplementary file6 (EPS 3130 KB)
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+Supplementary file7 (EPS 4184 KB)
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+Supplementary file8 (EPS 3717 KB)
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+Supplementary file9 (EPS 3281 KB)
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+Supplementary file10 (EPS 2499 KB)
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+Supplementary file11 (MP4 2227 KB)
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+Supplementary file12 (MP4 2176 KB)
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+Supplementary file13 (MP4 2257 KB)
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+Supplementary file14 (MP4 2242 KB)
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+Abbreviations
+
+ADCC Antibody-dependent cellular cytotoxicity
+
+BM MNC Bone marrow mononuclear cells
+
+CAR Chimeric antigen receptor
+
+CSR Chimeric switch receptor
+
+ICI Immune checkpoint inhibition
+
+ITAM Immunoreceptor tyrosine-based motif
+
+mAb Monoclonal antibody
+
+MM Multiple myeloma
+
+NK Natural killer
+
+pNK Primary natural killer
+
+PD1 Programmed death protein 1
+
+PD-L1 Programmed death ligand 1
+
+PD-L2 Programmed death ligand 2
+
+TCR T cell receptor
+
+TME Tumor microenvironment
+
+Acknowledgements
+
+We would like to express our gratitude toward the MedH FlowCytometry Core Facility for their help and support. Furthermore, we would like to acknowledge the NextGenNK Competence Center for advanced NK cell therapies (Vinnova 2019-00056) for use of the live cell imaging system IncuCyte S3.
+
+Authors contributions
+
+KHS, AL, AKW and EA designed and concepted the study. KHS, YAS, MK, MG, AB, MKe, KM and AKW acquired data and performed the experiments. KHS, YAS, AL, MK, JL, HGL, HN, AKW and EA contributed to data analysis and interpretation, and provided essential reagents. KHS, YAS, AKW, HGL and EA wrote the manuscript and prepared the figures. All authors revised and approved the manuscript.
+
+Funding
+
+Open access funding provided by Karolinska Institute. This work was supported by VINNOVA (2019-00056) and Radiumhemmets forskningsfonder (191063) and the Castenbäcks Stiftelse för cancer research. KHS received a young investigator award from the International Myeloma Society (IMS).
+
+Availability of data and material
+
+All data relevant to the study are included in the article or uploaded as supplementary information.
+
+Declarations
+
+Competing interests
+
+HN is employed by Genmab and a share holder of Genmab. EA, HG and AB are consultants for Vycellix. EA and HG are shareholders of Vycellix. AKW is consultant and shareholder of VyGenBio. A patent application has been submitted, covering this work (US Provisional application no 63286205).
+
+Ethics approval and consent to participate
+
+Ethical permits were granted by the Swedish Ethical Review board (Etikprövningsmyndighet) for work with patient derived PBMCs and bone marrow samples (Permit Numbers: 2019-04973 and 2020-02119).
+
+Consent for publication
+
+All authors declare their consent for publication.
+
+Publisher's Note
+
+Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
+==== Refs
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+

PMC10110729.txt

@@ -0,0 +1,247 @@
+
+==== Front
+Cancer Immunol Immunother
+Cancer Immunol Immunother
+Cancer Immunology, Immunotherapy
+0340-7004
+1432-0851
+Springer Berlin Heidelberg Berlin/Heidelberg
+
+36385211
+3322
+10.1007/s00262-022-03322-1
+Research
+Expanded natural killer cells potentiate the antimyeloma activity of daratumumab, lenalidomide, and dexamethasone in a myeloma xenograft model
+Thangaraj Jaya Lakshmi 123
+Jung Sung-Hoon shglory@hanmail.net
+
+12
+Vo Manh-Cuong 12
+Chu Tan-Huy 134
+Phan Minh-Trang Thi 5
+Lee Kyung-Hwa 6
+Ahn Seo-Yeon 12
+Kim Mihee 2
+Song Ga-Young 2
+Ahn Jae-Sook 2
+Yang Deok-Hwan 2
+Kim Hyeoung-Joon 2
+Cho Duck duck.cho@skku.edu
+
+578
+Lee Je-Jung drjejung@chonnam.ac.kr
+
+1234
+1 grid.411602.0 0000 0004 0647 9534 Research Center for Cancer Immunotherapy, Chonnam National University Hwasun Hospital, Chonnam National University Medical School, Hwasun, Republic of Korea
+2 grid.411602.0 0000 0004 0647 9534 Department of Hematology-Oncology, Chonnam National University Hwasun Hospital, Chonnam National University Medical School, Hwasun, Republic of Korea
+3 grid.14005.30 0000 0001 0356 9399 Department of Molecular Medicine, Chonnam National University, Gwangju, Republic of Korea
+4 grid.14005.30 0000 0001 0356 9399 BioMedical Sciences Graduate Program, Chonnam National University, Gwangju, Republic of Korea
+5 grid.414964.a 0000 0001 0640 5613 Cell and Gene Therapy Institute (CGTI), Samsung Medical Center, Seoul, Republic of Korea
+6 grid.14005.30 0000 0001 0356 9399 Department of Pathology, Chonnam National University Medical School, Gwangju, Republic of Korea
+7 grid.264381.a 0000 0001 2181 989X Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, Republic of Korea
+8 grid.264381.a 0000 0001 2181 989X Department of Laboratory Medicine and Genetics, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
+16 11 2022
+16 11 2022
+2023
+72 5 12331246
+21 3 2022
+31 10 2022
+© The Author(s) 2022
+https://creativecommons.org/licenses/by/4.0/ Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
+The development of new treatment agents in recent decades has significantly improved the survival of patients with multiple myeloma (MM). Nonetheless, MM remains an incurable disease; therefore, novel combination therapies are required. Natural killer (NK) cells are one of the safest immunotherapeutic options. In this study, we found that the anti-myeloma activity of expanded NK cells (eNKs) was improved by daratumumab, lenalidomide, and dexamethasone (DRd) in an MM xenograft mouse model. NK cells expanded from peripheral blood mononuclear cells collected from MM patients were highly cytotoxic against DRd pretreated tumor cells in vitro. To mimic the clinical protocol, a human MM xenograft model was developed using human RPMI8226-RFP-FLuc cells in NOD/SCID IL-2Rγnull (NSG) mice. MM bearing mice were randomly divided into six groups: no treatment, eNK, Rd, Rd + eNKs, DRd, and DRd + eNKs. DRd significantly enhanced the cytotoxicity of eNKs by upregulating NK cell activation ligands and effector function. DRd in combination with eNKs significantly reduced the serum M-protein level and prolonged mouse survival. In addition, DRd significantly increased the persistence of eNK and homing to MM sites. These results show that the anti-myeloma activity of ex vivo-expanded and activated NK cells is augmented by the immunomodulatory effect of DRd in MM-bearing mice, suggesting the therapeutic potential of this combination for MM patients.
+
+Supplementary Information
+
+The online version contains supplementary material available at 10.1007/s00262-022-03322-1.
+
+Keywords
+
+Natural killer cells
+Multiple myeloma
+Immunotherapy
+Daratumumab
+http://dx.doi.org/10.13039/501100003725 National Research Foundation of Korea 2018R1A5A2024181 Lee Je-Jung Ministry of Education, Science and Technology2021R1A2B5B0100214911 Lee Je-Jung issue-copyright-statement© Springer-Verlag GmbH Germany, part of Springer Nature 2023
+==== Body
+pmcIntroduction
+
+The development of monoclonal antibodies has changed the treatment paradigm of multiple myeloma (MM). In the phase 3, POLLUX and CASTOR trials, daratumumab (Dara), lenalidomide, and dexamethasone (DRd) or Dara, bortezomib, and dexamethasone (DVd) significantly prolonged progression-free survival, compared with lenalidomide/bortezomib and dexamethasone (Rd/Vd), in relapsed refractory MM (RRMM) [1–3]. Because the results were superior to the results of other triplet regimens such as carfilzomib, lenalidomide, and dexamethasone (KRd) or ixazomib, lenalidomide, and dexamethasone (IRd) in RRMM, DRd has been recommended for the first relapse of MM not refractory to lenalidomide [1, 4, 5]. However, most patients eventually relapse despite these combination therapies and require subsequent therapy. In addition, MM develops at older ages, when patients are frail; thus, a combination therapy with enhanced efficacy and low toxicity is needed [6–9].
+
+Natural killer (NK) cells are innate immune cells that mediate anti-tumor responses without prior sensitization [10–12]. Several clinical trials have proven the safety and efficacy of NK cells in MM and other malignancies [13, 14]. Therefore, NK cells are a potential immunotherapy for RRMM; however, high expression of NK inhibitory molecules and tumor microenvironmental factors in MM patients suppress the activity of NK cells [15–17]. We established a genetically modified K562 expressing OX40 ligand and membrane-bound (mb) interleukin (IL)-18 and IL-21 (K562-OX40L-mbIL-18/-21), capable of inducing robust expansion of NKs from MM patients with enhanced cytotoxicity against MM cells [18]. In addition to eNKs, we evaluated the effect of DRd in combination in vitro and in vivo. Dara is a CD38-targeting monoclonal antibody used clinically against MM and RRMM. Dara activates NK cells by inducing degranulation and antibody-dependent cell-mediated cytotoxicity (ADCC). However, Dara spares some of CD38-expressing immune cells such as T cells and NK cells, leading to immune depletion [19–23]. Adoptive transfer of ex vivo-expanded NK cells (eNKs) could overcome this issue [21, 24, 25]. In our previous study, eNKs anti-tumor activity and the survival of MM xenograft mice were greatly improved by the combination of DVd [26].
+
+Lenalidomide, an immunomodulatory drug, is the backbone of most treatment regimens for MM [1, 27]. Lenalidomide improves NK cell effector function and proliferation in vitro and in vivo; it is used in combination regimens to enhance immune effector cell function [28–30]. In a phase 3 study, DRd improved the progression-free survival of RRMM patients, compared with Rd [1, 4]. Therefore, we hypothesized that eNK–DRd combination would enhance eNK function and persistence, while sensitizing MM cells to NK cell-mediated lysis by inducing NK cell-activating ligands on the surfaces of MM cells.
+
+In this study, we evaluated whether DRd enhances the efficacy of adoptively transferred NK cells in an MM xenograft model. This treatment approach significantly improved disease-free survival and overall survival in MM-bearing mice, while reducing the serum M-protein level. DRd augmented the anti-myeloma effect of eNKs by attracting tumor-infiltrating NK cells to myeloma sites through upregulation of NK-activating ligands. These findings show that adoptive immunotherapy using eNKs with DRd can boost eNK activity in MM.
+
+Materials and methods
+
+Ethics declaration
+
+All experimental procedures were approved by the Institutional Review Board of Chonnam National University Hospital. Blood samples of patients with MM were collected, with informed consent. Animal experiments were performed in accordance with protocols approved by the Chonnam National University Animal Use and Care Committee.
+
+Cell lines, cytokines, and drugs
+
+The tumor cell lines (K562, U266, RPMI8226, and Raji) used in this study were purchased from the American Type Culture Collection (Manassas, VA, USA). Our recently established K562-OX40L-mbIL-18/-IL-21 cells were used to expand and activate NK cells [18, 31]. The cell lines and NK cells were cultured in RPMI-1640 medium supplemented with 10% heat-inactivated fetal bovine serum (Gibco) and 1% penicillin/streptomycin at 37 °C in a humidified 5% CO2 incubator. NK cells were expanded in the presence of recombinant human IL-2 and IL-15 (PeproTech, Rocky Hill, NJ, USA).
+
+Surface and intracellular staining for flow cytometry
+
+The phenotypic characteristics of the eNKs assessed by percentage of NK activating and inhibitory receptors on the surface of the eNK were analyzed by flow cytometry. In brief, eNKs (2 × 105 cells) were stained with fluorochrome-conjugated antibodies [CD3, CD56, CD16, NKp30, NKp44, NKp46, NKG2D, and NKG2C (BD Biosciences, USA)] for 15–20 min and 20,000 events/sample were acquired using a BD FACSCalibur. Tumor cells were pretreated with Dara (10 μg/mL; Janssen Pharmaceuticals, Johnson & Johnson, NJ, USA), lenalidomide (1 µM; Celgene), and dexamethasone (50 nM; Daewon Pharmaceuticals, Seoul, South Korea) for 24 h and stained with phycoerythrin-conjugated MICA, MICB, ULBP1, ULBP2, and Fas antibodies. Intracellular staining was performed using a BD Cytofix/Cytoperm™ Kit (BD Biosciences) to assess the interferon (IFN)-γ, granzyme-B, perforin, TRAIL, and FasL levels in DRd-pretreated eNKs by flow cytometry. Flow cytometric data were analyzed using FlowJo software (FLowJo LLC, USA).
+
+Ex vivo NK cell activation and expansion
+
+MM patients’ peripheral blood mononuclear cells (PBMCs) were isolated using Lymphoprep solution. To expand NK cells, PBMCs were co-cultured with K562-OX40L-mbIL-18/-21 feeder cells previously irradiated with 100 Gy in RPMI-1640 medium supplemented with 10% fetal bovine serum, 1% penicillin/streptomycin, and 4 mM L-glutamine with 10 IU/mL recombinant human IL-2 until day 7. From day 7, the IL-2 concentration was increased to 100 IU/mL and recombinant human IL-15 (10 IU/mL) was added to the medium. NK cells were replenished with fresh cytokine-containing medium, every 2–3 days. The expanded NK cells from day 14 were considered mature NK cells (purity > 90% by phenotype analysis) and used for in vitro and in vivo experiments.
+
+NK cell proliferation assay
+
+To assess lenalidomide-induced NK cell proliferation, PBMCs from patients with MM were cultured in RPMI-1640 medium containing 10% fetal bovine serum, 1% penicillin/streptomycin, and 4 mM L-glutamine in the presence of 1 µM lenalidomide alone or with 50 U/mL IL-2; the culture medium was changed every 2 days with lenalidomide or with IL-2 for 3 weeks. Lenalidomide-treated cells were stained with anti-human CD3 and CD56 antibodies to assess NK cell purity. To evaluate lenalidomide-induced eNK proliferation, D14 harvested eNKs were labeled with carboxyfluorescein diacetate succinimidyl ester (CFSE, Life Technologies), and cultured with 1 µM lenalidomide alone or with 50 U/mL IL-2 in RPMI-1640 medium containing 10% fetal bovine serum, 1% penicillin/streptomycin, and 4 mM L-glutamine. eNK proliferation was assessed by flow cytometry.
+
+Cytotoxicity assay
+
+eNK cytotoxicity was measured using a flow cytometry-based cytotoxicity assay. Tumor cells were stained with CFSE (Life Technologies, Carlsbad, CA) and were treated with Dara (10 μg/mL), lenalidomide (1 µM), and dexamethasone (50 nM) for 24 h and then co-cultured with eNKs at various effector-to-target (E:T) ratios at 37 °C for 4 h in a 5% CO2 incubator. After incubation, 1 μL of propidium iodide (Life Technologies) was added prior to FACS acquisition and analyzed using a BD FACSCalibur; 10,000 events/sample were collected. The proportion of dead cells among CFSE-positive cells was calculated by deducting the proportion of spontaneously dead cells.
+
+Degranulation assay
+
+To examine degranulation potential of the eNKs, DRd-pretreated target cells (K562, U266, RPMI8226, and Raji) or primary MM cells were co-cultured with eNKs at 1:1 effector-to-target cell (E:T) ratio with phycoerythrin-conjugated CD107a antibodies in 96-well U-bottom plates. At 1 h after co-culture, Brefeldin A and monensin (BD Biosciences) were added, and the cells were incubated for 3 h at 37 °C in a 5% CO2 incubator [32]. After 4-h incubation, the cells were stained for CD3 and CD56 for 15 min and analyzed using a BD FACSCalibur; 10,000 events/sample were collected. Flow cytometric data were analyzed using FlowJo software.
+
+eNK–DRd combination in the MM xenograft model
+
+NOD/SCID IL-2Rγnull (NSG) mice purchased from the Jackson Laboratory (Bar Harbor, MA, USA) were used to create a tumor xenograft model. The human MM xenograft model was established by intravenously injecting RPMI8226-RFP-FLuc cells (5 × 106 cells per mouse) into 9–12-week-old male and female NSG mice. RPMI8226-RFP-FLuc cells were intravenously injected into NSG mice (n = 15 per group). Ten days after tumor inoculation, MM-bearing mice were divided into the following six groups: no treatment (phosphate-buffered saline control), Rd, eNKs, Rd + eNKs, DRd, and DRd + eNKs. Dara (8 mg/kg/day) and dexamethasone (0.6 mg/kg/day) were injected on days 10, 17, 24, and 31 by intraperitoneal and intravenous injections, respectively. Lenalidomide (1 mg/kg/day) was administered orally from day 10 for 5 consecutive days for 4 weeks, with a 2-day interval in each week. One day after Dara and dexamethasone treatment, freshly harvested eNKs (2 × 107 cells per mouse) were injected on days 11, 18, 25, and 32. MM progression was monitored by bioluminescence imaging (BLI) in both dorsal and ventral views; mice were intraperitoneally injected with D-luciferin (150 mg/kg/mouse; PerkinElmer, Waltham, MA) 10 min before imaging, and imaged using the Night Owl System (Berthold Technologies, Bad Wildbad, Germany) [33]. Serum M-protein levels were quantified by human lambda free light chain (Bethyl Laboratories, USA) assays [34]. In vivo persistence and tumor infiltration of eNKs and myeloma clearance at various MM sites were assessed by flow cytometry.
+
+Quantification of cytokine levels in MM-bearing mice
+
+Human immune effector and regulatory cytokines in mouse serum samples were evaluated by enzyme-linked immunosorbent assay (ELISA). Human IFN-γ, tumor necrosis factor (TNF)-α, IL-6, IL-10 (BD OptEIA™), and human granzyme-B and perforin (Mabtech AB, Stockholm, Sweden) levels in serum were quantified using ELISA kits.
+
+Statistical analysis
+
+All data were analyzed using Prism 5 software (GraphPad Software Inc., San Diego, CA, USA). Statistical significance was determined using Student’s t-test or one-way analysis of variance. P-values < 0.05 were considered indicative of statistical significance. Data are expressed as means ± standard deviations or standard errors of the mean.
+
+Results
+
+DRd induces NK cell-activating ligands on tumor cells and primary MM cells in vitro
+
+To investigate the effect of DRd on NK cell activation, we analyzed the expression patterns of NKG2D activating ligands and Fas receptor on the surfaces of K562, U266, RPMI8226, and Raji cells, as well as in primary MM cells. Tumor cells and primary MM cells treated with DRd for 24 h showed significantly increased expression of MICA, MICB, ULBP1, ULBP2, and Fas receptor compared to Rd or untreated tumor cells (all p < 0.0001, Fig. 1 and Supplementary Fig. 1). This indicates that DRd combination treatment will sensitizes tumor cells and increases NK-mediated tumor killing by upregulating NKG2D-activating ligands.Fig. 1 DRd increases NK-activating ligand expression on tumor cells. A and B Flow cytometry histograms showing surface expression (MFI value) patterns of MICA, MICB, ULBP1, ULBP2, and Fas on K562, U266, RPMI8226, Raji, and primary MM cells after treatment with Rd or DRd. DRd significantly increased the expression levels of MICA, MICB, ULBP1, ULBP2, and Fas receptor in both cell lines and primary MM cells. (See also Supplementary Fig. 2)
+
+DRd induced NK cell proliferation and enhances antitumor effects of eNKs in vitro
+
+Lenalidomide has several immunomodulatory effects on various immune cells, including activation, proliferation, and direct tumor killing. We investigated the effect of lenalidomide on NK cell proliferation in MM patients. First, we evaluated lenalidomide-induced NK cell proliferation in MM patients’ PBMCs. Lenalidomide significantly increased the proportion of NK cells without cytokine stimulation in PBMCs of MM patients (Fig. 2A and B). Next, we assessed the lenalidomide-induced proliferation of eNK for 2 weeks with or without the addition of IL-2. eNK proliferation was significantly enhanced by lenalidomide (Fig. 2C and D). The impact of DRd on eNK effector functions was determined by evaluating the expression patterns of IFN-γ, granzyme-B, perforin, TRAIL, and FasL. DRd significantly increased the expression of IFN-γ and perforin in eNKs; however, the expression of granzyme-B, TRAIL, or FasL (Fig. 2E and F) is minimally affected with the treatment of either DRd or Rd combination. We evaluated the direct killing potential of DRd in tumor cells and primary MM cells. DRd alone was not enough to kill tumor cells or primary MM cells. Therefore, we assessed eNK-mediated cytotoxicity against DRd-pretreated tumor cells. Notably, DRd-pretreated tumor cells induced CD107a expression in eNKs (Fig. 3A and B). Furthermore, eNK cytotoxicity against DRd pretreated target cells was increased at all E:T ratios (Fig. 3C and D). These data suggest that DRd-pretreated tumor cells are sensitized to NK cells, which augments the antitumor effect of eNKs, thereby increasing cytotoxicity and presumably enhancing the anti-myeloma activity of eNKs.Fig. 2 Effect of DRd on effector molecules expression in eNKs. A Representative flow cytometry plots of the expression of NK cell markers in peripheral blood mononuclear cells from patients with MM that had been treated with lenalidomide. B Mean ± standard deviation (SD) NK cell proportion in peripheral blood mononuclear cells from patients with MM (n = 5) after lenalidomide treatment. Lenalidomide significantly increased the NK cell proportion without cytokine stimulation. C Representative histogram showing the proliferation of CFSE-labeled eNKs treated with lenalidomide (1 µm) with or without IL-2. D Mean ± SD CFSE labeled eNK (n = 5) proliferation after lenalidomide treatment. Lenalidomide significantly induced eNK proliferation for 2 weeks with or without IL-2. E Representative FACS plot showing the expression of IFN-γ, granzyme-B, perforin, TRAIL, and FasL in eNKs. eNKs were harvested on day 14 and treated with DRd or Rd for 12 h. F Mean ± SD eNK (n = 5) expression of effector molecules after DRd treatment. DRd significantly increased the expression levels of IFN-γ and perforin in eNKs (mean ± standard deviation; n = 5). *p < 0.05, **p < 0.001, ***p < 0.0001
+
+Fig. 3 eNKs exert a strong cytotoxic effect on DRd-pretreated tumor cells and primary MM cells. (A and B) Proportion of CD107a-expressing eNKs (CD3−CD56.+) after co-culture with DRd-pretreated K562, U266, RPMI8226, Raji cells, or primary MM cells (E:T ratio of 1:1) for 4 h. Flow cytometry revealed a higher level of CD107a in eNKs that had been co-cultured with DRd-treated tumor cells. (C and D) eNK-mediated cytotoxicity in DRd-pretreated tumor cells (K562, U266, RPMI8226, and Raji) and primary MM cells measured using a standard 4-h flow cytometry-based cytotoxicity assay. eNKs showed potent antitumor activity against DRd-pretreated tumor cells at all E:T ratios. *p < 0.05, **p < 0.001, ***p < 0.0001
+
+eNKs + DRd treatment exhibited improved anti-myeloma activity and prolonged survival in an RPMI8226-RFP-FLuc xenograft model
+
+The above data indicated that DRd activates eNKs in vitro. Therefore, we investigated the importance of DRd combination with eNKs for the anti-myeloma effect in vivo, using the RPMI8226-RFP-FLuc xenograft model. Untreated MM-bearing mice showed rapid tumor growth, leading to death within 7 weeks. Mice in the Rd, eNKs, Rd + eNKs, and DRd treatment groups also showed significantly inhibited tumor growth but relapsed after treatment ended (Fig. 4A and B; Supplementary Fig. 3A). DRd + eNKs showed the strongest inhibition of MM progression throughout the survival period; six mice in the DRd + eNKs group were tumor free with no detectable bioluminescence (Fig. 4B and C) at all time points. Furthermore, none of the DRD + eNKs-treated mice had visible tumors or detectable serum M-protein (Fig. 4C and D, Supplementary Fig. 3B, Supplementary Table 1). Detectable and visible plasmacytomas were present in all other treatment groups, except the DRd + eNKs group (Fig. 4E and Supplementary Table 1). Furthermore, mice treated with DRd + eNKs exhibited the highest survival rate (Fig. 4F; ***, p < 0.001), and no significant body weight loss occurred in mice treated with Rd + eNKs or DRd + eNKs (Supplementary Fig. 3C). These results indicated that DRd + eNKs was capable of plasmacytoma and exert a long-term systemic anti-myeloma effect.Fig. 4 eNKs + DRd inhibited MM progression in RPMI8226-RFP-FLuc xenograft mice. A Treatment of RPMI8226-RFP-FLuc-bearing mice with eNKs + DRd. NSG mice were intravenously injected with 5 × 10.6 RPMI8226-RFP-FLuc cells, and tumor growth was monitored weekly by BLI. B Representative BLI of six mice (n = 15) from each group (dorsal view). C BLI showed that DRd + eNKs treatment provided the strongest antitumor effect at all time points. D Serum M-protein level determined by quantifying the level of human lambda free light chain in mouse peripheral blood. Mice treated with DRd + eNKs had the lowest serum M-protein levels. E Representative ex vivo BLI of mouse tissues. DRd + eNKs treatment did not result in a detectable bioluminescence signal. F Kaplan–Meier survival curves (n = 15 mice per group). Statistical significance was determined by log-rank test. Mice treated with DRd + eNKs exhibited the longest survival. *p < 0.05, **p < 0.01, ***p < 0.001
+
+eNK function, persistence, and homing increase with DRd treatment
+
+To investigate the in vivo effector function underlying the enhanced anti-myeloma effect of DRd combined with eNKs, we evaluated the serum levels of effector and immunosuppressive cytokines [35]. Mouse PB serum was collected at three time points (D33, D39, and D45), and the levels of human IFN-γ, granzyme-B, perforin, TNF-α, IL-6, and IL-10 (Fig. 5A) were measured. DRd + eNKs significantly increased the levels of effector cytokines IFN-γ, granzyme-B, perforin, and TNF-α at all time points; however, the effector cytokine levels gradually decreased over time. In addition, DRd + eNKs-treated mice showed the lowest levels of the immunosuppressive cytokines IL-6 and IL-10 at all time points. These data indicated that DRd + eNKs treatment not only improves in vivo effector function and also controls immunosuppressive cytokine secretion, thereby improving anti-myeloma activity in vivo. We also investigated the persistence of functional eNKs treated with DRd. The treatment group containing the eNK infusion showed significant eNK persistence in peripheral blood. The Rd + eNKs and DRd + eNKs groups had higher percentages of circulating eNKs than the eNKs alone group. Moreover, the phenotypic characteristics (CD16, NKp30, NKp44, NKp46, NKG2D, and NKG2C) of circulatory eNKs are remained unaffected in DRd + eNKs group compared to the eNKs alone or Rd + eNKs groups (Fig. 5B, Supplementary Fig. 4).Fig. 5 DRd enhanced the in vivo effector function and persistence of eNKs in RPMI8226-RFP-FLuc-bearing mice. A In vivo effector function of circulating eNKs, determined based on the levels of various immune effector and immunosuppressive cytokines at three time points from 1 day after final cell infusion (i.e., D33, D39, and D45). Mice treated with DRd + eNKs had the highest levels of IFN-γ, granzyme-B, perforin, and TNF-α, which decreased over time; they also had the lowest levels of IL-6 and IL-10. B Representative flow cytometry plots showing the in vivo persistence of circulating eNKs expressing NK activation receptors (CD16, NKp30, NKp44, NKp46, NKG2D, and NKG2C) among the hCD45 population with hCD3−CD56.+ expression at D33, D39, and D45. *p < 0.01, **p < 0.001, ***p < 0.0001
+
+Next, we investigated the in vivo homing and tumor-targeting abilities of eNKs in the RPMI8226-RFP-FLuc human MM xenograft model. After the experimental end point, tissue samples (bone marrow [BM], brain, heart, kidney, liver, lung, and spleen) were collected to evaluate the biodistribution of eNKs. DRd + eNKs-treated mice showed the greatest infiltration of eNKs in the BM (Fig. 6A), brain, heart, kidney, liver, lung, and spleen (Supplementary Fig. 5). We also evaluated the functional stability of eNKs by assessing NK cell purity and expression of activation receptors (CD16, NKp30, NKp44, NKp46, NKG2D, and NKG2C) in the BM (Fig. 6B and C) and other tissues (Supplementary Fig. 5). The eNKs infused in this xenograft model were highly stable in vivo which remained unaffected by the tumor microenvironment for an extended period.Fig. 6 DRd enhanced eNK homing in vivo in RPMI8226-RFP-FLuc-bearing mice. A In vivo homing of eNKs in RPMI8226-RFP-FLuc-bearing mice (n = 10 per group) that were evaluated by flow cytometry. Mice were euthanized and BM samples were analyzed by flow cytometry for human NK cells (CD3−CD56+) and activation receptors (CD16, NKp30, NKp44, NKp46, NKG2D, and NKG2C) among the hCD45 population. (B, C) Quantification (mean ± SD) of in vivo eNK homing in the BM based on flow cytometry data. eNKs from mice treated with DRd + eNKs showed the greatest in vivo homing ability. *p < 0.01, **p < 0.001, ***p < 0.0001
+
+To evaluate eNK trafficking, mice in the eNKs, Rd + eNKs, and DRd + eNKs groups (n = 6 mice per group) received DIR (NIR dye)-labeled eNKs. DIR is a nontoxic near infrared dye that stains the cytoplasmic membrane of cells and is used to quantify the in vivo biodistribution of immune cells, in real time. eNK persistence and homing were assessed by real-time fluorescence imaging. Mice infused with DIR-labeled eNKs showed the greatest eNK homing to all tissues (Fig. 7A and B), and eNKs persisted until 5 weeks after eNK infusion. These findings were further confirmed by ex vivo fluorescence imaging and flow cytometry (Fig. 7C and D). eNK homing was more evident in the BM, liver, lung, and spleen compared with other tissues. Lenalidomide induced eNK persistence and homing, leading to NK proliferation and tumor infiltration. Therefore, the combination of Dara, lenalidomide, and dexamethasone increased the infiltration of eNKs into MM sites to a greater degree than Rd. Moreover, DRd combination treatment enhanced eNK-mediated tumor killing by suppressing the production of immunosuppressive cytokines in the MM microenvironment.Fig. 7 Trafficking of eNKs in DRd-treated RPMI8226-RFP-FLuc-bearing mice. (A) A separate set of RPMI8226-RFP-FLuc-bearing mice (n = 6 per group) were infused with DIR-labeled eNKs (2 × 107 cells per mouse). (B) Treatment group mice were subjected to fluorescence imaging once weekly after the final eNK infusion for 5 weeks. C and D Mice were euthanized, and BM and other tissues were collected and subjected to fluorescence imaging and flow cytometry. eNK infiltration was greatest in the BM, liver, lung, and spleen
+
+eNKs + DRd treatment enhances MM clearance and NK-activating ligands in vivo in RPMI8226-RFP-FLuc xenograft model
+
+The DRd + eNK-treated mice showed no visible signs of MM, no quantifiable serum M protein, and no detectable BLI signal. Therefore, we evaluated in vivo MM clearance and NK-activating ligand expression in all possible MM progression sites in the RPMI8226-RFP-FLuc MM xenograft model by flow cytometry. After the experimental end point, the mice were euthanized, and single-cell suspensions were generated from the BM, brain, heart, kidney, liver, lung, and spleen. The expression levels of MM markers (CD138 and CD38) and NK cell-activating ligands (MICA, MICB, ULBP1, ULBP2, and Fas) were evaluated by flow cytometry (Fig. 8A and B). Rd + eNKs and DRd with or without eNKs significantly cleared CD138 and CD38 cells in vivo, and DRd + eNKs significantly increased the expression levels of MICA, MICB, ULBP1, ULBP2, and Fas in all tissues (BM: Fig. 8A and B; other tissues, Supplementary Fig. 6).Fig. 8 DRd + eNKs treatment enhances MM clearance and NK-activating ligand expression levels in RPMI8226-RFP-FLuc xenograft mice. A Residual RPMI8226-RFP-FLuc cells in the BM evaluated by flow cytometry. Representative histograms showing the expression levels (MFI value) of CD138, CD38, MICA, MICB, ULBP1, ULBP 2, and Fas. B Quantification (mean ± SD) of residual myeloma cells in the BM. Mice treated with Rd + eNKs and DRd + eNKs exhibited lower CD138 and CD38 expression levels in the BM, compared with the other groups; they also exhibited higher MICA, MICB, ULBP1, ULBP2, and Fas expression levels. *p < 0.01, **p < 0.001, ***p < 0.0001
+
+Discussion
+
+NK cells are innate immune cells that kill tumor cells without prior sensitization, while avoiding the onset of graft-versus-host disease or other lethal adverse effects caused by CAR T cells [12–14]. Moreover, adoptive transfer of eNKs has several advantages over CAR T cells in terms of cancer immunotherapy [36–39]. However, the in vivo activity of NK cells can be suppressed by an immunosuppressive environment in the tumor; accordingly, combination treatments are needed to combat immunosuppression [14, 40]. We reported that Dara and bortezomib combination treatment significantly improved the anti-myeloma activity of eNKs [26]. In this study, we evaluated the ability of DRd to improve the in vivo effector function of adoptively transferred eNKs. DRd + eNKs treatment showed the greatest anti-myeloma effect on the in vivo eNK persistence and survival of RPMI8226-RFP-FLuc MM xenograft mice. DRd enhanced the anti-myeloma effect of eNKs in vivo by upregulating NK activating ligands, thereby enhancing eNK persistence, homing, and infiltration to myeloma sites.
+
+Lenalidomide is an immunomodulatory drug that increases NK cell function, activation, and proliferation [28, 30, 41, 42]. Lenalidomide increases IFN-γ, granzyme B, and perforin production, as well as NK-mediated cytotoxicity and degranulation [29, 30, 43]. Moreover, lenalidomide in combination with monoclonal antibodies enhances the cytotoxicity of NK cells [1, 2, 24, 30]. Dara is a monoclonal antibody against CD38 that improves the function and killing potential of NK cells via ADCC. The anti-myeloma activity of eNKs was significantly increased by Dara in MM xenograft models [19, 21, 25, 44]. In this study, DRd + eNK upregulated NKG2D ligands in tumor cells and primary MM cells and induced NK cell proliferation and effector function, resulting in a superior anti-myeloma effect.
+
+Cancer immunotherapy with the adoptive transfer of NK cells has several limitations such as a short lifespan, insufficient expansion, and poor in vivo persistence [39, 45, 46]. In general, a high dose of cytokines is administered to enhance the persistence of human NK cells in mouse models [32, 47–49]. However, the expression of membrane-bound IL-15 improved the survival and expansion of NK cells in vitro and in vivo without the administration of exogenous cytokines [50]. Additionally, IL-15-transduced cord blood-derived NK cells showed long-term antitumor activity and persistence in a mouse model of lymphoma [51]. In this study, we observed long-term circulatory maintenance of NK cells that had been expanded using genetically engineered K562-OX40L-mbIL-18/-21 feeder cells, in the absence of exogenous cytokines or CAR NK cells producing IL-15 [52]. Notably, DRd + eNKs-treated mice showed a significantly greater proportion of circulating eNKs than did mice treated with eNKs alone. Furthermore, DRd + eNKs-treated mice had the longest disease-free survival. Lenalidomide may significantly increase the NK cell proportion without cytokine stimulation. Importantly, graft-versus-host disease or cytokine release syndrome caused by the infusion of eNKs alone or in combination with DRd was not observed in our in vivo model. However, some mice were accidentally dead during the end point of study (90–100 days) without showing any signs of disease progression, and the reason for sudden death remains unclear.
+
+CAR-T cell therapy targeting B-cell maturation antigen has demonstrated promising results for RRMM [53]. However, cytokine release syndrome and neurotoxicity mediated by proinflammatory cytokines are problematic and hamper the use of CAR-T cells in elderly patients with MM. CAR-NK cells could overcome these limitations. In recent phase 1 and 2 studies in lymphoma patients, CAR-NK cells did not cause significant cytokine release syndrome, neurotoxicity, or graft-versus-host disease; 73% of patients responded to CAR-NK cells within 30 days [37]. Although CAR-NK cells have shown promising anti-tumor activity in xenograft models and clinical studies, CAR gene transfer to NK cells is challenging [10, 32, 54]. The viral-vector transduction efficiency of CAR NKs is low, requires repeated transductions, and could alter NK function [10, 55, 56]. In addition, CAR-NK or CAR-T cell therapy is costly. Therefore, eNKs + DRd treatment may be more effective and tolerable in RRMM.
+
+In conclusion, we investigated the potential of DRd to enhance the therapeutic efficacy of eNKs in vivo using an MM xenograft model. The anti-myeloma effect of eNKs was markedly enhanced by DRd combination treatment. These findings suggest that the eNKs plus DRd combination is a clinically feasible strategy for the treatment of MM.
+
+Supplementary Information
+
+Below is the link to the electronic supplementary material.Supplementary file1 (DOCX 4218 KB)
+
+Acknowledgements
+
+This study was supported by grants from the Basic Science Research Program through the National Research Foundation of Korea (NRF), funded by the Ministry of Education, Science and Technology (2018R1A5A2024181, 2020R1A2C2010098, 2021R1A2B5B0100214911) and a grant of the Korea Health Technology R & D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (grant number: HR22C1363). This work was supported by Korea Institute of Planning and Evaluation for Technology in Food, Agriculture and Forestry (IPET) through Companion Animal Life Cycle Industry Technology Development Program, funded by Ministry of Agriculture, Food and Rural Affairs(MAFRA)(grant number: 322092-4).
+
+Authors Contributions
+
+JLT, SHJ, DC, and JJL designed the study. JLT, MCV, THC, MTT, and KHL performed the experiments and interpreted the data. SYA, MK, GYS, DHY, and HJK contributed intellectually to the study. JLT and SHJ wrote the manuscript. DC and JJL supervised the study.
+
+Funding
+
+National Research Foundation of Korea, 2018R1A5A2024181, Ministry of Education, Science and Technology, 2021R1A2B5B0100214911.
+
+Data availability statement
+
+The data generated in this study are available in the article and the supplementary data files.
+
+Declarations
+
+Conflicts of interest
+
+The authors declare no conflicts of interest.
+
+Publisher's Note
+
+Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
+
+Jaya Lakshmi Thangaraj and Sung-Hoon Jung contributed equally to this work.
+==== Refs
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+52. Thangaraj JL Expanded natural killer cells augment the antimyeloma effect of daratumumab, bortezomib, and dexamethasone in a mouse model Cell Mol Immunol 2021 18 1652 1661 10.1038/s41423-021-00686-9 33980993
+53. Wudhikarn K Mailankody S Smith EL Future of CAR T cells in multiple myeloma Hematol Am Soc Hematol Educ Program 2020 2020 272 279 10.1182/hematology.2020000111
+54. Chu J CS1-specific chimeric antigen receptor (CAR)-engineered natural killer cells enhance in vitro and in vivo antitumor activity against human multiple myeloma Leukemia 2014 28 917 927 10.1038/leu.2013.279 24067492
+55. Hu W Wang G Huang D Sui M Xu Y Cancer immunotherapy based on natural killer cells: current progress and new opportunities Front Immunol 2019 10 1205 10.3389/fimmu.2019.01205 31214177
+56. Gong Y Klein Wolterink RGJ Wang J Bos GMJ Germeraad WTV Chimeric antigen receptor natural killer (CAR-NK) cell design and engineering for cancer therapy J Hematol Oncol 2021 14 73 10.1186/s13045-021-01083-5 33933160
+

PMC10115554.txt

@@ -0,0 +1,447 @@
+
+==== Front
+J Clin Oncol
+J Clin Oncol
+jco
+JCO
+Journal of Clinical Oncology
+0732-183X
+1527-7755
+Wolters Kluwer Health
+
+36548927
+JCO.22.01725
+10.1200/JCO.22.01725
+00016
+3
+ORIGINAL REPORTS
+Hematologic Malignancy
+Epcoritamab, a Novel, Subcutaneous CD3xCD20 Bispecific T-Cell–Engaging Antibody, in Relapsed or Refractory Large B-Cell Lymphoma: Dose Expansion in a Phase I/II Trial
+https://orcid.org/0000-0002-9941-2448
+Thieblemont Catherine MD, PhD 1
+https://orcid.org/0000-0003-2143-9672
+Phillips Tycel MD 2
+https://orcid.org/0000-0002-3131-3718
+Ghesquieres Herve MD, PhD 3
+https://orcid.org/0000-0001-7988-1565
+Cheah Chan Y. MBBS, DMSc 4 5
+https://orcid.org/0000-0003-3218-3633
+Clausen Michael Roost MD, PhD 6
+https://orcid.org/0000-0001-5158-1069
+Cunningham David MD 7
+Do Young Rok MD, PhD 8
+Feldman Tatyana MD 9
+https://orcid.org/0000-0003-1225-8757
+Gasiorowski Robin MBBS, PhD 10
+Jurczak Wojciech MD, PhD 11
+https://orcid.org/0000-0001-6145-4426
+Kim Tae Min MD, PhD 12
+https://orcid.org/0000-0001-5903-3788
+Lewis David John MD 13
+van der Poel Marjolein MD, PhD 14
+Poon Michelle Limei MD 15
+Cota Stirner Mariana MD, PhD 16
+Kilavuz Nurgul MSc 17
+https://orcid.org/0000-0001-9744-991X
+Chiu Christopher PhD 17
+Chen Menghui PhD 17
+Sacchi Mariana MD 17
+Elliott Brian MD 17
+Ahmadi Tahamtan MD, PhD 17
+https://orcid.org/0000-0003-3873-1741
+Hutchings Martin MD, PhD 18
+https://orcid.org/0000-0002-6735-8651
+Lugtenburg Pieternella J. MD, PhD 19
+1 Assistance Publique & Hôpitaux de Paris (APHP), Hôpital Saint-Louis, Hémato-oncologie, Université de Paris, Paris, France
+2 University of Michigan Comprehensive Cancer Center, Ann Arbor, MI
+3 Hospices Civils de Lyon, Centre Hospitalier Lyon Sud, Pierre-Bénite, France
+4 Sir Charles Gairdner Hospital, Perth, Australia
+5 Division of Internal Medicine, Medical School, University of Western Australia, Perth, Australia
+6 Vejle Hospital, Vejle, Denmark
+7 The Royal Marsden NHS Foundation Trust, Sutton, United Kingdom
+8 Keimyung University Dongsan Medical Center, Daegu, Republic of Korea
+9 Hackensack Meridian Health Hackensack University Medical Center, Hackensack, NJ
+10 Concord Hospital, University of Sydney, Sydney, Australia
+11 MSC National Research Institute of Oncology, Kraków, Poland
+12 Seoul National University Hospital, Seoul, Republic of Korea
+13 University Hospitals Plymouth NHS Trust, Derriford Hospital, Plymouth, United Kingdom
+14 On behalf of the Lunenburg Lymphoma Phase I/II Consortium-HOVON/LLPC, Maastricht, Department of Internal Medicine, Division of Hematology, GROW School for Oncology and Developmental Biology, Maastricht University Medical Center, Maastricht, the Netherlands
+15 National University Hospital, Singapore
+16 AbbVie, North Chicago, IL
+17 Genmab, Princeton, NJ
+18 Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
+19 On behalf of the Lunenburg Lymphoma Phase I/II Consortium-HOVON/LLPC, Erasmus MC Cancer Institute, University Medical Center, Department of Hematology, Rotterdam, the Netherlands
+Catherine Thieblemont, MD, PhD, Assistance Publique & Hôpitaux de Paris (APHP), Hôpital Saint-Louis, Hémato-oncologie, Université de Paris, 1 Ave Claude Vellefaux, 75010 Paris, France; e-mail: catherine.thieblemont@aphp.fr.
+20 4 2023
+22 12 2022
+22 12 2022
+41 12 22382247
+28 7 2022
+28 10 2022
+15 11 2022
+© 2022 by American Society of Clinical Oncology
+2022
+American Society of Clinical Oncology
+https://creativecommons.org/licenses/by/4.0/ Licensed under the Creative Commons Attribution 4.0 License: https://creativecommons.org/licenses/by/4.0/
+
+PURPOSE
+
+Epcoritamab is a subcutaneously administered CD3xCD20 T-cell–engaging, bispecific antibody that activates T cells, directing them to kill malignant CD20+ B cells. Single-agent epcoritamab previously demonstrated potent antitumor activity in dose escalation across B-cell non-Hodgkin lymphoma subtypes.
+
+PATIENTS AND METHODS
+
+In the dose-expansion cohort of a phase I/II study (ClinicalTrials.gov identifier: NCT03625037), adults with relapsed or refractory CD20+ large B-cell lymphoma and at least two prior therapy lines (including anti-CD20 therapies) received subcutaneous epcoritamab in 28-day cycles (once weekly step-up doses in weeks 1-3 of cycle 1, then full doses once weekly through cycle 3, once every 2 weeks in cycles 4-9, and once every 4 weeks in cycle 10 and thereafter) until disease progression or unacceptable toxicity. The primary end point was overall response rate by the independent review committee.
+
+RESULTS
+
+As of January 31, 2022, 157 patients were treated (median age, 64 years [range, 20‐83]; median of three [range, 2-11] prior therapy lines; primary refractory disease: 61.1%; prior chimeric antigen receptor (CAR) T-cell exposure: 38.9%). At a median follow-up of 10.7 months, the overall response rate was 63.1% (95% CI, 55.0 to 70.6) and the complete response rate was 38.9% (95% CI, 31.2 to 46.9). The median duration of response was 12.0 months (among complete responders: not reached). Overall and complete response rates were similar across key prespecified subgroups. The most common treatment-emergent adverse events were cytokine release syndrome (49.7%; grade 1 or 2: 47.1%; grade 3: 2.5%), pyrexia (23.6%), and fatigue (22.9%). Immune effector cell–associated neurotoxicity syndrome occurred in 6.4% of patients with one fatal event.
+
+CONCLUSION
+
+Subcutaneous epcoritamab resulted in deep and durable responses and manageable safety in highly refractory patients with large B-cell lymphoma, including those with prior CAR T-cell exposure.
+
+OPEN-ACCESSTRUE
+==== Body
+pmcINTRODUCTION
+
+Large B-cell lymphoma (LBCL) is a heterogeneous group of hematologic malignancies.1,2 Although new therapies have become available, management of relapsed or refractory LBCL remains a challenge.3 Outcomes are poor, particularly among patients with early relapse or primary refractory disease. Response rates range from 20% to 39%, and the median overall survival (OS) was 6.3 months in 636 patients with LBCL who relapsed or were refractory to first-line chemoimmunotherapy.3 Several therapies are approved in the United States, including polatuzumab vedotin in combination with bendamustine and rituximab, tafasitamab in combination with lenalidomide, loncastuximab tesirine, selinexor, and chimeric antigen receptor (CAR) T-cell therapies. CAR T-cell therapy represents a major advancement; however, consistency of bioengineering, manufacturing timelines, and access are limited globally.4,5 Thus, an unmet medical need still remains for effective, well-tolerated, and convenient therapies.
+
+CONTEXT
+
+Key Objective
+
+Outcomes are poor for patients with relapsed or refractory large B-cell lymphoma (LBCL). This study evaluated the efficacy and safety of subcutaneous epcoritamab, a bispecific antibody targeting CD3 and CD20, in a dose-expansion cohort of patients with relapsed or refractory LBCL.
+
+Knowledge Generated
+
+Single-agent epcoritamab demonstrated high overall response rates, including deep and durable complete responses. Adverse events were manageable, with few discontinuations; cytokine release syndrome was mostly low grade with predictable timing.
+
+Relevance (J.W. Friedberg)
+
+The observed high response rates with durability in patients with refractory LBCL treated with epcoritamab, including post–chimeric antigen receptor-T therapy, represent further evidence of the value of bispecific antibodies in this setting. Future studies need to evaluate feasibility of limited duration therapy, explore rational combinations, and incorporate this agent into earlier lines of treatment.*
+
+*Relevance section written by JCO Editor-in-Chief Jonathan W. Friedberg, MD.
+
+Epcoritamab (GEN3013) is a subcutaneously administered, bispecific antibody targeting CD3 and CD20 that redirects and activates T cells to kill CD20-expressing malignant cells.6 In preclinical evaluation, epcoritamab resulted in potent and selective T-cell–mediated cytotoxic activity against CD20+ malignant B cells.6,7 The dose-escalation portion met the primary end point, with the recommended phase II dose established and no dose-limiting toxicity in patients with relapsed or refractory CD20+ mature B-cell non-Hodgkin lymphoma.8 Here, we report results from the LBCL expansion cohort.
+
+METHODS
+
+Study Design and Patients
+
+This was a phase I/II, single-arm, multicenter, open-label, dose-escalation/dose-expansion study in patients with relapsed, progressive, and/or refractory mature B-cell lymphoma (EPCORE NHL-1; GCT3013-01; ClinicalTrials.gov identifier: NCT03625037).
+
+Eligible patients were at least age 18 years with an Eastern Cooperative Oncology Group performance status of 0 to 2 and documented CD20+ mature B-cell neoplasm (diffuse large B-cell lymphoma [DLBCL] or other aggressive non-Hodgkin lymphoma, including primary mediastinal LBCL, high-grade B-cell lymphoma, or follicular lymphoma grade 3B).1 Other inclusion criteria were relapsed or refractory disease, treatment with at least two prior lines of systemic therapy, including at least one anti-CD20–containing regimen, and prior failure or ineligibility for autologous stem-cell transplantation. Relapsed disease was defined as recurrence at least 6 months after completion of therapy, and refractory disease was defined as progression either during therapy or within 6 months of completion of therapy. Patients with prior CAR T-cell therapy were eligible (if ≥ 30 days since last treatment). There were no requirements for minimum life expectancy or absolute leukocyte count. Full inclusion and exclusion criteria are given in the Data Supplement (online only).
+
+Patients received subcutaneous epcoritamab with cycle 1 step-up dosing consisting of a 0.16-mg priming dose once on day 1, followed by a 0.8-mg intermediate dose once on day 8, and subsequent full 48-mg doses once on day 15 and beyond until disease progression or unacceptable toxicity. Epcoritamab was administered as a 1-mL injection once weekly in cycles 1-3, once every 2 weeks during cycles 4-9 (days 1 and 15), and once every 4 weeks from cycle 10. No initial B-cell–depleting treatment was administered. During cycle 1, prophylaxis for cytokine release syndrome (CRS) included prednisolone 100 mg orally (or intravenous equivalent) administered 30-120 minutes before each epcoritamab dose (once daily on days 1-4 for the priming dose, once daily on days 8-11 for the intermediate dose, once daily on days 15-18 for the first full dose, and once daily on days 22-25 for the second full dose). In addition, diphenhydramine 50 mg orally or intravenously (or equivalent) and acetaminophen 650-1,000 mg orally were administered once daily on days 1, 8, 15, and 22 of cycle 1. If grade 2 or higher CRS occurred after the fourth epcoritamab administration during cycle 1, corticosteroids were given with epcoritamab for 4 days or until resolution of CRS occurred.
+
+To ensure patient safety and to better characterize CRS, 24-hour inpatient monitoring was required for the first full epcoritamab dose.
+
+The Protocol (online only) was approved by site-specific institutional review boards and/or institutional or central ethics committees before study initiation. The study was conducted in accordance with the International Council for Harmonisation E6 (R2) guidelines on good clinical practice and the principles of the Declaration of Helsinki. All patients reviewed and signed informed consent forms before enrollment.
+
+End Points and Assessments
+
+The primary end point was overall response rate (ORR) by the independent review committee (IRC) using Lugano criteria.9 Secondary end points included duration of response (DOR), complete response (CR) rate, duration of CR, progression-free survival (PFS), time to response per IRC, and OS. Subgroup analyses were prespecified. In addition, minimal residual disease (MRD) was assessed by circulating tumor DNA using the clonoSEQ MRD assay (Adaptive Biotechnologies, Seattle, WA; Data Supplement). Safety end points included adverse events (AEs) and laboratory abnormalities. Relatedness of AEs to treatment was designated by the investigator.
+
+Imaging assessments for efficacy (mandatory fluorodeoxyglucose positron emission tomography and either computed tomography or magnetic resonance imaging), along with MRD evaluation and physical examination, took place at weeks 6, 12, 18, 24, 36, and 48 and every 24 weeks thereafter.
+
+AEs were coded using Medical Dictionary for Regulatory Activities (MedDRA) version 24.1, and severity was graded using National Cancer Institute Common Terminology Criteria for Adverse Events version 5.0. CRS and immune effector cell–associated neurotoxicity syndrome (ICANS) were graded using criteria from the American Society for Transplantation and Cellular Therapy criteria.10 Clinical tumor lysis syndrome was graded using criteria by Cairo-Bishop.11 Other assessments, including pharmacokinetics, antidrug antibodies, and patient-reported outcomes, are summarized in the Data Supplement.
+
+Statistical Analysis
+
+Enrollment occurred in two stages; 28 patients with DLBCL were enrolled in the first stage. Interim analysis was conducted when approximately 25 patients with DLBCL had follow-up of up to 12 weeks. Futility stopping criteria were not met; therefore, an additional 100 patients with DLBCL were enrolled in the second stage, with up to 30 additional patients having other types of aggressive non-Hodgkin lymphomas.
+
+ORR was defined as the proportion of patients who achieved best overall response of CR or partial response (PR). Best overall response per response criteria before initiation of subsequent antilymphoma therapy was summarized. The primary analysis of ORR was IRC-assessed response per Lugano criteria in the full analysis population (all patients who received at least one dose of epcoritamab). The ORR and the corresponding 95% exact CI were calculated.
+
+PFS was defined as time from day 1 of cycle 1 to first documented disease progression or death because of any cause, whichever occurred earlier. Patients who remained alive without disease progression at the cutoff date were censored at the date of last evaluable disease assessment before the start of subsequent antilymphoma therapy. For patients who remained alive with incomplete or no baseline tumor assessment, PFS was censored on day 1 of cycle 1.
+
+Time-to-event end points (DOR, PFS, and OS) were analyzed using Kaplan-Meier estimates (median time and 95% CI) with the number and percentage of patients with an event or censoring reported. Efficacy analyses were performed on the full analysis set; the safety analysis set was identical to the full analysis set (all patients who received at least one dose of epcoritamab). A landmark analysis was conducted for PFS by MRD status up to cycle 3 day 1 (day 60, considering ± 3-day window). Data were analyzed using SAS software version 9.4 (SAS Institute, Inc, Cary, NC; Data Supplement).
+
+RESULTS
+
+Patients and Treatment Exposure
+
+Between June 19, 2020, and the data cutoff date of January 31, 2022, there were 157 patients who were enrolled at 54 global sites and treated with epcoritamab. At a median follow-up of 10.7 months, 106 patients discontinued study treatment: 83 (52.9%) because of disease progression, 11 (7.0%) because of AEs, seven (4.5%) because of decision to undergo allogeneic transplantation, four (2.5%) because of patient withdrawal, and one (0.6%) after achieving a PR to undergo CAR T-cell therapy. Demographic and baseline disease characteristics are given in Table 1. Patients had received a median of three prior lines of therapy (range, 2-11); 96 (61.1%) patients had primary refractory disease; 119 (75.8%) patients were refractory to two or more consecutive lines of therapy. The median time from initial diagnosis was 1.6 years (19 months). Sixty-one patients (38.9%) received prior CAR T-cell therapy, 46 (75.4%) of whom had progressive disease (PD) within 6 months of CAR T-cell therapy.
+
+TABLE 1. Demographic and Clinical Characteristics at Baseline in Patients With LBCL
+
+Patients received a median of five cycles (15 doses) of epcoritamab therapy (range, 1-20). As of the data cutoff date, 51 patients (32.5%) continued receiving study treatment; 56.1% continued into the follow-up period.
+
+Efficacy
+
+All 157 patients were efficacy and safety evaluable. The ORR per IRC using Lugano criteria was 63.1% (n/N = 99/157; 95% CI, 55.0 to 70.6), and the CR rate was 38.9% (n/N = 61/157; 95% CI, 31.2 to 46.9). Efficacy outcomes are summarized in Table 2. Best percentage changes from baseline in sum-of-product perpendicular diameters of target lesions are shown in Figure 1A. The median DOR (mDOR) per Kaplan-Meier estimates was 12.0 months in patients with LBCL (95% CI, 6.6 to not reached; Fig 1B). mDOR among complete responders was not reached. An estimated 88.7% of complete responders remained in response at 6 and 9 months. The median time to response was 1.4 months (range, 1.0-8.4). The median time to CR was 2.7 months (range, 1.2-11.1). Most CRs were achieved by the first or second assessment; however, nine patients converted from a PR to a CR at or after the week 36 tumor assessment (range, 32.3-48.1 weeks; Data Supplement).
+
+TABLE 2. Summary of Efficacy End Points (per IRC; Lugano Criteria) in Patients With Large B-Cell Lymphoma
+
+FIG 1. Efficacy results (per IRC; Lugano criteria) with epcoritamab in patients with LBCL. (A) shows best percentage change in sum-of-product perpendicular diameters of target lesions for patients with LBCL. Asterisks represent patients with prior exposure to CAR T-cell therapy. (B) shows the Kaplan-Meier curve for DOR. Results were similar in the DLBCL population (data not shown). (C) shows the Kaplan-Meier plot of PFS. A PFS ad hoc analysis using the Mantel-Byar approach showed a hazard ratio (95% CI) for patients with CR versus nonresponders of 0.11 (0.04 to 0.25) and a hazard ratio (95% CI) for patients with PR versus nonresponders of 0.47 (0.26 to 0.86). Thirty-six patients had disease progression (n = 28) or died (n = 8) within the first 6 weeks of treatment. Data cutoff: January 31, 2022. CAR, chimeric antigen receptor; CR, complete response; DLBCL, diffuse large B-cell lymphoma; DOR, duration of response; IRC, independent review committee; LBCL, large B-cell lymphoma; PD, progressive disease; PFS, progression-free survival; PR, partial response; SD, stable disease.
+
+The median PFS was 4.4 months (95% CI, 3.0 to 7.9; Fig 1C), and the PFS rate at 6 months was 43.9% (95% CI, 35.7 to 51.7). Median PFS among complete responders was not reached (95% CI, 14.5 to not reached). Twenty-eight patients had disease progression within the first 6 weeks of treatment, and eight deaths occurred within the first 6 weeks of treatment; reasons for these early deaths included PD in five patients and AEs in three patients (one each with COVID-19 disease, hepatotoxicity in a patient with PD in the liver, and myocardial infarction). Median OS was not reached (95% CI, 11.3 to not reached; Data Supplement). Of 107 MRD-evaluable patients, 49 (45.8%) were MRD-negative (95% CI, 36.1 to 55.7). An estimated 78.7% of these patients remained MRD-negative at 6 months. Patients who achieved MRD negativity had longer PFS versus those who were MRD-positive (Data Supplement).
+
+Concordance between the IRC and investigator assessments was high at 82.8% (kappa: 0.77 [95% CI, 0.69 to 0.84]; Data Supplement). For key subgroups, benefit with epcoritamab was consistent with that of the overall population (Figs 2A and 2B). In patients with primary refractory disease (n = 96), the ORR was 55.2% and the CR rate was 30.2%. In patients who received prior CAR T-cell therapy (n = 61), the ORR was 54.1% and the CR rate was 34.4%, with a mDOR of 9.7 months (95% CI, 5.4 to not reached); mDOR in patients with CR was not reached. In patients who did not receive prior CAR T-cell therapy (n = 96), the ORR was 68.8% and the CR rate was 41.7%, with a mDOR of 12.0 months (95% CI, 5.6 to not reached); mDOR in patients with CR was not reached. Of note, regional differences in ORRs and CR rates were observed, likely because of a higher proportion of patients with prior CAR T-cell exposure in North America.
+
+FIG 2. Response (per IRC; Lugano criteria) in prespecified subgroups of patients with LBCL. (A) ORRs. (B) CR rates. Data cutoff: January 31, 2022. aA greater proportion of patients in North America had exposure to CAR T-cell therapy compared with other regions. bPer the statistical analysis plan, subgroup analyses were not performed on subgroups with fewer than 20 patients. The ORR in patients who were not refractory to prior CAR T-cell therapy (n = 15) was 80.0% (95% CI, 51.9 to 95.7). The CR rate in patients who were not refractory to prior CAR T-cell therapy (n = 15) was 53.0% (95% CI, 26.6 to 78.7). ABC, activated B-cell; ASCT, autologous stem-cell transplantation; CAR, chimeric antigen receptor; CR, complete response; CRR, complete response rate; DLBCL, diffuse large B-cell lymphoma; GCB, germinal center B-cell; IPI, International Prognostic Index; IRC, independent review committee; LBCL, large B-cell lymphoma; ORR, overall response rate.
+
+Safety
+
+Treatment-emergent AEs observed with epcoritamab are summarized in Table 3. Grade 3 and higher AEs were observed in 61.1% of patients; treatment-related grade 3 and higher AEs were observed in 26.8% of patients. The most common treatment-related AEs were CRS (49.7%), injection site reaction (19.7%), and neutropenia (17.8%; Data Supplement). Most AEs (including treatment-related AEs) occurred in the first 12 weeks (cycles 1-3) of epcoritamab treatment, and the incidence of AEs declined after 12 weeks. Only one treatment-related serious AE occurred after week 12 (grade 1 CRS). Grade 3 or 4 infections are listed in the Data Supplement.
+
+TABLE 3. Treatment-Emergent AEs by Worst Grade During the Treatment Period in Patients With LBCL
+
+Treatment-emergent AEs leading to discontinuation occurred in 12 patients (7.6%); three patients discontinued because of treatment-related AEs, including worsening of chronic lymphocytic inflammation with pontine perivascular enhancement responsive to steroids (see the description in the Data Supplement), CRS, and ICANS (one patient each). Nine patients (5.7%) had fatal treatment-emergent AEs, including COVID-19 disease in two patients and one case each with myocardial infarction, hepatotoxicity, progressive multifocal leukoencephalopathy, loss of consciousness, general health deterioration, pulmonary embolism, and ICANS. None of these AEs were considered related to epcoritamab by the investigator, except for the one fatal ICANS event, which had multiple concurrent confounding factors (Data Supplement). ICANS events occurred in 10 (6.4%) patients, including seven patients with grade 1, two patients with grade 2, and one fatal event. At least one event of CRS was observed in 49.7% of patients, mostly grade 1 in severity (n = 50; grade 2: n = 24; grade 3: n = 4); no grade 4 or 5 events were observed. Most CRS events occurred after the first full dose (Data Supplement) on day 15 of cycle 1 with a median time to onset of 0.8 days (20 hours). CRS resolved in 77 of 78 patients (98.7%); the median time to resolution from onset after first full dose was 2 days (48 hours). CRS was treated with tocilizumab in 22 (28.2%) patients and with corticosteroids (beyond those required for CRS prophylaxis) in 16 (20.5%) patients. Clinical tumor lysis syndrome occurred in two patients (grade 3 in severity) and was considered related to treatment. COVID-19–related events occurred in 10 (6.4%) patients; two of these cases were fatal but deemed to be unrelated to treatment.
+
+The overall incidence of neutropenia was 21.7% (34 of 157). Febrile neutropenia was observed in four patients (2.5%) and was considered treatment-related in one patient (0.6%). Treatment with granulocyte colony-stimulating factor was required in 16 patients (10.2%).
+
+Patient-Reported Outcomes
+
+Patients reported improvements in their lymphoma-related symptoms and overall quality of life during treatment. Clinically meaningful improvements were reported in the Functional Assessment of Cancer Therapy-Lymphoma scores (ie, lymphoma subscale, trial outcome index, Functional Assessment of Cancer Therapy-General total score, and Functional Assessment of Cancer Therapy-Lymphoma total score) and the EuroQol-5 Dimensions-3 Levels health utility score and EuroQol visual analog scale from day 1 of cycle 1 to day 1 of cycle 9 (Data Supplement).
+
+DISCUSSION
+
+Subcutaneous epcoritamab achieved rapid, deep, and durable responses, including CRs and MRD negativity, in this cohort of patients with challenging-to-treat and highly refractory LBCL. Overall response and CR rates were 63.1% (95% CI, 55.0 to 70.6) and 38.9% (95% CI, 31.2 to 46.9), respectively. Responses were primarily observed early, by either the first or the second response assessment (scheduled at weeks 6 and 12). Among complete responders, mDOR was not reached at the time of analysis. Median PFS was not reached in complete responders, and median OS was not reached at the time of analysis. As of the data cutoff date, 32.5% of patients continued to receive study treatment and 56.1% continued into the follow-up period. This cohort represents a heavily pretreated, heterogeneous patient population. Patients had a median of three prior lines of therapy at a median of 19 months from diagnosis: 61.1% had primary refractory disease, 28.8% with DLBCL had transformed DLBCL, and 38.9% had received prior CAR T-cell therapy, 75.4% of whom were refractory to CAR T-cell therapy. The aggressive disease of this patient population was further demonstrated in that 36 patients had disease progression (n = 28) or died (n = 8) within the first 6 weeks. Responses to epcoritamab were consistent across several prespecified subgroups, including age, line of therapy, primary refractory disease, and prior exposure to CAR T-cell therapy. Forty-nine (45.8%) of 107 patients were MRD-negative per circulating tumor DNA analysis, with most (an estimated 78.7%) remaining MRD-negative after 6 months. Furthermore, MRD negativity was associated with longer PFS, highlighting the depth and durability of response to continuous epcoritamab treatment.
+
+The safety profile of epcoritamab was consistent with previous reports. Notably, the majority of AEs occurred early (ie, in the first 12 weeks) in the treatment course. CRS, observed in 78 (49.7%) patients, was mostly low grade, predictable in terms of timing, and resolved. CRS occurred most frequently after the first full epcoritamab dose; tocilizumab was used to manage CRS in 22 of 78 (28.2%) patients. ICANS events were limited to mostly grade 1, and all resolved apart from the one fatal ICANS event in a patient with several confounding factors.
+
+Results observed with epcoritamab in patients with relapsed or refractory LBCL favorably compare with those observed in approved antilymphoma immunotherapies, although differences in patient populations and study designs should be considered. CAR T-cell therapies (axicabtagene ciloleucel, tisagenlecleucel, and lisocabtagene maraleucel) demonstrated high ORRs (52%-82%) and CR rates (40%-54%) in phase I/II studies.12-14 However, many patients are ineligible for or do not receive CAR T-cell therapy because of rapidly progressing disease, complex manufacturing, limited accessibility, or patient preference.4,15 Notably, 38.9% of patients in the present study received prior CAR T-cell therapy, to which most were refractory; the number of patients in our study with prior CAR T-cell exposure is among the largest reported to date in LBCL. Among patients with relapsed or refractory DLBCL who received CAR T-cell therapy, a 43% risk of relapse has been shown and outcomes after relapse are poor.16 In the present study, epcoritamab showed clinical activity in patients who had received prior CAR T-cell therapy (n = 61), with an ORR of 54% and a CR rate of 34%. Numerically higher clinical activity was observed in patients without prior CAR T-cell therapy (n = 96), with an ORR of 69% and a CR rate of 42%. Recently approved therapies in the United States (polatuzumab vedotin plus bendamustine and rituximab, tafasitamab plus lenalidomide, selinexor, and loncastuximab tesirine) vary with regard to their mechanisms of action, safety profiles, and clinical activity.17-22 In the pivotal studies for polatuzumab vedotin plus bendamustine and rituximab and tafasitamab plus lenalidomide, which enrolled patients with fewer lines of prior therapy and few patients with primary refractory disease or CAR T-cell exposure, response rates were 60%-63% in patients ineligible for transplantation with at least one prior systemic regimen, and CR rates were 43%-50%.17,19 Despite shorter follow-up times, clinical activity observed with single-agent epcoritamab was comparable with that seen for CAR T-cell therapy and is favorable to other approved treatment options, considering the more refractory and difficult-to-treat population. Subcutaneous administration of epcoritamab may be a convenient alternative to intravenous therapies for both long-term and first-line use.
+
+CD3xCD20 bispecific antibodies are a treatment modality being developed for treatment of non-Hodgkin lymphomas, with one compound approved in the European Union for relapsed or refractory follicular lymphoma.23-26 Responses with epcoritamab were shown to deepen from PR to CR at the week 36 assessment or later in nine patients, eight of whom had ongoing responses, thereby suggesting added clinical benefit with continuous treatment in a subset of patients.
+
+Given the lack of direct comparison, there are currently no data to suggest whether the best outcome for heavily pretreated patients can be achieved by treating until disease progression or by stopping after a fixed number of treatment cycles. Treatment until progression ensures ongoing anti-CD20–directed, T-cell–mediated tumor suppression/surveillance. Continuation of subcutaneous epcoritamab at reduced frequency in later cycles may provide the greatest chance of durable remissions with limited burden for patients, but further studies would be helpful to determine the best treatment duration strategy for patients who achieve a CR. In this study, epcoritamab led to patient-reported improvements in their lymphoma-related symptoms and overall quality of life while receiving therapy. One limitation of our study is that it was a single-arm design with no control group for comparison.
+
+Single-agent epcoritamab demonstrated a high ORR, including deep and durable CRs, in a challenging-to-treat and highly refractory patient population with relapsed or refractory LBCL. Efficacy was consistent across key subgroups. Epcoritamab was mostly well tolerated, with few discontinuations because of AEs. CRS was manageable, with predictable timing, and was mostly grade 1 or 2. As long-term treatment, epcoritamab is administered on a once monthly basis as a subcutaneous injection, making it an attractive and convenient off-the-shelf alternative to other antilymphoma immunotherapies. These results support ongoing and future clinical trials of epcoritamab both as monotherapy and in combination in late and earlier lines of treatment for B-cell non-Hodgkin lymphoma.
+
+ACKNOWLEDGMENT
+
+We acknowledge and thank the patients and their families for their participation in this study. We also acknowledge and thank all the participating study sites, investigators, the data monitoring committee, and other research personnel for their support of this trial. The authors thank Huaibao Feng and Kevin Liu for statistical analysis support. Medical writing and editorial assistance were provided by Amy Zannikos, PharmD of Peloton Advantage, LLC, an OPEN Health company, and funded by Genmab A/S and AbbVie.
+
+PRIOR PRESENTATION
+
+SUPPORT
+
+CLINICAL TRIAL INFORMATION
+
+DATA SHARING STATEMENT
+
+Clinical trial data can be requested by qualified researchers for use in rigorous, independent scientific research as long as the trials are not part of an ongoing or planned regulatory submission. Sharing of data is subject to protection of patient privacy and respect for the patient's informed consent. The data will be provided following review and approval of a research proposal and Statistical Analysis Plan and execution of a Data Sharing Agreement. For approved requests, the data will be accessible for 12 months, with possible extensions considered. For more information on the process or to submit a request, contact clinicaltrials@genmab.com.
+
+AUTHOR CONTRIBUTIONS
+
+Conception and design: Catherine Thieblemont, Tycel Phillips, Mariana Cota Stirner, Christopher Chiu, Brian Elliott, Tahamtan Ahmadi, Martin Hutchings, Pieternella J. Lugtenburg
+
+Provision of study materials or patients: Herve Ghesquieres, Chan Y. Cheah, Michael Roost Clausen, David Cunningham, Young Rok Do, Robin Gasiorowski, Wojciech Jurczak, Tae Min Kim, Marjolein van der Poel, Martin Hutchings, Pieternella J. Lugtenburg
+
+Collection and assembly of data: Catherine Thieblemont, Tycel Phillips, Chan Y. Cheah, Michael Roost Clausen, Young Rok Do, Tatyana Feldman, Robin Gasiorowski, Wojciech Jurczak, David John Lewis, Marjolein van der Poel, Michelle Limei Poon, Nurgul Kilavuz, Christopher Chiu, Brian Elliott, Tahamtan Ahmadi, Martin Hutchings
+
+Data analysis and interpretation: Catherine Thieblemont, Tycel Phillips, Herve Ghesquieres, Chan Y. Cheah, Michael Roost Clausen, David Cunningham, Young Rok Do, Robin Gasiorowski, Wojciech Jurczak, Tae Min Kim, Marjolein van der Poel, Michelle Limei Poon, Mariana Cota Stirner, Nurgul Kilavuz, Christopher Chiu, Menghui Chen, Mariana Sacchi, Brian Elliott, Tahamtan Ahmadi, Martin Hutchings, Pieternella J. Lugtenburg
+
+Manuscript writing: All authors
+
+Final approval of manuscript: All authors
+
+Accountable for all aspects of the work: All authors
+
+AUTHORS' DISCLOSURES OF POTENTIAL CONFLICTS OF INTEREST
+
+Epcoritamab, a Novel, Subcutaneous CD3xCD20 Bispecific T-Cell–Engaging Antibody, in Relapsed or Refractory Large B-Cell Lymphoma: Dose Expansion in a Phase I/II Trial
+
+The following represents disclosure information provided by authors of this manuscript. All relationships are considered compensated unless otherwise noted. Relationships are self-held unless noted. I = Immediate Family Member, Inst = My Institution. Relationships may not relate to the subject matter of this manuscript. For more information about ASCO's conflict of interest policy, please refer to www.asco.org/rwc or ascopubs.org/jco/authors/author-center.
+
+Open Payments is a public database containing information reported by companies about payments made to US-licensed physicians (Open Payments).
+
+Catherine Thieblemont
+
+Honoraria: Celgene, AbbVie, Bayer, Janssen, Roche, Incyte, Novartis, Gilead Sciences
+
+Research Funding: Roche
+
+Travel, Accommodations, Expenses: Roche, Janssen-Cilag, Kite/Gilead, Novartis, AbbVie
+
+Tycel Phillips
+
+Honoraria: Seattle Genetics, Lymphoma & Myeloma Connect
+
+Consulting or Advisory Role: Seattle Genetics, Pharmacyclics, Incyte, Genentech, Bayer, Gilead Sciences, Curis, Kite/Gilead, Celgene, Genmab, TG Therapeutics, ADC Therapeutics, Lilly
+
+Research Funding: AbbVie, Pharmacyclics/Janssen, Bayer, Genentech
+
+Herve Ghesquieres
+
+Honoraria: Gilead Sciences, Roche, Takeda
+
+Consulting or Advisory Role: Gilead Sciences, Roche
+
+Travel, Accommodations, Expenses: AbbVie
+
+Chan Y. Cheah
+
+Honoraria: Roche/Genentech, Janssen-Cilag, TG Therapeutics, Loxo/Lilly, AstraZeneca, Bristol Myers Squibb, Gilead Sciences, BeiGene, Novartis
+
+Consulting or Advisory Role: Janssen-Cilag, Roche/Genentech (Inst), TG Therapeutics, Loxo/Lilly, Gilead Sciences, AstraZeneca, Bristol Myers Squibb, Ascentage Pharma, Merck
+
+Research Funding: Roche/Genentech (Inst), Bristol Myers Squibb (Inst), AbbVie (Inst), Merck (Inst)
+
+Travel, Accommodations, Expenses: Roche
+
+Michael Roost Clausen
+
+Consulting or Advisory Role: AbbVie, Janssen, Kite, a Gilead company, Genmab
+
+Speakers' Bureau: AbbVie, Janssen
+
+Travel, Accommodations, Expenses: AstraZeneca/Merck, Janssen, Genmab, AbbVie
+
+David Cunningham
+
+Stock and Other Ownership Interests: OVIBIO
+
+Consulting or Advisory Role: OVIBIO
+
+Research Funding: Celgene (Inst), MedImmune (Inst), Bayer (Inst), 4SC (Inst), Clovis Oncology (Inst), Lilly (Inst), Roche (Inst), Leap Oncology (Inst)
+
+Tatyana Feldman
+
+Honoraria: Seattle Genetics, Pharmacyclics/Janssen, AbbVie, Bristol Myers Squibb, Kite, a Gilead company, Bayer, Takeda
+
+Consulting or Advisory Role: Seattle Genetics, Bristol Myers Squibb, Genmab, ADC Therapeutics, AstraZeneca
+
+Speakers' Bureau: Seattle Genetics, Kite, a Gilead company
+
+Research Funding: Bristol Myers Squibb (Inst), Seattle Genetics (Inst), Portola Pharmaceuticals (Inst), Eisai (Inst), Kyowa Hakko Kirin (Inst), Amgen (Inst), Viracta Therapeutics (Inst), Cell Medica (Inst), Roche (Inst), Trillium Therapeutics (Inst), Pfizer (Inst)
+
+Travel, Accommodations, Expenses: Seattle Genetics, Takeda
+
+Robin Gasiorowski
+
+Honoraria: MSD, AbbVie, Astellas Pharma, Janssen, Novartis, Otsuka
+
+Wojciech Jurczak
+
+Consulting or Advisory Role: Roche, AstraZeneca, BeiGene
+
+Research Funding: Acerta Pharma, TG Therapeutics, Sandoz-Novartis, Roche, Takeda, Epizyme, Janssen-Cilag, BeiGene, MorphoSys, MEI Pharma
+
+Tae Min Kim
+
+Consulting or Advisory Role: Hanmi, AstraZeneca/MedImmune, Janssen Oncology, Novartis, Takeda, Yuhan
+
+Speakers' Bureau: Takeda, Roche/Genentech
+
+Research Funding: AstraZeneca
+
+Uncompensated Relationships: AstraZeneca/MedImmune, Novartis, Bayer, Sanofi, Boryung, Roche/Genentech
+
+David John Lewis
+
+Consulting or Advisory Role: Janssen Oncology, Kite/Gilead, BeiGene
+
+Travel, Accommodations, Expenses: Kite, a Gilead company
+
+Marjolein van der Poel
+
+Consulting or Advisory Role: Takeda
+
+Travel, Accommodations, Expenses: Jazz Pharmaceuticals, Daiichi Sankyo, AbbVie
+
+Mariana Cota Stirner
+
+Employment: AbbVie
+
+Stock and Other Ownership Interests: AbbVie
+
+Travel, Accommodations, Expenses: AbbVie
+
+Nurgul Kilavuz
+
+Employment: ADC Therapeutics, Genmab
+
+Stock and Other Ownership Interests: Bristol Myers Squibb/Celgene, Genmab, ADC Therapeutics
+
+Christopher Chiu
+
+Employment: Janssen Research & Development, Genmab
+
+Stock and Other Ownership Interests: Genmab
+
+Patents, Royalties, Other Intellectual Property: Related to Daratumumab with Janssen, Related to Epcoritamab with Genmab
+
+Menghui Chen
+
+Employment: Genmab, Janssen
+
+Stock and Other Ownership Interests: Genmab, Merck/Schering Plough
+
+Mariana Sacchi
+
+Employment: Genmab
+
+Leadership: Genmab
+
+Stock and Other Ownership Interests: Genmab
+
+Brian Elliott
+
+Employment: Genmab, Novartis
+
+Stock and Other Ownership Interests: Novartis, Genmab
+
+Patents, Royalties, Other Intellectual Property: Patents pending related to Genmab development of epcoritamab
+
+Travel, Accommodations, Expenses: Genmab, Novartis
+
+Tahamtan Ahmadi
+
+Employment: Genmab
+
+Leadership: Genmab
+
+Stock and Other Ownership Interests: Genmab
+
+Martin Hutchings
+
+Consulting or Advisory Role: Takeda, Roche, Genmab, Janssen, AbbVie
+
+Research Funding: Celgene (Inst), Genmab (Inst), Roche (Inst), Takeda (Inst), Novartis (Inst), Janssen (Inst), Merck (Inst), AbbVie (Inst), AstraZeneca (Inst)
+
+Pieternella J. Lugtenburg
+
+Consulting or Advisory Role: Takeda, Roche/Genentech, Genmab, Celgene, Regeneron, Incyte, AbbVie, Y-mAbs Therapeutics
+
+Research Funding: Takeda (Inst), Servier (Inst)
+
+Travel, Accommodations, Expenses: Celgene
+
+No other potential conflicts of interest were reported.
+
+Presented at the European Hematology Association Annual Congress, Vienna, Austria, June 9-17, 2022.
+
+Sponsored by Genmab A/S and AbbVie.
+
+NCT03625037
+==== Refs
+REFERENCES
+
+1. Swerdlow SH Campo E Pileri SA : The 2016 revision of the World Health Organization classification of lymphoid neoplasms. Blood 127 :2375-2390, 2016 26980727
+2. Wienand K Chapuy B : Molecular classification of aggressive lymphomas-past, present, future. Hematol Oncol 39 :24-30, 2021 34105819
+3. Crump M Neelapu SS Farooq U : Outcomes in refractory diffuse large B-cell lymphoma: Results from the international SCHOLAR-1 study. Blood 130 :1800-1808, 2017 28774879
+4. Myers GD Verneris MR Goy A : Perspectives on outpatient administration of CAR-T cell therapy in aggressive B-cell lymphoma and acute lymphoblastic leukemia. J Immunother Cancer 9 :e002056, 2021 33846220
+5. Bhaskar ST Dholaria BR Sengsayadeth SM : Role of bridging therapy during chimeric antigen receptor T cell therapy. eJHaem 3 :39-45, 2022 35844303
+6. Engelberts PJ Hiemstra IH de Jong B : DuoBody-CD3xCD20 induces potent T-cell-mediated killing of malignant B cells in preclinical models and provides opportunities for subcutaneous dosing. EBioMedicine 52 :102625, 2020 31981978
+7. van der Horst HJ de Jonge AV Hiemstra IH : Epcoritamab induces potent anti-tumor activity against malignant B-cells from patients with DLBCL, FL and MCL, irrespective of prior CD20 monoclonal antibody treatment. Blood Cancer J 11 :38, 2021 33602901
+8. Hutchings M Mous R Clausen MR : Dose escalation of subcutaneous epcoritamab in patients with relapsed or refractory B-cell non-Hodgkin lymphoma: An open-label, phase 1/2 study. Lancet 398 :1157-1169, 2021 34508654
+9. Cheson BD Fisher RI Barrington SF : Recommendations for initial evaluation, staging, and response assessment of Hodgkin and non-Hodgkin lymphoma: The Lugano classification. J Clin Oncol 32 :3059-3067, 2014 25113753
+10. Lee DW Santomasso BD Locke FL : ASTCT consensus grading for cytokine release syndrome and neurologic toxicity associated with immune effector cells. Biol Blood Marrow Transplant 25 :625-638, 2019 30592986
+11. Coiffier B Altman A Pui CH : Guidelines for the management of pediatric and adult tumor lysis syndrome: An evidence-based review. J Clin Oncol 26 :2767-2778, 2008 18509186
+12. Schuster SJ Bishop MR Tam CS : Tisagenlecleucel in adult relapsed or refractory diffuse large B-cell lymphoma. N Engl J Med 380 :45-56, 2019 30501490
+13. Abramson JS Palomba ML Gordon LI : Lisocabtagene maraleucel for patients with relapsed or refractory large B-cell lymphomas (TRANSCEND NHL 001): A multicentre seamless design study. Lancet 396 :839-852, 2020 32888407
+14. Neelapu SS Locke FL Bartlett NL : Axicabtagene ciloleucel CAR T-cell therapy in refractory large B-cell lymphoma. N Engl J Med 377 :2531-2544, 2017 29226797
+15. Sehn LH Salles G : Diffuse large B-cell lymphoma. N Engl J Med 384 :842-858, 2021 33657296
+16. Di Blasi R Le Gouill S Bachy E : Outcomes of patients with aggressive B-cell lymphoma after failure of anti-CD19 CAR T-cell therapy: A DESCAR-T analysis. Blood 140 :2584-2593, 2022
+17. Sehn LH Herrera AF Flowers CR : Polatuzumab vedotin in relapsed or refractory diffuse large B-cell lymphoma. J Clin Oncol 38 :155-165, 2020 31693429
+18. Smith SD Lopedote P Samara Y : Polatuzumab vedotin for relapsed/refractory aggressive B-cell lymphoma: A multicenter post-marketing analysis. Clin Lymphoma Myeloma Leuk 21 :170-175, 2021 33431309
+19. Salles G Duell J González Barca E : Tafasitamab plus lenalidomide in relapsed or refractory diffuse large B-cell lymphoma (L-MIND): A multicentre, prospective, single-arm, phase 2 study. Lancet Oncol 21 :978-988, 2020 32511983
+20. Kalakonda N Maerevoet M Cavallo F : Selinexor in patients with relapsed or refractory diffuse large B-cell lymphoma (SADAL): A single-arm, multinational, multicentre, open-label, phase 2 trial. Lancet Haematol 7 :e511-e522, 2020 32589977
+21. Caimi PF Ai W Alderuccio JP : Loncastuximab tesirine in relapsed or refractory diffuse large B-cell lymphoma (LOTIS-2): A multicentre, open-label, single-arm, phase 2 trial. Lancet Oncol 22 :790-800, 2021 33989558
+22. Nowakowski GS Yoon DH Peters A : Improved efficacy of tafasitamab plus lenalidomide versus systemic therapies for relapsed/refractory DLBCL: RE-MIND2, an observational retrospective matched cohort study. Clin Cancer Res 28 :4003-4017, 2022 35674661
+23. Budde LE Assouline S Sehn LH : Single-agent mosunetuzumab shows durable complete responses in patients with relapsed or refractory B-cell lymphomas: phase I dose-escalation study. J Clin Oncol 40 :481-491, 2022 34914545
+24. Hutchings M Morschhauser F Iacoboni G : Glofitamab, a novel, bivalent CD20-targeting T-cell–engaging bispecific antibody, induces durable complete remissions in relapsed or refractory B-cell lymphoma: a phase I trial. J Clin Oncol 39 :1959-1970, 2021 33739857
+25. Dickinson M Carlo-Stella C Morschhauser F : Glofitamab in patients with relapsed/refractory (R/R) diffuse large B-cell lymphoma (DLBCL) and ≥2 prior therapies: Pivotal phase II expansion results. J Clin Oncol 40 , 2022 (suppl 16; abstr 7500)
+26. Bannerji R Arnason JE Advani RH : Odronextamab, a human CD20×CD3 bispecific antibody in patients with CD20-positive B-cell malignancies (ELM-1): Results from the relapsed or refractory non-Hodgkin lymphoma cohort in a single-arm, multicentre, phase 1 trial. Lancet Haematol 9 :e327-e339, 2022 35366963
+

PMC10117456.txt

@@ -0,0 +1,201 @@
+
+==== Front
+Afr Health Sci
+Afr Health Sci
+African Health Sciences
+1680-6905
+1729-0503
+Makerere Medical School Kampala, Uganda
+
+37092066
+jAFHS.v22.i4.pg64
+10.4314/ahs.v22i4.9
+Articles
+Multiple myeloma with unexplained isolated anaemia in a 24year old man- a case report
+Aworanti Oladapo Wale 1
+Ogundeji Sunday Peter 2
+Adeoye Olateni Asake 1
+Shokunbi Wuraola Adebola 2
+1 University College Hospital Ibadan, Department of Haematology
+2 University of Ibadan College of Medicine, Department of Haematology
+Corresponding author: Sunday Peter Ogundeji, University of Ibadan College of Medicine, Department of Haematology peterogundeji22@gmail.com
+12 2022
+22 4 6469
+© 2022 Aworanti OW et al.
+2022
+https://creativecommons.org/licenses/by/4.0/ Licensee African Health Sciences. This is an Open Access article distributed under the terms of the Creative commons Attribution License (https://creativecommons.org/licenses/BY/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
+Background
+
+Multiple myeloma (MM) is a disease of the elderly with a median age at presentation of 70 years. It is rare to diagnose MM in individuals less than 40 years and even extremely rare in those less than 30 years of age. MM is usually suspected in those aged 50 years and above having a combination of hypercalcemia, renal insufficiency, anaemia and bone lesions. Although anaemia is a common clinical feature of MM, it is very rare that anaemia would be the only clinical presentation, hence the need to report this index case.
+
+Case Presentation
+
+We present a rare case of MM in a 24-year- old male who presented with only symptomatic anaemia. Investigations for the cause of anaemia, including Bone marrow aspiration cytology revealed a diagnosis of MM ISS stage II. Here, we highlighted the need to seek early haematologist consultation in investigating patients' whose cause of anaemia is not immediately obvious from the clinical presentation and routine laboratory investigations.
+
+Conclusion
+
+MM can present at a younger age with unexplained anaemia without bone pains or renal insufficiency. High level of suspicion for MM is required in young patients with unexplained anaemia
+
+Multiple myeloma
+isolated anaemia
+young patient
+==== Body
+pmcIntroduction
+
+Multiple Myeloma (MM) is a B cell malignancy characterized by monoclonal expression and accumulation of abnormal plasma cells in the bone marrow1,2. Multiple myeloma belongs to a group of blood disorder referred to as Plasma cell dyscrasia, other disease components in the group include Benign Monoclonal disease such as Monoclonal Gammopathy of Undetermined Significant (MGUS), Indolent Lymphoma and Heavy chain diseases. In Caucasians, MM constitutes about 1% of all malignancies, and it is 2nd most common blood cancer, accounting for about 10% of haematological malignancies3. In Nigeria, MM accounts for about 9% of all hematological malignancies and it is ranks 4th after chronic myeloid leukemia, non-Hodgkin's lymphoma, and chronic lymphocytic leukemia in frequency.1 4,5
+
+The male: female ratio ranged between 1.1: 1 to 4:11,3,4 and it is commoner in blacks 6. The incidence of Multiple myeloma increases with increasing age worldwide, with a median age at presentation of 60 years. This is due to a reduction in the ability of the elderly immune system to clear potential myeloma precursors. In MM, there is clonal proliferation of plasma cells, these cells are confined to the bone marrow but they may also be seen in the peripheral blood in end stage myeloma or in Plasma cell leukaemia.3
+
+Myeloma cells are long lived plasma cells which have been exposed to antigen stimulation, having undergone B cell maturation processes. They are post germinal centre plasma cells which have undergone immunoglobulin gene recombination, class switching and somatic hyper- mutation, and subsequently home to the marrow.
+
+The causative factor of MM is unknown but exposure to chemicals like dioxins, solvents, radiation and viral infections (Hepatitis, HIV, EBV, Herpes and Cytomegalovirus) have been implicated in the pathogenesis6.
+
+MM patients present with different complaints but the dominating symptoms consist of bone pains, features of anaemia and kidney disease2,3,4. About 96% of MM patients in our institution had low back and waist pain.3
+
+The purpose of this report is to highlight the unusually young age of the patient, who presented with a four months history of isolated anaemia.
+
+Case report
+
+He is a 24-year- old man who presented with about 4 months history of progressive exercise intolerance and breathlessness on mild exertion. There was an episode of fainting spells. No history suggestive of acute or chronic blood loss or haemolysis. There was also no bone pain, facial swelling and no reduction in urinary output or other features suggestive of renal impairment. No history of cardiac abnormalities.
+
+At the onset of the illness he was managed in a private facility for an infection (typhoid enteritis). He was transfused with two units of blood at a packed cell volume (PCV) of 12%.
+
+He is not a known hypertensive, diabetic, asthmatic or PUD patient. His haemoglobin phenotype is A. He is not on any routine medications but takes herbal concoction. He does not smoke cigarette nor take alcoholic drinks. He works as a fashion designer though he had Ordinary National Diploma in Civil Engineering.
+
+Examination at presentation showed a young man in no obvious distress, severely pale, anicteric, afebrile (T-37°C), a cyanosed, fair hydration status with no significant peripheral lymphadenopathy.
+
+Cardiovascular system examination showed a pulse rate of 112 beats per minute, blood pressure of 110/60mmHg, 1st, 2nd and 3rd heart sound were heard with gallop rhythm.
+
+Physical examination of the Chest, Abdominal, and Central Nervous system were normal.
+
+Laboratory investigations include Complete Blood count: Haematocrit- 15%, White Blood Cell count- 6,880cells/mm3, platelets of 113,000cells /mm3. White cell differential was as follows: Neutrophil: 46%, Lymphocyte: 40%, Monocyte: 13%, Eosinophil: 1%. The haematological investigations are as shown in Table I.
+
+Table I Haematological parameters of the patient
+
+Parameters	At Presentation	Week one	Week two	
+Haematocrit (%)	15.0	*21.0	18.0	
+White blood cell count (c/mm3)	6,880	7,700	7,900	
+Absolute Neutrophil count (c/mm3)	3,160	3,800	3,700	
+Absolute Lymphocyte count (c/mm3)	2,710	2,500	3,000	
+Absolute Monocyte count(c/mm3)	920	1,000	980	
+Platelet count(c/mm3)	113,000	127,000	154,000	
+ESR (mm in 1st hour)	>130	-	-	
+Reticulocyte count (%)	3.1	-	-	
+Absolute Reticulocyte
+count (x109/L)	73.47	-	-	
+Corrected reticulocyte count (%)	1.5	-	-	
+Direct coombs Test	Negative	-	-	
+* Post two units of red cell concentrate
+
+The review of peripheral blood film showed normocytic normochromic red cells with marked rouleaux. The white cell morphology was normal and platelets were adequate. He was urgently transfused with two units of group identical Blood group- B Rh “D” positive and compatible packed cells.
+
+Bone Marrow Aspiration cytology showed abnormal plasmacytosis constituting about 80% of the nucleated marrow elements, and the plasma cells were of varying morphology and stages of differentiation. There was nuclear-cytoplasmic dissociation of the plasma cells.
+
+Bone marrow histology showed hypercellularity with fat: cell ratio of 1%: 99%. The bone marrow cellular elements were nearly completely replaced by pleomorphic plasmacytoid cells.
+
+Other investigations done include Electrolytes, Urea, Creatinine, Liver function test, as shown in Tables II, III and IV.
+
+Table II Electrolytes, Urea, Creatinine and Liver Function Test Profile of the Patient
+
+Parameters	Value	Reference range	
+Sodium	137mmol/L	135–145	
+Potassium	3.3mmol/L	3.5–5.0	
+Bicarbonate	21mmol/L	20–30	
+Chloride	101mmol/L	95–110	
+Urea	11mg/dl	15–45	
+Creatinine	0.9mg/dl	0.5–1.5	
+Calcium	11.2 mg/dl	8.5–10.0	
+Phosphate	4.0 mg/dl	2.5–4.5	
+Uric acid	7.2mg/dl	2.0–7.0	
+Total Protein	13.2g/dl	6.0–8.0	
+Albumin	2.5g/dl	3.5–5.0	
+Alanine Transaminase	27IU/L	0–40	
+Aspartate Transaminase	42 IU/L	0–37	
+Alkaline Phosphatase	53 IU/L	40–130	
+GGT	35 IU/L	7–50	
+Total Bilirubin	0.5mg/dl	0.2–1.0	
+Direct Bilirubin	0.4mg/dl	0.0–0.4	
+β2 microglobulin	4.8mg/dl	<2.4	
+
+Table III Serum Protein Electrophoresis
+
+Parameters	Value	Normal reference	
+S-Alpha 1 Globulin	4g/L	2–6	
+S-Alpha 2 Globulin	7g/L	3–10	
+S-Beta 1 Globulin	4g/L	3–6	
+S-Beta 2 Globulin	2g/L	2–6	
+S-Gamma Globulin	2g/L	6–15	
+S- ‘M’ Component	89g/L	0	
+SPE showed a monoclonal peak in the late gamma region measuring approximately 89g/L
+
+Table IV Immunoglobulin Quantitation Profile of the Patient
+
+Parameters	Results	Reference range	
+S-IgA	0.26g/L	0.41–3.49	
+S-IgG	112.30g/L	6.5–16.0	
+S-IgM	0.40g/L	0.50–3.00	
+
+Immunoglobulin qu antitation showed immune paresis with markedly elevated IgG fraction of 112.3g/L (Ref: 6.5- 16g/L)
+
+A diagnosis of IgG myeloma, ISS stage II was made. The patient was duly counselled about the diagnosis, disease spectrum/course, and treatment options were discussed including autologous haematopoietic stem cell transplantation. The choice of induction chemotherapy was Velcade, Thalidomide and Dexamethasome (VTD) as per standard protocol.
+
+Discussion
+
+Multiple myeloma is a common haematological malignancy which affects predominantly the elderly, mainly as a result of the inability of the immune system of the elderly to clear the myeloma precursors 3. Previous reports from Nigeria showed that the age range at presentation is 54 to 62 years and that there is increased incidence with increasing age as reported globally. Previous studies reported that only 15% of MM patients presented at an age less than 55years and very few cases presented at age less than 40years 3,4. In this report, we highlighted the presentation of a 24year-old man diagnosed with MM in our facility, an unusual presentation because only less than 2% of cases are diagnosed below 40years. Muhammad et al reported a 20-year-old female diagnosed with MM who presented with a bleeding disorder due to acquired factor X inhibitor.7
+
+The index patient presented only with features of recurrent anaemia about four months prior to presentation despite the extensive bone marrow involvement. Pathogenesis of anaemia in MM is related to bone marrow suppression of normal haemopoiesis and renal impairment leading to reduced erythropoietin secretion. Anaemia is a component of the diagnostic criteria for MM (CRAB) 6. There are however no other features suggestive of bone disease, renal impairment or hypercalcemia in this patient. Isolated anaemia has been previously reported in MM but this has to be supported by rouleaux formation of the red cells in the peripheral blood film review, when this is combined with isolated anaemia, there should be high index of suspicion for MM 3. Previous studies done showed that most patients with MM presents with bone pain 1–4,8–9.
+
+Bone marrow cytology in this patient showed that 80% of nucleated marrow elements were plasma cells. Bone marrow histology showed that the marrow is completely replaced by plasma cells. Bone marrow plasmacytosis of only 10% and above with other two major criteria is diagnostic of MM 3,8,9 This underscores the importance of early bone marrow studies in patients with unexplained anaemia as this will enable the physician in streamlining the investigations.
+
+Serum protein electrophoresis and Immunoglobulin quantitation showed that there was a monoclonal peak at late gamma region measuring 89g/L and increased IgG with immune paresis. In a previous study done in our facility, IgG MM has been found to be the commonest type and this is also consistent with other previous studies on MM 3,6
+
+The patient also had elevated β2 microglobulin of 4.8mg/dl, a level that puts the patient in ISS stage II despite the absence of other features suggestive of Bone and/or renal disease 6
+
+Conclusion
+
+Multiple myeloma can present at a younger age with unexplained isolated anaemia without features of involvement of bone or kidney. High level of suspicion for MM is required in young patients with isolated anaemia.
+
+Acknowledgement
+
+We acknowledge the nursing staff and all medical doctors involved in the management of this patient.
+
+Limitations
+
+No genetic studies were done for the patient, as our health care centre do not have the necessary equipment to do this investigation and the patient is not financially buoyant to send the sample outside the country.
+
+Ethical approval
+
+This study was carried out in compliance with the guidelines of the Helsinki Declaration on biomedical research involving human subjects. Confidentiality of the identity of the patients and personal health information was maintained.
+
+Consent for publication
+
+Written informed consent was obtained from the patient for publication of this case report and any accompanying images. A copy of the written consent is available for review by the Editor-in-Chief of this journal.
+
+Availability of Data and Materials
+
+Not applicable
+
+Competing Interests
+
+Nil
+
+Funding
+
+Nil
+
+Authors' contributions
+
+This work was carried out in collaboration among the authors. Authors OWA and SPO made the draft, authors OWA, SPO and WAS managed the literature searches. Authors OWA, OAA, SPO and WAS corrected the draft. All the authors read and approved the final manuscript.
+
+Figures Ia and Ib Show bone marrow cytology and histology respectively
+==== Refs
+1 Dosunmu AO Akinbami AA Uche E A review of epidemiology and management of Multiple Myeloma in a resource poor country Ann Trop Pathol 2018 9 99 105
+2 Fasola FA Eteng KII Shokunbi WA Akinyemi JO Salako BL Renal status of Multiple Myeloma patients in Ibadan, Nigeria Ann Ibd. Pg. Med 2012 10 2 28 33
+3 Olaniyi JA Fowodu FO Multiple Myeloma: The burden and clinic-laboratory characteristics in a Nigerian foremost tertiary hospital J Appl Hematol 2015 6 58 63 PubMed
+4 Salawu L Durosinmi MA Myelomatosis: Clinical and Laboratory features in Nigerians West Afr J Med 2005 24 54 57 PubMed 15909712
+5 Akinbami A Dada M Dosumu AO Balogun M Adult Haematooncology cases: A six-year review at Lagos state University Teaching Hospital, Ikeja Internet J Hematol 2009 6 1 6 PubMed
+6 Terpos Evangelos Rahemtulla Amin Hoffbrand AV Lewis SM Tuddenham EG Myeloma Postgraduate Haematology 2005 Oxford Butterwort-Heinemann 681 713
+7 Muhammad M Omkar M A Rare Case of Early Onsent Multiple Myeloma in 20-Year-Old Female with Factor X Inhibitor J Hematol 2018 5 7 2 69 71) 32300415
+8 Madu AJ Ocheni Sunday Nwagha TU ObikeIbegbulam Sabastine UA Multiple myeloma in Nigeria: An insight to the clinical, laboratory features and outcomes Niger J Clin Pract 2014 17 2 212 217 24553034
+9 Odunukwe NN Madu AJ Nnodu OE Okocha OE Akingbola TS Asuquo IM Multiple myeloma in Nigeria: a multicenter epidemiological and biomedical study Pan Afr Med J 2015 22 292 298 PubMed 26966488
+

PMC10134288.txt

@@ -0,0 +1,409 @@
+
+==== Front
+Cancer Med
+Cancer Med
+10.1002/(ISSN)2045-7634
+CAM4
+Cancer Medicine
+2045-7634
+John Wiley and Sons Inc. Hoboken
+
+36583504
+10.1002/cam4.5551
+CAM45551
+CAM4-2022-03-1010.R3
+Review
+REVIEWS
+Clinical Cancer Research
+Chimeric antigen receptor T (CAR‐T) cells: Novel cell therapy for hematological malignancies
+Abbasi et al.
+Abbasi Samane 1
+Totmaj Milad Asghari 2
+Abbasi Masoumeh 3
+Hajazimian Saba 4
+Goleij Pouya 5
+Behroozi Javad 6
+Shademan Behrouz 7
+Isazadeh Alireza https://orcid.org/0000-0002-8781-1177
+4
+Baradaran Behzad https://orcid.org/0000-0002-8642-6795
+4 baradaranb@tbzmed.ac.ir
+
+1 Department of Biology, Faculty of Sciences University of Guilan Rasht Iran
+2 Department of Clinical Immunology, Faculty of Medicine The University of Manchester Manchester UK
+3 Department of Microbiology, Malekan Branch Islamic Azad University Malekan Iran
+4 Immunology Research Center Tabriz University of Medical Sciences Tabriz Iran
+5 Department of Genetics, Faculty of Biology Sana Institute of Higher Education Sari Iran
+6 Department of Genetics and Biotechnology, School of Medicine AJA University of Medical Sciences Tehran Iran
+7 Department of Medical Biology, Faculty of Medicine Ege University Izmir Turkey
+* Correspondence
+Behzad Baradaran, Immunology Research Center, Tabriz University of Medical Sciences, Gholghasht Ave, Tabriz 5166614766, Iran.
+Email: baradaranb@tbzmed.ac.ir
+
+30 12 2022
+4 2023
+12 7 10.1002/cam4.v12.7 78447858
+23 7 2022
+12 3 2022
+03 12 2022
+© 2022 The Authors. Cancer Medicine published by John Wiley & Sons Ltd.
+https://creativecommons.org/licenses/by/4.0/ This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
+
+Abstract
+
+Over the last decade, the emergence of several novel therapeutic approaches has changed the therapeutic perspective of human malignancies. Adoptive immunotherapy through chimeric antigen receptor T cell (CAR‐T), which includes the engineering of T cells to recognize tumor‐specific membrane antigens and, as a result, death of cancer cells, has created various clinical benefits for the treatment of several human malignancies. In particular, CAR‐T‐cell‐based immunotherapy is known as a critical approach for the treatment of patients with hematological malignancies such as acute lymphoblastic leukemia (ALL), multiple myeloma (MM), chronic lymphocytic leukemia (CLL), acute myeloid leukemia (AML), Hodgkin lymphoma (HL), and non‐Hodgkin's lymphoma (NHL). However, CAR‐T‐cell therapy of hematological malignancies is associated with various side effects. There are still extensive challenges in association with further progress of this therapeutic approach, from manufacturing and engineering issues to limitations of applications and serious toxicities. Therefore, further studies are required to enhance efficacy and minimize adverse events. In the current review, we summarize the development of CAR‐T‐cell‐based immunotherapy and current clinical antitumor applications to treat hematological malignancies. Furthermore, we will mention the current advantages, disadvantages, challenges, and therapeutic limitations of CAR‐T‐cell therapy.
+
+The chimeric antigen receptor T‐cells (CAR‐T) are engineered T cells that recognize tumor‐specific membrane antigens, and cause death of cancer cells. This approach has created various clinical benefits for the treatment of several human hematological malignancies.
+
+chimeric antigen receptor T cells
+hematological malignancies
+immune therapy
+T‐cell therapy
+tumor immunology
+source-schema-version-number2.0
+cover-dateApril 2023
+details-of-publishers-convertorConverter:WILEY_ML3GV2_TO_JATSPMC version:6.2.7 mode:remove_FC converted:27.04.2023
+Abbasi S , Totmaj MA , Abbasi M , et al. Chimeric antigen receptor T (CAR‐T) cells: Novel cell therapy for hematological malignancies. Cancer Med. 2023;12 :7844‐7858. doi:10.1002/cam4.5551
+==== Body
+pmc1 INTRODUCTION
+
+Various therapeutic approaches have been developed during the last years for treating hematological malignancies, but these malignancies still are an important cause of cancer death worldwide. 1 , 2 Currently, the main treatment methods of hematological malignancies are stem cell transplantation, chemotherapy, and radiotherapy. With the increase of current knowledge about molecular genetics basis of hematological malignancies, emerging immunotherapy approaches have become a novel possibility for the treatment of these diseases. In addition, more knowledge about interaction between cancer cells and immune system cells have been a great promise for development of immunotherapy approaches. 3 , 4 , 5
+
+Previously, immunotherapy was deemed a potential favorable issue, but currently immunotherapy has become an applied cancer treatment approach that revolutionized the cancer therapy landscape in the past decade. 6 , 7 One of the most promising immunotherapeutic approaches is chimeric antigen receptor (CAR) T‐cell therapy that is highly efficient in the treatment of hematological malignancies. 8 , 9 This immunotherapy method prolongs the survival of patients with hematological malignancies, even if current standard therapeutic methods have failed. 10 CAR‐T cells genetically engineered to recognizing specific tumor‐associated antigens (TAAs), and activate T cells independently of major histocompatibility complex (MHC) molecules. 11 The antitumor mechanism of CAR‐T is summarized in Figure 1.
+
+FIGURE 1 The antitumor mechanism of chimeric antigen receptors (CARs) T‐cell therapy. (A) The T‐cell receptor (TCR) recognizes intracellular and extracellular tumor‐associated antigens (TAAs) depending on presentation of MHC; but often expression of MHC downregulated by tumor cells in order to escape from killer T cells. (B) However, CAR‐T cells are able to recognize the specific TAAs in a MHC‐independent manner. Next, T cells were activated by phosphorylation of immunoreceptor tyrosine‐based activation motif (ITAM) followed by enhanced cytotoxicity, T‐cell proliferation, as well as secretion of cytokines (such as IL‐2, IL‐4, IFN‐γ, IL‐12, and TNF). Interleukin‐12 (IL‐12) recruit and reinforce functions of macrophages and NK cells. The activated CAR‐T and T cells creates cytotoxicity through production and secretion of granzyme and perforin, as well as through induction of the death receptor pathway (such as Fas/Fas‐L).
+
+Immunotherapy of hematological malignant with the use of CAR‐T cells has recently provided significant progress. It has already been approved by the European Medicines Agency (EMA) and the US Food and Drug Administration (FDA) for treatment of some hematological malignancies. Besides the impressive benefits of CAR‐T‐cell therapy, recently reported serious toxicities and adverse events in some cases that have been treated with this therapeutic method. In addition, failure and relapse of CAR‐T‐cell therapy were reported in some cases. 12 , 13 , 14 Therefore, further studies are needed to minimize the limitations and enhance the efficacy of this emerging immunotherapy approach.
+
+This review study will provide the current knowledge of CAR‐T‐based immunotherapy, including current clinical application for treatment of various hematological malignancies. In addition, we will describe the advantages, disadvantages, challenges, and therapeutic limitations of this novel therapeutic approach.
+
+2 CAR‐T‐CELL THERAPY
+
+CAR‐T‐cell therapy acts through reprogramming the immune system to combat tumor cells without any dependency on HLA presentation. The intended T cells are genetically engineered in order to presentation of monoclonal antibodies that recognize tumor‐specific antigens, and infused to the patient (Figure 2). Recognition of these cognate cancer‐specific antigens by the engineered antibodies causes to initiation of some signaling pathways in T cells that induce production of several pro‐inflammatory cytokines (IFN‐γ, TNF‐α, IL‐6, and IL‐2) and cytolysis (osmotic lysis) of cancer cells. 15 This unique function of CAR‐T cells can help compensate for limitations of immune response mediated by T‐cell receptor (TCR), such as low affinities for antigen in T cells and MHC loss on tumor cells. 16 , 17 For the first time, Zelig Eshhar and Gideon Gross engineered T cells with chimeric molecule during 1989–1993 in Israel. 18 The history of CAR‐T‐cell therapy progress and milestones is presented in Figure 3.
+
+FIGURE 2 The process of CAR‐T‐cell therapy. Peripheral blood samples are taken from the patient. T cells are isolated and genetically engineered to present chimeric antigen receptors (CARs) and recognize a specific tumor associated antigen (TAAs). The obtained CAR‐T cells are expanded, and infused to the patient.
+
+FIGURE 3 The history of CAR‐T cells progress and milestones in previous years. CAR‐T, chimeric antigen receptor‐T; ALL, acute lymphoblastic leukemia; CLL, chronic lymphocytic leukemia; DLBCL, diffuse large B‐cell lymphoma; MCL, mantle cell lymphoma; MM, multiple myeloma; LBCL, diffuse large B‐cell lymphoma.
+
+CARs are artificial proteins that are composed of three major components: transmembrane domain, intracellular signaling motif, and extracellular tumor‐specific antibody. 19 , 20 The extracellular tumor‐specific antibody is the key component in antigen targeting and incorporates a single‐chain fragment (scFv) derived from natural tumor‐specific antibodies. 21 This component is involved in binding of CAR‐T cells to cancer cells, which subsequently stimulate activation and proliferation of T cells for production of cytokines and cytolytic degranulation. 22 The intracellular signaling motif provides persistence, quality, and strength of T‐cell response to cancer‐specific antigens and is commonly engineered in order to increase the anticancer potency of CAR‐T cells. 21
+
+So far, five generations of CARs have been developed. In first generation, endo‐domain (intracellular signaling motif) is comprised of only CD3‐ζ chain that provides insufficient T‐cell proliferation and cytokine production. 23 Therefore, in second generation, an intracellular co‐stimulatory domain (CD28 or 4‐1BB) has been added in order to ameliorate T‐cell proliferation and persistence. 24 , 25 In third generation, both CD28 and 4‐1BB have been added intracellularly in order to further increase T‐cell proliferation and persistence. 26 , 27 In fourth generation, various cytokines such as IL‐12 have been added to endo‐domain of the second generation of CARs, which stimulates activation of both T cells and natural killer cells against cancer cells. 28 These stimulated natural killer cells can recruit cytokine cassettes and help increase cytotoxicity against cancer cells. 29 This emergence prolongs the lifespan of CAR‐T cells as well as stimulates CAR‐T cells against antigen‐negative cancer cells and tumor microenvironments. 30 Ultimately, in fifth generation, a binding site for STAT3 transcription factor and IL‐2 receptor has been added to induce cytokine storm. 28 All five generations of CAR‐T cells are indicated in Figure 4.
+
+FIGURE 4 Different generations of CAR‐T cells. (A) The first generation contains only CD3ζ as an intracellular domain. (B) The second generation also consists of CD28 or 4‐1BB motifs. (C) The third generation contains both CD28 and 4‐1BB motifs. (D) The fourth generation contains IL‐12 or IL‐18 encoding genes that are tethered to the intracellular domain. (E) The fifth generation contains IL‐2 receptor and STAT3 transcription factor binding site to induce cytokine storm.
+
+Up to now, numerous approaches have been applied to increase the efficiency of CAR‐T cells' functions. Preclinical models have demonstrated that combined CD28 and 4‐1BB costimulation can lead to enhanced CAR‐T‐cell persistence, IL‐2 secretion, and cytolytic activity. The modified T cells with CD40L will lead to an increase in production and secretion of pro‐inflammatory cytokines, such as interferon‐gamma (IFNγ), tumor necrosis factor alpha (TNFα), IL‐2, and IL‐12. 31 IL‐12 plays several critical roles in the anti‐cancer activity of CAR‐T cells through recruit and reinforce of the innate immune cells such as macrophage and NK cells, increase cytotoxic T‐cell activation, increase T helper type 1 (Th1) response, and decrease angiogenic activities. 32 , 33 , 34 In this regard, T cells redirected for universal cytokine killing (TRUCK) method was developed in recent years. TRUCK can redirect CAR‐T cells through production and secretion of transgenic factors (e.g., IL‐12) in order to stimulate the immune system against cancer cells that are unrecognizable to CAR‐T cells. 35 In addition to targeting cancer‐specific antigen, CAR‐T cells produce IFN‐γ cytokine that plays a role in antigen‐independent destruction of cancer cells through interaction with IFNγ receptors (IFNγR) that are expressed in tumor stroma. 36
+
+3 CAR‐T‐CELL THERAPY FOR HEMATOLOGICAL MALIGNANCIES
+
+The first time in 2012, a child with acute lymphoblastic leukemia (ALL) received the CD19‐targeted CAR‐T‐cell therapy and exhibited a complete and promising response with no relapse or refractory for more than 5 years. 37 This event provided a novel strategies of CAR‐T‐cell therapy for hematological malignancies. Afterward, several studies reported successful results with 60% to 93% remission rate as well as a minimal residual disease after CAR‐T‐cell therapy of patients with hematological malignancies. 38 , 39 , 40 With rapid progress in this area, for the first time in 2017, tisagenlecleucel was approved by FDA as first CAR‐T‐cell therapy medication for treatment of under 25 years old patients with relapsed and refractory ALL. 41 , 42 After 2 months in the same year, the second CAR‐T‐cell therapy medication (axicabtagene ciloleucel) was approved by FDA for treatment of patients with relapsed or refractory large B‐cell lymphoma. 43 The anti‐CD19 CAR‐T medications are the first products that received regulatory approval for treatment of patients with B‐cell ALL (B‐ALL) and B‐cell non‐Hodgkin lymphomas (NHL). Another successful example of FDA‐approved CAR‐T‐cell therapy is related to axicabtagene ciloleucel/Yescarta, (Gilead/Kite), which is used to treatment of patients with NHL. 44 , 45 In recent years, Gardner et al. produced CAR‐T cells that indicated 93% complete response among patients with leukemia. 38 Another important milestone of CAR‐T products is FDA‐approved liso‐cel/Breyanzi for treatment of NHL, due to remarkable efficacy and low toxicity. 46 , 47 These promising results in hematological malignancies have spurred a tidal wave of clinical trials on CAR‐T‐cell therapy (Table 1). 48 , 49 , 50 Due to further encouraging results in hematological malignancies, CAR‐T‐cell therapy was suggested for treatment of various solid tumors. However, the results of CAR‐T‐cell therapy in solid tumors were less efficient as compared to hematological malignancies. 51 This can be due to limited T‐cell expansion, insufficient CAR‐T cells infiltrating and traveling to a solid tumor, poor persistence due to immunosuppressive tumor microenvironment, and low expression of target tumor‐specific antigen on the solid cancer cells. 52 , 53 Five FDA‐approved medications of CAR‐T cells for hematological malignancies are presented in Table 2.
+
+TABLE 1 Some of the clinical trials for CAR‐T‐cell therapy of hematological malignancies.
+
+Clinical trial	Phase	Start date	Estimated completion date	Disease	Estimated participants	Ages eligible	Target antigen	Location	
+NCT04599556	I/II	2020	2023	ALL	108	3–70 years (child, adult, older adult)	CD7	China	
+NCT01044069	I	2010	2023	ALL	93	18 ≤ years (adult, older adult)	CD19	United states	
+NCT02028455	I/II	2014	2036	ALL	167	1–26 years (child, adult)	CD19	United States	
+NCT02772198	I/II	2016	2022	ALL	300	1–50 years (child, adult)	CD19	Israel	
+NCT02435849	II	2015	2022	ALL	97	25 ≤ years (adult, older adult)	CD19	United States	
+NCT01029366	I	2010	2016	CLL	26	18 ≤ years (adult, older adult)	CD19	United States	
+NCT01416974	I	2011	2019	CLL	13	18 ≤ years (adult, older adult)	CD19	United States	
+NCT01865617	I/II	2013	2021	CLL	204	18 ≤ years (adult, older adult)	CD19	United States	
+NCT03331198	I/II	2017	2026	CLL	259	18 ≤ years (adult, older adult)	CD19	United States	
+NCT00924326	I	2009	2021	DLBCL	43	18–70 years (adult, older adult)	CD19	United States	
+NCT02631044	I	2016	2022	DLBCL	314	18 ≤ years (adult, older adult)	CD19	United States	
+NCT02348216	I/II	2015	2035	DLBCL	307	18 ≤ years (adult, older adult)	CD19	United States	
+NCT02445248	II	2015	2023	DLBCL	115	18 ≤ years (adult, older adult)	CD19	United States	
+NCT02215967	I	2014	2019	MM	30	18–73 years (adult, older adult)	BCMA	United States	
+NCT02658929	I	2015	2022	MM	67	18 ≤ years (adult, older adult)	BCMA	United States	
+NCT03958656	I	2019	2021	MM	13	18–73 years (Adult, Older Adult)	SLAM7	United States	
+NCT04288726	I	2020	2037	LH	18	12–75 years (child, adult, older adult)	CD30	United States	
+NCT04136275	I	2020	2024	LH	18	18 ≤ years (adult, older adult)	CD37	United States	
+NCT03904069	I	2022	2029	AML	40	12 ≤ years (child, adult, older adult)	FLT3	United States	
+NCT03081910	I	2017	2039	T‐ALL	42	75 ≤ years (child, adult, older adult)	CD5	United States	
+Abbreviations: ALL, acute lymphoblastic leukemia; CLL, chronic lymphocytic leukemia; DLBCL, diffuse large B‐cell lymphoma; MM, multiple myeloma; HL, Hodgkin lymphoma; AML, acute myeloid leukemia; T‐ALL, T‐cell acute lymphoblastic leukemia.
+
+TABLE 2 The FDA‐approved CAR‐T‐cell medications for hematological malignancies.
+
+Medication	Abecma (idecabtagene vicleucel)	Breyanzi (lisocabtagene maraleucel)	Kymriah (tisagenlecleucel)	Tecartus (brexucabtagene autoleucel)	Yescarta (axicabtagene ciloleucel)	
+FDA approval	Multiple myeloma: 2021	Large B‐cell lymphoma: 2021	acute lymphoblastic leukemia: 2017
+
+Large B‐cell lymphoma: 2018
+
+	Mantle cell lymphoma: 2020	Large B‐cell lymphoma: 2017
+
+Follicular lymphoma: 2021
+
+	
+CAR Construct	CD19scFv, 4‐1BB, CD3‐ζ	CD19scFv, CD28, CD3‐ζ	CD19scFv, 4‐1BB, CD3‐ζ	CD19scFv, CD28, CD3‐ζ	CD19scFv, CD28, CD3‐ζ	
+Vector	Lentiviral vector	Lentiviral vector	Lentiviral vector	Retroviral vector	Retroviral vector	
+Target antigen	Anti‐CD38 monoclonal antibody	Anti‐CD19 monoclonal antibody	Anti‐CD19 monoclonal antibody	Anti‐CD20 monoclonal antibody	Anti‐CD19 monoclonal antibody	
+Bridging chemotherapy	Yes: 87%	Yes: 59%	Yes: 59%	Yes: 37%	No: ‐	
+CAR‐T dose	450 × 106 CAR‐T cells/kg	50 × 106 CAR‐T cells/kg	3 × 108 CAR‐T cells/kg	2 × 106 CAR‐T cells/kg	2 × 106 CAR‐T cells/kg	
+Efficacy	Overall response: 72%
+
+Complete response: 33%
+
+	Overall response: 61%
+
+Complete response: 44%
+
+	Overall response: 52%
+
+Complete response: 40%
+
+	Overall response: 85%
+
+Complete response: 59%
+
+	Overall response: 82%
+
+Complete response: 54%
+
+	
+Safety	Cytokine release syndrome: 84%
+
+Neurotoxicity: 18%
+
+	Cytokine release syndrome: 42%
+
+Neurotoxicity: 30%
+
+	Cytokine release syndrome: 58%
+
+Neurotoxicity: 21%
+
+	Cytokine release syndrome: 91%
+
+Neurotoxicity: 63%
+
+	Cytokine release syndrome: 93%
+
+Neurotoxicity: 64%
+
+	
+Side effects	Cytokine release syndrome	Cytokine release syndrome	B‐cell aplasia, off‐target activity	Cytokine release syndrome	Cytokine release syndrome	
+
+3.1 Acute lymphoblastic leukemia
+
+ALL is a hematological malignancy with a high proliferation of abnormal primitive cells as well as naive cells in bone marrow. Several preclinical studies demonstrated that CAR‐T‐cell therapy is an appropriate strategy with remarkable efficacy for the treatment of ALL. 54 , 55 So far, several clinical trials have investigated the efficiency of anti‐CD19 CAR‐T‐cell therapy of patients with B‐ALL, which indicated promising partial remission and complete remission rates. 56 , 57 Two different studies from Pennsylvania and Philadelphia groups have reported that from 30 patients with ALL that received anti‐CD19 CAR‐T‐cell therapy, 27 cases (90%) indicated complete remission. 57 In an interesting study, 57 patients with relapsed or refractory ALL were treated by CAR‐T cells, and the results indicated that 28 patients (83%) achieved complete remission. 58 In another clinical study on 75 patients with ALL that received anti‐CD19 CAR‐T‐cell therapy reported a complete remission rate of 60%. 59 Although anti‐CD19 CAR‐T cells is an ideal therapeutic method for ALL, often administered for patients with B‐ALL; this approach presents a limited efficacy in patients with T‐cell ALL (T‐ALL). However, a previous preclinical study on xenograft mouse models reported that anti‐CD5 CAR‐T‐cell therapy could be used effectively to treat patients with T‐ALL. 60 Despite significant progress in this therapeutic method, several clinical trials to treatment ALL by CAR‐T‐cell therapy through targeting CD19, CD20, and CD22, as well as combination therapy by anti‐CD19 and anti‐CD20, are in progress. 61 In a clinical trial on 27 patients with relapsed or refractory B‐ALL that received anti‐CD22 CAR‐T cells and anti‐CD19 CAR‐T cells, reported that 24 patients (89%) reached complete remission. 62 These evidence indicates that combination and multitargeted CAR‐T‐cell therapy can be a promising therapeutic method for impressive treatment of ALL patients.
+
+3.2 Chronic lymphocytic leukemia
+
+Chronic lymphocytic leukemia (CLL) is a common subtype of leukemia that indicates poor prognosis in cases with multiple relapsed or refractory CLL. 63 Targeting anti‐CD19 CAR‐T‐cell therapy has been introduced as an effective therapeutic method for treatment of patients with CLL. A study by Porter et al. investigated the efficiency of CAR‐T‐cell therapy through targeting CD137 and CD3zeta in patients with CLL. They reported that the number of anti‐CD137 CAR‐T cells and anti‐CD3zeta CAR‐T cells significantly expanded, and the patients were completely relieved. Moreover, they reported that the designed CARs were expressed for 6 months in bone marrow and blood of patients. 64 In another study, Porter et al. reported that total effective rate of anti‐CD19 CAR‐T‐cell therapy was 57% among 14 patients with CLL, in which 4 patients (28%) achieved complete remission among them. 65 In addition, combined therapy with chemotherapy and CAR‐T‐cell therapy was performed by Geyer et al. in order to treatment of eight patients with CLL. 66 This study reported that two patients (25%) achieved complete remission for more than 28 months after treatment by infliximab chemotherapy, anti‐CD19 and anti‐CD28 CAR‐T‐cell therapy. 66 Another study by Gauthier et al. investigated the efficiency of CAR‐T‐cell therapy along with ibrutinib in 19 patients with CLL. They reported that 83% of patients achieved complete remission. They suggested that simultaneous use of CAR‐T‐cell therapy and ibrutinib was well tolerated in patients. 67 In addition, the possibility of concomitantly targeting CD19 and CD37 has been explored preclinically. 68 This evidence demonstrated that CAR‐T‐cell therapy is an impressive therapeutic method for treatment of patients with CLL.
+
+3.3 Acute myeloid leukemia
+
+Acute myeloid leukemia (AML) is one of the common subtypes of leukemia in children that its main feature is uncontrolled immature myeloid cells proliferation in bone marrow. So far, CAR‐T‐cell therapy of AML has not been successful like ALL. The early efforts for CAR‐T‐cell therapy of AML were performed through targeting CD123 and CD33. 14 In one of the first efforts, CAR‐T‐cell therapy targeting CD33 was performed in a patient with relapsed or refractory AML and reported that the tumor burden of this patient was significantly decreased in the bone marrow after anti‐CD33 CAR‐T‐cell therapy. 69 After that, CD123 was introduced as a novel potential antigen target. However, the anti‐CD123 CAR‐T‐cell therapy indicated a low efficiency due to the relative expression of CD123 on normal cells (monocytes and endothelial cells), though it is lower than AML cells. 70 Due to disappointing results, further preclinical studies were performed, and a large number of antigens were tried as new targets, such as Lewis‐Y (LeY) and CLEC12A. 71 , 72 In a phase I clinical trial study by Ritchie et al., the safety and persistence of autologous anti‐LeY CAR‐T‐cell therapy were examined in three patients with AML. They reported that one patient indicated cytogenetic remission, one patient indicated reduction of blood blasts, and one patient indicated protracted remission. However, all the three patients experienced disease progression despite the persistence of CAR‐T cells. 71 In a recent study by Morsink et al., anti‐CLEC12A‐CD33 CAR‐T cells were applied for the treatment of a 44‐year‐old woman with AML. They reported that this female tolerated this treatment approach and achieved complete remission after 44 days of infusion. 73 It is noteworthy that a transiently expressed mRNA anti‐CD33 CAR has been designed preclinically in order to increase the persistence of anti‐CD33 CAR‐T‐cell therapy as a potential therapeutic method for treatment of patients with AML. 74
+
+3.4 Multiple myeloma
+
+Multiple myeloma (MM) is a neoplastic malignancy of B cell in bone marrow that its main features include monoclonal immunoglobulin production and plasma cells proliferation. 75 In recent years, immunotherapy of MM by CAR‐T‐cell therapy has expanded. Inhibition of myeloma cells growth using CAR‐T cells against various targets (CS1, CD138, BCMA, and NKG2D) reported by preclinical studies. 76 For the first time, a clinical trial demonstrated promising anti‐CD269 CAR‐T‐cell therapy for treatment of patients with MM. 77 CD269, or B‐cell maturation antigen (BCMA), is a membrane antigen found on both malignant and normal plasma cells. 78 Efficacy of BCMA CAR‐T‐cell therapy were investigated in phase I clinical trial. This study suggested that the overall response rate of this therapeutic approach was 85% among 33 patients with MM, with a 45% complete remission rate. 79 A previous preclinical study have shown that CD138 is an effective target for the treatment of MM. 80 In other clinical study. Heffner et al. reported a high efficiency for anti‐CD138 CAR‐T‐cell therapy in refractory MM. 81 CD138 or syndecan‐1 is a membrane antigen found on both malignant and normal plasma cells that is an appropriate target for CAR‐T‐cell therapy. 82 However, CD138 can also be found on the surface of epithelial cells and is not specifically found on myeloma cells. Some issues have been raised on the toxicity and specificity of this approach. Due to the absence in most tissues, BCMA is a better candidate as compared to CD138. Therefore, BCMA CAR‐T‐cell therapy is more effective and presents a great clinic outcome. 83 The first BCMA‐directed CAR was developed less than a decade ago, showing preclinical evidence of functional targetability. 79 Another successful experience of CAR‐T‐cell therapy is obtained by targeting CD19 in a 43‐year‐old patient with MM. A clinical study reported that five (55%) patients with MM achieved remission among nine patients after treatment by anti‐CD19 CAR‐T‐cell therapy. 84 CD19 or B‐lymphocyte antigen expression in malignant plasma cells has been reported at lower levels as compared normal plasma cells. 85 This data indicated that CAR‐T‐cell therapy is a promising therapeutic method for treatment of patients with MM.
+
+3.5 Hodgkin lymphoma
+
+Hodgkin lymphoma (HL) is a B‐cell malignancy, which B‐cell‐specific antigens have lost, and expression of CD30 is increased. Therefore, CD30 is an appropriate target for CAR‐T‐cell therapy of patients with HL. 14 Despite CD30 expression on activated normal T cells as well as challenges ahead in anti‐CD30 CAR‐T‐cell therapy of HL, numerous promising results have been reported. In phase 1 clinical trial by Ramos et al., no toxicities were observed to anti‐CD30 CAR‐T‐cell therapy among seven patients with relapsed or refractory HL and reported that two patients achieved complete remission, as well as three patients achieved transient remission after treatment by anti‐CD30 CAR‐T cell. 86 In another phase 1 clinical trial by Wang et al., patients with HL were treated by anti‐CD30 CAR‐T‐cell therapy and reported that seven patients achieved partial remission, whereas six patients remained with stable disease. They reported that all patients tolerated anti‐CD30 CAR‐T‐cell infusion without any side effects. 87 This evidence has indicated safety, tolerability, as well as potential of anti‐CD30 CAR‐T‐cell therapy for treatment of patients with relapsed or refractory HL.
+
+3.6 Non‐Hodgkin lymphoma
+
+NHL is a group of B‐cell malignancies that includes several types of lymphomas such as DLBCL, mantle cell lymphoma (MCL), Burkitt lymphoma (BL), follicular lymphoma (FL), Li‐Fraumeni syndrome (LFS), and B‐cell lymphoblastic lymphoma (B‐LBL). Stem cell transplantation, chemotherapy, and radiotherapy are the common treatment methods for patients with NHL. However, the mortality rate from NLH has not declined. Due to remarkable success in treating relapsed or refractory lymphoma, CAR‐T‐cell therapy has recently received more attention. 14
+
+DLBCL is an important subtype of NHL with aggressive clinical features. In a study Jensen et al. reported that anti‐CD20 CAR‐T‐cell therapy indicated no clinical responses and toxicities in treating two patients with relapsed or refractory DLBCL. 88 Another study by Kochenderfer et al. reported that four of seven chemoresistance patients with relapsed or refractory DLBCL achieved remission. 89 In addition, Schuster et al. reported that 6 of 14 adult patients with DLBCL achieved remission after treatment by anti‐CTL019 CAR‐T‐cell therapy. 90 Furthermore, Stirrups et al. used anti‐CD19 CAR‐T‐cell therapy in order to treatment of 101 patients with relapsed or refractory DLBCL. They reported that 55 cases (54%) achieved complete remission as well as 28 cases (28%) achieved partial remission. 91
+
+MCL is another common subtype of NHL that includes 7% of all NHL. 92 CAR‐T‐cell therapy is an efficient therapeutic method for treatment of patients with MCL and causes complete remission in numerous of patients. In a preclinical trial, Till et al. investigated the efficiency and toxicity of anti‐CD20 CAR‐T‐cell therapy on four patients with MCL. They reported a good tolerance for this approach without any toxicity, although some transient infusion symptoms were observed in one patient. In this study, 2 patients indicated no progress for 12 and 24 months after treatment, but a partial remission occurred in 1 patient that relapsed 12 months after injection. 93
+
+BL is also a common subtype of NHL that a high proportion of patients indicate poor prognosis after chemotherapy. In a clinical study by Du et al., anti‐CD19, anti‐CD20, and anti‐CD22 CAR‐T‐cell therapy were applied for treatment of an 8‐year‐old boy with BL. They observed no obvious response after treatment by anti‐CD19 CAR‐T‐cell therapy. However, by anti‐CD22 CAR‐T‐cell therapy, the child experienced partial remission, but the disease relapsed quickly, unfortunately. Finally, an encouraging result was obtained after treatment with anti‐CD20 CAR‐T cell, and the patient achieved remission. 94
+
+In addition, several studies also reported the high efficiency of CAR‐T‐cell therapy for treatment of other NHL. A recent phase IIa study Schuster et al. investigated the efficiency of anti‐CTL019 CAR‐T‐cell therapy in 14 patients with follicular lymphoma (FL). They reported a disease progression after treatment by anti‐CTL019 CAR‐T‐cell therapy within 2 years. 90 In another study, Neelapu et al. treated 66 patients with aggressive and refractory NHL by FMC‐63, a single‐chain antibody that recognizes CD19 on cancer cells. They reported 52% complete effective rate as well as 79% total effective rate. 95 Moreover, Chen et al. evaluated efficiency of anti‐CD19 and anti‐CD22 CAR‐T‐cell therapy in a patient with relapsed or refractory acute B‐LBL. They reported a complete tumor remission in the studied patient. 96
+
+4 DISADVANTAGES AND CHALLENGES
+
+4.1 Cytokine release syndrome
+
+Cytokine release syndrome (CRS), a systemic immune inflammation, rapidly produces and secretes inflammatory cytokines after injection of CAR‐T cells to patients. CRS is known as a most important side effect of CAR‐T‐cell therapy, which commonly causes several signs such as hypoxia, fever, hypotension, and neurological alterations. 97 The diagnostic criteria for severe CRS can be investigated by systemic analysis of serum cytokines as well as clinical analysis 21 days after injection of CAR‐T cell. Moreover, serum levels of C‐reactive protein (CRP) is a dependable factor in order to investigate severity of CRS and is a disease management way in clinical centers that presents CAR‐T‐cell therapy. 98 Tocilizumab is a humanized monoclonal antibody against IL‐6 receptor, which was approved by FDA for treatment of CRS. After taking tocilizumab, CRS subsides rapidly and does not affect the efficiency of CAR‐T‐cell therapy. 99 A study by Caimi et al. reported that use of prophylactic tocilizumab followed by anti‐CD19 CAR‐T‐cell therapy cause reduce of incidence and severity of CRS. 100 In another study, Jiang et al. reported that severe CRS after CAR‐T‐cell therapy could cause disseminated intravascular coagulation (DIC). They suggested that corticosteroids and immunosuppressive agents could be used to prevent CRS‐related coagulation and appropriate management of CAR‐T treatment. 101
+
+4.2 Neurotoxicity
+
+Neurotoxicity is one of the main side effects of CAR‐T‐cell therapy that is associated with numerous symptoms such as confusion, delirium, seizures, and mild headaches, visual hallucination, acute encephalopathy, and cerebral edema. 102 , 103 The onset of neurotoxicity is less than CRS and usually occurs after CRS onset and a few days after CAR‐T‐cell therapy. Pathogenesis of neurotoxicity is unclear and may be correlated with T‐cell trafficking or cytokines diffusion in the brain. 40 , 104 Neurotoxicity is usually solved within a few days and is uncommon after a perfect treatment. 40 Strategies to deal with the CAR‐T‐cell‐associated neurotoxicity are aimed to reduction of inflammatory response. Siltuximab is a IL‐6 antagonist monoclonal antibody that prevents translocation of IL‐6 from blood–brain barrier (BBB) and plays an important role in managing neurotoxicity. 102 Antiepileptic agents or levetiracetam are other drugs prevention for severe neurotoxicity as well as seizures prophylaxis. 105 However, further studies are required to optimize the management of neurotoxicity after CAR‐T‐cell therapy and identify underlying molecular mechanisms and risk factors of neurotoxicity.
+
+4.3 On‐target/off‐tumor toxicity
+
+On‐target off‐tumor is toxicity specific to CAR‐T‐cell therapy resulting from a direct attack on normal tissues. 106 This event may occur in the form of manageable depletion as B‐cell aplasia or severe toxicity, which observed in various organ systems such as hematologic, pulmonary, and gastrointestinal. 107 B‐cell aplasia, absence, and elimination of B cells, commonly occur after anti‐CD19 or anti‐CD22 CAR‐T‐cell therapy cause various types of infectious diseases. 108 , 109 Persistence and efficacy of CAR‐T‐cell therapy can be investigated by B‐cell aplasia rate. B‐cell aplasia also can be used for prediction of disease relapse. 109 To overcome this toxicity, several novel CARs are designed that are able to distinguish malignant and healthy cells. These CAR constructs include masked CARs, inhibitory CAR (iCAR), universal CARs (UniCARs), and Logic‐Gated CAR‐T Cells. 110 One of the important key to success of CAR‐T‐cell therapy is detection of a specific antigen that expressed on tumor cells surface, but not expressed on normal cells. CD19 is one of the promising target that expressed on surface of B‐cell malignancies. 51 However, clinical studies have reported that relapse rate is approximately 30% after anti‐CD19 CAR‐T‐cell therapy, which may be due to low persistence of CAR‐T cells, antigen escape, antigen loss, and antigen downregulation. In addition, other targeted antigens may lead to on‐target/off‐tumor toxicities that is unacceptable or even fatal. 59 , 111 Therefore, combination of multi‐antigen targets is a potential strategy to increase effectiveness of CAR‐T‐cell therapy. In this regard, Boolean logic gates system (AND, OR, and NOT) has been introduced to improving multi‐antigen targeted CAR‐T‐cell therapy, reduce on‐target/off‐tumor toxicities, and prevent tumor antigen escape. 112 , 113
+
+5 THERAPEUTIC LIMITATIONS
+
+CAR‐T‐cell therapy has become an encouraging therapeutic method for treatment of various hematological malignancies, but there are still several limitations for broadly application of this therapeutic method. The availability and cost are the first important factors that limits application of CAR‐T‐cell therapy. The modified CAR‐T cells are highly personalized and produced from immune cells isolated from the patient. In contrast to the other immunotherapeutic approaches (such as inhibition of immune checkpoints), CAR‐T cells cannot be mass‐produced and not universal. These factors cause to increase costs of therapy and decrease number of equipment and facilities that required for an appropriate therapy. Various advanced instrument and technologies (such as gene‐editing tools and viral vectors) are required for genetic modification of T cells that may not be available in smaller therapeutic centers and laboratories. Moreover, a highly sterile and controlled fully equipped environment as well as continuous monitoring is essential in order to avoid infections in patients that received CAR‐T‐cell therapy. 114 , 115
+
+In addition, possibility of resistance to CAR‐T‐cell therapy is one of the important limitation that can be occur in response to prolonged exposure to genetically engineered CAR‐T cells. Resistance to CAR‐T‐cell therapy especially is observed in ALL patients with negative CD19 expression. 116
+
+The numerous barriers and several complex mechanisms have characterized that cause to transient improvement as well as decrease efficiency of CAR‐T‐cell therapy. One of the main cause of treatment failures by CAR‐T‐cell therapy is limited persistence or insufficient expansion of genetically engineered T cells in body of the patient. Another important cause of therapy failure is lower or loss of antigen that can occur in some patients. Relapse of malignancy in some patients cause that malignant tumor cells no longer express the TAAs targeted by the first modified CAR‐T cells. 117 The majority of CAR targets are TAAs that are upregulated on surface of cancer cells. 118 , 119 The risk of on‐target off‐tumor toxicity is associated with overexpression of TAAs on surface of nonmalignant cells. Low expression of TAAs on nonmalignant cells minimize the risk of on‐target off‐tumor toxicity. 120 The combinatorial antigen is a most common strategy for increase specificity of CAR‐T‐cell therapy. This method increases ability of CAR‐T cells to discriminate between target and off‐target cells. 121
+
+The other main technically limitations of CARs therapy are include: immunosuppressive tumor microenvironment (design of a CARs that able to overcome immunosuppressive factors such as immune checkpoints), CAR‐T trafficking, and infiltration of tumors (design of a CARs that increase penetration from physical barriers), On‐target/off‐tumor effects (binding to target antigen on cancer cells that also expressed on normal cells), CAR‐T‐cell‐associated toxicities (alteration of CARs structure to ameliorate of toxicity), and antigen escape (design of a CARs that able to target multiple antigens), 117 which are current challenges in extensive use of this approach (Figure 5).
+
+FIGURE 5 Limitations in use of CAR‐T‐cell therapy. (A) Immunosuppressive tumor microenvironment or engineering CARs cells to overcome to immunosuppressive factors. (B) CAR‐T trafficking and infiltration of tumors or engineering CARs that increase penetration from physical barriers. (C) On‐target/off‐tumor effects or binding to target antigen on cancer cells that also expressed on normal cells. (D) Antigen escape or design of a CARs that able to target multiple antigens. (E) CAR‐T‐cell‐associated toxicities or alteration of CARs structure to ameliorate toxicity.
+
+6 FUTURE DIRECTION
+
+In recent years, CAR‐T‐cell therapy has provided enormous development in treatment of hematological malignancies. However, there are still numerous challenges and limitations that need to be addressed. The main problems in in this therapeutic approach are include increase of durability and effectiveness of CAR‐T cells in body of patients as well as decrease the side effects after CAR‐T‐cell therapy. 106
+
+The durability and effectiveness of CAR‐T‐cell therapy can be improved through use of oncolytic viruses' carrier of chemokine encoding genes to more recruit CAR‐T cells. Previous experimental studies demonstrated that oncolytic viruses are able to increase duration of exposure to CAR‐T cells as well as directly attacks malignant cells, which may have great potential to increase permanence and efficiency of CAR‐T‐cell therapy for treatment of human hematological malignancies. 122 , 123
+
+The side effects after CAR‐T‐cell therapy also can be partially resolved through application of corticosteroids and tocilizumab as the main therapeutic drugs. 124 Moreover, eliminate of CAR‐T cells by several strategies after a period of improvement can reduce CAR‐T‐cell‐associated toxicities. 124 Constructs that allow switching the CAR expression on and off are currently in preclinical development and if successful would provide better control of CAR‐T‐related toxicity. 125 Preclinical evidence demonstrated that the main strategies include use of anti‐CD19 CAR‐T‐cell‐mediated B cell that eliminate CAR‐T cells by B cells as well as use of suicide gene system such as induced caspase 9 (iCas9) dimerization that eliminate CAR‐T cells by cell death. 126 , 127 These strategies may avoid the side effects after CAR‐T‐cell therapy, and provide a novel perspective for future directions.
+
+Furthermore, the problem of access to patients' autologous T cells and high cost of therapy can be addressed through development of universal CARs. Application of clustered regularly interspaced short palindromic repeats (CRISPR) in order to modification of allogeneic genes can offers high potential to production of universal CAR‐T‐cell therapy for treatment of hematological malignancies. 128 , 129
+
+7 CONCLUSION
+
+CAR‐T‐cell therapy has provided potential for treatment of numerous hematological malignancies such as AML, ALL, CLL, MM, HL, and NHL. The main aim of this therapy is screen out tumor‐specific target antigens and design of CAR‐T cells to injection to patients against tumor cells. This strategy has been applied relatively successful in clinical treatment of hematological malignancies for, and have gained headway in this field. However, there are still several disadvantages, such as neurotoxicity, CRS, and off‐tumor toxicity that decrease efficiency as well as side effects of CAR‐T‐cell therapy. Therefore, further studies are required to identification of underlying molecular mechanisms and overcome these deficiencies.
+
+AUTHOR CONTRIBUTIONS
+
+Samane Abbasi, Milad Asghari Totmaj, Masoumeh Abbasi, Saba Hajazimian, and Pouya Goleij wrote the manuscript. Javad Behroozi and Alireza Isazadeh prepared figures for the manuscript. Alireza Isazadeh, and Javad Behroozi, and Behzad Baradaran edited and provided comments to improve the manuscript. Behzad Baradaran performed significant studies in the subject area of this manuscript.
+
+CONFLICT OF INTEREST
+
+All authors declare no conflict of interest.
+
+ETHICAL STATEMENT
+
+Being a review article, ethical committee approval was not required.
+
+ACKNOWLEDGMENTS
+
+Non applicable.
+
+DATA AVAILABILITY STATEMENT
+
+All the information provided in the article are available.
+==== Refs
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+78 Carpenter RO , Evbuomwan MO , Pittaluga S , et al. B‐cell maturation antigen is a promising target for adoptive T‐cell therapy of multiple myeloma. Clin Cancer Res. 2013;19 (8 ):2048‐2060.23344265
+79 Raje N , Berdeja J , Lin YI , et al. Anti‐BCMA CAR T‐cell therapy bb2121 in relapsed or refractory multiple myeloma. N Engl J Med. 2019;380 (18 ):1726‐1737.31042825
+80 Sun C , Mahendravada A , Ballard B , et al. Safety and efficacy of targeting CD138 with a chimeric antigen receptor for the treatment of multiple myeloma. Oncotarget. 2019;10 (24 ):2369‐2383.31040928
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+85 Kumar S , Kimlinger T , Morice W . Immunophenotyping in multiple myeloma and related plasma cell disorders. Best Pract Res Clin Haematol. 2010;23 (3 ):433‐451.21112041
+86 Ramos CA , Ballard B , Zhang H , et al. Clinical and immunological responses after CD30‐specific chimeric antigen receptor–redirected lymphocytes. J Clin Invest. 2017;127 (9 ):3462‐3471.28805662
+87 Wang CM , Wu ZQ , Wang Y , et al. Autologous T cells expressing CD30 chimeric antigen receptors for relapsed or refractory Hodgkin lymphoma: an open‐label phase I trial. Clin Cancer Res. 2017;23 (5 ):1156‐1166.27582488
+88 Jensen MC , Popplewell L , Cooper LJ , et al. Antitransgene rejection responses contribute to attenuated persistence of adoptively transferred CD20/CD19‐specific chimeric antigen receptor redirected T cells in humans. Biol Blood Marrow Transplant. 2010;16 (9 ):1245‐1256.20304086
+89 Kochenderfer JN , Dudley ME , Kassim SH , et al. Chemotherapy‐refractory diffuse large B‐cell lymphoma and indolent B‐cell malignancies can be effectively treated with autologous T cells expressing an anti‐CD19 chimeric antigen receptor. J Clin Oncol. 2015;33 (6 ):540‐549.25154820
+90 Schuster SJ , Svoboda J , Chong EA , et al. Chimeric antigen receptor T cells in refractory B‐cell lymphomas. N Engl J Med. 2017;377 (26 ):2545‐2554.29226764
+91 Stirrups R . CAR T‐cell therapy in refractory large B‐cell lymphoma. Lancet Oncol. 2018;19 (1 ):e19.29249304
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+94 Du J , Zhang Y . Sequential anti‐CD19, 22, and 20 autologous chimeric antigen receptor T‐cell (CAR‐T) treatments of a child with relapsed refractory Burkitt lymphoma: a case report and literature review. J Cancer Res Clin Oncol. 2020;146 (6 ):1575‐1582.32222815
+95 Neelapu SS . An interim analysis of the ZUMA‐1 study of KTE‐C19 in refractory, aggressive non‐Hodgkin lymphoma. Clin Adv Hematol Oncol. 2017;15 (2 ):117‐120.28398281
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+123 Shaw AR , Porter CE , Watanabe N , et al. Adenovirotherapy delivering cytokine and checkpoint inhibitor augments CAR T cells against metastatic head and neck cancer. Mol Ther. 2017;25 (11 ):2440‐2451.28974431
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+

PMC10183269.txt

@@ -0,0 +1,99 @@
+
+==== Front
+Intern Med
+Intern Med
+Internal Medicine
+0918-2918
+1349-7235
+The Japanese Society of Internal Medicine
+
+36104190
+10.2169/internalmedicine.0035-22
+Case Report
+Incidentally Detected Extramedullary Plasmacytoma of the Gallbladder: A Case Report and Literature Review
+Ono Hideki 1
+Iwatsu Shinichi 1
+Otsuka Eiichi 2
+Kato Yuji 1
+1 Department of Gastroenterology, Oita Prefectural Hospital, Japan
+2 Department of Hematology, Oita Prefectural Hospital, Japan
+Correspondence to Dr.　Hideki Ono, spgh6299@dune.ocn.ne.jp
+
+13 9 2022
+15 4 2023
+62 8 11451149
+28 3 2022
+24 7 2022
+Copyright © 2023 by The Japanese Society of Internal Medicine
+https://creativecommons.org/licenses/by-nc-nd/4.0/ The Internal Medicine is an Open Access journal distributed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. To view the details of this license, please visit (https://creativecommons.org/licenses/by-nc-nd/4.0/).
+Extramedullary plasmacytoma (EMP) can rarely occur in conjunction with multiple myeloma (MM). EMPs are usually detected in the upper aerodigestive tract (UAD) but can also occur along the digestive tract. However, the involvement of gallbladder is uncommon. Gastrointestinal tract symptoms often lead to the diagnosis of EMP in the gallbladder. An 81-year-old man was referred to our hospital with suspected primary gallbladder carcinoma. He was subsequently operated on, and the pathological findings showed EMP of the gallbladder without MM.
+
+plasmacytoma
+gallbladder
+multiple myeloma
+==== Body
+pmcIntroduction
+
+Multiple myeloma (MM) is a common hematologic malignancy characterized by intramedullary clonal plasma cell proliferation. Extramedullary plasmacytoma (EMP) involves any tissue outside the skeleton and is rarely seen in patients with MM (1,2). On preoperative imaging, it is difficult to distinguish gallbladder EMP from gallbladder carcinoma, the most common malignant gallbladder tumor (2).
+
+We herein report a case of incidentally detected gallbladder EMP without MM.
+
+Case Report
+
+An 81-year-old man was admitted to our hospital because of an asymptomatic gallbladder tumor detected by transabdominal ultrasonography (TAUS) at a periodic health checkup. He had a history of appendectomy, pulmonary emphysema, and prostate hypertrophy but no malignant neoplasms, including a hematopoietic organ tumor.
+
+An initial blood examination revealed slightly low hemoglobin (11.9 g/dL) and normal urea and electrolyte levels. The levels of tumor markers CEA and CA19-9 were within the normal range. Myeloma protein (M protein) was undetected in blood and urine samples. TAUS and endoscopic ultrasonography (EUS; convex array type) revealed a low-echoic mass with an extended base and asymmetric shape (Fig. 1A, B). The hyperechoic outer layer of the gallbladder was thin (Fig. 1B) following EUS. However, the common bile duct was normal. Computed tomography (CT) revealed an enhanced gallbladder mass (Fig. 2A, B). Gadoxetic acid-enhanced magnetic resonance imaging (GA-MRI) revealed a gallbladder nodule, and diffusion-weighted imaging revealed a nodule with extension in the gallbladder (Fig. 3A, B, C, and D). However, there was no evidence of invasion of the liver or tumors in other abdominal or pelvic organs. Esophagogastroduodenoscopy and total colonoscopy revealed no malignancy. We therefore diagnosed him with primary gallbladder carcinoma and referred the patient for open cholecystectomy.
+
+Figure 1. Images of TAUS and EUS. TAUS revealed a low echoic mass with an extent base and asymmetry shape (A). In EUS, the outer layer of the gallbladder was thinning (B, arrows). TAUS: transabdominal ultrasonography, EUS: endoscopic ultrasonography
+
+Figure 2. Contrasted CT revealed an enhanced mass in the gallbladder. Simple CT (A) and contrasted CT (B, arrows). CT: computed tomography
+
+Figure 3. MRI findings of the patient. Simple MRI (A). Ga-enhanced MRI (B, 20 seconds and C, 120 seconds) and diffusion-weighted imaging (D). A tumor nodule with smooth extension in the gallbladder wall was confirmed (D, arrows). MRI: magnetic resonance imaging, DWI: diffusion-weighted imaging
+
+After the operation, the resected specimen revealed a protruded tumor in the gallbladder (Fig. 4A). The cut surface of the specimen showed infiltrating gallbladder serosa, and a microscopic examination revealed abnormal plasma cells infiltrating the gallbladder serosa (Fig. 4B, C). In addition, an immunohistochemistry analysis confirmed positivity for CD138 (Fig. 4D, yellow quadrangle in Fig. 4C) and kappa light chain and negativity for CD20, CD3, CD4, CD5, CD8, and CD19. No liver invasion or lymph node metastasis was noted. Furthermore, bone marrow aspirate revealed normocellular marrow. Positron emission tomography-CT showed no abnormal accumulation in other organs. Therefore, the final diagnosis for the patient was gallbladder EMP without MM. The patient had no remarkable complications after surgery and has a follow-up evaluation scheduled for 26 months.
+
+Figure 4. Pathological findings of the patient. A macroscopic protruded tumor was noted in the gallbladder (A). The tumor was macroscopically recognized as infiltrating the gallbladder serosa at the cut surface of the specimen (B, arrows). The tumor contained abnormal plasma cells infiltrating the serosa of the gallbladder (C). The abnormal plasma cells were positive on immunoperoxidase stain for CD138, a plasma cell marker (400×) (D).
+
+Discussion
+
+EMP accounts for 3% of MM cases, with an age range at the diagnosis of 55-60 years old (3). Most solitary EMPs are in the head and neck, primarily in the upper aerodigestive tract (UAD) (4). Holler et al. summarized 1,134 cases of EMP from 1999 to 2021, and 131 cases of EMP were observed (about 11%) in the gastrointestinal tract (4). The liver accounted for 9% of 226 EMP cases reported in Bladé et al.'s study (5). Cases of EMP of the pancreas have been reported in the PubMed database. We searched the PubMed database for all relevant studies published until May 31, 2022, using the search term [plasmacytoma] AND [gallbladder] and found nine case reports (Table). Abughanimeh et al. (6) cited two other cases of gallbladder EMP with MM (7, 8; lower Table row). Gallbladder EMP can present with gastrointestinal symptoms, and the 11 total cases showed some symptoms or were associated with MM (1-3,6-13). In addition, case reports by St Romain et al., Fakhri et al., and Abt et al. incidentally detected gallbladder EMPs with MM (1,2,7).
+
+Table. Reference	Year	Age/Sex	Background MM	Presenting gastrointestinal symptoms	
+(5)	1995	53/M	Yes	Painless obstructive jaundice	
+(4)	2007	66/M	Yes	Right upper quadrant pain	
+(5)	2008	70/M	Yes	Right upper quadrant pain (due to cholecystitis)	
+(8)	2010	63/F	No	Epigastric pain, jaundice	
+(6)	2012	69/M	No	Right upper quadrant pain	
+(5)	2015	53M	Yes	Incidental imaging findings (CT)	
+(3)	2016	65/F	Yes	Incidental imaging findings (PET-CT)	
+(5)	2018	66/F	Yes	Right upper quadrant pain	
+(3)	2019	77/F	Yes	Sepsis to biliary source	
+(2)	1969	53/F	Yes	Asymptomatic gallbladder stone	
+(4)	2012	80/M	Yes	Obstructive jaundice	
+Our case	2021	81/M	No	Incidental imaging findings (TAUS)	
+MM: multiple myeloma, CT: computed tomography, PET-CT: positron emission tomography/computed tomography, TAUS: transabdominal ultrasonography
+
+Conversely, our case was asymptomatic and showed no evidence of MM. CT in previous cases reportedly detected a thickened gallbladder wall, dilated biliary ducts, or enlarged lymph nodes in gallbladder EMP or biliary ducts (2,6). In addition, primary gallbladder adenocarcinoma, the most common primary gallbladder polyp, shows similar radiological findings. While MRI was not performed in all 11 cases, a tumor nodule with smooth extension in the gallbladder wall was confirmed by diffusion-weighted MRI in our case. Although the differential diagnosis between primary gallbladder carcinoma and EMP of the gallbladder without histopathological findings remains challenging, MRI findings may aid in the preoperative diagnosis of EMP of the gallbladder. EUS fine-needle aspiration (FNA) has been widely used for biliary tract lesions. St Romain et al. reported a case of gallbladder EMP with MM diagnosed by EUS-FNA (1). EUS-FNA for biliary tract lesions should be performed with care due to risks such as biliary fistula and membrane dissemination (14). Majerović et al. and Hwang et al. reported cases of gallbladder EMP without MM that were surgically treated and had a good course (3,9). In Holler et al.'s report, patients with EMP who received radiation, surgery, a combination of surgery and radiation, and another therapy showed an overall local recurrence rate of 12.8%, with MM developing in 10.2% (4). Furthermore, among patients with EMP of the UAD undergoing surgery alone, 5% developed MM, whereas among those with EMP of non-UAD regions, 3.4% developed MM. Whether or not cholecystectomy alone contributes to a good course in patients with gallbladder EMP without MM is uncertain (10). Wirk et al. reported that EMP non-progressive of MM is usually indolent (15). To our knowledge, this is the first report of a rare case of asymptomatic gallbladder EMP without MM. Therefore, EMP should be considered in the differential diagnosis of gallbladder tumors, regardless of MM.
+
+The authors state that they have no Conflict of Interest (COI).
+
+Acknowledgement
+
+The authors wish to thank Dr. Haruka Sato from the Department of Radiology for her support in the diagnostic imaging.
+==== Refs
+1. St Romain P , Desai S , Bean S , Jiang X , Burbridge RA . Extramedullary plasmacytoma of the gallbladder diagnosed by endoscopic ultrasound fine-needle aspiration (EUS-FNA). J Gastrointest Oncol 6 : 7-9, 2015.
+2. Fakhri AA , Rodrigue PD , Fakhri AF . Extramedullary plasmacytoma of the gallbladder detected on fluorine 18-fluorodeoxyglucose positron emission tomography/computed tomography. J Clin Imaging Sci 6 : 40, 2016.27761300
+3. Majerović M , Bogdanić B , Drinković N , Kinda SB , Jakić-Razumović J , Gašparović V . Extramedullary plasmacytoma imitating neoplasm of the gallbladder fossa after cholecystectomy. Coll Antropol 36 : 331-333, 2012.22816242
+4. Holler A , Cicha I , Eckstein M , et al . Extramedullary plasmacytoma: tumor occurrence and therapeutic concepts -a follow up. Cancer Med. Forthcoming.
+5. Bladé J , Beksac M , Caers J , et al . Extramedullary disease in multiple myeloma: a systemic literature review. Blood Cancer J 12 : 45, 2022.35314675
+6. Abughanimeh O , Qasrawi A , Abu Omar M , Bahaj W , Abu Ghanimeh M . A case of multiple myeloma associated with extramedullary plasmacytoma of the gallbladder manifesting as acute cholecystitis. Cureus 10 : e2688, 2018.30050743
+7. Abt AB , Deppisch LM . Multiple myeloma involving the extrahepatic biliary system. J Mt Sinai Hosp NY 36 : 48-54, 1969.
+8. Fukatsu H , Hiramatsu Y , Takagi S , Morishita H . Multiple myeloma involving the extrahepatic bile duct. Intern Med 52 : 829-830, 2013.23545686
+9. Hwang DW , Lim CS , Jang JY , et al . Primary hematolymphoid malignancies involving the extrahepatic bile duct or gallbladder. Leuk Lymphoma 51 : 1278-1287, 2010.20572800
+10. Mouchli M , Grider DJ , Yeaton P . Gallbladder metastasis: a report of two cases. Case Rep Oncol 19 : 235-240, 2019.
+11. Kondo H , Kainuma O , Itami J , Minoyama A , Nakada H . Extramedullary plasmacytoma of maxillary sinus with later involvement of the gall bladder and subcutaneous tissues. Clin Oncol 7 : 330-331, 1995.
+12. Schuster D , Klosterhalfen B , Fiedler C , Prescher A . Metastasis of medullary plasmacytoma as the cause of acute cholecystitis. Dtsch Med Wochenschr 132 : 612-615, 2007 (in German).17357904
+13. Heckmann M , Uder M , Grgic A , Adrian N , Bautz W , Heinrich M . Extraosseous manifestation of multiple myeloma with unusual appearance in computed tomography - case report. Röntgenpraxis 56 : 249-253, 2008.19294871
+14. Tanaka K , Katanuma A , Hayashi T , Kin T , Takahashi K . Role of endoscopic ultrasound for gallbladder disease. J Med Ultrasonic 48 : 187-198, 2021.
+15. Wirk B , Wingard JR , Moreb JS . Extramedullary disease in plasma cell myeloma: the iceberg phenomenon. Bone Marrow Transplant 48 : 10-18, 2013.22410751
+

PMC10207982.txt

@@ -0,0 +1,270 @@
+
+==== Front
+Oncol Res
+Oncol Res
+OR
+Oncology Research
+0965-0407
+1555-3906
+Tech Science Press USA
+
+26913
+10.32604/or.2022.026913
+Review
+Histone deacetylase inhibitors as a novel therapeutic approach for pheochromocytomas and paragangliomas
+Histone deacetylase inhibitors as a novel therapeutic approach for pheochromocytomas and paragangliomas
+Histone Deacetylase Inhibitors in Pheochromocytomas and Paragangliomas
+MANTA ASPASIA 1
+KAZANAS SPYRIDON 2
+KARAMAROUDIS STEFANOS 3
+GOGAS HELEN 2
+ZIOGAS DIMITRIOS C. 2ziogasdc@gmail.com
+
+1 Endocrine Unit, Second Department of Internal Medicine Propaedeutic & Research Institute, Medical School, National and Kapodistrian University of Athens, Attikon University Hospital, Athens, 12461, Greece
+2 First Department of Internal Medicine, Medical School, National and Kapodistrian University of Athens, Laikon General Hospital, Athens, 11527, Greece
+3 Department of Obstetrics & Gynecology, General Hospital of Elefsina Thriassio, Elefsina, 19600, Greece
+* Address correspondence to: Dimitrios C. Ziogas, ziogasdc@gmail.com
+2022
+3 2 2023
+30 5 211219
+03 10 2022
+11 1 2023
+© 2022 Manta et al.
+2022
+Manta et al.
+https://creativecommons.org/licenses/by/4.0/ This work is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
+
+Epigenetic mechanisms, such as DNA methylation and histone modifications (e.g., acetylation and deacetylation), are strongly implicated in the carcinogenesis of various malignancies. During transcription, the expression and functionality of coding gene products are altered following the histone acetylation and deacetylation. These processes are regulated by histone acetyltransferases (HATs) and histone deacetylases (HDACs), respectively. HDAC inhibitors (HDACis) have been developed as promising therapeutic agents, to limit exposure to traditional and toxic chemotherapies and offer more alternatives for some specific malignant diseases with limited options. Mechanistically, these agents affect many intracellular pathways, including cell cycle arrest, apoptosis and differentiation, and their mechanism of action mainly depends on the type of cancer. Currently, five HDACis have been approved for the treatment of several hematological malignancies (e.g., T-cell lymphoma subtypes and multiple myeloma); while, many of them are tested for further therapeutic indications in solid tumors (e.g., colorectal, thyroid, breast, lung and pancreatic cancer). Herein, we review the literature and gather all available evidence, from in vitro and in vivo data to clinical trial results, that recognizes the antitumor activity of HDACis on pheochromocytomas and paragangliomas; and supports their clinical implementation in the treatment of these rare neuroendocrine tumors at metastatic setting.
+
+HDACis
+HDAC inhibitors
+Neuroendocrine tumors
+Epigenetics
+Histone deacetylation
+Cancer
+==== Body
+pmcIntroduction
+
+Pheochromocytomas (PCCs) and paragangliomas (PGLs) are rare neuroendocrine tumors that arise from chromaffin cells and frequently secrete one or more catecholamines. Pheochromocytomas arise from the adrenal medulla, whereas PGLs originate from extra-adrenal sympathetic or parasympathetic ganglia [1]. The diagnosis may be disregarded during life and be discovered in 0.05%–0.1% of autopsies [1]. Usually, PCCs are detected by chance (21.1%–57.6%) and constitute approximately 4%–8% of all adrenal incidentalomas [2,3]. Updating the previous epidemiological data where 10% of PCCs/PGLs were identified as malignant [4], all PCCs/PGLs are now considered potentially metastatic [5], and all patients should be advised for genetic counseling [1]. Currently, 25%–30% or more of these tumors are attributed to genetic background [6]; at least 15 PCC/PGL-related genes have been recognized, and 12 syndromes have been described [7].
+
+The diagnosis of PCC/PGL is based on detecting urinary metanephrines [8]. Following the biochemical diagnosis, CT scanning should be performed [1]. At the same time, functional imaging should also be used in suspicion of metastatic disease, including positron emission tomography (PET)/CT with various radiotracers [9] and 123I-metaiodobenzylguanidine (123I-MIBG) scintigraphy, especially to recognize those patients that could also be treated with 131I-MIBG [10]. After a multidisciplinary team consideration, most PCCs and PGLs can be treated surgically [1]. Still, for some unfit cases, therapy with 131I-MIBG could also be a reasonable option, if 123I-MIBG scintigraphy is positive [11,12]. For metastatic PCCs/PGLs, different combinations of conventional chemotherapy, mainly the regimen cyclophosphamide, vincristine and dacarbazine, have been used for many years [13–15], but recently, novel agents, including tyrosine kinase inhibitors [16], somatostatin analogs [17], hypoxia-inducible factor (HIF) inhibitors, mTOR inhibitors, histone deacetylase inhibitors (HDACis), DNA-alkylating agents and immune checkpoint inhibitors [18] are under testing for the treatment of metastatic/unresectable setting of these rare neuroendocrine tumors.
+
+Among those approaches, the inhibition of histone deacetylation via HDACis has been entered into the focus of this study. The imbalance between histone acetylation and deacetylation can epigenetically change the expression of tumor suppressor genes and/or proto-oncogenes [19–21], that control cancer evolution and progression [22,23]. So far, 18 human HDACs have been identified into two families according to the implicated co-factor [24–26]. Different classes of HDACs are located in different cellular compartments [23,27]. An overview of the classification of human HDACs, their cellular localization and their tissue expression is presented in Table 1 [25–28].
+
+Table 1 HDAC classification, cellular localization and tissue expression
+
+HDAC classes	Cellular localization	Normal tissue expression	
+Classical HDAC family
+Zn 2+ -dependent	Class I	HDAC1	Nucleus	All tissues	
+HDAC2	Nucleus	All tissues	
+HDAC3	Nucleus	All tissues	
+HDAC8	Nucleus, Cytoplasm	Smooth muscle	
+Class II
+	IIA	HDAC4	Nucleus, Cytoplasm	Brain, heart, skeletal muscle	
+HDAC5	Nucleus, Cytoplasm	Brain, heart, skeletal muscle	
+HDAC7	Nucleus, Cytoplasm	Heart, lungs, placenta, pancreas, skeletal muscle, thymus	
+HDAC9	Nucleus, Cytoplasm	Brain, skeletal muscle	
+IIB	HDAC6	Cytoplasm	Heart, brain, skeletal muscle	
+HDAC10	Nucleus, Cytoplasm		
+Class IV	HDAC11	Nucleus		
+Sirtuin family
+NAD + -dependent	Class III	SIRT1	Nucleus		
+SIRT2	Cytoplasm	All tissues	
+SIRT3	Mitochondria	All tissues	
+SIRT4	Mitochondria	All tissues	
+SIRT5	Mitochondria	All tissues	
+SIRT6	Nucleus	All tissues	
+SIRT7	Nucleus	All tissues	
+
+HDACs are overexpressed in hematologic and solid malignancies, and their inhibition became a promising anti-cancer theory. HDACis include both natural and synthetic compounds. Some HDACis selectively inhibit specific HDAC classes while others are pan-HDAC inhibitors [29]. HDACis increase acetylation of both histone and non-histone proteins to a significant degree, resulting in cell cycle arrest, cell differentiation, induction of cell death/apoptosis (e.g., oxidative stress generation, disruption of mitosis and mitotic cell death, autophagy, etc.), as well as blocking of angiogenesis [30]. Fig. 1 depicts the multiple mechanisms of action of HDACis [29]. The main HDACis under clinical testing, their targeted HDACs, their chemical nature and their approved indications are presented in Table 2 [23,29,31,32]. The antitumor activity of these agents depends on the specific type and stage of cancer, the characteristics of each patient and the administered dose [23,33].
+
+FIGURE 1 Summary of HDACis’ mechanisms of action in cancer.
+
+Table 2 HDACis under clinically testing as anticancer agents
+
+HDACi	HDAC Target	Chemical Class	Cancer Type	FDA Approval	
+Vorinostat	Class I, II and IV	Hydroxamic acid	CTCL, Melanoma, Gastric, Breast, NSCLC, Ovarian, Thyroid	CTLC	
+Belinostat	Class I, II and IV	Hydroxamic acid	PTCL, Breast, Ovarian, SCLC, Neuroendocrine	PTLC	
+Panobinostat	Class I, II and IV	Hydroxamic acid	MM, Breast, CML, SCLC, Prostate, Nasopharyngeal, Renal, Melanoma	MM	
+Pracinostat	Class I, II and IV	Hydroxamic acid	AML, Prostate		
+Givinostat	Class I and II	Hydroxamic acid	CLL, HL, MM		
+Resminostat	Class I and II	Hydroxamic acid	Colorectal, HCC, HL, CTCL		
+Abexinostat	Class I and II	Hydroxamic acid	CLL, HL, NHL, Breast, Melanoma, Sarcoma, Renal, DLBCL		
+Quisinostat	Class I and II	Hydroxamic acid	CTCL, Leukemia, NSCLC, Ovarian, MM,		
+Nanatinostat	Class I	Hydroxamic acid	Nasopharyngeal, EBV-Related Tumors		
+Rocilinostat	HDAC 6	Hydroxamic acid	MM, Breast, CLL, Cholangiocarcinoma		
+Mocetinostat	Class I and IV	Benzamide	Solid Tumors, Melanoma, NSCLC		
+Domatinostat	Class I	Benzamide	Advanced Hematologic Malignancies		
+AR-42	Class I, II, and IV	Hydroxamic acid	MM, AML		
+Entinostat	Class I	Benzamide	Solid Tumors, Melanoma, Lymphoma, AML, Breast, Colon		
+Alteminostat	Class I and II	Hydroxamic acid	DLBCL, MM		
+Tacedinaline	Class I	Benzamide	Lung, Pancreatic, MM		
+Tucidinostat/ Chidamide	HDAC 1, 2, 3, 10	Benzamide	Breast, PTCL, Cervical, Gastric, Esophageal, NSCLC	PTCL	
+Romidepsin	Class I	Cyclic peptides	CTCL, PTCL, HL, NHL, Breast	CTLC, PTLC	
+Valproic Acid	Class I and II	Fatty acid	Hematologic & Solid Tumors, CLL, Brain		
+Sodium butyrate	Class I and II	Fatty acid	Hematologic & Solid Tumors		
+Pivanex	Class I and II	Fatty acid	NSCLC, MM, CLL		
+Nicotinamide	Class III	Sirtuins Inhibitor	Laryngeal, Bladder, NSCLC		
+Abbreviations: CTCL: Cutaneous T-cell Lymphoma; PTCL: Peripheral T-cell Lymphoma; DLBCL: Diffuse Large B Cell Lymphoma; MM: Multiple Myeloma; AML: Acute Myeloid Leukemia; HCC: Hepatocellular Carcinoma; HL: Hodgkin Lymphoma; NHL: Non-Hodgkin Lymphoma; CML: Chronic Myeloid Leukemia; CLL: Chronic Lymphocytic Leukemia; NSCLC: Non-Small-Cell Lung Carcinoma.
+
+In this review, we summarize the research background and the development status of currently tested HDACis for treating metastatic/unresectable PCCs/PGLs and gather all published evidence from in vitro and in vivo studies up to clinical trials, supporting their implementation in oncological practice. The most important clinical trials investigating the use of HDACis in the treatment of PCCs/PGLs and NETs are presented in Table 3.
+
+Table 3 Major clinical trials investigating the use of HDACis in the treatment of advanced PCCs/PGLs and NETs
+
+Study	Type	Agent(s) used	Design	No. of patients	Diagnosis	Result	Comments	
+Mohammed et al., 2011 [34]	Phase II	Valproic acid	Twice daily PO	8	Low-grade NET	Stabilization of progressive disease and improvement of NET biomarker chromogranin A. Increase of Notch-1 post-treatment.	Patients with PCC/PGL excluded.	
+Fu et al., 2015 [35]	Phase I	Vorinostat + pazopanib	600 mg daily of pazopanib + 300 mg daily of vorinostat	78
+(1 PCC)	Advanced solid tumors	No meaningful antitumor activity overall.	Patients with a hotspot T53 mutation had a notably better response.	
+Kelly et al., 2005 [36]	Phase I	Vorinostat	400 mg once daily/200 mg twice daily/300 mg twice daily for 3 days/week	73
+(1 PGL)	Advanced solid and hematologic malignancies	Proposed dosing schedules safe for prolonged treatment, sufficient inhibition of HDAC activity.	Only 30% of patients remained in the study for 4 or more months.	
+DuBois et al., 2015 [37]	Phase I multicenter	Vorinostat + 131I-MIBG	Oral vorinostat once daily on days 1–14, intravenous 131I-MIBG on Day 3	27	Relapsed or refractory neuroblastoma	Histone acetylation increased post-treatment. MTD determined at 180 mg/m2/dose for vorinostat and 18 mCi/kg for MIBG.	Potentially useful results for other tumors treated with 131I-MIBG.	
+Pollard et al, 2021 [38]	Pilot study	Vorinostat	4 days of vorinostat followed by imaging with 123I-MIBG and 68Ga-DOTATOC	50	Metastatic midgut NET	Significant increase in 68Ga-DOTATOC uptake in some liver metastases.	Further research for pretreatment with HDACis needed.	
+DuBois et al., 2021 [39]	Phase II	131I-MIBG, 131I-MIBG + Vincristine + Irinotecan, 131I-MIBG + Vorinostat	One course of treatment-Group A: 131I-MIBG only, Group B: 131I-MIBG + IV Vincristine + IV Irinotecan, group C: 131I-MIBG + Vorinostat orally once daily	105	Relapsed or refractory neuroblastoma	The group treated with 131I-MIBG + Vorinostat had the best response rate with minimum toxicity.	Potentially useful results for other tumors treated with 131I-MIBG.	
+Balasubramaniam et al., 2018 [40]	Phase I	Belinostat + cisplatin + etoposide	48 h continuous IV infusion of belinostat	28
+(2 PCC/
+PGL)	Advanced NETs and SCLC	Safe dose for future studies confirmed at 500 mg/m2/24 h. In patients with PCC/PGL, the treatment resulted in one stable disease after 4 cycles of treatment and one progressive disease after 2 cycles of treatment.	A specific group of patients with the UGT1A1 genotype was notably affected by AEs.	
+Jin et al., 2016 [41]	Phase II	Panobinostat	Oral panobinostat 20 mg once daily 3 days/week	15	Metastatic low-grade NETs	High stable disease rate, median progression free survival of 9.9 months. Treatment relatively well tolerated.	Different NETs possibly respond differently to therapeutic agents.	
+
+HDACis in Pheochromocytomas & Paragangliomas
+
+Valproic acid (VPA)
+
+Valproic acid is a branched short-chained fatty acid that was synthesized in 1882 and was approved for treating epilepsy in 1967 [42]. Since then, it has been used as an effective anticonvulsant medication in many neurological indications. After discovering that VPA can inhibit both HDAC classes I and IIA, many in vitro studies and clinical trials examined its use in different tumor types, including ovarian, breast, lung, pancreatic and thyroid cancer [43,44]. Adler et al. observed that rat PCC cells treated with increasing doses of VPA reduced their neuroendocrine tumor (NET)-related biomarkers, achaete-scute complex-like1 (ASCL1) and chromogranin A (CgA), and subsequently decreased their hormone secretion. The authors concluded that VPA-treated cells suppressed their growth rate by nearly 70% compared to controls in a dose-dependent manner by activating apoptotic pathways, mainly Notch-1 signaling [45]. Notch-1 is inactive in NETs, and its activation is associated with tumor growth inhibition and analogical decrease in NET-related biomarkers [46]. In another experiment in rat PCC cells, treatment with VPA for 48 h showed a similar dose-dependent effect on HDAC-mediated cancer cell growth [47]. In the clinical setting, an interesting phase II study including patients with low-grade neuroendocrine neoplasms was published in 2011 (Table 3). Although patients with PCC/PGL were excluded from this study, treatment with VPA resulted in stable disease in most of included patients, improving the levels of chromogranin A. It is worth noting that post-treatment Notch-1 was upregulated by ten times on average, in the VPA-treated population [34].
+
+Vorinostat (Suberanilohydroxamic acid–SAHA)
+
+Vorinostat is an antitumor agent with inhibitory effects on both classes of HDACs (I and II). At doses that have little or no toxicity on normal cells, it was found that it can cause growth arrest and cell death [48] and subsequently, its therapeutic efficacy was examined as a monotherapy or in combined regimens with promising results [48,49]. Since October 2006, vorinostat has received approval from FDA for patients with cutaneous T-cell lymphoma (CTCL) who have progressive, persistent or recurrent disease on or following two prior systemic therapies [50]. Repositioning some new therapeutic options to the metastatic PCC/PGL, Giubellino et al. suggested that the synergistic combination of the HDAC inhibitor SAHA and the topoisomerase inhibitor epirubicin could represent an example of possible successful treatment [51]. In patients with PCC/PGL, a mutation in the mitochondrial complex II subunit (succinate dehydrogenase subunit B, SDHB) is associated with more aggressive and extensive disease [52,53]. Yang et al. showed that SDHB expression was reduced in SDHB-mutated tumors, and the administration of vorinostat could prevent further degradation and restore the quantity of functional SDHB, blocking the proliferative signaling [54]. The effect of vorinostat on advanced solid and hematologic malignancies has been examined by 3 phase I and 1 phase II clinical trials as well as by a pilot study on imaging of metastatic midgut NET (Table 3). The dosing schedules were safe with sufficient inhibition of HDAC activity either as vorinostat monotherapy or in a combination with other agents, including pazopanib and [131I]-MIBG. Overall, the efficacy results from these trials were modest [35,38], and only Kelly et al. [36] achieved keeping 30% of patients with advanced solid and hematologic malignancies in the study for more than four months [47–50]. Recently, a randomized phase II clinical trial compared 131I-MIBG plus vorinostat vs. 131I-MIBG alone and vs. 131I-MIBG plus vincristine plus irinotecan in 105 patients with neuroblastoma. The combination of 131I-MIBG plus vorinostat offered the highest response rate [39].
+
+Belinostat (PXD101)
+
+Belinostat is a hydroxamic acid that was approved for treating peripheral T-cell lymphoma (PTCL) in 2014. It is currently under investigation for the treatment of both hematologic and solid malignancies, as monotherapy or in a combination with other anticancer agents [23,29]. It is considered a pan-HDACi, blocking Class I, II and IV HDACs [22]. Recently, a prodrug of belinostat, ZL277, was reported to be superior in bioavailability and efficacy. Because of its superior biocompatibility and intratumoral penetration, ZL277 was found to be more effective than belinostat in vivo, not only preventing tumor development but also dramatically lowering tumor sizes in an MCF-7 xenograft tumor model [55]. However, these results are preliminary enough, and more studies are needed to support its potential use in humans [52]. A phase I clinical trial tried to determine the maximum tolerated dose of the combination of belinostat (in a 48-h continuous IV infusion on days 1–2, reached 500 mg/m2/24 h) with cisplatin (a 1-h IV infusion of 60 mg/m2 on day 2), and etoposide (a 1-h IV infusion of 80 mg/m2 on days 2, 3, and 4) in 28 patients with advanced small cell lung cancer (SCLC) (Table 3). The combination was safe and active in SCLC and other neuroendocrine cancers. Objective responses were observed in 11 (39%) of 28 patients and seven (47%) of 15 patients with neuroendocrine tumors. In the 2 included patients with PCC/PGL, the combination resulted in one stable disease after four cycles, and one progressive disease after two cycles of treatment Patients carrying more than three copies of variant UGT1A1 (*28 and *60) had higher serum levels of belinostat because of slower clearance. Future phase II studies incorporating the genotyping information for UGT1A1*28 and UGT1A1*60 are needed to identify candidates for this combination [40].
+
+Sodium butyrate (NaB)
+
+Sodium Butyrate is a short-chain fatty acid and one of the oldest identified HDACis [56,57]. It is typically produced in the gastrointestinal tract through anaerobic bacterial fermentation of dietary fibers. Among the fatty acids, NaB displays the most remarkable efficacy in inhibiting HDAC activity, including most HDAC classes. Many studies have examined its use in different types of cancer. Primarily due to its production in the colon, NaB may have a protective role in the development of colon cancer [58,59]. The impact of NaB in rat PCC cells was initially described in 1987, when Byrd et al. observed that treatment with NaB ceased cell division and altered the cellular “malignant” phenotype [56]. In rat PCC cells, NaB reduced cell growth and the levels of NET biomarkers ASCL1 and CgA in a dose-dependent manner, activating the Notch-1 pathway and subsequent carcinogenesis, as mentioned above. The tumor growth was inhibited due to the concurrent arrest of the cell cycle and the induction of apoptosis [60].
+
+Trichostatin A (TSA)
+
+Trichostatin A is a metabolic compound isolated from the strains of Streptomyces hygroscopicus in 1975 primarily used for its antifungal activity [61]. TSA displays a similar inhibitory activity in Classes I and II HDAC, keeping some slight differences among particular HDAC counterparts, but remains ineffective in Class III HDAC [62,63]. It has shown anti-proliferative properties, inducing cell cycle arrest, differentiation and apoptosis [64]. Treatment with TSA inhibited the proliferation of mouse pheochromocytoma cells (MPC) in a dose- and time-dependent manner, while increased specific [3H]-norepinephrine and 123I-MIBG uptake. In vivo experiments showed that TSA-treated tumor-bearing mice presented an increased uptake of 123I-MIBG and 18F-fluorodopamine in their metastatic liver lesions. Although TSA may enhance the response to 131I-MIBG treatment and make more effective the123I-MIBG-mediated diagnosis of metastatic disease, its poor in vivo availability will never permit its use in clinical trials in patients [65].
+
+Romidepsin (FR901228 or FK228)
+
+Romidepsin is a bicyclic depsipeptide that was isolated from Chromobacterium violaceum cultures, first reported in the literature in 1994 [66]. In 1998, Nakajima et al. showed that romidepsin could inhibit intracellular HDAC. Its mechanism of action is similar to TSA, despite their chemical and structural differences [67]. Romidepsin was found to inhibit the growth of tumor cell lines, but its anticancer efficacy varied against different tumor tissues [68]. Currently, romidepsin has been approved for treating patients with CTCL or PTCL who have received at least one prior line of therapy [25]. Martiniova et al. found that in vitro exposure of MPC cells to romidepsin increases the uptake of 3H-norepinephrine, 18F-fluorodopamine and 123I-MIBG and causes a dose- and time-dependent decrease of cell proliferation. Further in vivo studies revealed that mice with metastatic PCC treated with romidepsin had increased uptake of 123I-MIBG and 18F-fluorodopamine in their liver lesions. Taken together these results, we could suggest that romidepsin may work as a useful diagnostic and therapeutic tool, improving the accuracy of 123I-MIBG scintigraphy or 18F-fluorodopamine PET, and in parallel, increasing the response of 131I-MIBG treatment in patients with PCC [65].
+
+Suberoyl bis-hydroxamic acid (SBHA)
+
+Suberoyl bis-hydroxamic acid is a close analog of SAHA that acts as a HDACi and has been tested in treating NETs. It activates Notch-1 signaling, suppresses the secretion of NET-related biomarkers and hormones and inhibits cell proliferation by inducing cell cycle arrest or apoptosis in various cancer cell lines, including gastrointestinal and pulmonary carcinoid cells and medullary thyroid cancer cells [69–71]. Adler et al. treated rat PCC cells with gradually increasing doses of HDACis. Treatment of PCC cells with SBHA had similar results as treatment with VPA. The NET biomarkers ASCL1 and CgA were decreased. SBHA in a dose of 40μM suppressed tumor growth in more than 70% of PCC cells after six days of treatment and activated the apoptotic pathway. More specifically, the Notch-1 signaling pathway was upregulated 3-fold upon treatment with 40 μM of SBHA [45].
+
+(−)-Epigallocatechin-3-gallate (EGCG)
+
+(−)-Epigallocatechin-3-gallate (EGCG) is one of the most substantial polyphenolic extracts in green tea. It has been reported that EGCG inhibits DNA methyltransferase and reactivates methylation-silenced genes in various cancer cell lines (e.g., human colon cancer HT-29 cells, esophageal cancer KYSE 150 cells, and prostate cancer PC3 cells) [72]. This induced inhibition of DNA methylation by a commonly consumed dietary constituent suggested a potential use of EGCG for the reversal of related gene silencing in the prevention of carcinogenesis. EGCG acts in a concentration-dependent manner affecting class I HDACs, a especially HDAC1 [73,74]. Hu et al. studied the effect of EGCG on PCC-xenografted mice. Treatment with EGCG affected both tumor growth and apoptosis via activating the caspases 3 & 7 and decreasing amyloid precursor protein (APP) levels [75]. This specific APP protein seems to play a significant role in various diseases, including Alzheimer’s disease. It has been studied as a diagnostic tumor biomarker or as a targetable molecule in distinct cancer types, including pancreatic adenocarcinoma or colon carcinoma. Except EGCG, studies have shown that also other HDACis (such as VPA) could downregulate the levels of APP, leading to decreased tumor growth, invasion and angiogenesis [76].
+
+Panobinostat
+
+Panobinostat is a pan-HDAC inhibitor with high efficacy that has shown substantial antineoplastic activity in various cancer cell lines and is currently being clinically tested against hematologic and solid malignancies [77]. In 2015, it was approved for treating multiple myeloma in patients who have received at least two previous treatments [78]. A Phase II clinical trial showed that treatment with panobinostat resulted in a high rate of stable disease and a median progression-free survival (PFS) of 9.9 months with tolerable toxicity in patients with metastatic low-grade neuroendocrine tumors. Still, no data is available regarding PCCs/PGLs (Table 3) [41].
+
+Conclusion
+
+PCCs and PGLs are rare neuroendocrine tumors with complicated genetic backgrounds and an unmet need for a more individualized approach, especially in the metastatic or unresectable setting. The benefits from conventional chemotherapy are limited, and the prognosis remains poor. Based on some promising preclinical results, HDACis grew the expectations of being a considerable alternative treatment for these tumors with the limited options in the metastatic context. The preliminary clinical studies confirmed that HDACis could inhibit tumor growth, activate specific molecular pathways and act synergistically with other already approved treatments, such as 131I-MIBG. However, more data are required to prove the benefit of their use, to determine the specific HDACis compound that should be used in the rare indication of metastatic PCCs/PGLs, and to detect the optimal dosing and the way of administration (as monotherapy or in combination with other agents), following in parallel their safety profile.
+
+Funding Statement
+
+The authors received no specific funding for this study.
+
+Author Contributions
+
+Aspasia Manta: Conceptualization, Writing, Reviewing and Editing. Spyridon Kazanas: Data collection and curation, Writing-original draft preparation. Stefanos Karamaroudis: Data collection and curation, Writing-original draft preparation. Helen Gogas: Reviewing and Editing. Dimitrios C. Ziogas: Supervision, Conceptualization, Reviewing and Editing. All authors approved the final version of the manuscript.
+
+Availability of Data and Materials
+
+Data supporting this article are included within the reference list. Please contact corresponding author for any further information.
+
+Ethics Approval
+
+Not applicable
+
+Conflicts of Interest
+
+Helen Gogas has received grants and personal fees by Roche, BMS, MSD, Novartis and personal fees by Amgen and Pierre Fabre, outside the submitted work. All other authors declare no conflict of interest.
+==== Refs
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+

PMC10309407.txt

@@ -0,0 +1,111 @@
+
+==== Front
+Rev Bras Ginecol Obstet
+Rev Bras Ginecol Obstet
+10.1055/s-00030576
+RBGO Gynecology & Obstetrics
+0100-7203
+1806-9339
+Thieme Revinter Publicações Ltda Rio de Janeiro, Brazil
+
+28834996
+10.1055/s-0037-1605373
+0007
+Case Report
+Uterine Extramedullary Plasmacytoma as a Primary Manifestation of Multiple Myeloma
+Plasmocitoma extramedular uterino como manifestação primária de mieloma múltiploCodorniz Ana 1
+Cunha Renato 2
+Fernandes Fernando 1
+Pais Maria João 3
+Neves Tiago 4
+Quintana Carlos 5
+1 Gynecology Department, Hospital do Espírito Santo de Évora, Évora, Portugal
+2 Medical Oncology Department, Hospital do Espírito Santo de Évora, Évora, Portugal
+3 Medicine II Department, Hospital do Espírito Santo de Évora, Évora, Portugal
+4 Orthopedics Department, Hospital do Espírito Santo de Évora, Évora, Portugal
+5 Pathological Anatomy Department, Hospital do Espírito Santo de Évora, Évora, Portugal
+Address for correspondence Ana Codorniz, Medical Resident Serviço de Ginecologia do Hospital do Espírito Santo de ÉvoraLargo Senhor da Pobreza, 7000-811, ÉvoraPortugalcodorniza@hotmail.com
+23 8 2017
+9 2017
+1 8 2017
+39 9 516520
+28 3 2017
+30 6 2017
+https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License, which permits unrestricted reproduction and distribution, for non-commercial purposes only; and use and reproduction, but not distribution, of adapted material for non-commercial purposes only, provided the original work is properly cited.
+The association between plasmacytomas and multiple myeloma (MM) is well-described, and in about one third of the cases of plasmacytoma the additional study will lead to the diagnosis of MM. The finding of plasmacytomas in the genital tract is extremely rare, with sparse cases described in the literature, and these cases pose a challenge regarding the optimal guidance and treatment. This paper describes a case of uterine extramedullary plasmacytoma in a 79-year-old woman with complaints of postmenopausal abnormal uterine bleeding. The complementary study led to the diagnosis of uterine plasmacytoma and, subsequently, of MM. Despite the unfavorable outcome of this case, we consider pertinent to report it because it constitutes a differential diagnosis to be taken into account in the approach of pelvic masses.
+
+Resumo
+
+A associação entre plasmocitomas e mieloma múltiplo (MM) encontra-se bem demonstrada, e em cerca de um terço dos casos de plasmocitoma o estudo adicional conduzirá ao diagnóstico de MM. O achado de plasmocitomas no trato genital é extremamente raro, havendo um número muito limitado de casos descritos na literatura, o que dificulta concluir sobre a melhor forma de orientação e tratamento destes casos. O presente trabalho descreve um caso de plasmocitoma extramedular uterino em mulher de 79 anos estudada por queixas de hemorragia uterina anômala pós-menopáusica. O estudo complementar levou ao diagnóstico de plasmocitoma uterino e, posteriormente, de MM. Apesar do desfecho desfavorável do caso, consideramos pertinente o seu relato por se tratar de um diagnóstico diferencial a levar em consideração na abordagem de massas pélvicas.
+
+Keywords
+
+plasmacytoma
+multiple myeloma
+Palavras-chave
+
+plasmocitoma
+mieloma múltiplo
+==== Body
+pmcIntroduction
+
+Plasma cells dyscrasias refer to a group of neoplasms that is characterized by the proliferation of a monoclonal population of plasmocytes that secretes monoclonal immunoglobulins. These neoplasms may present in single or multiple lesions (solitary plasmacytomas or multiple myeloma respectively). The association between plasmacytomas and multiple myeloma (MM) is well-established,1 and in about one third of the cases the additional study of a plasmacytoma will lead to the diagnosis of MM.2 These tumors can appear in the bone or in different organs, and are classified as bone or extramedullary plasmacytomas respectively.3 Extramedullary plasmacytomas in the female genital tract are quite rare, either as solitary plasmacytomas or as part of a disseminated MM. There are few cases described in the literature,3 4 5 6 7 considering that 80% of extramedullary plasmacytomas arise in the head or neck, mostly in the superior respiratory and digestive tracts.
+
+Case Report
+
+We present a case of a 79-year-old woman, bedridden and totally dependent on other people to perform her daily activities, with a past history of breast cancer at 54 years old treated with conservative surgery followed by radiotherapy. She had no other relevant personal antecedents, made no chronic use of medications, and had no smoking or drinking habits. Her menarche occurred at 11 years old, and she had regular cycles and 1 pregnancy (with a vaginal delivery at 27 years old). There was no history of use of hormonal contraceptives, and she had spontaneous menopause at 41 years without hormonal therapy.
+
+The patient arrived to the emergency department with complaints of postmenopausal abnormal uterine bleeding (AUB) since the previous month, with no other symptoms. Upon physical examination, there were no signs of hemodynamic instability. Upon the speculum examination, a moderate amount of necrotic tissue and blood with fetid odor was found in the vagina and through the external cervical orifice; they were sent for a histological test. The blood work was normal, with a hemoglobin level of 11.7 g/dL. The pelvic ultrasound showed a heterogenic endometrial thickening of 21 mm, atrophic ovaries, and no adnexal masses. The histological study revealed a neoplasm with diffuse infiltration of atypical plasma cells, suggestive of myeloma. Immunohistochemistry: CD138 and CD56 positive, cytokeratins, S100, estrogen receptors, actin, desmin, CD20 and CD79a negative (Fig. 1). The kappa and lambda light chain analysis was inconclusive. She had an antigen Ki-67 level of 60%.
+
+Fig. 1 Histological study of fragments of the endometrium. (A and B) Hematoxylin-eosin (HE). Immunohistochemistry with positivity for CD56 (C) and CD138 (D) antibodies.
+
+Shortly after, the patient returned to the emergency with a history of severe pain and functional disability of the right arm after brushing her teeth. Radiography identified a supracondylar pathological fracture of the right humerus, which reinforced the diagnosis hypothesis of MM. Additionally, she maintained the AUB, but with associated anemia (hemoglobin level of 7.2 g/dL). The patient was then admitted to the Department of Orthopedics. During hospitalization and a complementary study, a rise in the β-2-microglobulin and kappa chain levels was noted. Computed tomography (CT) showed diffuse osteopenia and two expansive lesions: one with 6.9 × 7.9 cm, solid and heterogeneous, well-delimited, with moderate contrast-enhanced, located on the left hypochondrium, between the diaphragmatic cupula, stomach, pancreas and spleen, with apparent cleavage plan with adjacent structures; the other lesion measured 10 × 11 × 9 cm, and was heterogeneous, with macrolobulated contours, and intense contrast-enhanced, located on the pelvis, probably of neoformative origin, and was responsible for an increase in the uterine volume (Fig. 2).
+
+Fig. 2 Enlarged uterus with a heterogeneous uterine lesion of macrolobulated contours and intense contrast-enhanced, of probable neoformative origin.
+
+Conservative treatment for the fracture was decided, and the patient was referred to a Hematology-Oncology appointment. In the follow-up, the diagnosis of MM was assumed, considering that the patient had a histologically-confirmed plasmacytoma, bone lesion of target organ and increase in biomarkers. However, as the patient was totally dependent, bedridden, without conditions for intensive chemotherapy regimens and presented progressive worsening of the general condition with performance status 4 at the time of the appointment, we decided to immediately start a treatment with melphalan combined with prednisolone, without waiting for other tests, namely the bone biopsy.
+
+The patient maintained a gradual worsening of the general condition, and only had a chemotherapy cycle before dying shortly after, in a palliative care unit.
+
+Discussion
+
+The extramedullary and bone plasmacytomas correspond to localized forms of plasma cells neoplasms,8 and result from the proliferation of monoclonal plasma cells.9
+
+The mean age of onset of these lesions is 55 years, with a predominance of females,1 and the most common location of extramedullary plasmacytomas is the upper respiratory and digestive tracts (82%), followed by the gastrointestinal tract, the urogenital tract, the skin, the lung and the breast.10 The initial form of presentation may correspond to the finding of one or more localized swellings and/or the onset of nonspecific symptoms related to its location.11
+
+The association between plasmacytomas, especially bone plasmacytomas, and MM has been well-described.11 Extramedullary involvement, however, is less frequent, and it generally presents in more advanced stages of the disease.12 Not only is there a risk of progression of solitary plasmacytomas to MM, but plasmacytomas may occur in naturally as secondary forms of MM.13
+
+In less than 5% of patients with a plasma cell dyscrasia, the onset of the disease is the detection of a plasmacytoma with no manifestations of systemic disease.14 The incidence of extramedullary plasmacytomas at the time of the diagnosis of MM is around 7–18%, and 6–20% of patients will develop this type of tumor during the MM follow-up,15 with a better survival prognosis in the latter situation.1
+
+The diagnosis of primary plasmacytoma (bone or extramedullary) differs from MM because there is a histological confirmation but no evidence of plasma cells involving the bone marrow, with no evidence of lytic lesions in the bone study, and absence of hypercalcemia, anemia or insufficiency associated renal disease.10
+
+Due to its important association with MM and prognosis implications,10 the initial investigation of patients with extramedullary plasmacytoma should include a detailed study to confirm or exclude this diagnosis.8 Similarly, the follow-up of patients with the diagnosis of plasmacytoma should include adequate surveillance to allow the early detection of MM, although the duration and frequency of such follow-up have not yet been well-established.8
+
+The occurrence of plasmacytomas in the female genital tract is rare, with few cases described.3 4 5 6 7 14 16 17 Due to the scarcity of available information, the optimal follow-up and treatment are also to be clarified.
+
+Regarding the treatment, the distinction between primary plasmacytoma and MM is essential, as the approaches are quite different. While the former has a good response to radiotherapy (the first-line treatment), in the latter, the systemic treatment is the choice.
+
+We describe a rare diagnosis of a uterine extramedullary plasmacytoma detected by postmenopausal AUB. In this case, although the complementary study was not concluded due to the rapid worsening of the general health state, the finding of bone lesions and histologically-confirmed plasmacytoma led to the diagnosis of MM. This report is relevant because it constitutes a differential diagnosis to be presented in the study of pelvic masses with important management and prognosis implications.
+
+Conflicts to Interest The authors have no conflicts of interest to declare.
+==== Refs
+References
+
+1 Schols S E Tick L L Recurrent extramedullary plasmacytoma in asymptomatic multiple myeloma: a case report J Med Case Reports 2015 9 37
+2 Ruiz Santiago F Tello Moreno M Martín Castro A Guzmán Alvarez L Navarrete González P Soft tissue extramedullary plasmacytoma Case Rep Med 2010 2010 307902 20204131
+3 Fischer E G Bocklage T J Rabinowitz I Smith H O Viswanatha D S Primary plasmacytoma arising in an endocervical polyp with detection of neoplastic cells on papanicolaou test. A case report and review of the literature Arch Pathol Lab Med 2003 127 01 e28 e31 12562291
+4 Mondal S K Chatterjee S Mandal S Bhattacharjee D Primary extramedullary plasmacytoma of ovary: Report of a rare neoplasm J Cancer Res Ther 2015 11 04 923 924 26881544
+5 Zhong Y P Zhang J J Huang X N Multiple myeloma with rupture of ovarian plasmacytoma Chin Med J (Engl) 2012 125 16 2948 2950 22932097
+6 Emery J D Kennedy A W Tubbs R R Castellani W J Hussein M A Plasmacytoma of the ovary: a case report and literature review Gynecol Oncol 1999 73 01 151 154 10094897
+7 Shakuntala P Praveen S Shankaranand B Rajshekar K Umadevi K Bafna U A rare case of plasmacytoma of the ovary: a case report and literature review Ecancermedicalscience 2013 7 288 23390453
+8 Pinto J A Sônego T B Artico M S Leal C FA Bellotto S Extramedullary plasmacytoma of the larynx Int Arch Otorhinolaryngol 2012 16 03 410 413 25991967
+9 Kilciksiz S Karakoyun-Celik O Agaoglu F Y Haydaroglu A A review for solitary plasmacytoma of bone and extramedullary plasmacytoma Sci World J 2012 2012 895765
+10 Ooi G C Chim J C Au W Y Khong P L Radiologic manifestations of primary solitary extramedullary and multiple solitary plasmacytomas AJR Am J Roentgenol 2006 186 03 821 827 16498114
+11 Guo S Q Zhang L Wang Y F Prognostic factors associated with solitary plasmacytoma Onco Targets Ther 2013 6 1659 1666 24259986
+12 Huang H Bazerbachi F Mesa H Gupta P Asymptomatic multiple myeloma presenting as a nodular hepatic lesion: a case report and review of the literature Ochsner J 2015 15 04 457 467 26730235
+13 Park Y M Imaging findings of plasmacytoma of both breasts as a preceding manifestation of multiple myeloma Case Rep Med 2016 2016 6.59561E6
+14 Huang C C Liu M T Pi C P Chung C Y Primary plasmacytoma of the uterine cervix treated with three-dimensional conformal radiotherapy Singapore Med J 2008 49 12 e361 e364 19122936
+15 Bladé J de Larrea C F Rosiñol L Extramedullary involvement in multiple myeloma Haematologica 2012 97 11 1618 1619 23125242
+16 Johansen B Ahlbom G Ostergård B Extramedullary solitary plasmocytoma at the uterine cervix as a cause of postcoital bleeding Acta Obstet Gynecol Scand 1989 68 03 279 280 2618615
+17 Sun N Wang L Li W A case of extramedullary solitary plasmacytoma arising at the uterine cervix Eur J Gynaecol Oncol 2012 33 04 423 424 23091904
+

PMC10314844.txt

@@ -0,0 +1,472 @@
+
+==== Front
+J Cancer Res Clin Oncol
+J Cancer Res Clin Oncol
+Journal of Cancer Research and Clinical Oncology
+0171-5216
+1432-1335
+Springer Berlin Heidelberg Berlin/Heidelberg
+
+35987926
+4184
+10.1007/s00432-022-04184-x
+Research
+Impact of the changing landscape of induction therapy prior to autologous stem cell transplantation in 540 newly diagnosed myeloma patients: a retrospective real-world study
+http://orcid.org/0000-0001-5433-8750
+Wang Song-Yau Song-Yau.Wang@medizin.uni-leipzig.de
+
+1
+Holzhey Tanja 1
+Heyn Simone 1
+Zehrfeld Thomas 2
+Fricke Susann 2
+Hoffmann Franz Albert 3
+Becker Cornelia 3
+Braunert Leanthe 4
+Edelmann Thomas 4
+Paulenz Inessa 5
+Hitzschke Marcus 6
+Flade Franziska 7
+Schwarzer Andreas 7
+Fenchel Klaus 8
+Franke Georg-Nikolaus 1
+Vucinic Vladan 1
+Jentzsch Madlen 1
+Schwind Sebastian 1
+Hell Saskia 1
+Backhaus Donata 1
+Lange Thoralf 9
+Niederwieser Dietger 1
+Scholz Markus 10
+Platzbecker Uwe 1
+Pönisch Wolfram Wolfram.Poenisch@medizin.uni-leipzig.de
+
+1
+1 grid.411339.d 0000 0000 8517 9062 Hematology and Cell Therapy, Medical Clinic and Policlinic 1, University Hospital Leipzig, University of Leipzig, Liebigstraße 22, 04103 Leipzig, Germany
+2 Hospital Torgau, Christianistraße 1, 04860 Torgau, Germany
+3 Hematology Practice, Biedermannstraße 84, 04277 Leipzig, Germany
+4 Hematology Practice, Theodor - Heuss-Str. 2, 04435 Schkeuditz, Germany
+5 Hospital Dessau, Auenweg 38, 06847 Dessau-Roßlau, Germany
+6 Hospital Borna, Rudolf-Virchow-Straße 2, 04552 Borna, Germany
+7 Hematology Practice, Strümpellstraße 42, 04289 Leipzig, Germany
+8 Hematology Practice, Bahnhofstraße 12, 07318 Saalfeld, Saale Germany
+9 Hospital Weißenfels, Naumburger Straße 76, 06667 Weißenfels, Germany
+10 grid.9647.c 0000 0004 7669 9786 Institute for Medical Informatics, Statistics and Epidemiology (IMISE), University of Leipzig, Härtelstraße 16‑18, 04107 Leipzig, Germany
+20 8 2022
+20 8 2022
+2023
+149 7 37393752
+4 4 2022
+1 7 2022
+© The Author(s) 2022
+https://creativecommons.org/licenses/by/4.0/ Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
+Introduction
+
+Autologous stem cell transplantation (ASCT) is the standard treatment for younger patients with newly diagnosed multiple myeloma (MM). However, due to restrictive exclusion criteria, more than half of eligible patients are usually excluded from transplant studies.
+
+Methods
+
+This retrospective monocentric analysis included 540 patients with MM who received an ASCT between 1996 and 2019.
+
+Results
+
+Up to 2005, induction therapy consisted mainly of conventional chemotherapies, e.g. vincristine/doxorubicin/dexamethasone (VAD). In the following years, the triple-combinations based on bortezomib coupled with doxorubicin/dexamethasone (PAD), melphalan/prednisolone (VMP), cyclophposphamide/dexamethasone (VCD) or bendamustine/prednisolone (BPV) became the most popular treatment options. A progressive improvement in PFS was observed in patients treated with the two current induction therapies BPV (47 months) or VCD (54 months) compared to VAD (35 months, p < 0.03), PAD (39 months, p < 0.01 and VMP (36 months, p < 0.01). However, there was no significant difference in median OS (VAD 78, PAD 74, VMP 72, BPV 80 months and VCD not reached). In our analysis, we also included 139 patients who do fulfill at least one of the exclusion criteria for most phase 3 transplant studies (POEMS/amyloidosis/plasma cell leukemia, eGFR < 40 mL/min, severe cardiac dysfunction or poor general condition). Outcome for these patients was not significantly inferior compared to patients who met the inclusion criteria for most of the transplant studies with PFS of 36 vs 41 months (p = 0.78) and OS of 78 vs 79 months (p = 0.34).
+
+Conclusions
+
+Our real-world data in unselected pts also stress the substantial value of ASCT during the first-line treatment of younger MM pts.
+
+Keywords
+
+Multiple myeloma
+Autologous stem cell transplantation
+Bortezomib
+Real-world evidence
+Universität Leipzig (1039)Open Access funding enabled and organized by Projekt DEAL.
+
+issue-copyright-statement© Springer-Verlag GmbH Germany, part of Springer Nature 2023
+==== Body
+pmcIntroduction
+
+Multiple Myeloma (MM), the second most common hematologic malignancy, has an estimated incidence of more than nine cases per 100,000 with around 7600 new cases in Germany in 2020 (Robert Koch-Institut, 2020). The median age at MM diagnosis is 73 years with approximately 35% of patients being younger than 65 years (Klausen et al. 2019). For these patients, high-dose therapy followed by autologous stem cell transplantation (ASCT) is the standard treatment (Cavo et al. 2011). The Intergroupe Francophone du Myélome (IFM) 90 study was the first to demonstrate the superiority of ASCT over conventional chemotherapy (Attal et al. 1996). A meta-analysis examined nine randomized studies that compared conventional chemotherapy with high-dose chemotherapy and ASCT (Koreth et al. 2007). In most of these studies, ASCT significantly improved the rate of complete response (CR) and progression-free survival (PFS), although an overall survival (OS) benefit could only be demonstrated in three studies. Before the era of novel agents, the combination of vincristine, doxorubicin and dexamethasone (VAD) had long been the standard induction regimen prior to ASCT (Barlogie et al. 2006; Sonneveld et al. 2012). Trials published in the ASCT setting showed an overall response rate (ORR) ranging from 32 to 85% (CR 2–8%) after 2–4 VAD cycles and an ORR of 68–93% (CR 9–29%) after ASCT with a median PFS of 22–29 and OS of 47–70 months. In the last 15 years, the introduction of novel agents, particularly bortezomib, into induction therapy for transplant-eligible patients has markedly improved the management of MM. In the IFM 2005-01 study, bortezomib plus dexamethasone was shown to be a highly active induction treatment prior to ASCT, resulting in an ORR of 79% and a CR rate of 6% after induction therapy, and an ORR of 80% and a CR rate of 16% after subsequent ASCT, with a median event-free survival of 36 months and an OS of 81% at 3 years (Harousseau et al. 2010). Further intensification of the induction regimen to include three-drug combinations of bortezomib with alkylating chemotherapy (e.g. cyclophosphamide, bendamustine), anthracycline (doxorubicin) or immunomodulatory drugs (thalidomide and lenalidomide) has resulted in superior ORR and PFS. These triple combinations resulted in clinically relevant improvements in ORR (63–93%) and CR rate (7–35%) after induction therapy as well as increasing ORR (79–97%) and CR rates (21–44%) after ASCT with a median PFS of 35–55 months and a 3-year OS of 75–90% (Cavo et al. 2010; Moreau et al. 2011; Sonneveld et al. 2012; Sonneveld et al. 2013; Mai et al. 2015; Mateos et al. 2015; Attal et al. 2017; Tacchetti et al. 2020). These studies are also used to evaluate the efficacy and tolerability of the respective combination therapy and potentially resulting approvals. However, due to restrictive inclusion and exclusion criteria, more than half of patients with newly diagnosed MM (NDMM) are routinely excluded from randomized phase 3 trials with ASCT (Blimark et al. 2018; Klausen et al. 2019). In contrast, we included additional patients with kidney failure (estimated glomerular filtration rate (eGFR) < 40 mL/min), congestive heart failure (left ventricular ejection fraction < 40%), WHO performance status > 2 and other severe comorbidities in our study. With the aim of reflecting more closely on the conditions of routine practice in the changing treatment landscape, we conducted a retrospective study to determine the feasibility and efficacy for all NDMM patients treated in the Department of Hematology and Oncology at the University of Leipzig with a single, tandem ASCT or autologous/reduced-intensity conditioning allogeneic (auto-RICallo) SCT in the period from 1996 to 2019.
+
+Methods
+
+Patients
+
+This retrospective analysis included all consecutive patients with NDMM who received first-line induction therapy followed by high-dose therapy and subsequent ASCT in the university hospital of Leipzig between January 1st, 1996 and December 31st, 2019. The data were collected from an electronic database containing patient records. All patients had a histologically or cytologically proven MM with symptomatic disease based on the CRAB-criteria of the International Myeloma Working Group (IMWG) (Rajkumar et al. 2014). Patients with significantly compromised general conditions were also considered. For better comparability, we have deployed the main exclusion criteria of the four-phase 3 studies [StaMINA trial (Stadtmauer et al. 2019); IFM2013-04 trial (Moreau et al. 2016); IFM2009 trial (Attal et al. 2017) and the EMN02/HO95 trial (Cavo et al. 2020)], which were also used by Klausen et al. (2019): POEMS/amyloidosis/plasma cell leukemia, eGFR < 40 mL/min, severe cardiac dysfunction (NYHA classification III-IV, ejection fraction < 40%), poor general condition caused mainly by MM with an Eastern Cooperative Oncology Group performance status (ECOG) 3/4, prior malignancies within 5 years or other severe comorbidities. All patients gave written informed consent for the applied treatment and the use of anonymized personal data for clinical research. This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Ethics Committee of the Medical Faculty, Leipzig University (IRB 00,001,750; registration number 118/18-e).
+
+Treatment protocols
+
+The most commonly used induction treatments included VAD, bortezomib, melphalan and prednisone (VMP), bendamustine, prednisone and bortezomib (BPV), bortezomib and dexamethasone (VD), bortezomib, adriamycin and dexamethasone (PAD) and bortezomid, cyclophosphamide and dexamethasone (VCD).
+
+Peripheral blood stem cell (PBSC) collection was performed 2–3 weeks after induction treatment. The mobilization regimen consisted of cyclophosphamide 4 g/m2 or in case of severe renal insufficiency or preexisting heart disease 2 g/m2. All patients received G-CSF (2 × 5 μg/kg) until the completion of stem cell collection. PBSC collection was started when the required number of CD 34+ cells (≥ 20 × 106/L) was detected in peripheral blood. The target for all patients was to collect stem cells for two to three transplants. In patients with a poor stem cell yield in the first leukapheresis session, plerixafor was added before the next apheresis.
+
+The pre-transplantation conditioning therapy consisted of melphalan 200 mg/m2. In case of concomitant heart amyloidosis or severe renal insufficiency, the dose of melphalan was reduced to 100 or 140 mg/m2. G-CSF (5 μg/kg body weight) was given on day 4 after stem cell reinfusion and continued until reconstitution of leukocytes ≥ 1.0 × 109/L.
+
+Definition of response
+
+Evaluation of response was based on the international uniform response criteria for multiple myeloma (Durie et al. 2006). In addition, the terms ‘near complete response’ (nCR) was included. Treatment responses were verified after the end of induction therapy and three months after the first ASCT. OS was measured from the start of induction treatment to the time of death, and PFS from the start of induction treatment to the time at which a relapse, progression or death was observed. The degree of improvement of renal function was assessed according to the criteria of the IMWG consensus statement (Dimopoulos et al. 2010).
+
+Evaluation of efficacy
+
+Patients were examined within seven days prior to initiation of induction therapy. Staging was performed for each patient comprising medical history, physical examination including a detailed neurological examination, determination of World Health Organization Performance Status, determination of laboratory parameters (including β2-microglobulin, serum protein, serum protein electrophoresis, myeloma typing of serum and urine, serum-free light chain assay (Freelite®), serum creatinine, serum calcium and C-reactive protein), electrocardiogram, low dose CT and bone marrow examination. Myeloma protein concentration was determined by the integral of the area under the myeloma protein curve (based on electrophoresis data) and by relating it to the total serum protein. Renal function was assessed by the eGFR using the Modification of Diet in Renal Disease (MDRD) formula (Levey et al. 1999). Patients were followed-up at 3 to 4-weekly intervals during the period of induction therapy/ASCT and thereafter at 12-weekly intervals until disease progression.
+
+Statistical methods
+
+Descriptive statistics were calculated for demographic and baseline variables. Regarding survival follow-up, the data set was freezed on May 15th, 2020. All patients who commenced treatment until this date were included in the analysis. OS and PFS were estimated by the Kaplan–Meier method. Survival curves are compared by Log Rank tests (IBM SPSS Statistics, Version 24). Transplant-related mortality (TRM) was determined as death from any cause other than progression or relapse before day + 100 from the first ASCT. p-values of group differences were calculated applying the Wilcoxon rank-sum test or Student’s t-test. Categorical variables were compared using the χ2-test. p-values < 0.05 were considered significant.
+
+Results
+
+Patient characteristics
+
+This retrospective analysis included 540 patients with NDMM treated with induction therapy followed by ASCT in the first-line therapy. Baseline demographics and disease characteristics are shown in Table 1. Median age at diagnosis was 59 (range 29–75) years. There were 203 females (38%) and 337 males (62%).Table 1 Patient characteristics of 540 newly diagnosed multiple myeloma patients grouped in four time-periods depending on the time of diagnosis
+
+Parameter	Cohort 1
+1996–2005
+n = 71	Cohort 2
+2006–2010
+n = 125	Cohort 3
+2011–2015
+n = 191	Cohort 4
+2016–2019
+n = 153	
+Median age,	57	59	59	62	
+years (range)	(31–70)	(41–70)	(29–71)	(40–75)	
+ECOG 0/1, n (%)	45 (81)	85 (71)	98 (52)	97 (64)	
+ ≥ 2, n (%)	10 (19)	34 (29)	92 (48)	55 (36)	
+Unavailable, n	16	6	1	1	
+MM-Type					
+ IgG, n (%)	43 (61)	62 (50)	99 (52)	80 (52)	
+ IgA, n (%)	11 (15)	28 (22)	37 (19)	36 (24)	
+ IgD, n (%)	0	1 (1)	1 (1)	2 (2)	
+ Light chain, n (%)	15 (21)	26 (21)	53 (28)	34 (22)	
+ Asecretory, n (%)	2 (3)	8 (6)	1 (1)	1 (1)	
+ISS					
+ I, n (%)	24 (61)	66 (54)	94 (49)	78 (51)	
+ II, n (%)	8 (21)	28 (23)	59 (31)	45 (29)	
+ III, n (%)	7 (18)	28 (23)	38 (20)	30 (20)	
+ Unavailable, n	32	3	0	0	
+Cytogeneticsa					
+ High-riskb, n (%)	0	8 (13)	33 (23)	38 (32)	
+Standard-risk, n (%)	16 (100)	52 (87)	110 (77)	81 (68)	
+ Unavailable, n	55	65	48	34	
+Transplant methodsc					
+ Single ASCT, n (%)	40 (56)	62 (50)	158 (83)	134 (86)	
+ Tandem ASCT, n (%)	6 (8)	49 (39)	12 (6)	15 (10)	
+ Auto-RICallo-SCT, n (%)	25 (35)	14 (11)	21 (11)	4 (3)	
+Maintenance (IMiDs), n (%)	4 (6)	7 (6)	27 (14)	75 (49)	
+Median PFS (months)	39	36	39	43	
+Median OS (months)	80	78	74	nr	
+Abbreviation: nr not reached
+
+a Results available from 338 patients
+
+b High-risk: del(17p), t(4;14), t(14;16), t(16;20)
+
+cSingle ASCT: n = 394, tandem ASCT n = 82, auto-RICallo-SCT n = 64
+
+Response and survival
+
+The majority of patients (n = 430; 80%) responded after induction therapy with 21 stringent complete response (sCR) (4%), 11 CR (2%), 31 nCR (6%), 101 very good partial response (VGPR) (19%) and 266 partial response (PR) (49%). The median duration from the start of induction therapy to first ASCT was 159 (range 59–517) days. After the first ASCT, the ORR increased to 97% with 76 sCR (14%), 41 CR (8%), 88 nCR (16%), 168 VGPR (31%) and 149 PR (28%). TRM was 0.6% (n = 3), with two patients dying due to septicemia and one patient with intracerebral hemorrhage. With a median follow-up of 85 months, the median PFS was 39 (95% CI 36.7–41.3) and median OS 79 (95% CI 74.1–83.9) months. In accordance with the Danish MM registry (Klausen et al. 2019), we also included 139 (26%) patients in our analysis, who fulfilled at least one of the exclusion criteria for most clinical phase 3 transplant studies [POEMS/amyloidosis/plasma cell leukemia (n = 18, 3%), eGFR < 40 mL/min (n = 73, 14%), severe cardiac dysfunction (n = 15, 3%), ECOG 3/4 (n = 30, 6%), prior malignancies within 5 years (n = 21, 4%) and other severe comorbidities (n = 7, 1%)]. Outcome for these patients was not significantly inferior compared to those meeting the inclusion criteria for the majority of transplant studies: PFS of 36 vs 41 months (p = 0.78) and OS of 78 vs 79 months (p = 0.34) (Fig. 1 a, b). There was also no difference between the groups in terms of the ≥ CR rate (37/139, 27% vs 80/401, 20%, p = 0.10).Fig. 1 Progression-free survival (PFS) (a) and overall survival (OS) (b) of 139 patients who do not fulfill the most commonly used inclusion criteria of ASCT studies compared to 401 study-eligible patients. Outcome for those patients who did not meet the inclusion criteria was not significantly inferior to the outcome of those meeting the inclusion criteria for the majority of transplant studies: median PFS (36 vs 41 months; p = 0.78) and median OS (78 vs 79 months; p = 0.34)
+
+Outcome according to the time of MM diagnosis
+
+According to the time of MM diagnosis, we divided the patients into four cohorts. Cohort 1 comprises 71 patients with their first MM diagnosis between 1996 and 2005, cohort 2 125 patients between 2006 and 2010, cohort 3 191 patients between 2011 and 2015 and cohort 4 153 patients between 2016 and 2019 (Table 1). The median age at diagnosis increased from 57 (range 31–70) years in the first cohort up to 62 (range 40–75) years in the period after 2015 (p < 0.001). There was no difference between the four cohorts regarding the subtype of MM and ISS stage. In the later period after 2010, we transplanted significantly more patients with a moderately/severe restricted general condition (ECOG ≥ 2) (p < 0.01). Between the first and last cohorts, there was a significant increase in both the ≥ VGPR rate (39 vs 81%, p < 0.001) and the ≥ CR rate (11 vs 25%, p < 0.03) after the first ASCT. However, there was no significant difference in PFS between the four cohorts (39 vs 36 vs 39 vs 43 months; p > 0.05) (Fig. 2 a). While no improvement was seen in the OS during the first three cohorts, the survival of the patients treated within the last four years was significantly better (p < 0.01) (Fig. 2 b). The 48-months OS for patients diagnosed in the last cohort was 85% compared with 61% in cohort 1, 74% in cohort 2 and 76% in cohort 3. Based on EMA approval, 75 patients received lenalidomide maintenance therapy since 2017. With a short median follow-up time of only 17 months, there was no significant benefit in PFS and OS for the patients on maintenance therapy. No consolidation therapy was performed due to the lack of EU approval.Fig. 2 Progression-free survival (PFS) (a) and overall survival (OS) (b) according to time of MM diagnosis: 1996–2005 (n = 71), 2006–2010 (n = 125), 2011–2015 (n = 191) and 2016–2019 (n = 153). There was no significant difference in median PFS between the four cohorts (39 vs 36 vs 39 vs 43 months). While no improvement was seen in the median OS during the first three cohorts, the survival of the patients treated within the last four years was significantly better (p < 0.01)
+
+Impact of induction therapies
+
+In the last 25 years, we applied a large variety of different induction therapies to reduce tumor burden prior to ASCT (Fig. 3). In the first period up to 2005, induction therapy consisted mainly of conventional chemotherapies, e.g. VAD, VCAP (vincristine, cyclophosphamide, adriblastin, prednisolone) or BP (bendamustine and prednisolone). Following the introduction of the new substances starting in 2006, bortezomib-containing therapies progressively replaced conventional chemotherapies. VMP, which was only approved for non-transplant eligible patients, was temporarily used by us as induction therapy in transplant-eligible patients during the period pending approval of bortezomib-containing induction therapies prior to ASCT. From 2011 onwards, the triple-combination of PAD, VCD and BPV became the most frequent treatment options. Table 2 summarizes the patient characteristics and basic information on therapy for the most commonly used induction regimens. Response rates after induction treatment and first ASCT are shown in Table 3. After completion of induction therapy, the ORR in patients treated with conventional chemotherapy VAD was only 66% with a ≥ VGPR rate of 14% and a ≥ CR rate of 2%. The implementation of various bortezomib-containing regimens significantly improved the ORR to 77–86% (p < 0.005), with a ≥ VGPR rate between 29 and 41% (p < 0.001) and a ≥ CR rate between 3 and 11% (p = 0.09). The majority of BPV-treated patients (n = 141; 83%) responded after a median of 2 (range 1–6) 3-weekly induction cycles with 10 sCR (6%), 4 CR (2%), 12 nCR (7%), 40 VGPR (24%) and 75 PR (44%). In contrast, the other induction regimens required longer treatment periods to achieve comparable response rates [e.g. median 3 (range 1–4) 3-weekly PAD cycles (p < 0.005) or median 4 (range 2–6) 3-weekly VCD cycles (p < 0.001)]. This resulted in a significantly shorter time between initiation of BPV induction therapy and the start of stem cell mobilization e.g. BPV median 71 (range 26–309) days vs PAD median 89 (range 59–183) days (p < 0.01) or VCD median 102 (range 45–198) days (p < 0.001). There was no significant difference between various induction therapies in the median number of apheresis (1–2) performed and the median yield of CD34+ cells (6.5–14.2 × 106 CD34+ cells/kg) harvested (Table 2). It was remarkable that a sufficient number of stem cells could also be collected after VMP induction. The median time from the start of induction treatment to ASCT was also significantly shorter in the BPV group at 120 (range 68–431) days compared to VCD 154 (range 98–286) days (p < 0.01), PAD 156 (93–328) days (p < 0.03), VMP 169 (87–363) days (p < 0.001), VD 185 (95–330) days (p < 0.001) and VAD 195 (59–517) days (p < 0.001). The conditioning therapy prior to ASCT consisted of melphalan 200 mg/m2. In patients with concomitant severe renal or pre-existing cardiac impairment, the melphalan dose was reduced to 100 mg/m2 (n = 28) or 140 mg/m2 (n = 68). Autografts contained between 1.1 × 106 and 36.9 × 106 (median 5.2 × 106) CD34+ cells/kg. Engraftment was successful in 539 of 540 patients. The ORR after ASCT showed no difference between the patients initially treated with VAD (96%) or with bortezomib-containing therapies (97%). However, the ≥ VGPR (45% vs 76%; p < 0.001) and the ≥ CR rate (13% vs 23%; p < 0.03) were significantly higher with bortezomib-containing induction regimens. The comparison of the various triple-combinations based on bortezomib showed similar ≥ VGPR and ≥ CR rates (Table 3). An improvement in PFS was observed in patients treated with the two current induction therapies BPV (47 months) or VCD (54 months) compared to patients treated with the previously used VAD (35 months, p < 0.03), VMP (36 months, p < 0.01), PAD (39 months, p < 0.01) and VD (31 months, p < 0.005) (Fig. 4a). The very short follow up time of only 21 months in the VCD group does not allow a comparison of the OS with the other induction therapies (Fig. 4b). Among the five other treatment groups, median OS was not significantly different (BPV 80, VAD 78, VMP 72, PAD 74, VD 64 months).Fig. 3 Changing landscape of induction therapies prior to autologous stem cell transplantation in 540 patients divided into four cohorts over time from 1996 to 2019. VAD: vincristine, adriamycin and dexamethasone; VMP: bortezomib, melphalan and prednisone; BPV: bendamustine, prednisone and bortezomib, VD: bortezomib and dexamethasone; PAD: bortezomb, adriamycin and dexamethasone; VCD: bortezomid, cyclophosphamide and dexamethasone. Others: Regimens used up to and including 2010: VCAP (vincristine, cyclophosphamide, adriblastin, prednisolone), BP (bendamustine and prednisolone) and from 2011: VTD (bortezomib, thalidomide and dexamethasone), VRD (bortezomid, lenalidomide and dexamethasone), DVD (daratumumab, bortezomib and dexamethasone)
+
+Table 2 Patient characteristics depending on the most commonly used induction regimens prior to ASCT and basic information on induction and stem cell mobilization
+
+Parameter	VAD
+n = 95	VMP
+n = 93	BPV
+n = 169	VD
+n = 33	PAD
+n = 29	VCD
+n = 70	
+Median age, years (range)	57
+
+(38–70)
+
+	59
+
+(41–71)
+
+	60
+
+(32–75)
+
+	60
+
+(40–73)
+
+	61
+
+(48–71)
+
+	63
+
+(29–72)
+
+	
+eGFR (mL/min)							
+ ≥ 60, n (%)	60 (73)	73 (80)	116 (69)	27 (82)	23 (79)	54 (78)	
+ 30–59, n (%)	18 (22)	16 (18)	23 (14)	3 (9)	4 (15)	12 (17)	
+ 15–29, n (%)	4 (5)	1 (1)	15 (9)	3 (9)	2 (7)	3 (4)	
+  < 15, n (%)	1 (1)	1 (1)	15 (9)	0	0	1 (1)	
+ Unavailable, n	12	2	0	0	0	0	
+Number of cycles median (range)	4
+
+(2–6)
+
+	2
+
+(1–6)
+
+	2
+
+(1–6)
+
+	5
+
+(2–8)
+
+	3
+
+(1–4)
+
+	4
+
+(2–6)
+
+	
+Time to start mobilizationa, days, median (range)	156
+
+(59–352)
+
+	102
+
+(42–310)
+
+	71
+
+(26–309)
+
+	121
+
+(56–240)
+
+	89
+
+(59–183)
+
+	102
+
+(45–198)
+
+	
+CD34+cells (× 106/kg), median (range)	12.6
+
+(3.3–70.6)
+
+	10.3
+
+(2.5–30.5)
+
+	13.5
+
+(1,7–31)
+
+	14.2
+
+(2.8–25.2)
+
+	6.5
+
+(1–26.7)
+
+	11.9
+
+(3.9–67.1)
+
+	
+Number of apheresis, median (range)	1
+
+(1–3)
+
+	2
+
+(1–4)
+
+	1
+
+(1–4)
+
+	2
+
+(1–3)
+
+	2
+
+(1–3)
+
+	1
+
+(1–3)
+
+	
+Time to ASCT, days, median (range)b	195
+
+(59–517)
+
+	169
+
+(87–363)
+
+	120
+
+(68–431)
+
+	185
+
+(95–330)
+
+	156
+
+(93–328)
+
+	154
+
+(98–286)
+
+	
+VAD: vincristine, adriamycin and dexamethasone; VMP: bortezomib, melphalan and prednisone, BPV: bendamustine, prednisone and bortezomib; VD: bortezomib and dexamethasone; PAD: bortezomb, adriamycin and dexamethasone; VCD: bortezomid, cyclophosphamide and dexamethasone
+
+aTime from start induction treatment to stem cell mobilization
+
+bTime from start induction treatment to ASCT
+
+Table 3 Best confirmed hematological response after completion of induction therapy and three months after the first ASCT
+
+Parameter	VAD
+n = 95	VMP
+n = 93	BPV
+n = 169	VD
+n = 33	PAD
+n = 29	VCD
+n = 70	
+Response after induction							
+ sCR, n (%)	1 (1)	7 (8)	10 (6)	0	1 (3)	1 (1)	
+ CR, n (%)	1 (1)	3 (3)	4 (2)	1 (3)	0	1 (1)	
+ nCR, n (%)	0	9 (10)	12 (7)	3 (9)	1 (3)	2 (3)	
+ VGPR, n (%)	11 (12)	10 (11)	40 (24)	7 (21)	10 (34)	16 (23)	
+ PR, n (%)	50 (53)	43 (46)	75 (44)	17 (51)	13 (45)	39 (56)	
+  ≥ CR, n (%)	2 (2)	10 (11)	14 (8)	1 (3)	1 (3)	2 (3)	
+  ≥ VGPR, n (%)	13 (14)	29 (31)	66 (39)	11 (33)	12 (41)	20 (29)	
+  ORR, n (%)	64 (66)	72 (77)	141(83)	28 (85)	25 (86)	59 (84)	
+Response after first ASCT							
+ sCR, n (%)	5 (5)	12 (13)	35 (21)	5 (15)	5 (17)	5 (7)	
+ CR, n (%)	7 (7)	7 (8)	13 (8)	2 (6)	2 (7)	4 (6)	
+ nCR, n (%)	5 (5)	17 (18)	33 (20)	4 (12)	11 (38)	13 (19)	
+ VGPR, n (%)	26 (27)	23 (25)	56 (33)	13 (39)	5 (17)	33 (47)	
+ PR, n (%)	48 (51)	32 (34)	29 (17)	8 (24)	5 (17)	11 (16)	
+  ≥ CR, n (%)	12 (13)	19 (20)	48 (28)	7 (21)	7 (24)	9 (13)	
+  ≥ VGPR, n (%)	43 (45)	59 (63)	137 (81)	24 (73)	23 (79)	55 (79)	
+  ORR, n (%)	91 (96)	91 (98)	166 (98)	32 (97)	28 (97)	66 (94)	
+Median PFS (months)	35	36	47	31	39	54	
+Median OS (months)	78	72	80	64	74	nra	
+Italic values represent cumulative results
+
+sCR: stringent complete response; CR: complete response; nCR: near-complete response; VGPR: very good partial response; PR: partial response; ORR: overall response rate; nr: not reached
+
+aMedian observation time in the VCD group is only 21 months
+
+Fig. 4 Progression-free survival (PFS) (a) and overall survival (OS) (b) depending on different induction therapies. An improvement in median PFS was observed in patients treated with the two current induction therapies BPV (47 months) or VCD (54 months) compared to patients treated with the previously used VAD (35 months, p < 0.03), VMP (36 months, p < 0.01), PAD (39 months, p < 0.01) and VD (31 months, p < 0.005). The very short follow-up time of only 21 months in the VCD group does not allow a comparison of the OS with the other induction therapies. Median OS was not significantly different between the five other treatment groups, (BPV 80, VAD 78, VMP 72, PAD 74, VD 64 months)
+
+Role of auto, tandem-auto or auto-RICallo-SCT
+
+Patients received either single ASCT (n = 394), or tandem ASCT (n = 82) or auto-RICallo SCT (n = 64). In particular, patients with partial response to first ASCT were candidates for tandem ASCT or RICallo SCT until 2010. Later, a tandem or RICallo transplant was predominantly performed in patients with high-risk cytogenetics. PFS in patients undergoing single ASCT was 39 and OS 80 months and in the tandem ASCT group, PFS was 39 and OS 78 months. Due to the different transplantation approaches, we refrained from comparing the single to the tandem transplant in our analysis. In 64 patients with an HLA-identical sibling, a RICallo SCT was performed after the first ASCT. The RIC regimen consisted of fludarabine 30 mg/m2 for 3 days plus total-body irradiation 2 Gy (Maloney et al. 2003; Björkstrand et al. 2011). There was no difference in median PFS between auto-RICallo and single/tandem transplanted patients (39 vs 39 months; p = 0.134) and OS (76 vs 79 months; p = 0.964) (Fig. 5a, b). Non-relapse mortality at 24 months was 9% in the auto-RICallo group compared with 2% in the single/tandem auto group (p < 0.001). This resulted in a significantly improved 24-month PFS (74 vs 62%; p < 0.01) and OS (92 vs 76%; p < 0.03) for autologous transplanted patients. However, there was a benefit for auto-RICallo compared to the single/tandem auto patients in the long-term follow-up after 120 months with a PFS of 25 vs 6% (p < 0.03) and OS 32 vs 23% (p < 0.03), respectively.Fig. 5 Progression-free survival (PFS) (a) and overall survival (OS) (b) depending on transplant schedule: auto (n = 394), tandem-auto (n = 82) or auto-RICallo-SCT (n = 64). There was no difference in median PFS between auto-RICallo and single/tandem transplanted patients (39 vs 39 months; p = 0.134) and median OS (76 vs 79 months; p = 0.964)
+
+Impact of renal function
+
+In total 187/518 evaluable patients (36%) had mild renal dysfunction (eGFR: 60–89 mL/min), 85 (16%) moderate renal dysfunction (eGFR: 30–59 mL/min), 32 (6%) severe renal dysfunction (eGFR: 15–29 mL/min) and 19 (4%) renal failure/dialysis (eGFR < 15 mL/min). The majority of patients with severe renal dysfunction/ renal failure/dialysis (n = 30; 59%) received induction treatment with BPV. We observed no difference in median PFS between patients with mild, moderate, severe renal dysfunction and renal failure/dialysis: 39 vs 37 vs 46 vs 34 months (p = 0.58), and in median overall survival: 79 vs 79 vs 75 vs 78 months (p = 0.72) (Fig. 6a, b). Forty-one of the 51 patients with eGFR < 30 mL/min (80%) improved their renal function after the first ASCT. Seventeen (33%) patients reached CRrenal, 5 (10%) patients PRrenal and 19 (37%) patients MRrenal. Seven of the 11 dialysis-dependent patients became dialysis-independent.Fig. 6 Progression-free survival (PFS) (a) and overall survival (OS) (b) according to the renal function: eGFR ≥ 60 mL/min (n = 382), eGFR 30- < 60 mL/min (n = 85), eGFR 15- < 30 mL/min (n = 32), eGFR < 15 mL/min (n = 19). There was no difference in median PFS between patients with mild, moderate, severe renal dysfunction and renal failure/dialysis (39 vs 37 vs 46 vs 34 months; p = 0.58) and in median OS (79 vs 79 vs 75 vs 78 months; p = 0.72)
+
+Discussion
+
+In this retrospective study, we present the results of a large, single-center-cohort of 540 unselected NDMM patients treated with ASCT. In our analysis, the median PFS of 39 months and OS of 79 months are comparable to those observed in other ASCT studies conducted in the last 30 years (Fermand et al. 1998; Harousseau et al. 2010; Sonneveld et al. 2012). In addition to the 401 patients who fulfill the restrictive criteria for inclusion in most clinical phase 2/3 studies, our analysis included 139 patients with at least one clinically relevant comorbidity, which would usually have led to study exclusion. In this subgroup of MM patients who did not fulfill the inclusion criteria for clinical trials, but who were considered as transplant eligible by us, PFS was shortened only slightly and OS was not reduced. This is concordant with the results of the Danish MM registry, which also found no difference in OS between these two groups (Klausen et al. 2019). This suggests that in clinical practice, significantly more patients could benefit from an ASCT than are usually included in transplant studies.
+
+During the last 20 years, there has been a complete shift in pre-transplant induction therapies from conventional chemotherapy to combination therapies using novel agents (Nooka et al. 2013; Kumar et al. 2014). This change in induction therapy is mirrored in our retrospective analysis. While VAD (75%) was the predominant conventional chemotherapy until 2005, a transition phase up to 2010 was followed by a complete switch to bortezomib-containing combinations. The remission rates after ASCT achieved in our first cohort up to 2005 with a CR rate of 11% and a ≥ VGPR rate of 39% are comparable to the CR rates between 6 and 44% and ≥ VGPR rates between 36 and 57% in other transplant studies using conventional chemotherapy induction regimens (Koreth et al. 2007; Sonneveld et al. 2012; Nooka et al. 2013). However, our observed PFS of 39 months and OS of 80 months after induction therapy with VAD was considerably longer than in most studies, comparable PFS of 24 and 42 months and OS of 65 and 82 months being achieved only with additional consolidation or maintenance therapy (Fermand et al. 1998; Bladé et al. 2005; Goldschmidt et al. 2018). The reason for our favorable results in the first cohort could be the high proportion of auto-RICallo transplanted patients (35%), our early extensive implementation of thalidomide monotherapy starting in 2000 and our use of triple combinations including thalidomide (Pönisch et al. 2008) and bortezomib since 2004 (Pönisch et al. 2013) in a relapse setting. After the introduction of bortezomib-containing triple combinations (VMP, BPV, PAD and VCD) in induction therapy, there was a significant improvement in response rates after induction. In comparing these different triple therapies, we found similar ORR between 77 and 86% and ≥ VGPR rates between 29 and 41%. The median duration of BPV induction to achieve best response was only 6 weeks and thus significantly shorter than the 9–12 weeks required for other bortezomib-containing triple combinations. This shorter time to best response is clinically relevant because rapid tumor control is usually associated with a corresponding improvement in clinical symptoms and a lower risk of bortezomib-associated polyneuropathy. The mobilization and collection of stem cells were feasible and effective among the various triple combinations, comparable to the published data for PAD, VCD (Mai et al. 2015) and BPV (Poenisch et al. 2015). It was remarkable, however, that a sufficient number of stem cells could also be collected after VMP induction. The reason for this could be the low cumulative dose of oral melphalan (median 72 mg/m2) in the VMP induction, resulting in limited stem cell toxicity. Following the first ASCT, the ORR of the different bortezomib-containing triple combinations increased to 94–98% with a ≥ VGPR rate between 63 and 81% and CR rate between 13 and 28%. These response rates compare favorably with those reported by Moreau et al. (2011); Sonneveld et al. (2012); Moreau et al. (2019) and Goldschmidt et al. (2020) for alternative bortezomib-based regimens PAD, VCD and VTD. Both the BPV and VCD induction therapies, which we have used preferentially since 2011, are associated with a significantly better PFS compared to the other triplets, although BPV had only a slightly (but not statistically significant) better OS compared to the triplets VMP and PAD. Compared to the bortezomib-containing triple combinations, the doublet bortezomib and dexamethasone showed significantly shorter PFS and OS despite the significantly longer duration of induction therapy. Therefore, our results indicate a higher efficacy of triplet compared to doublet therapies. Our retrospective analysis found no difference in median PFS and OS between patients transplanted with single and tandem ASCT, or auto-RICallo SCT. The auto-RICallo transplant patients showed significantly better long-term survival. However, a significantly increased early mortality needs to be expected in the first few years. This is in line with the results of the EBMT-NMAM2000 study, which compared auto-RICallo SCT with tandem ASCT in a prospective phase 3 trial (Björkstrand et al. 2011). The majority of patients with severe renal dysfunction or renal failure/dialysis received an induction treatment with BPV. As induction therapy, this combination has shown high efficacy and good tolerability in both transplant-eligible and non-transplant-eligible MM patients with renal impairment (Pönisch et al. 2014; Poenisch et al. 2015; Holzhey et al. 2021). Specifically, this combination induced a rapid reduction in monoclonal LC production in the first few days of treatment, potentially preventing the development of irreversible renal failure (Pönisch et al. 2015; Tessenow et al. 2017; Holzhey et al. 2021). The German-Speaking Myeloma Multicenter Group (GMMG) previously reported that the bortezomib-based triplet therapy PAD before ASCT could overcome the negative prognostic impact of renal impairment (Scheid et al. 2014). Our results confirm this for the bortezomib-containing inductions used predominantly here, as we found no differences in PFS and OS in patients with severe renal impairment compared to patients with normal or moderate restricted renal function.
+
+In conclusion, our real-world data demonstrate the substantial value of ASCT as a first-line treatment for younger MM patients. In addition to patients meeting restrictive inclusion and exclusion criteria for clinical studies, patients with relevant comorbidities (e.g. severe renal impairment) classified as eligible for transplantation also benefit from an ASCT. The risk profile of patients transplanted in our clinic has changed substantially over the past 25 years: median age increased from 57 to 62 years and significantly more patients with reduced general condition (ECOG ≥ 2) were transplanted. While conventional chemotherapy was administered until 2005 after a transitional phase from 2011, exclusively bortezomib-based combinations became established as induction therapy prior to ASCT. The different bortezomib-containing triplets resulted in similar ORR and OS. Only improvement in PFS was observed in patients treated with the two current induction therapies BPV and VCD in comparison with the previously used VMP, PAD and the doublet VD. In addition, the significantly shorter duration of BPV induction therapy indicates a superior efficacy of this combination compared to the other bortezomib-based inductions.
+
+Acknowledgements
+
+The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.
+
+Author Contributions
+
+All authors contributed to the study’s conception and design. Material preparation, data collection and analysis were performed by Song-Yau Wang, Tanja Holzhey and Wolfram Pönisch. The first draft of the manuscript was written by Song-Yau Wang and Wolfram Pönisch and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.
+
+Funding
+
+Open Access funding enabled and organized by Projekt DEAL. The authors have no relevant financial or non-financial interests to disclose.
+
+Declarations
+
+Competing interests
+
+The authors declare no competing interests.
+
+Conflict of interest
+
+The authors declare no conflict of interests.
+
+Publisher's Note
+
+Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
+==== Refs
+References
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+Maloney DG Molina AJ Sahebi F Stockerl-Goldstein KE Sandmaier BM Bensinger W Storer B Hegenbart U Somlo G Chauncey T Bruno B Appelbaum FR Blume KG Forman SJ McSweeney P Storb R Allografting with nonmyeloablative conditioning following cytoreductive autografts for the treatment of patients with multiple myeloma Blood 2003 102 3447 3454 10.1182/blood-2002-09-2955 12855572
+Mateos MV Oriol A Rosiñol L de Arriba F Puig N Martín J Martínez-López J Echeveste MA Sarrá J Ocio E Ramírez G Martínez R Palomera L Payer A Iglesias R de la Rubia J Alegre A Chinea AI Bladé J Lahuerta JJ San Miguel JF Bendamustine, bortezomib and prednisone for the treatment of patients with newly diagnosed multiple myeloma: results of a prospective phase 2 Spanish/PETHEMA trial Haematologica 2015 100 1096 1102 10.3324/haematol.2015.124818 25911554
+Moreau P Avet-Loiseau H Facon T Attal M Tiab M Hulin C Doyen C Garderet L Randriamalala E Araujo C Lepeu G Marit G Caillot D Escoffre M Lioure B Benboubker L Pégourié B Kolb B Stoppa AM Fuzibet JG Decaux O Dib M Berthou C Chaleteix C Sebban C Traullé C Fontan J Wetterwald M Lenain P Mathiot C Harousseau JL Bortezomib plus dexamethasone versus reduced-dose bortezomib, thalidomide plus dexamethasone as induction treatment before autologous stem cell transplantation in newly diagnosed multiple myeloma Blood 2011 118 5752 5758 10.1182/blood-2011-05-355081 21849487
+Moreau P Hulin C Macro M Caillot D Chaleteix C Roussel M Garderet L Royer B Brechignac S Tiab M Puyade M Escoffre M Stoppa AM Facon T Pegourie B Chaoui D Jaccard A Slama B Marit G Laribi K Godmer P Luycx O Eisenmann JC Allangba O Dib M Araujo C Fontan J Belhadj K Wetterwald M Dorvaux V Fermand JP Rodon P Kolb B Glaisner S Malfuson JV Lenain P Biron L Planche L Caillon H Avet-Loiseau H Dejoie T Attal M VTD is superior to VCD prior to intensive therapy in multiple myeloma: results of the prospective IFM2013-04 trial Blood 2016 127 2569 2574 10.1182/blood-2016-01-693580 27002117
+Moreau P Attal M Hulin C Arnulf B Belhadj K Benboubker L Béné MC Broijl A Caillon H Caillot D Corre J Delforge M Dejoie T Doyen C Facon T Sonntag C Fontan J Garderet L Jie KS Karlin L Kuhnowski F Lambert J Leleu X Lenain P Macro M Mathiot C Orsini-Piocelle F Perrot A Stoppa AM van de Donk NW Wuilleme S Zweegman S Kolb B Touzeau C Roussel M Tiab M Marolleau JP Meuleman N Vekemans MC Westerman M Klein SK Levin MD Fermand JP Escoffre-Barbe M Eveillard JR Garidi R Ahmadi T Zhuang S Chiu C Pei L de Boer C Smith E Deraedt W Kampfenkel T Schecter J Vermeulen J Avet-Loiseau H Sonneveld P Bortezomib, thalidomide, and dexamethasone with or without daratumumab before and after autologous stem-cell transplantation for newly diagnosed multiple myeloma (CASSIOPEIA): a randomised, open-label, phase 3 study Lancet 2019 394 29 38 10.1016/S0140-6736(19)31240-1 31171419
+Nooka AK Kaufman JL Behera M Langston A Waller EK Flowers CR Gleason C Boise LH Lonial S Bortezomib-containing induction regimens in transplant-eligible myeloma patients: a meta-analysis of phase 3 randomized clinical trials Cancer 2013 119 23 4119 4128 10.1002/cncr.28325 24005889
+Poenisch W Plötze M Holzvogt B Andrea M Schliwa T Zehrfeld T Hammerschmidt D Schwarz M Edelmann T Becker C Hoffmann FA Schwarzer A Kreibich U Gutsche K Reifenrath K Schwarzbach H Heyn S Franke GN Jentzsch M Leiblein S Schwind S Lange T Vucinic V AlAli HK Niederwieser D Stem cell mobilization and autologous stem cell transplantation after pretreatment with bendamustine, prednisone and bortezomib (BPV) in newly diagnosed multiple myeloma J Cancer Res Clin Oncol 2015 141 2013 2022 10.1007/s00432-015-1984-4 25976868
+Pönisch W Rozanski M Goldschmidt H Hoffmann FA Boldt T Schwarzer A Ritter U Rohrberg R Schwalbe E Uhlig J Zehrfeld T Schirmer V Haas A Kreibich U Niederwieser D East German Study Group of Haematology and Oncology (OSHO) Combined bendamustine, prednisolone and thalidomide for refractory or relapsed multiple myeloma after autologous stem-cell transplantation or conventional chemotherapy: results of a Phase I clinical trial Br J Haematol 2008 143 191 200 10.1111/j.1365-2141.2008.07076.x 18752593
+Pönisch W Bourgeois M Moll B Heyn S Jäkel N Wagner I Rohrberg R Hurtz HJ Schmalfeld M Aßmann M Edelmann T Mohren M Hoffmann FA Becker C Schwarzer A Schönfelder U Zehrfeld T Hensel G Löschcke K Krahl R Ali HA Niederwieser D Combined bendamustine, prednisone and bortezomib (BPV) in patients with relapsed or refractory multiple myeloma J Cancer Res Clin Oncol 2013 139 499 508 10.1007/s00432-012-1339-3 23184429
+Pönisch W Holzvogt B Plötze M Andrea M Bourgeois M Heyn S Zehrfeld T Hammerschmidt D Schwarz M Edelmann T Becker C Hoffmann FA Schwarzer A Kreibich U Gutsche K Reifenrath K Winkelmann C Krahl R Remane Y Hennig E Schliwa T Lindner T Kaiser T Vucinic V Behre G Niederwieser D Bendamustine and prednisone in combination with bortezomib (BPV) in the treatment of patients with newly diagnosed/untreated multiple myeloma J Cancer Res Clin Oncol 2014 140 1947 1956 10.1007/s00432-014-1737-9 24942335
+Rajkumar SV Dimopoulos MA Palumbo A Blade J Merlini G Mateos MV Kumar S Hillengass J Kastritis E Richardson P Landgren O Paiva B Dispenzieri A Weiss B LeLeu X Zweegman S Lonial S Rosinol L Zamagni E Jagannath S Sezer O Kristinsson SY Caers J Usmani SZ Lahuerta JJ Johnsen HE Beksac M Cavo M Goldschmidt H Terpos E Kyle RA Anderson KC Durie BG Miguel JF International Myeloma Working Group updated criteria for the diagnosis of multiple myeloma Lancet Oncol 2014 15 e538 548 10.1016/S1470-2045(14)70442-5 25439696
+Scheid C Sonneveld P Schmidt-Wolf IG van der Holt B el Jarari L Bertsch U Salwender H Zweegman S Blau IW Vellenga E Weisel K Pfreundschuh M Jie KS Neben K van de Velde H Duehrsen U Schaafsma MR Lindemann W Kersten MJ Peter N Hänel M Croockewit S Martin H Wittebol S Bos GM van Marwijk-Kooy M Wijermans P Goldschmidt H Lokhorst HM Bortezomib before and after autologous stem cell transplantation overcomes the negative prognostic impact of renal impairment in newly diagnosed multiple myeloma: a subgroup analysis from the HOVON-65/GMMG-HD4 trial Haematologica. 2014 99 148 154 10.3324/haematol.2013.087585 23996482
+Sonneveld P Schmidt-Wolf IG van der Holt B El Jarari L Bertsch U Salwender H Zweegman S Vellenga E Broyl A Blau IW Weisel KC Wittebol S Bos GM Stevens-Kroef M Scheid C Pfreundschuh M Hose D Jauch A van der Velde H Raymakers R Schaafsma MR Kersten MJ van Marwijk-Kooy M Duehrsen U Lindemann W Wijermans PW Lokhorst HM Goldschmidt HM Bortezomib induction and maintenance treatment in patients with newly diagnosed multiple myeloma: results of the randomized phase III HOVON-65/ GMMG-HD4 trial J Clin Oncol 2012 30 2946 2955 10.1200/JCO.2011.39.6820 22802322
+Sonneveld P Goldschmidt H Rosiñol L Bladé J Lahuerta JJ Cavo M Tacchetti P Zamagni E Attal M Lokhorst HM Desai A Cakana A Liu K van de Velde H Esseltine DL Moreau P Bortezomib-based versus nonbortezomib-based induction treatment before autologous stem-cell transplantation in patients with previously untreated multiple myeloma: a meta-analysis of phase III randomized, controlled trials J Clin Oncol 2013 31 3279 3287 10.1200/JCO.2012.48.4626 23897961
+Stadtmauer EA Pasquini MC Blackwell B Hari P Bashey A Devine S Efebera Y Ganguly S Gasparetto C Geller N Horowitz MM Koreth J Knust K Landau H Brunstein C McCarthy P Nelson C Qazilbash MH Shah N Vesole DH Vij R Vogl DT Giralt S Somlo G Krishnan A Autologous transplantation, consolidation, and maintenance therapy in multiple myeloma: results of the BMT CTN 0702 Trial J Clin Oncol 2019 37 589 597 10.1200/JCO.18.00685 30653422
+Tacchetti P Pantani L Patriarca F Petrucci MT Zamagni E Dozza L Galli M Di Raimondo F Crippa C Boccadoro M Barbato S Tosi P Narni F Montefusco V Testoni N Spadano A Terragna C Pescosta N Marzocchi G Cellini C Galieni P GIMEMA (Gruppo ItalianoMalattieEmatologichedell'Adulto Italian Myeloma Network) Bortezomib, thalidomide, and dexamethasone followed by double autologous haematopoietic stem-cell transplantation for newly diagnosed multiple myeloma (GIMEMA-MMY-3006): long-term follow-up analysis of a randomised phase 3, open-label study Lancet Haematol 2020 12 e861 e873 10.1016/S2352-3026(20)30323-9
+Tessenow H Holzvogt M Holzvogt B Andrea M Heyn S Schliwa T Schwarz M Zehrfeld T Becker C Pfrepper C Franke GN Krahl R Jentzsch M Leiblein S Schwind S Bill M Vucinic V Lange T Niederwieser D Pönisch W Successful treatment of patients with newly diagnosed/untreated light chain multiple myeloma with a combination of bendamustine, prednisone and bortezomib (BPV) J Cancer Res Clin Oncol 2017 143 2049 2058 10.1007/s00432-017-2439-x 28534173
+

PMC10315871.txt

@@ -0,0 +1,250 @@
+
+==== Front
+Int J Surg Pathol
+Int J Surg Pathol
+IJS
+spijs
+International Journal of Surgical Pathology
+1066-8969
+1940-2465
+SAGE Publications Sage CA: Los Angeles, CA
+
+35912479
+10.1177/10668969221113490
+10.1177_10668969221113490
+Original Articles
+The Prevalence of Epstein-Barr Virus in Plasma Cell Neoplasms is Higher in HIV-Positive Individuals
+https://orcid.org/0000-0002-0525-7134
+Penzhorn Ingrid H MMed (Anat Path), FCPath (SA) Anat 1
+https://orcid.org/0000-0001-5187-6756
+Schneider Johann W MMed (Anat Path), FCPath (SA) Anat 1
+Sher-Locketz Candice MMed (Anat Path), FCPath (SA) Anat 12
+1 Division of Anatomical Pathology, Department of Pathology, Faculty of Medicine and Health Sciences, National Health Laboratory Service, 98826 University of Stellenbosch , Tygerberg Hospital, Cape Town, South Africa
+2 Anatomical Pathology, 484973 PathCare, Cape Town , South Africa
+Ingrid Penzhorn, 2 Belleisle, 4a Norfolk Road, Sea Point, Cape Town, South Africa, 8005. Email: ingrid.penzhorn@gmail.com
+1 8 2022
+8 2023
+31 5 564571
+16 10 2021
+07 6 2022
+20 6 2022
+© The Author(s) 2022
+2022
+SAGE Publications
+https://creativecommons.org/licenses/by/4.0/ This article is distributed under the terms of the Creative Commons Attribution 4.0 License (https://creativecommons.org/licenses/by/4.0/) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page (https://us.sagepub.com/en-us/nam/open-access-at-sage).
+Aims. Epstein-Barr virus (EBV) is causally associated with many hematolymphoid malignancies. This laboratory-based study aimed to establish the prevalence of EBV in plasma cell neoplasms in a large South African cohort and to determine whether there is any correlation between EBV-positivity and human immunodeficiency virus (HIV) status in patients with plasma cell neoplasms, including plasma cell myeloma and plasmacytoma (solitary plasmacytoma of bone and extraosseous plasmacytoma). Methods. This single-institution retrospective study included all patients with a histopathologic diagnosis of plasma cell neoplasm between 2003 and 2020. EBV-expression in the plasma cell neoplasms was assessed by EBV-encoded RNA (EBER) in situ hybridization (ISH) and correlated with HIV status. HIV status was determined by retrieving prior serologic results. Formalin-fixed paraffin-embedded tissue from HIV-unknown patients underwent HIV-1 p24 antibody testing. Results. Sixteen of 89 plasma cell neoplasms (18%) were EBV-positive. There was a significant correlation between EBV and HIV infection in plasma cell neoplasms, with 6/10 tumors from HIV positive patients showing EBV-positivity in tumor cells. The EBV-positive cohort was significantly younger than the EBV-negative group. Conclusion. EBV-positivity in plasma cell neoplasms in this study is higher than previously reported. The significant occurrence of EBV in plasma cell neoplasms from HIV-positive patients suggests a co-carcinogenic relationship between the two viruses.
+
+plasma cell neoplasm
+plasmacytoma
+Epstein-Barr virus
+EBER-ISH
+HIV
+p24
+National Health Laboratory Service https://doi.org/10.13039/501100010753 GRANT004_94771 typesetterts19
+==== Body
+pmcIntroduction
+
+Plasma cell myeloma and plasmacytoma are plasma cell neoplasms with similar histopathologic but different clinical characteristics. Plasmacytomas are usually solitary bone or extraosseous lesions with no or minimal (<10%) clonal plasma cells in the bone marrow, while plasma cell myeloma presents with multiple lytic bone lesions and invariable bone marrow involvement. Nearly all patients with plasma cell myeloma have monoclonal antibodies, or so-called M-protein, in urine or serum, with resultant end-organ damage (renal failure, anemia and hypercalcemia). M-protein is less commonly detected in plasmacytoma, and end-organ damage is absent by definition. The neoplastic cells encountered in these two entities typically display a plasmacytic phenotype resembling mature plasma cells. Although plasmacytic morphology is most commonly encountered, plasma cell neoplasms can also display anaplastic or plasmablastic morphology. 1 The latter is defined by immature cells with a high nuclear/cytoplasm ratio, dispersed nuclear chromatin, prominent nucleoli and absent or inconspicuous perinuclear clearing.2–4
+
+Epstein-Barr virus (EBV) is a ubiquitous gamma-herpesvirus that will infect more than 95% of humans during their lifetime. 5 EBV is associated with many non-neoplastic and neoplastic entities. One of them is plasmablastic lymphoma—an aggressive B-cell lymphoma that can be a histologic mimicker of plasma cell neoplasms. Plasmablastic lymphoma and plasma cell neoplasms with plasmablastic morphology share similar cytologic features and plasmacytic immunoprofiles, complicating the histologic distinction between these entities. 6 These overlapping features pose a diagnostic dilemma, as plasmablastic lymphoma and plasma cell neoplasms run diverging clinical courses and require different treatment protocols.7–9 Currently, the most reliable distinguishing factor is EBV-positivity in plasmablastic lymphoma, with The World Health Organization reporting EBV-encoded RNA (EBER) in situ hybridization (ISH) testing to be positive in 60–75% of plasmablastic lymphoma. 1 The presence of EBV in plasma cell neoplasms is historically so unusual that single or low-number cases still warrant reporting in journals (Supplementary Table 1).
+
+Competent T-cell immune surveillance is required to control EBV-infection and curb the production of “immortal” B lymphoblastoid cell lines that can lead to lymphoid malignancies.5, 10 Therefore, it is not surprising that immunodeficiency increases the risk of EBV-associated lymphoproliferative disorders. 11 EBV-positive plasma cell neoplasms are usually reported in post-transplant patients12–18 and less commonly in HIV-positive patients,3, 19–23 with the prevalence dramatically lower in immunocompetent individuals. 20 HIV predisposes infected individuals to many cancers, including several hematolymphoid malignancies. While plasmablastic lymphoma is considered an (EBV-driven) AIDS-defining malignancy, it is unclear if HIV-infection is causally associated with plasma cell neoplasms.24–28 Meta-analyses of studies reporting the incidence of plasma cell neoplasms in HIV-infected patients in high-income countries have revealed increased standardized incidence ratios26, 27, 29 with patients presenting at a younger age24, 30 and succumbing to a more aggressive disease course. 27 Good quality epidemiological data on HIV and plasma cell neoplasms in low- and middle-income countries are much more challenging. 31 Dhokotera et al 28 did not report an increase in plasma cell myeloma in HIV-positive individuals after reviewing South African National Cancer Registry data for ten years.
+
+This laboratory-based study aims to establish the prevalence of EBV in plasma cell neoplasms in a large South African cohort and to determine whether there is any correlation between EBV-positivity and HIV status in patients with plasma cell neoplasms.
+
+Materials and Methods
+
+Case Selection, Morphologic and Immunohistochemical Review
+
+The study cohort included 97 patients diagnosed with plasmacytoma (solitary plasmacytoma of bone and extraosseous plasmacytoma) or plasma cell myeloma at a single South African institution between 1 January 2003 and 31 March 2020. Bone marrow aspirates and trephine biopsies were excluded. A pathologist and pathology trainee independently reviewed each tumor's histomorphology and immunohistochemical panels. The pathologist and trainee found no diagnostic discrepancies with the initial pathology reports. All EBV-positive tumors and all tumors included in the tissue microarrays were reviewed by a second pathologist. Tumors with a minor (less than 30%) component of blastic tumor cells were categorized as plasmacytic, while tumors comprising more than 30% of blastic cells were classified as plasmablastic. 9 The minimum immunohistochemical requirements for inclusion of a tumor were positivity for multiple myeloma (MUM)-1 and CD138, evidence of immunoglobulin light chain restriction (kappa or lambda) and weak or absent staining for CD20. Bone marrow aspirate and serum/urine electrophoresis reports were reviewed as further support for diagnosing plasma cell myeloma (Supplemental material). Tumors with blastic morphology were only included if there was clinical consensus to support a diagnosis of plasmablastic myeloma; cases with reasonable concern for plasmablastic lymphoma were excluded. This study did not include reactive plasma cell lesions and other lymphoid neoplasms with plasmacytic differentiation, like extranodal marginal zone lymphoma. Cases with insufficient residual tumor tissue for performing EBER-ISH and/or p24 immunohistochemistry were excluded.
+
+Immune Status
+
+HIV status was recorded based on available results of HIV enzyme-linked immunoassay (ELISA) testing or HIV viral load testing. Also included were tumors from patients with a known history of HIV infection and an available CD4 count. HIV status was recorded as positive, negative, or unknown. None of the patients had undergone solid organ or stem cell allograft transplants.
+
+Tissue Microarray Assembly
+
+Formalin-fixed paraffin-embedded (FFPE) tissue blocks with sufficient tumor tissue were used to construct two tissue microarrays (TMA)s. The procedure entailed extracting 1mm diameter tumor tissue cores from suitable blocks using a UNITMA microarrayer (catalog #IW-UT06, immunohistochemistry (IHC) World, Ellicott City, MD, USA) and re-embedding these cores into a gridded paraffin block. Fifty tumors (one core per tumor) were successfully incorporated into the two TMAs. Ovarian, appendiceal and skin tissue were used as control place markers.
+
+EBV-Encoded Small RNA in Situ Hybridization
+
+EBV-encoded small RNA (EBER) in situ hybridization (ISH) was performed on FFPE sections from tissue blocks (47 tumors) and sections from the TMAs (representing 50 tumors) using the Leica BOND Ready-to-use chromogenic EBER probe (Leica Biosystems, Newcastle upon Tyne, UK) and according to the supplier's protocol. An RNA control to demonstrate the presence of suitable RNA for hybridization was not used. Results were independently interpreted by three investigators using conventional light microscopy. Tumors were assigned as positive if more than 5% of neoplastic cells showed brown nuclear staining 32 without confounding artefactual staining.
+
+Immunohistochemistry: p24 Antigen
+
+HIV-1 p24 antibody recognizes part of a capsid gag protein unique to the human immunodeficiency virus. 33 In this study, p24 immunohistochemistry was performed to determine whether HIV was present in tumoral tissue from patients in which the HIV status could not be conclusively determined from prior serologic testing or clinical information captured from laboratory request forms. Tumors from patients with an unknown HIV status were stained with anti-HIV-1 p24 antibody from Dako Diagnostics (Agilent Dako, Burlington, ON, Canada, 1:10) on the Dako Link Autostainer 48. Lymph node tissue from an HIV-positive patient was used as external control and showed positive cytoplasmic staining in follicular dendritic cells. Two tumors from HIV-positive patients were included in the TMAs as an “internal positive control” representing exclusive tumoral tissue.
+
+Statistical Analysis
+
+Chi-squared, Fisher’s exact and Mann-Whitney U tests were performed using GraphPad Prism, version 5. Hypotheses were two-tailed, and P-values of <.05 were considered significant.
+
+Results
+
+Tumor Sites, Demographics, and Morphology
+
+The patient cohort comprised 54% men and 46% women (1.07:1) with a mean age of 55 years (Table 1). Of the 97 tumors included in the study, 64 biopsies were from bone sites and had undergone decalcification. The remainder of the biopsies were from the upper aerodigestive tract, lower respiratory tract, skin, liver, lymph nodes, submandibular salivary gland and soft tissue sites (Table 2). Plasmacytic morphology occurred in most tumors (Table 2; Figure 1A), with only 7/97 (7%) showing plasmablastic morphology (Table 2; Figure 1B).
+
+Figure 1. Plasma cell neoplasm histology, hematoxylin and eosin,  × 400. (A) Plasmacytic morphology. (B) Plasmablastic morphology.
+
+Table 1. Patient Demographics and HIV Status.
+
+Age a	Gender	HIV status	
+11 to 84 (55)	Female
+45 (46%) b	Male
+52 (54%) b	Positive
+10 (16%) c	Negative
+56 (84%) c	
+a Age range in years with mean; bTotal of 97 patients; cSixty-six patients had a known HIV status.
+
+Table 2. Tumor Sites and Morphology.
+
+Biopsy site	Axial skeleton	Appendicular skeleton	Bone, NOS	Liver	Upper aerodigestive tract	Lower respiratory tract	Soft tissue	Skin	Lymph node	Salivary gland	
+Plasmacytic a	29	29	1	2	9	2	13	2	2	1	
+Plasmablastic b	3	1	0	0	1	0	0	1	1	0	
+Number of cases with apredominantly mature plasma cells (<30% plasmablastic cells); bmore than 30% plasmablastic cells.
+
+EBER-ISH
+
+Of the 97 tumors, eight were excluded from statistical analysis due to equivocal staining. The EBER-equivocal tumors showed staining artefacts which included nuclear staining mainly at the periphery of the tissue sections, weak nuclear staining (Figure 2A), stronger cytoplasmic than nuclear staining, and non-specific background staining confounding the interpretation of nuclear staining. Six of the eight tumors with equivocal staining had been decalcified. The external control tissue used for EBER-ISH testing was processed in the same laboratory and had not been decalcified; it showed crisp nuclear staining without background artefact. With the equivocal tumors removed, 16/89 tumors (18%) showed convincing nuclear staining (Table 3; Figure 2B), with the remainder having legitimate negative staining without excessive artefact (Figure 2C). There was no gender difference (P-value 1.0), but the EBV-positive cohort was significantly younger than the EBV-negative group (P-value .00124) (Table 3).
+
+Figure 2. EBER-ISH stain. (A) Weak nuclear staining interpreted as equivocal, × 400. (B) Positive nuclear staining, × 400. (C) Negative nuclear staining, × 400. (D) Positive nuclear staining in stromal cells, endothelial cells, osteoblasts, and osteocytes, × 200.
+
+Table 3. EBV Status Versus Age and Gender.
+
+Positive EBV-status a	EBV-status versus mean age in years*	EBV-status versus gender**	
+Negative	Positive	Negative	Positive	
+16/89 (18%)	57.58	44.25	Male 38	Female 35	Male 8	Female 8	
+a Based on EBER-ISH result; *P-value of Mann-Whitney U test is .00124. **P-value of chi-squared test is 1.0.
+
+The EBV-positive tumors were significantly linked to positive HIV patient status (P-value .0027) (Table 4). There was no significant correlation between EBV status and plasmacytic versus plasmablastic morphology (P-value .106).
+
+Table 4. EBV Versus HIV Status.
+
+EBV-status in HIV-negative patients*	EBV-status in HIV-positive patients*	EBV-status in HIV-unknown patients a	
+Negative	Positive	Negative	Positive	Negative	Positive	
+42	8	4	6	27	2	
+*P-value of chi-squared test is .0027; aThe two EBV-positive tumors with unknown HIV patient status were not included in the statistical analysis.
+
+An interesting finding was that three tumors—two showing positive staining in tumor cells, one being negative—had convincing nuclear EBER-ISH staining in endothelial cells, stromal cells and even osteoblasts and osteocytes in the absence of any background staining (Figure 2D). All three of these tumors arose in bony sites (humerus, thoracic vertebra and iliac wing), and all three had undergone decalcification.
+
+The three independent observers showed >90% correlation in their interpretation of the EBER-ISH stains.
+
+HIV Status and P24 Immunohistochemical Staining
+
+Of the 97 plasma cell neoplasms included in this study, 64 (66%) had a known HIV patient status (Table 1). Ten patients were HIV-positive (16%), in line with the most recent estimate of a 14% HIV prevalence in South Africa. 34 None of the tumors from patients with unknown HIV status that underwent HIV-1 p24 immunohistochemical testing showed positive staining. It is important to note that the two tumors with known HIV-positive patient status included in the TMAs also showed entirely negative p24 staining. Due to this finding of ‘false negative’ staining in the internal positive controls, the tumors with unknown patient status were not assigned a negative HIV status and instead remained HIV-unknown.
+
+Discussion
+
+Our finding of 18% EBV-positivity in plasma cell neoplasms is higher than previously reported in larger-number studies. Chang et al 35 had reported four of 58 plasma cell neoplasms to be EBV-positive (6.9%); Yan et al 36 described 4/46 EBV-positive tumors (8.7%), and Nael et al 20 reported 6/131 (4.6%). The historically uncommon association of EBV with plasma cell neoplasms, as opposed to some other B-cell lymphomas, is primarily ascribed to the absence of the EBV receptor CD21 on plasma cells.12, 37, 38 The tumorigenesis of plasma cell neoplasms is still incompletely understood, as myeloma cells are notoriously difficult to culture. Matsui et al 39 have suggested that the ‘stem cells’ giving rise to plasma cell neoplasms are CD138-negative, CD20-positive B-cells that eventually differentiate into clonal mature CD138-positive, CD20-negative plasma cells. Whether plasma cell neoplasm “stem cells’ express CD21 receptors required for EBV infection has not yet been determined. 38 The current incomplete understanding of the pathogenic role of EBV infection in plasma cell neoplasms is potentially hampering the effective treatment of this disease, as EBV-positive B-cell lymphomas are known to be biologically distinct from EBV-negative lymphomas, requiring different treatment approaches. 40
+
+Most EBV-related malignancies display latent EBV-infection, but the lytic phase is also implicated in oncogenesis. 41 Four EBV-latency phases (0-III) have been described,41, 42 with their different gene expression and protein products implicated in varying aspects of oncogenesis.43, 44 EBERs are abundantly expressed during all four EBV latency phases,45, 46 and therefore, its presence does not distinguish between the different latency phases. EBER-positivity also does not confirm that EBV is indeed in the latent phase of infection. Although EBERs are generally thought to be downregulated (absent) during the lytic phase, 47 Naidoo has reported co-expression of EBER with BamHI Z fragment leftward open reading frame (BZLF) 1, a lytic phase-specific gene, in diffuse large B-cell lymphoma. 48 Latent membrane protein (LMP) 1 immunohistochemistry has only been performed on a handful of plasma cell neoplasm tumors (Supplementary Table 1). Therefore, further studies exploring other latency phase proteins (LMP2A/B, EBV-encoded nuclear antigens (EBNAs), non-transcribed BamHI-A rightward transcripts [BART] RNAs) 43 and lytic phase gene expression in EBV-positive plasma cell neoplasms are required to characterize the nature of EBV-infection in plasma cell neoplasms.
+
+The significant correlation of EBER-positivity with positive HIV status suggests a co-carcinogenic relationship between the two viruses. HIV does not primarily infect plasma cells, 31 therefore other biologic mechanisms have been proposed to explain reports of increased incidences of plasma cell neoplasms in HIV-positive patients. These mechanisms include B-cell proliferation due to chronic antigenic stimulation by HIV proteins, 49 the more potent effect of oncogenic viruses like EBV in impaired immunity and elevated levels of interleukin-6, which is an integral plasma cell growth factor associated with plasma cell neoplasm tumorigenesis.30, 50–52 As the vast majority of reports regarding EBV-positive plasma cell neoplasms originate in high-income countries (Supplementary Table 1), where HIV infection is a less common cause of immunosuppression than in low- and middle-income countries, 53 data on the relationship between EBV and HIV in plasma cell neoplasms have been underrepresented in the literature. Better characterizing this relationship could potentially benefit the management of HIV patients, as plasma cell neoplasms in post-transplant immunosuppressed patients are known to behave more like post-transplant lymphoproliferative disorder (PTLD) B-cell lymphomas in prognosis and treatment response than plasma cell neoplasms encountered in immunocompetent patients. 16 Whether this is also the case in HIV patients with plasma cell neoplasms remains to be seen.
+
+Equivocal EBER-ISH staining was a limitation observed in eight tumors, of which six were decalcified bone specimens. Although RNA degradation due to decalcification could have negatively affected EBER-ISH testing, statistical analysis did not reveal a significant difference in equivocal staining between decalcified and non-decalcified tissue (P-value .71). Using control RNA probes to confirm RNA integrity after decalcification should be considered in future studies.
+
+The finding of crisp nuclear EBER-ISH staining in stromal, endothelial and bone cells was unexpected. In vitro cell culture studies have shown EBV to be present in endothelial cells, 54 and EBER-expression has been documented in endothelial cells in EBV-associated nasopharyngeal carcinoma. 45 Only one in vivo study refers to EBER-ISH staining in tumoral stromal cells—this was reported in sclerosing angiomatoid nodular transformation (SANT) of the spleen. SANT is a non-neoplastic reactive lesion in which the stromal cells are considered part of the lesion; 55 an entirely different biologic milieu than plasma cell neoplasms. The oncogenic qualities of EBV have been widely studied, focusing on its ability to evade the immune system through latency and its ability to create immortal B-cell lines. Less is known about its role in establishing or promoting a microenvironment where a neoplasm can flourish. 56 Although the finding of stromal and endothelial staining is possibly non-specific, it could be worthwhile to explore in future studies.
+
+Although some association between EBV-positivity and plasmablastic morphology in plasma cell neoplasms has been reported,20, 35 our data did not reveal a significant correlation.
+
+P24 immunohistochemistry did not contribute to this study outcome. The most likely reason for the pervasive negative p24 staining, including the ‘false negative’ staining in tumors from patients with confirmed HIV-positive status, is the absence of cell types infected by HIV in plasma cell neoplasm tumoral tissue. HIV primarily infects CD4 + T-cells and dendritic cells, 57 with B-cells and plasma cells seemingly spared. 31 Germinal center follicular dendritic cells are the most reliable cells to express the p24 antigen in FFPE tissue derived from HIV-infected individuals.58, 59 Staining is also demonstrated in the mantle zone, intrafollicular and paracortical lymphocytes in lymphoid tissue. P24 staining has not been reported in epithelial cells, stromal cells and plasma cells. 58 Another consideration for the absent p24 staining is p24’s specificity for the HIV-1 viral type. Although cross-reactivity between p24 and HIV-2 infected cells has been reported in cell culture studies, 60 the DAKO p24 antibody is not expected to stain HIV-2 infected cells. 61 This is unlikely to be a contributing factor, however, as HIV-1 infects most South African patients living with HIV. 62
+
+While the finding of 18% EBV-positivity in plasma cell neoplasms could potentially contribute to the reconsideration of pathologists’ reliance on EBER-ISH in distinguishing between plasma cell neoplasms with plasmablastic morphology and plasmablastic lymphoma, this study had several limitations. Due to its retrospective nature, clinical information, including radiology, the presence of end organ damage, detection of M-protein, and HIV status, could only be gleaned from laboratory records and the information provided to the pathologist at the time of biopsy. HIV status was unavailable in 34% of patients, and p24 immunohistochemistry did not contribute to determining HIV status in tumoral tissue. Also, the clinical outcome of EBV-positive versus EBV-negative patients, and HIV-positive versus HIV-negative patients, could not be determined. Another limitation was not using RNA probes in decalcified tissue to determine RNA integrity before performing EBER-ISH.
+
+Several questions arose during the execution of this study, which might aid in guiding future research: In which latency phase is EBV when detected in plasma cell neoplasms? To what degree does decalcification affect the detection of EBER using situ hybridization? If EBV is genuinely present in stromal cells, does it play a role in establishing a microenvironment allowing for tumor development? Do HIV and EBV have a co-carcinogenic relationship, and how does that affect clinical outcomes?
+
+Supplemental Material
+
+sj-docx-1-ijs-10.1177_10668969221113490 - Supplemental material for The Prevalence of Epstein-Barr Virus in Plasma Cell Neoplasms is Higher in HIV-Positive Individuals
+
+Click here for additional data file.
+
+Supplemental material, sj-docx-1-ijs-10.1177_10668969221113490 for The Prevalence of Epstein-Barr Virus in Plasma Cell Neoplasms is Higher in HIV-Positive Individuals by Ingrid H Penzhorn, Johann W Schneider and Candice Sher-Locketz in International Journal of Surgical Pathology
+
+Acknowledgments
+
+Thank you to Nadine Solomons (Stellenbosch University) for constructing the TMAs and to Zaineb Mia and Ursula Paulsen (NHLS Tygerberg) for performing the EBER-ISH. Biostatisticians from the Division of Epidemiology and Biostatistics, Department of Global Health, Stellenbosch University, assisted with the statistical analysis.
+
+All data relevant to the study are included in this article or supplied as supplemental material.
+
+The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
+
+Ethical Approval: The Health Research Ethics Committee of Stellenbosch University approved this study (approval number S17/10/235) on 07/05/2018.
+
+Funding: The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the National Health Laboratory Service, (grant number GRANT004_94771).
+
+Informed Consent: The Health Research Ethics Committee of Stellenbosch University granted a waiver of informed consent for this retrospective study (approval number S17/10/235).
+
+ORCID iDs: Ingrid H Penzhorn https://orcid.org/0000-0002-0525-7134
+
+Johann W Schneider https://orcid.org/0000-0001-5187-6756
+
+Trial Registration: N/A
+
+Supplemental Material: Supplemental material for this article is available online.
+==== Refs
+References
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+

PMC10357481.txt

@@ -0,0 +1,373 @@
+
+==== Front
+Immunology
+Immunology
+10.1111/(ISSN)1365-2567
+IMM
+Immunology
+0019-2805
+1365-2567
+John Wiley and Sons Inc. Hoboken
+
+35318648
+10.1111/imm.13471
+IMM13471
+Original Article
+Original Articles
+Tumour‐associated antigenic peptides are present in the HLA class I ligandome of cancer cell line derived extracellular vesicles
+HLA‐I ligandome of extracellular vesicles
+Kumar et al.
+Kumar Pankaj 1 pk20@st-andrews.ac.uk
+
+Boyne Caitlin 1
+Brown Sydney 1
+Qureshi Ayesha https://orcid.org/0000-0003-0910-153X
+1
+Thorpe Peter 1
+Synowsky Silvia A. 2 3
+Shirran Sally 2 3
+Powis Simon J. https://orcid.org/0000-0003-4218-2984
+1 3 sjp10@st-andrews.ac.uk
+
+1 School of Medicine University of St Andrews St Andrews UK
+2 School of Biology University of St Andrews St Andrews UK
+3 Biomedical Sciences Research Complex University of St Andrews St Andrews UK
+* Correspondence
+Simon J. Powis and Pankaj Kumar, School of Medicine, University of St Andrews, St Andrews KY16 9TF, UK.
+Email: sjp10@st-andrews.ac.uk and pk20@st-andrews.ac.uk
+
+20 4 2022
+6 2022
+20 4 2022
+166 2 10.1111/imm.v166.2 249264
+16 12 2021
+22 9 2021
+17 1 2022
+© 2022 The Authors. Immunology published by John Wiley & Sons Ltd.
+https://creativecommons.org/licenses/by/4.0/ This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
+
+Abstract
+
+The recent success of monoclonal antibody checkpoint inhibitor therapies that enhance the ability of CD8+ T cells to detect cancer‐related antigenic peptides has refocused the need to fully understand the repertoire of peptides being presented to the immune system. Whilst the peptide ligandome presented by cell surface human leucocyte antigen class I (HLA‐I) molecules on cancer cells has been studied extensively, the ligandome of extracellular vesicles (EVs) remains poorly defined. Here, we report the HLA‐I ligandome of both the cell surface and EVs from eight breast cancer cell lines (MCF7, MDA‐MB‐231, MDA‐MB‐361, MDA‐MB‐415, MDA‐MB‐453, HCC 1806, HCC 1395, and HCC 1954), and additionally the melanoma cell line ESTDAB‐056 and the multiple myeloma line RPMI 8226. Utilizing HLA‐I immunoisolation and mass spectrometry, we detected a total of 6574 peptides from the cell surface and 2461 peptides from the EVs of the cell lines studied. Within the EV HLA‐I ligandome, we identified 150 peptides derived from tumour associated antigenic proteins, of which 19 peptides have been shown to elicit T‐cell responses in previous studies. Our data thus show the prevalence of clinically relevant tumour‐associated antigenic peptides in the HLA‐I ligandome presented on EV.
+
+The HLA‐I peptidomes of both EV and cell were identified from 10 cancer cell lines and 150 peptides derived from tumour‐associated antigens were identified in HLA‐I ligandome of EVs. Our data, show, for the first time the prevalence of clinically relevant tumour‐associated antigenic peptides in the HLA‐I ligandome presented on EV and the potential of EV ligandomes for identification of CD8+ T‐cell epitopes.
+
+breast cancer
+extracellular vesicles
+HLA ligandome
+T‐cell epitopes
+tumour associated antigen (TAA)
+Breast Cancer Now 10.13039/100009794 2018JulPR1086 Wellcome Trust 10.13039/100010269 105621/Z/14/Z source-schema-version-number2.0
+cover-dateJune 2022
+details-of-publishers-convertorConverter:WILEY_ML3GV2_TO_JATSPMC version:6.3.2 mode:remove_FC converted:20.07.2023
+Kumar P , Boyne C , Brown S , Qureshi A , Thorpe P , Synowsky SA , et al. Tumour‐associated antigenic peptides are present in the HLA class I ligandome of cancer cell line derived extracellular vesicles. Immunology. 2022;166 :249–264. 10.1111/imm.13471 35318648
+
+Funding information Breast Cancer Now, Grant/Award Number: 2018JulPR1086; Wellcome Trust, Grant/Award Number: 105621/Z/14/Z
+==== Body
+pmcAbbreviations
+
+CL cell lysates
+
+EVs extracellular vesicles
+
+HLA‐I human leucocyte antigen class I
+
+TAA tumour‐associated antigenic proteins
+
+TAApep peptides derived from tumour‐associated antigenic proteins
+
+INTRODUCTION
+
+Human leucocyte antigen class I molecules (HLA‐I) display short peptides, typically 9–11 amino acids in length, primarily derived from the cellular proteome, to CD8+ T cells which permits the detection of intracellular pathogens such as viruses, and also up‐regulated or mutated proteins relevant in cancer [1] However, a key problem in the context of cancer cells is their ability to evade elimination by cytotoxic CD8+ T cells through the expression of immune checkpoint proteins such as PD‐L1. In recent years, checkpoint inhibitor therapies in the form of monoclonal antibodies directed against PD‐L1 and PD1 have displayed significant clinical success by allowing CD8+ T cells to more readily detect the array of antigenic peptides on cancer cells [2, 3, 4]. This in turn has led to enhanced efforts to fully describe the cancer HLA‐I peptide proteome in ever more detail [5]. Such efforts have naturally concentrated on the cell surface HLA‐I ligandome. However, the nature of the HLA‐I ligandome present on extracellular vesicles (EVs) released by cancer cells remains essentially unknown.
+
+Extracellular vesicles, including exosomes, are typically 50–200 nm membrane bound vesicles secreted by cells into extracellular environment [6]. EVs released into the extracellular space can carry bioactive molecules including RNAs (mRNA, microRNA, and long non‐coding RNA), lipids, and proteins. Depending on their origin and biological information carried, EVs can instigate changes in the recipient cells according to the information transferred [7]. Once EVs have entered the extracellular space or circulation, they are thought to elicit effects by one of two mechanisms: by fusing with the plasma membrane of recipient cells, resulting in the release of their contents to the cell [8], or second, by direct interaction with receptors on the surface of the target cells, initiating the activation of various signalling pathways [9]. EVs have been proposed as key players in cell communication, and in particular the communication that exists between cancer cells and the host microenvironment, both locally and at a distant site [10]. EVs derived from many cancer cells or within the tumour microenvironment are thought to be key factors in oncogenic transformation, drug resistance, and tumour metastasis. On the other hand, EVs are also involved in tumour rejection [11] indicating that the role of EVs in tumour progression relies on their cargo. Therefore, defining the EV cargo is crucial to fully understand the role of EVs in tumour growth or rejection. Additionally, tumour‐derived EVs or exosomes (TEX) have been proposed as cancer prognosis markers for a range of tumours [12, 13]. For example, TEX and T‐cell derived EVs were used for monitoring head and neck cancer patients' response to oncology therapy [14].
+
+It has been known for many years that tumour‐derived EVs are present in malignant effusions and that these EVs are enriched in HLA molecules as well as tumour antigens such as HER2/neu and melan‐A [15]. Additionally, EVs derived from tumour cells can modulate antigen specific CD8+ T‐cell response either by direct presentation or cross‐presentation, indicating that EVs carry functional HLA‐peptide complexes [16, 17]. However, the actual HLA‐I ligandome of EVs released by tumour cells and its potential in identifying tumour antigens (TAs), has not been studied extensively. We hypothesized that defining the HLA‐I ligandome of EVs released by tumour cells may potentially be utilized to identify clinically relevant tumour antigens. This hypothesis is based on our previous finding that the HLA‐I ligandome of EVs is similar to the cell ligandome [18]. Here, we report the results of a study aimed at the identification of EV HLA‐I ligandomes, and more crucially their potential in identification of tumour antigens, from breast cancer, melanoma, and myeloma cell lines.
+
+EXPERIMENTAL PROCEDURES
+
+Cell lines and flow cytometry
+
+The breast cancer cell lines, MDA‐MB‐231, MDA‐MB‐361, MDA‐MB‐415, MDA‐MB‐453, MCF7, HCC 1395, HCC 1806, and HCC 1954, were obtained from the ATCC Breast Cancer Cell panel (ATCC 30‐4500K, LGC Standards). Multiple myeloma cell line RPMI 8226 was obtained from LGC Standards and melanoma cell line ESTDAB‐056 was a gift from Prof Federico Garrido, (University of Granada, Spain). All cell lines were cultured in DMEM or RPMI‐1640 supplemented with 5% fetal bovine serum (FBS) and 50 μg/ml kanamycin (all ThermoFisher Scientific). For flow cytometry, cells were harvested from culture flasks with a 1–2 min incubation with trypsin (0.025%)‐EDTA (0.01%) solution at 37° celsius. Cells were then resuspended in culture medium to inactivate trypsin and centrifuged at 300g for 10 min at 4° celsius, then resuspended in PFN buffer (PBS, 2% FBS, 0.1% sodium azide). Cells were stained with primary mouse IgG anti‐HLA‐A, ‐B, and ‐C monoclonal antibody W6/32 [19] for 20 min at 4° celsius, followed by two washes with centrifugation steps as above in PFN. Cells were then stained with FITC‐anti‐mouse IgG (Sigma‐Aldrich UK, F2012) at a dilution of 1/100 for 20 min, and washed as above. Control cells received second stage FITC anti‐mouse IgG alone. Cells resuspended in PFN and were analysed on a Merck‐Millipore Guava 8HT flow cytometer with a 488‐nm laser using Guavasoft 2.7 software.
+
+Characterization of EV
+
+For the initial characterization of EVs, cell lines were grown in serum‐free medium (Ex‐Cell 610 HSF serum free, Sigma‐Aldrich, UK) to exclude the possibility of EV contamination from serum. For nanoparticle tracking analysis (NTA), cells were cultured for 24 hr, conditioned media were then collected and centrifuged at 300g for 10 min at 4° celsius to remove cells and large debris. EV‐containing supernatants were then filtered with 0.22‐μm Millex‐GP syringe filters (Merck). NTA was performed using an LM‐10 unit (Malvern) equipped with a 638‐nm laser. A minimum of three videos of 30 s were recorded for each sample using settings of shutter speeds of 17 or 30 ms. Data analysis was performed using NTA 2.3 software with detection thresholds of 2 or 3 and blur, min track length, and min expected size on auto settings. One representative data plot is shown for each cell line.
+
+For immunoblotting, cells were grown in 175 cm2 flasks for 48 hr in serum‐free medium as above. After 48 hr, conditioned media were collected, spun, and filtered through 0.22‐μm filters as above, and culture supernatants were then spun at 100 000g for 2 hr using a Sw32Ti swing‐out rotor in a Beckman L100 ultracentrifuge. EV pellets were resuspended in lysis buffer (1% NP40, 150 mM NaCl, 10 mM Tris pH 7.4, supplemented with Pierce mini‐protease inhibitor tablets). Cell lysates were prepared concomitantly by lysis of the cells remaining in the culture flasks with the same lysis buffer, with an additional spin at 20 000g for 10 min at 4° celsius to remove insoluble debris. Protein estimations were performed by Bradford assay (ThermoFisher Scientific). Approximately, 3 μg of cell and EV lysates were electrophoresed on 4%–20% gradient sodium dodecyl sulphate‐polyacrylamide gel electrophoresis gels (ThermoFisher Scientific) and transferred to nitrocellulose filters (BA85, ThermoFisher Scientific). Membranes were probed with anti‐CD9, CD63, and CD81 antibodies (ThermoFisher Scientific clones Ts9, Ts63, and M38, respectively) at 1:5000 dilution, anti‐HLA‐B and ‐C mouse monoclonal antibody HC10 [20] at 1:1000 dilution, anti‐human TAP1 (Merck, clone 148.1) at 1:1000 dilution, or rabbit monoclonal anti‐human calnexin (Abcam, ab213243) at 1:1000 dilution. Immunoblot signals were revealed with 1:10 000 diluted IR Dye800cw anti‐mouse or anti‐rabbit IgG (LI‐COR) and visualized using a LI‐COR Odyssey scanner.
+
+Large‐scale EV and cell isolation for HLA‐I peptide isolation
+
+Large‐scale cultures for EV and cell isolation were maintained in EV‐depleted FBS. EV‐depleted FBS was prepared by ultracentrifugation of FBS at 100 000g for 4 hr at 4° celsius using a SW32Ti rotor in a Beckman L100 ultracentrifuge, followed by 0.2‐μm filtration. All cell lines were either grown in DMEM (breast cancer cell lines) or in RPMI‐1640 (myeloma and melanoma cell lines) supplemented with 2.5% EV‐depleted FBS and 50 μg/ml kanamycin. Suspension cell line RPMI 8226 was cultured in 175‐cm2 flasks and cultured to approximately 2 million cells per ml. Conditioned medium containing cells and EVs was collected and centrifuged at 300g for 10 min at 4° celsius. The supernatant containing EVs was filtered with 0.2‐μm syringe filter and stored at −20° celsius and RPMI 8226 cells were re‐plated in EV‐depleted medium. Conditioned medium was collected again after 48 hr and processed as described above.
+
+All adherent cells were grown in either multiple 175‐cm2 flasks or in Falcon 5‐layer multi‐flasks with a surface area of 875 cm2 (ThermoFisher Scientific). At around 80% cell confluence, fresh culture medium was added to flasks and conditioned medium was harvested every 48 hr and processed as above, with new medium then added to the flasks. In total 800 ml conditioned media were harvested from each breast cancer cell line and 250 ml conditioned medium was harvested from ESTDAB‐056 and suspension cell line RPMI 8226. The EV harvests were thawed and spun at 100 000g for 2 hr using a Sw32Ti rotor and the EV pellet lysed in lysis buffer as above, and stored at −20° celsius until immunoprecipitation. At culture closedown, cells were harvested from culture flasks with a 1–2 min incubation with trypsin (0.025%)‐EDTA (0.01%) solution at 37° celsius and spun at 300g for 10 min at 4° celsius. Cell pellets were lysed in lysis buffer as above, spun at 20 000g for 10 min at 4° celsius to remove debris and the cell lysates were stored at −20° celsius until immunoprecipitation. EV and cell isolation experiments were independently repeated two to three times for breast cancer cell lines and six times for myeloma and melanoma cell lines.
+
+Immunoprecipitation and mass spectrometry analysis
+
+EV and cell lysates (CL) were thawed and incubated with 0.5 ml anti‐HLA‐A, ‐B, and ‐C antibody, W6/32, coupled to Protein G‐Sepharose beads (crosslinked to Protein G using BS3 crosslinker according to manufacturer instructions, ThermoFisher Scientific) for 1–2 hr. The beads were then washed extensively with wash buffer (150 mM NaCl, 10 mM Tris pH 7.4), and resuspended in 1 ml of 1% TFA for 10 min at room temperature to release HLA‐I and bound peptides. Eluted HLA‐I and peptides were then bound to Pierce C18 100‐μl tips (87784, ThermoFisher Scientific) based on the manufacturer's instructions. The peptide fraction was eluted in 30% acetonitrile and 0.1% TFA and dried down by speedvac for mass spectrometry.
+
+Peptides were then analysed on an AB Sciex TripleTOF 5600+ system mass spectrometer (Sciex) coupled to an Eksigent nanoLC AS‐2/2Dplus system. The samples were loaded in loading buffer (2% acetonitrile and 0.05% trifluoroacetic acid) and bound to an Aclaim pepmap 100 μm × 2 cm trap (ThermoFisher Scientific), and washed for 10 min to waste after which the trap was turned in‐line with the analytical column (Aclaim pepmap RSLC 75 μm × 15 cm). The analytical solvent system consisted of buffer A (2% acetonitrile and 0.1% formic acid in water) and buffer B (2% water with 0.1% formic acid in acetonitrile) at a flow rate of 300 nl/min with the following gradient: linear 1%–20% of buffer B over 90 min, linear 20%–40% of buffer B for 30 min, linear 40%–99% of buffer B for 10 min, isocratic 99% of buffer B for 5 min, linear 99–1% of buffer B for 2.5 min, and isocratic 1% solvent buffer B for 12.5 min. The mass spectrometer was operated in the DDA top 20 positive ion mode, triggering on +2 to +5 charge states, with 120 and 80 ms acquisition time for the MS1 (m/z 400–1250) and MS2 (m/z 95–1800) scans, respectively, and 15‐s dynamic exclusion. Rolling collision energy was used for fragmentation.
+
+Peptide identification
+
+LC–MS/MS data were searched against the human proteome (uniprot database UP000005640 containing 74 464 protein entries, downloaded on 2 August 2019) with an additional contaminant list from the global proteome machine (cRAP protein sequences). We used decoy fusion method of PEAKS DB search to estimate false discovery rate during peptide identification [21]. Peptide identification was performed with PEAKS Studio X (https://www.bioinfor.com/peaks-studio). Following mass spectrometry data acquisition, data files from the AB Sciex Triple TOF 5600+ were converted into mzML format using MSConvert software of proteowizard. Converted data files were imported into PEAKS Studio X for peptide identification. Data files were subjected to default data refinement followed by PEAKS de novo and PEAKS DB (database) searches to identify peptide sequences. PEAKS de novo and PEAKS DB searches were carried out by setting parent mass error tolerance to 15 parts per million (ppm), the fragment mass error tolerance to 0.1 Da, no enzyme selection, unspecific digestion mode, and filtering charge between 2 and 5. No post‐translational modification (PTM) was selected in PEAKS de novo and PEAKS DB searches. A 0.1% FDR cut off was applied during PEAK DB searches to select high confidence peptides and peptides only identified by PEAKS DB were selected for further analysis.
+
+Mass spectrometry proteomic data have been deposited to the ProteomeXchange Consortium via the PRoteomics IDEntifications (PRIDE) repository with the dataset identifier PXD025345.
+
+HLA typing of cell lines
+
+The HLA‐I genotypes of breast cancer cell lines, MDA‐MB‐231, MDA‐MB‐361, MDA‐MB‐415, MDA‐MB‐453, HCC 1806, HCC 1395, and HCC 1954, were identified by using paired end reads in fastq format as input to the Optitype version 1.3.1 [22]. Scripts used to perform HLA‐I typing can be found on: https://github.com/peterthorpe5/Cancer_cell_line_RNAseq_assemblies. RNA‐seq data of breast cancer cell lines were obtained from the Gene Expression Omnibus (GEO) (accession GSE73526). The HLA‐I genotypes of breast cancer cell line, MCF7, was obtained from previously reported HLA‐I genotypes of cancer lines [23]. HLA‐I genotype of ESTDAB‐056 was identified from immune polymorphism database (https://www.ebi.ac.uk/cgi-bin/ipd/estdab/print_cell.cgi?ESTDAB-056) and RPMI 8226 HLA‐type was determined from previous report [24]. HLA‐I types of all cancer cell lines are presented in File S1.
+
+HLA‐I peptide binding affinity and data analysis
+
+Eluted peptides identified from each replicate were combined, duplicate peptides were removed and filtered to select 8–15 amino acid long peptides. The total number of 8‐15‐mer peptides was defined as HLA‐I ligandome of cell and EVs from each cell line. Peptides present in HLA‐I ligandome were assessed for their predicted allele‐binding and affinity using algorithm netMHCpan 4.0 (http://www.cbs.dtu.dk/services/NetMHCpan/) [25] following default settings. Peptides with IC50 up to 1000 nM were tabulated and plotted using Prism 8 (GraphPad, Inc.) software. For identification of immunogenic peptides reported in previous studies and the peptides derived from TAA proteins, peptides with 8–15 length were searched against IEDB database (https://www.iedb.org/database_export_v3.php; tcell_full_v3_zip) and Tantigen database (http://projects.met-hilab.org/tadb/index.php).
+
+RESULTS
+
+Characterization of cell lines and extracellular vesicles
+
+We first characterized the expression of HLA‐I molecules on our chosen cell lines. Cell surface flow cytometry was performed on the breast cancer cells lines MCF7, MDA‐MB‐231, MDA‐MB‐361, MDA‐MB‐415, MDA‐MB‐453, HCC 1806, HCC 1395, and HCC 1954, the melanoma cell line ESTDAB‐056, and the myeloma cell line RPMI 8226, using the HLA‐A, B, and C specific monoclonal antibody W6/32 (Figure 1a). HLA‐I expression levels varied, with cell line MBA‐MB‐361 displaying the lowest apparent levels and ESTDAB‐056 displaying the highest levels of relative expression, but overall, all of the cell lines expressed cell surface HLA‐I molecules.
+
+FIGURE 1 (a) Flow cytometry of cell surface HLA‐A, B, and C as detected by antibody W6/32 compared to control second‐stage FITC anti‐IgG alone (ctrl). (b) NTA analysis of particles released by cell lines. One comparative graph of three recordings is shown, with mean and modes indicated. (c) Immunoblotting analysis of detergent cell lysates (CL) and EV lysates of all cell lines, probed for HLA‐B and C, CD9, CD63, CD81, and control proteins transporter associated with antigen processing (TAP1) and calnexin (CXN)
+
+To characterize the EVs released by each of these cell lines and to determine HLA‐I expression on EVs, cell lines were cultured in serum‐free media to eliminate potential contamination from FBS‐derived EVs. We performed nanoparticle tracking analysis (NTA) on conditioned medium collected from cells grown for 24 hr in these serum‐free conditions (Figure 1b). All cell lines released particles that were detected by NTA with means and modes that were within the 90–200 nm range. We also performed immunoblot analysis on cell lysates (CL) and EV lysates (EV isolated by filtration and ultracentrifugation) from cultures grown in serum‐free conditions for 48 hr (Figure 1c). The expression of characteristic markers for exosomes and EVs varied, but all EV lysates from breast cancer lines were positive for CD9 and CD81, whilst the presence of CD63 was more variable. The cell and EV lysates from myeloma line RPMI 8226 was positive for CD9 but negative for CD63 and CD81. The EV lysate from melanoma line ESTDAB‐056 showed a high signal for CD9 (image saturated in Figure 1c) but was low for CD63 and CD81. EV lysates from all lines were negative for the control endoplasmic reticulum proteins TAP1 or calnexin (CXN) indicating EV lysates were devoid of contaminating cellular components. The detection of HLA‐I molecules was variable, with cell lines such as MDA‐MB‐415, ESTDAB‐056, and RPMI 8226 displaying strong signals. Lower HLA‐I signals were observed in cell lines, HCC 1806, HCC 1395, and HCC 1954. HLA‐I signal was not detected in cell lines MCF‐7, MDA‐MB‐361, MDA‐MB‐231, and MDA‐MB‐453. It should be noted however that the monoclonal antibody HC10 used in these immunoblots reacts only with the heavy chain of HLA‐B and C molecules. Therefore, HLA‐A molecules will be under‐reported in these data. Taken together, we characterized our vesicle preparation as typical of a population described as extracellular vesicles based on MISEV guidelines [26].
+
+HLA‐I ligandome of cells and EVs
+
+Based on our previous study [18], we used a protocol that promotes speed of isolation and processing of both CL and EV lysates, to reduce the risk that lower affinity peptides bound by HLA‐I molecules would be lost due to HLA‐I protein unfolding. Thus, cell culture supernatants grown in EV‐depleted serum containing medium were centrifuged at 300 x g to remove debris, filtered at 0.2 μm and then ultracentrifuged at 100 000g for 2 hr to isolate EV, followed by detergent lysis and immunoisolation with antibody W6/32 coupled to Sepharose beads. CLs were processed concomitantly. After release of the peptides by acidification in trifluoracetic acid and clean up on C18 matrix tips, peptides were analysed by mass spectrometry. HLA‐I immunoisolated and eluted peptides were identified by PEAKS DB software (hereinafter defined as eluted peptides). Eluted peptides from independent experiments of cell and EV lysates of each cell line were combined together and duplicate peptides (peptides identified in more than one experiment) were removed to obtain a complete list of unique eluted peptides from cell or EVs. Peptides which are 8–15 amino acids long have been reported to bind HLA‐I molecules and elicit immune responses. Therefore, eluted peptides identified by PEAKS DB, were filtered to select 8‐15‐mer peptides for further analysis. The total number of 8‐15‐mer peptides identified from cell and EV lysates is hereinafter defined as cell or EV ligandome, respectively.
+
+The details of eluted peptides and HLA‐I ligandomes from each cell lines are presented in File S2 and summarized in Tables 1 and 2. A total of 6574 and 2461 eluted peptides were identified from cell and EVs of all 10 cell lines, respectively (Column 2 of Tables 1 and 2). A total of 6144 and 2406 peptides were identified as the cell and EV ligandomes from all 10 cell lines, respectively (Column 3 of Tables 1 and 2). The highest number of peptides were identified in ligandomes of cell lines, HCC 1954, HCC 1806, ESTDAB‐056, MDA‐MB‐415, and HCC 1395 (Column 3 of Tables 1, 2 and File S2), which is in agreement with the flow cytometry data of HLA‐I expression on cell surface (Table 1 and Figure 1a). A similar correlation was observed between HLA‐I expression detected in immunoblots and number of peptides in EV ligandomes of different cell lines. For example, expression of HLA‐I was not detected/below detection with mAb HC10 in immunoblots of EVs of MCF7, MDA‐MB‐231, MDA‐MB‐361, and MDA‐MB‐453 (Figure 1c) which are the cell lines with least number of peptides in their respective EV ligandomes (Column 3 of Table 2 and File S2). The cell and EV ligandomes were also compared with each other to determine the proportion of peptide common between cell and EVs. To facilitate this analysis, peptides from cell ligandomes of all cell lines were combined and duplicate peptides i.e., the same peptide identified from more than one cell line, were removed. Similarly, peptides of EV ligandomes were combined and duplicate peptides were removed before comparing the HLA‐I ligandomes of cell and EVs (File S3). A total of 5503 and 2244 unique peptides were found to constitute the cell and EV ligandomes of all 10 cell lines, respectively. Of 2244 peptides found in the EV ligandome, 74% peptides were also detected in the cell ligandome. However, 26% (589) peptides of the EV ligandome were not detected in cells (Figure S1 and File S3). A similar observation, albeit varying proportion of common and unique peptides, was made when peptides of cell and EV ligandomes were compared separately for breast cancer, myeloma and melanoma cell lines (Figure S1 and File S3).
+
+TABLE 1 Details of total eluted peptides, HLA‐I ligandome, netMHCpan4.0 predicted binding peptides, previously reported T‐cell epitopes and peptides derived from TAA proteins from the cell surface of breast cancer, melanoma and myeloma cell lines
+
+1. Cell lines	2. Number of eluted peptides	3. Number of peptides in HLA‐I ligandome (8‐15‐mer peptides)	4. Number of netMHCpan 4.0 binding peptides	5. Number of known immunogenic peptides identified in the study	6. Number of peptides derived from known TAA (TAApep)	
+MCF7	625	461	214	11	22	
+MDA‐MB‐231	412	406	359	11	25	
+MDA‐MB‐361	178	168	134	0	9	
+MDA‐MB‐415	827	818	700	2	52	
+MDA‐MB‐453	621	569	453	3	18	
+HCC 1806	873	756	633	6	36	
+HCC 1395	727	693	606	2	39	
+HCC 1954	842	828	720	3	44	
+ESTDAB‐056	804	795	717	4	40	
+RPMI 8226	665	650	580	1	37	
+Total	6574	6144	5116	43	322	
+
+TABLE 2 Details of total eluted peptides, HLA‐I lignadome, netMHCpan4.0 predicted binding peptides, previously reported T‐cell epitopes and peptides derived from TAA proteins from the EV of breast cancer, melanoma, and myeloma cell lines
+
+1. Cell lines	2. Number of eluted peptides	3. Number of peptides in HLA‐I ligandome (8‐15‐mer peptides)	4. Number of netMHCpan 4.0 binding peptides	5. Number of known immunogenic peptides identified in the study	6. Number of peptides derived from known TAA (TAApep)	
+MCF7	43	18	5	2	0	
+MDA‐MB‐231	38	38	32	1	0	
+MDA‐MB‐361	67	67	35	1	4	
+MDA‐MB‐415	336	336	275	2	19	
+MDA‐MB‐453	127	120	115	2	5	
+HCC 1806	363	345	306	3	18	
+HCC 1395	534	533	502	2	32	
+HCC 1954	331	330	293	2	23	
+ESTDAB‐056	472	472	433	3	24	
+RPMI 8226	150	147	112	1	6	
+Total	2461	2406	2108	19	131	
+
+Peptides present in HLA‐I ligandomes were further processed through the HLA‐I binding affinity prediction algorithm netMHCpan 4.0. Details of HLA‐I binding peptides from each cell line, for each expressed HAL‐I allele, and their respective predicted binding affinities (BA) are presented in Figures 2 (HLA‐A and HLA‐B) and S2 (HLA‐C). Predicted mean binding affinities are presented in Table S1. It was noted that the mean predicted binding affinity of peptides for HLA‐A molecules was frequently lower (strong binding) in the EV pool compared to the cell surface pool (shown in Table S1). Two‐tailed Mann–Whitney tests indicated no significant differences in HLA‐I binding affinities between the cell and EV peptidomes, except in cases such as HLA‐A*2:01 in MDA‐MB‐231, HLA‐A*68:02 in RPMI 8226 (Figure 2). However, low numbers of peptides in the EV peptidome limits the interpretation of significance values in these samples. In addition, it was noted that the HLA‐I binding affinity predictions for alleles A*02:01 and A*02:17 in MDA‐MB‐231 were very distinct from each other. These alleles differ at key positions 95, 97, and 99 in the middle of the HLA‐I peptide‐binding groove which will likely impact on peptide selections. Peptide ligands from EVs and cells were also compared to determine peptide length distribution and sequence motifs. As reported previously [18], we also found that peptide from cells and EVs distributed similarly in length and were composed of peptides with similar sequence motifs (data not shown).
+
+FIGURE 2 Predicted HLA‐A‐ and B‐binding affinities of peptides from cells (CL) and EV, determined using algorithm netMHCpan 4.0. Each dot represents a single identified peptide. The numbers above each plot indicates the number of ligands for each respective HLA‐I allele. Two‐tailed Mann–Whitney test was performed, with mean predicted affinity ± SE shown in red. ns, not significant; *p < 0.05
+
+Identification of peptides derived from tumour‐associated antigens and putative immunogenic peptides
+
+Owing to the presence of HLA‐I molecules, EVs have long been suspected to carry tumour antigens. A detailed description of the peptidome and the full extent and identity of tumour antigens has, however, not been reported for EVs. Therefore, EV ligandomes of each cell line were searched for the presence of tumour antigens. For clarity, herein we define TAA as tumour‐associated antigenic proteins and TAApep as the peptides derived from tumour‐associated antigenic proteins that are presented on HLA‐I molecules.
+
+First, we determined if EVs can carry immunogenic peptides which have been identified in previous studies. The cell and EV ligandomes were searched for the presence of peptides showing sequence match to known T‐cell epitopes using IEDB and Tantigen databases since IEDB and Tantigen databases are currently amongst the most comprehensive tumour antigen databases. The number of identified peptides showing sequence match with known T‐cell epitopes from EV and cell ligandomes are presented in Tables 1 and 2. A total of 43 peptides from cell ligandomes were identified to match 31 known T‐cell epitopes (Table S3; Table 1, Column 5). In EV ligandomes, 19 peptides were found to match with 15 known T‐cell epitopes (Table 2, Column 5; Table 3). Importantly, known T‐cell epitope sequences were detected in the EV ligandomes of all cell lines (Tables 2 and 3) including MCF7, MDA‐MB‐361, and MDA‐MB‐231 where only a limited number of peptides were identified in the EV ligandome (Table 2). For example, in the EV ligandome of MCF7, only 18 peptides were identified, but amongst them were two known T‐cell epitope sequences (NTDSPLRY and ALSDHHIYL, Table 3). The peptide NTDSPLRY is derived from the 40S ribosomal protein SA, an oncofetal antigen expressed by tumours [27].
+
+TABLE 3 List of peptides matching with previously identified T‐cell epitopes from EV HLA‐I ligandomes of breast cancer, melanoma, and myeloma cell lines
+
+Number	Antigen name	Parent protein	Epitope ID	Position	Uniprot entry name	PubMed ID	T‐cell epitope	HLA ‐I restriction	Peptides identified in the study	Cell line	
+1	Carcinoma‐associated mucin	P15941	T000434	130–138, multiple positions	MUC1_HUMAN	10361129	STAPPAHGV	HLA‐A*02:01, HLA‐A*11:01	STAPPAHGV	RPMI 8226	
+2	Ubiquitin‐conjugating enzyme E2 D2	P62837	243 855	59–66	UB2D2_HUMAN	22869377	YPFKPPKV	HLA‐B*51
+
+	YPFKPPKVTF*	HCC 1954	
+YPFKPPKV	HCC 1806	
+3	Chromatin Assembly Factor 1 Subunit A	Q13111	T001077	772–781	CAF1A_HUMAN	24048523	SPRSPSTTYL	HLA‐B*07:02	SPRSPSTTYL	ESTDAB‐056	
+4	Clathrin heavy chain 1	Q00610	442 923	311–320	CLH1_HUMAN	22869377	ATAGIIGVNR	HLA‐A*11	ATAGIIGVNR	HCC 1806	
+5	G1/S‐specific cyclin‐D1	P24385	T000697	115–124	CCND1_HUMAN	12384544	ETIPLTAEKL	HLA‐A*68:01	ETIPLTAEKL	HCC 1806	
+6	Cytochrome P450 1B1	Q16678	T000701	5–13	CP1B1_HUMAN	12869499	FLDPRPLTV	HLA‐A*02:01	FLDPRPLTV	MDA‐MB‐231	
+7	E3 ubiquitin‐protein ligase Mdm4	Q00987	236 802	273–281	MDM2_HUMAN	25548167	DEVYQVTVY	HLA‐B*18	DEVYQVTVY	MDA‐MB‐415	
+8	Fructose‐bisphosphate aldolase A	P04075	2874	216–224	ALDOA_HUMAN	11782012	ALSDHHIYL	HLA‐A*02:01	ALSDHHIYL	MCF7	
+9	Histone H3.3	P84243	T000946	59–67	H33_HUMAN	16196104	ELLIRKLPF	HLA‐B*08	ELLIRKLPF	HCC 1395	
+	ELLIRKLPF	MDA‐MB‐453	
+10	HER2 receptor	P04626	67 385	63–71	ERBB2_HUMAN	17397516	TYLPTNASL	HLA‐A*24	TYLPTNASLSF*	HCC 1954	
+11	Mammaglobin‐A	Q13296	64 399	32–40	SG2A2_HUMAN	15538043	TINPQVSKT	HLA‐A*02	KTINPQVSKTEY*	MDA‐MB‐415	
+12	Poly (ADP‐ribose) polymerase family, member 12	Q9H0J9	T000954	669–677	PAR12_HUMAN	16033845	VYPEYVIQY	HLA‐C*07:02	VYPEYVIQY	ESTDAB‐056	
+13	Poly(RC) Binding Protein 2	Q15366	T001070	183–191	PCBP2_HUMAN	24048523	RPKPSSSPV	HLA‐B*07:02	RPKPSSSPVIF*	ESTDAB‐056	
+14	Similar to retinoblastoma‐binding protein 4, partial	Q09028	215 983	245–253	RBBP4_HUMAN	22869377	NLKLKLHSF	HLA‐B*57	NLKLKLHSF	HCC 1395	
+15	40S ribosomal protein SA	P08865	T000873	146–154	RSSA_HUMAN	16709854	ALCNTDSPL	HLA‐A*02:01	NTDSPLRY*	MDA‐MB‐361	
+NTDSPLRY*	MDA‐MB‐453	
+NTDSPLRY*	MCF7	
+Note: Peptides showing partial match with known T‐cell epitopes are marked with an asterisk (*). Epitope IDs are the reference number of T‐cell epitope listed on Tantigen (IDs beginning with T) and IEDB (IDs beginning with number).
+
+Additionally, the list of known T‐cell epitope sequences identified in the EV ligandome includes peptides derived from antigens which are either associated with cancer or used as diagnostic and prognostic biomarkers. For example, peptides KTINPQVSKTEY and TYLPTNASLSF detected in the EV ligandomes of MDA‐MB‐415 and HCC 1954, respectively, are known T‐cell epitopes from Mammaglobin‐A and Her‐2, and thus relevant to breast cancer. Similarly, the peptide STAPPAHGV identified in the EV ligandome of myeloma cell line RPMI 8226, is a T‐cell epitope of MUC1 which has been identified as a tumour antigen in several multiple myeloma cell lines [28, 29, 30]. These results show for the first time that EVs released by cancer cells frequently carry T‐cell epitope peptides which may be clinically relevant to cancer.
+
+The cell and EV ligandomes of all cell lines were also searched for the presence of additional peptides (TAApep) derived from TAA proteins. We predicted these would be present in our ligandomes because of the extended HLA‐I allele cohort present in our study, such that peptides derived by antigen processing of other parts of the TAA would likely be detected. We used Tantigen 2.0 database to identify peptides derived from the full‐length protein sequences of TAA. A total of 277 additional TAApep derived from 107 TAA proteins were identified in cell ligandomes of all lines (File S4). Out of the 277 TAApep, 39 TAApep were shared 45 times between different cell lines (representing a total of 322 TAApep; 5.2% of cell ligandome; Table 1, Column 6 and File S4). In EV ligandomes, 122 TAApep from 68 TAA proteins were identified (File S4). Of the 122 TAApep identified in the EV ligandome, 9 TAApep were shared amongst different EV ligandomes (total 131 TAApep; 5.4% of EV ligandome, Table 2, Column 6 an, File S4). A shortlist of TAApep identified in EV ligandomes is presented in Table 4 which contains TAApep derived from TAA proteins which have at least one defined T‐cell epitope in Tantigen database.
+
+TABLE 4 Shortlist of peptides derived from TAA proteins (TAApep) detected in EV HLA‐I ligandome of breast cancer, melanoma, and myeloma cell lines
+
+Number	Antigen full name	Antigen name	Antigen accession	Protein accession	Uniprot protein entry	Number of peptides	Peptide	Cell lines	
+1	Canalicular multispecific organic anion transporter 2	ABCC3	Ag000410	O15438	MRP3_HUMAN	2	AEKAFVSV	HCC 1395	
+AYLHTTTTF	MDA‐MB‐361	
+2	Ataxin‐2‐like protein	ATXN2L	Ag004315	Q8WWM7	ATX2L_HUMAN	1	AHYPSQPVF	HCC 1806, RPMI 8226 8226	
+3	G1/S‐specific cyclin‐D1	CCND1	Ag000285	P24385	CCND1_HUMAN	4	NYLDRFLSL	HCC 1954	
+AEETCAPSV	HCC 1395	
+EVFPLAMNY	ESTDAB‐056	
+EVFPLAMNYL	ESTDAB‐056	
+4	Cell division control protein 45 homologue	CDC45	Ag004342	O75419	CDC45_HUMAN	1	RPVNVVNVY	HCC 1954	
+5	Cleavage and polyadenylation specificity factor subunit 1	CPSF1	Ag000125	Q10570	CPSF1_HUMAN	2	SVLPAYLSY	HCC 1395	
+ETVSGLKGY	ESTDAB‐056	
+6	Catenin beta‐1	CTNNB1	Ag000058	P35222	CTNB1_HUMAN	2	HPPSHWPLI	HCC 1806	
+							AQNAVRLHY	HCC 1806	
+7	Receptor tyrosine‐protein kinase erbB‐2	ERBB2	Ag000001	P04626	ERBB2_HUMAN	2	TPTAENPEY	HCC 1954	
+MPNPEGRYTF	HCC 1954	
+8	ets variant 5	ETV5	Ag004221	P41161	ETV5_HUMAN	1	KVAGERYVY	ESTDAB‐056	
+9	Neutral alpha‐glucosidase AB	GANAB	Ag004500	Q14697	GANAB_HUMAN	1	AVAAVAARR	HCC 1806	
+10	Glycoprotein NMB	GPNMB	Ag000250	Q14956	GPNMB_HUMAN	1	STINYKWSF	ESTDAB‐056	
+11	Transcription factor HIVEP2	HIVEP2	Ag004270	P31629	ZEP2_HUMAN	1	SPLIRSNSV	HCC 1395	
+12	Heme oxygenase (decycling) 1	HMOX1	Ag000461	P09601	HMOX1_HUMAN	1	EVIPYTPAM	ESTDAB‐056	
+13	Heterogeneous nuclear ribonucleoprotein L	HNRNPL	Ag000394	P14866	HNRPL_HUMAN	2	VEFDSVQSA	HCC 1954	
+IYIAGHPAF	HCC 1806	
+14	Heat shock 70 kDa protein 1B	HSPA1B	Ag000092	P0DMV9	HS71B_HUMAN	2	KQTQIFTTY	HCC 1806	
+TVFDAKRLIGR	HCC 1806	
+15	Heat shock protein beta‐1	HSPB1	Ag004328	P04792	HSPB1_HUMAN	1	NEITIPVTF	MDA‐MB‐415	
+16	Insulin‐like growth factor 2 mRNA binding protein 3	IGF2BP3	Ag000506	O00425	IF2B3_HUMAN	1	ETAVVNVTY	ESTDAB‐056	
+17	Microtubule‐Actin cross‐linking factor 1 isoforms 1/2/3/5	MACF1	Ag004285	Q9UPN3	MACF1_HUMAN	3	EEAFHQGLISA	HCC 1395	
+AEKFWYDMA	HCC 1395	
+NQKPPSAEY	RPMI 8226 8226	
+18	Melanoma‐associated antigen C2	MAGEC2	Ag000036	Q9UBF1	MAGC2_HUMAN	1	NAVGVYAGR	HCC 1806	
+19	E3 ubiquitin‐protein ligase Mdm2 (Fragment)	MDM2	Ag000287	Q00987	MDM2_HUMAN	1	DEVYQVTVY	MDA‐MB‐415	
+20	ATPase MORC2	MORC2	Ag004287	Q9Y6X9	MORC2_HUMAN	1	IETELIYKY	MDA‐MB‐415	
+21	Nucleolar Protein Interacting With The FHA Domain Of MKI67	NIFK	Ag004290	Q9BYG3	MK67I_HUMAN	1	SQFGTVTRF	RPMI 8226 8226	
+22	2′‐5′‐oligoadenylate synthase 3	OAS3	Ag000403	Q9Y6K5	OAS3_HUMAN	2	TVLELVTQY	HCC 1954	
+AEIISEIRA	HCC 1395 EV	
+23	Serine/threonine‐protein kinase PAK 2	PAK2	Ag000472	Q13177	PAK2_HUMAN	1	NENPLRALY	MDA‐MB‐415	
+24	Protein mono‐ADP‐ribosyltransferase PARP12	PARP12	Ag000523	Q9H0J9	PAR12_HUMAN	2	EYQKVWNLF	HCC 1954	
+							DEFGSWQEY	MDA‐MB‐415	
+25	Poly(rC)‐binding protein 2 (Fragment)	PCBP2	Ag004321	Q15366	PCBP2_HUMAN	1	LEGPPLEAY	MDA‐MB‐415	
+26	Serine/threonine‐protein phosphatase PP1‐alpha catalytic subunit	PPP1CA	Ag004293	P62136	PP1A_HUMAN	1	KYPENFFLL	HCC 1954	
+27	Replication protein A 70 kDa DNA‐binding subunit (Fragment)	RPA1	Ag000443	P27694	RFA1_HUMAN	2	AEAILGQNAA	HCC 1395	
+KVIDQQNGLY	MDA‐MB‐415	
+28	Protein SON	SON	Ag004333	P18583	SON_HUMAN	3	SAYERSMM	HCC 1395	
+YTDSYTDTY	MDA‐MB‐453	
+SPMAERSMM	ESTDAB‐056	
+29	Serine/Arginine Repetitive Matrix 2	SRRM2	Ag004276	Q9UQ35	SRRM2_HUMAN	1	SPRKPIDSL	ESTDAB‐056	
+30	Signal transducer and activator of transcription	STAT1	Ag000451	P42224	STAT1_HUMAN	3	EELEQKYTY	MDA‐MB‐415	
+SEVLSWQF	MDA‐MB‐415	
+DQYSRFSL	MDA‐MB‐415	
+31	STAGA complex 65 subunit gamma	SUPT7L	Ag000225	O94864	ST65G_HUMAN	1	GSSPVFNQR	HCC 1806	
+32	Tensin‐3	TNS3	Ag004302	Q68CZ2	TENS3_HUMAN	3	YITERIIAV	HCC 1395	
+QQMVVAHQY	MDA‐MB‐415	
+TERIIAVSF	MDA‐MB‐415	
+33	DNA topoisomerase 2‐alpha	TOP2A	Ag000436	P11388	TOP2A_HUMAN	1	ATKTKFTM	HCC 1395	
+34	Topoisomerase (DNA) II beta 180 kDa	TOP2B	Ag000441	Q02880	TOP2B_HUMAN	1	SPRYIFTML	ESTDAB‐056	
+35	Targeting protein for Xklp2	TPX2	Ag004304	Q9ULW0	TPX2_HUMAN	1	KSSDQPLTV	HCC 1954	
+36	Thymidylate synthase	TYMS	Ag000483	P04818	TYSY_HUMAN	1	DAHIYLNHI	HCC 1806	
+37	Serine/threonine‐protein kinase WNK2 (Fragment)	WNK2	Ag000399	Q9Y3S1	WNK2_HUMAN	2	HESDVKIVA	HCC 1954	
+QEHVPTSSA	HCC 1954	
+38	Zinc Finger CCCH‐Type Containing 14	ZC3H14	Ag004311	Q6PJT7	ZC3HE_HUMAN	1	KTTNVRQTY	ESTDAB‐056	
+Note: The shortlist is selected for TAAs which have at least one immunogenic peptide defined in Tantigen database.
+
+In summary, our data indicate that the EV ligandomes of common cancer cell lines spanning breast, melanoma, and myeloma carry peptides from known TAAs, potential T‐cell epitopes, and additional TAApep derived from the protein sequences of known and established TAA.
+
+DISCUSSION
+
+In this study, we report the first direct evidence of known tumour‐associated antigens and T‐cell epitopes presented on HLA‐I molecules on extracellular vesicles released by cancer cell lines. We have analysed the EV HLA‐I ligandomes of 10 cell lines, predominately representing breast cancer, but also including melanoma and myeloma to indicate that the presence of HLA‐I associated TAA on EV is likely to be a widespread observation and not exclusive to just one cancer type.
+
+In recent years, EVs have emerged as promising tools in both cancer diagnosis and cancer immunotherapy [31]. EVs represent a cell‐free system for direct delivery of immune relevant peptides in association with HLA molecules [16, 17]. EVs derived from different cells have already been used in clinical trials; however, the majority of early trials have displayed only modest success in stimulation of an anti‐tumour response in effector T cells [32, 33]. Therefore, the challenge of selecting suitable target antigens that could be used for effective vaccine development remains. Use of EVs as cell free vaccines with preloaded antigen requires existing knowledge about the antigen [16]. Identifying EV HLA‐I ligandome of cancer cells offers the opportunity to study and identify complex peptidome to potentially gain insight of overall immuno‐peptidome of the EVs as well as the selection of target antigens. Moreover, identification of target antigens from EVs can be relatively non‐invasive since EVs can be collected from several body fluids. For example, EVs derived from ascites or malignant effusions have been used in in the immunotherapy of colorectal cancer [34].
+
+Despite the clinical relevance and therapeutic potential of EVs, the EV HLA‐I ligandome of tumour cells has not been established in detail. To date, only two reports of the HLA‐I EV ligandome of cell lines have been published [35]. This low level of reporting on the HLA‐I ligandome is most likely due to the challenges of obtaining sufficient EV source material for mass spectrometry‐based sequencing of HLA‐I peptides. Nevertheless, our results show that despite low expression of HLA‐I molecules on EVs released by several of the breast cancer cell lines, it is still possible to identify tumour antigenic peptides and thus potential T‐cell epitopes. For example, HLA‐I expression was not detected by HC10 immunoblot analysis in EVs of cell lines MCF7, MDA‐MB‐231, and MDA‐MB‐361 (Figure 1c), yet tumour antigen peptides with known T‐cell activity were identified.
+
+We have identified peptides in HLA‐I ligandomes of 10 cancer cell line. The peptide repertoire reported here may be used as database to identify multiple markers for diagnosis, prognosis of tumour as well as targets for immunotherapy. Interestingly, 26% of peptides present in EV HLA‐I ligandome were not found in HLA‐I ligandome of cells. The presence of unique peptides in HLA‐I ligandomes was also detected in previous two studies of Jesthom and JY cells [18, 35]. The reasons for this discrepancy is not yet understood, and may be a technical issue related to the amounts of input material, whereby much larger‐scale purification of cells and EV would result in better overlaps of cell and EV ligandomes. However, it remains a possibility that the cell and EV ligandomes do not fully overlap due to the biogenesis of the exosome component of the EV pool having originated in the endosomal/MVB pathway, where the possibility of peptide exchange may occur, generating new antigens in the EV ligandome [36]. Of some significance, the constituent 8‐15‐mer peptides of HLA‐I EV ligandomes of all cell lines contained 150 peptides representing both known T‐cell epitopes and additional TAApep derived from TAA proteins. Furthermore, the analysis settings of the mass spectrometry data we have used in this study are of relatively high stringency, and therefore a wider range of antigenic peptides of interest may be present and revealed by other analysis methodologies.
+
+The presence of cell surface antigenic peptides presented on HLA‐I derived from TAA proteins, be it from differentiation antigens, over‐expressed cellular antigens, cancer/testis antigens or cancer related viral antigens allows the immune system the opportunity to mount responses that can impact on the progression of cancer growth [37]. Their presence on EV, as reported here, raises several interesting questions. Firstly, if HLA‐I TAA on EV were released by cancer cells into the bloodstream, they could potentially act as diagnostic, prognostic, and treatment monitoring biomarkers for cancer. Low recovery and heterogeneity of EVs by current isolation methods has largely restricted the detection of lowly expressed EV molecules. However, methods are available for the rapid enrichment of EV from serum or plasma by simple size exclusion columns [38, 39], and the EV can then be processed by our techniques herein to reveal the EV HLA‐I ligandome. Potentially, cancer‐specific EV could be enriched even further by monoclonal antibody‐affinity‐based methods [40], thus isolating only those EV released by cancer cells, allowing the characterization of the cancer‐specific EV HLA‐I ligandome occurring in that patient. Second, the presence of HLA‐I‐associated TAA on EV released by cancer cells could potentially represent a novel immune‐evasion strategy. Cancer specific CD8+ T cells approaching a tumour site could theoretically encounter a ‘cloud’ or gradient of EV containing HLA‐I presented TAA that could activate the T cell off‐target. There have, to date, been very few studies on the ability of HLA‐I on EV to activate CD8+ T cells, but EV loaded in vitro with common antigenic viral peptides can stimulate IFNγ secretion in purified CD8+ T cells, thus indicating that EV can indeed directly activate T cells [41].
+
+Studies of patients undergoing monoclonal antibody checkpoint inhibitor therapy, which unleashes wider T cell activity, reveal that the best responses occur against tumours that typically have increased numbers of mutations, that is, which have the potential to generate large numbers of neo‐epitopes [42]. In recent years, it has become clear that the range of peptide sources for presentation by HLA‐I molecules is far wider than just from traditional proteins, and now encompasses non‐canonical sources such as from mis‐translations of normal mRNA, non‐coding RNA, and in addition cis‐ and trans‐peptide splicing events that occur in the proteasome [37]. At present, it is not known to what extent these sources of HLA‐I peptides contribute to the EV HLA‐I ligandome. However, we propose that the EV HLA‐I ligandome may provide a rich source for the identification of clinically relevant antigenic peptides in both health and disease from a relatively easy to access biofluids.
+
+CONFLICTS OF INTEREST
+
+The authors declare no conflicts of interest.
+
+AUTHOR CONTRIBUTIONS
+
+Simon J. Powis and Sally Shirran conceived the research plan. Pankaj Kumar, Simon J. Powis, and Sally Shirran designed the experiments. Pankaj Kumar, Simon J. Powis, Caitlin Boyne, Sydney Brown, and Ayesha Qureshi performed experiments. Sally Shirran and Silvia A. Synowsky performed mass spectrometry analysis of samples. PT identified HLA type of cell lines. Pankaj Kumar, Caitlin Boyne, Sydney Brown, and Simon J. Powis analysed data. Pankaj Kumar, Simon J. Powis, and Caitlin Boyne wrote manuscript.
+
+Supporting information
+
+Supporting information
+
+Click here for additional data file.
+
+TFile S1Containing details of HLA genotypes of cell lines used in the study.
+
+Click here for additional data file.
+
+TFile S2Containing details of HLA‐I peptides identified by PEAKS DB search from cell and EV lysates of each cell line
+
+Click here for additional data file.
+
+File S3Containing details of cell and EV ligandomes and their comparisons
+
+Click here for additional data file.
+
+File S4TAApep identified in EVs of breast cancer, melanoma and myeloma cell lines
+
+Click here for additional data file.
+
+ACKNOWLEDGMENTS
+
+This work was funded by grants from Breast Cancer Now UK (2018JulPR1086), and the Melville Trust for the Care and Cure of Cancer UK. Bioinformatics and computational biology analyses were supported by the University of St Andrews Bioinformatics Unit funded by Wellcome Trust ISSF award 105 621/Z/14/Z.
+
+DATA AVAILABILITY STATEMENT
+
+Mass spectrometry proteomic data have been deposited to the ProteomeXchange Consortium via the PRoteomics IDEntifications (PRIDE) repository which can be accessed by reviewers (PRIDE dataset identifier: PXD025345).
+==== Refs
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+

PMC10390622.txt

@@ -0,0 +1,217 @@
+
+==== Front
+Clin Exp Med
+Clin Exp Med
+Clinical and Experimental Medicine
+1591-8890
+1591-9528
+Springer International Publishing Cham
+
+36495369
+968
+10.1007/s10238-022-00968-0
+Review
+The dynamic functions of IRF4 in B cell malignancies
+Maffei Rossana maffei.rossana@aou.mo.it
+
+1
+Fiorcari Stefania 2
+Atene Claudio Giacinto 2
+Martinelli Silvia 1
+Mesini Nicolò 2
+Pilato Flora 1
+Lagreca Ivana 2
+Barozzi Patrizia 2
+Riva Giovanni 1
+Nasillo Vincenzo 1
+Paolini Ambra 1
+Forghieri Fabio 2
+Potenza Leonardo 2
+Trenti Tommaso 1
+Tagliafico Enrico 1
+Luppi Mario 2
+Marasca Roberto 2
+1 Department of Laboratory Medicine and Pathology, Diagnostic Hematology and Clinical Genomics, AUSL/AOU Modena, Modena, Italy
+2 grid.7548.e 0000000121697570 Department of Medical and Surgical Sciences, Section of Hematology, University of Modena and Reggio Emilia, Modena, Italy
+10 12 2022
+10 12 2022
+2023
+23 4 11711180
+31 10 2022
+30 11 2022
+© The Author(s) 2022
+https://creativecommons.org/licenses/by/4.0/ Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
+The trajectory of B cell development goes through subsequent steps governed by complex genetic programs, strictly regulated by multiple transcription factors. Interferon regulatory factor 4 (IRF4) regulates key points from pre-B cell development and receptor editing to germinal center formation, class-switch recombination and plasma cell differentiation. The pleiotropic ability of IRF4 is mediated by its “kinetic control”, allowing different IRF4 expression levels to activate distinct genetic programs due to modulation of IRF4 DNA-binding affinity. IRF4 is implicated in B cell malignancies, acting both as tumor suppressor and as tumor oncogene in different types of precursors and mature B cell neoplasia. Here, we summarize the complexity of IRF4 functions related to different DNA-binding affinity, multiple IRF4-specific target DNA motif, and interactions with transcriptional partners. Moreover, we describe the unique role of IRF4 in acute leukemias and B cell mature neoplasia, focusing on pathogenetic implications and possible therapeutic strategies in multiple myeloma and chronic lymphocytic leukemia.
+
+Keywords
+
+IRF4
+B cell development
+Kinetic control
+B cell neoplasia
+issue-copyright-statement© Springer Nature Switzerland AG 2023
+==== Body
+pmcIntroduction
+
+IRF4 is a member of interferon regulatory factor (IRF) family of transcription factors, also called Pip, LSIRF, ICSAT and MUM1. It is a 19.7-Kb gene located at the 6p25.3 locus. Members of the IRF family (IRF1 through-9) are characterized by two conserved functional domains joined by a flexible linker: an N-terminal helix-turn-helix DNA-binding domain (DBD) with a unique tryptophan pentad repeat and a C-terminal interferon activation domain (IAD) critical in mediating protein–protein interactions. The DNA-binding activity of IRF4 relays on the formation of homo- or heterodimers with multiple partners that increase the DNA affinity. Differently from other IRF proteins, IRF4 binds DNA with low affinity due to an autoinhibitory conformation and needs different partners to relieve the inhibitory mechanism and recognize the DNA sequence containing Ets-IRF composite elements (EICEs) or AP1-IRF-consensus elements [1]. However, when at high concentrations, IRF4 regulates genes containing ISRE sites, presumably by homodimerization, and this property is critical for plasma cell differentiation [2].
+
+IRF4 is expressed in cells of immune system, including lymphocytes, dendritic cells, and macrophages, in which it can regulate several functions such as proliferation, apoptosis and differentiation. IRF4 is an essential factor that controls several stages of B cell development including pre-B cell development, receptor editing, germinal center (GC) formation, class-switch recombination (CSR), and plasma cell (PC) differentiation. Mice with germline deletion of IRF4 show a differentiation arrest at the transition from immature to mature B cells, thus lacking to generate the progeny of germinal center B cells and plasma cells. IRF4-deficient mice showed impairments in immunoglobulin production and in antibody response. In addition, cytotoxic and antitumor response by T cells were reported to be affected in mice deficient in IRF4 [3].
+
+The heterogeneity of regulated functions is due both to alternative interactions with several cofactors including PU1, E47, IRF8 and STAT6 and to a graded expression throughout B cell development and maturation. In B cell population, IRF4 has a biphasic function acting both during early B cell development and in mature B cells during germinal center reaction after antigen engagement. IRF4 controls the sequential rearrangement of immunoglobulin loci to generate a functional B cell receptor (BCR) restraining pre-B cell proliferation and influencing pre-B cell positioning inside bone marrow niches. Furthermore, IRF4 participates to the intermingled network of signals that define the cell fate of mature B cells upon antigen engagement toward apoptosis or plasma cell differentiation throughout the regulation of germinal center formation and affinity maturation.
+
+In this review, we summarize the recent advances in the definition of the pleiotropic functions of IRF4 during early B cell development and in mature B cells. Then, we describe the unique role of IRF4 in acute leukemias and B cell mature neoplasia, focusing on biological mechanisms and possible therapeutic strategies in multiple myeloma (MM) and chronic lymphocytic leukemia (CLL). Therapeutic nucleic acid-based approaches, including antisense oligonucleotides (ASOs), are promising strategies offering the potential to target transcription factors, like IRF4, that have proven to be intractable to alternative drug modalities.
+
+IRF4 controls early B cell development in redundant manner with IRF8
+
+During the development, B cells engage the sequential rearrangement of immunoglobulin loci. The H chain locus is rearranged at the pro-B stage, while the L chain locus at the pre-B stage. After the generation of a productive heavy (H) chain, it interacts with surrogate light (L) chain Vpre-B, forming a pre-BCR on the cell surface. The pre-B cells firstly undergo a clonal expansion phase characterized by high proliferation rate, followed by a resting phase, in which cells arrest their proliferation and proceed to L chain rearrangement, thereby generating IgM + B cells. The BCR is subsequently expressed on the surface of immature B cells and autoreactive cells are culled by central tolerance mechanisms [4].
+
+IRF4 is involved in early B cell development. IRF4 acts at the two key stages of pre-B cells, negatively regulating pre-B cell expansion and promoting L chain rearrangement and transcription, directly binding to Ig kappa (k) and lambda enhancer. IRF4 was originally discovered as the partner of the Ets transcription factor PU.1 in the immunoglobulin k light chain enhancer [5]. The IRF family member IRF8 also interacts with PU.1 and acts redundantly with IRF4 in early B cell development. Both IRF4 and IRF8 interact very weakly to IRF DNA-binding sites but are recruited to EICEs through interaction with other transcription factors related to ETs family, PU.1 and Spi.B. These heterodimeric complexes are implicated in the control of Ig L chain transcription [5–8]. In the absence of both IRF4 and IRF8, B cell development is arrested at the proliferative stage of pre-B cells, failing to down-regulate pre-BCR [9]. IRF4 and IRF8 regulate the switch between cycling pre-B cells and immature B cells by downregulating the expression of surrogate light chain genes and concomitantly promoting conventional light chain rearrangement and transcription [10]. IRF4 also collaborates with the transcription factor FOXO1 to reactivate Rag gene expression critical for recombination of IgL chain [11, 12].
+
+IRF4 together with its partner IRF8 negatively controls pre-B cell proliferation by inducing the expression of the transcription factors IKAROS and AIOLOS. These factors down-regulate MYC while promoting the expression of the cell cycle inhibitor p27KIP [13]. Moreover, IRF4 attenuates the pre-B cell expansion by limiting IL-7 receptor signaling. IRF4 increases the expression of the chemokine receptor CXCR4, promoting the migration of cycling pre-B cells to niches with low level of IL-7 to decrease the proliferative signal [14]. IL-7 signaling counteracts pre-B cell differentiation by directly repressing light chain rearrangements [15]. Therefore, the chemotaxis of pre-B cells to niches with low levels of IL-7 would be relevant to restrain their expansion and to initiate productive light chain rearrangements [14, 15]. IRF4-CXCR4 feedforward loop would be implicated in B cell migration into CXCL12-rich BM niches, reducing the expression of mediators of B cell proliferation MYC and STAT5, while inducing IRF4 expression and light chain rearrangement [16]. Furthermore, IRF4 has a unique role in inducing the pre-B cell marker CD25, limiting IL-7 responsiveness, and promoting migration to CXCR4 [17].
+
+IRF4 deficiency contributes to transformation of acute leukemias
+
+Given its role as a key transcription factor limiting pre-B cells expansion and favoring pre-B cell differentiation, IRF4 functions as a tumor suppressor against pre-B cell transformation. IRF4 is expressed at low levels in certain myeloid and early lymphoid B cell malignancies [18–20]. However, IRF4−/− mice do not generate pre-B acute lymphoblastic leukemia (ALL), but IRF4 deficiency promotes leukemogenesis in mouse model in cooperation with oncogenes such as BCR-ABL [21] and MYC [22]. In particular, IRF4 deficiency accelerates the progression of BCR-ABL-positive B-ALL in mice, and its forced up-regulation suppresses transformation both in vitro and in vivo, negatively regulating cell cycle progression. IRF4 is up-regulated in blast cells transformed by the BCR-ABL oncogene during treatment with BCR-ABL tyrosine kinase inhibitors [21]. Accordingly, microarray analysis showed low IRF4 mRNA levels in patients with Ph+ B-ALL [20]. Moreover, MYC-induced leukemia was greatly accelerated in IRF4 ± deficient mice showing hyperproliferative large leukemic pre-B cells resistant to apoptosis. The deficiency of IRF4 accelerates the loss of p27KIP which restrains cell cycle progression [22].
+
+IRF4/IRF8 double deficient mice develop an aggressive chronic myelogenous leukemia-like disease at early age with expansion of granulocyte–monocyte progenitors. Then, all mice die of B-lymphoblastic leukemia/lymphoma [23]. Partial block at the transition from pre-B cells to immature B cells characterizes PU.1/IRF4-deficient mice. Of note, all PU.1/IRF4 and about 50% PU.1/IRF8 double deficient mice developed pre-B ALL with reduced expression of the tumor-suppressor genes IKAROS, Blnk and Spi-B. Restoration of IKAROS and Spi-B expression reduced leukemic cell growth [24]. Very recently, Das Gupta and colleagues described the spontaneous emergence of pre-B leukemia in IRF4−/− mice with age, showing clonal preB-I mononuclear cells infiltrating bone marrow, lung, and liver. Enlarged pre-B cell compartment is detected already in healthy IRF4−/− mice, due to unrestrained proliferation in response to IL-7, suggesting the presence of a preleukemic population vulnerable to immortalization. Due to unchecked growth of preleukemic cells and activation-induced deaminase (AID) induction, a second acquired genetic alteration may arise in some cases to promote leukemia development. The IL7-JAK-STAT signaling was found to be altered by mutations in JAK1 and JAK3 genes [25]. Furthermore, the oncomir microRNA-125b is up-regulated in several types of leukemias, including acute myeloid leukemia (AML) and B-ALL and is reported to inhibit IRF4 expression while inducing tumorigenesis in hematopoietic progenitor cells and myeloid and B cell neoplasms [26].
+
+IRF4 is involved in plasma cell differentiation
+
+Upon antigen engagement, activated mature B cells generate GC where they undergo affinity maturation, isotype switching and terminal differentiation of PCs. GCs are transient follicular structures which generate inside secondary lymphoid tissue to develop a T cell-dependent B cell activation upon antigen engagement. A complex network of signaling pathways is intermingled to control key processes inside GC. IRF4 and IRF8 are factors known to control GC formation, CSR, somatic hypermutation (SHM) and plasma cell differentiation in a non-redundant manner [27]. The generation of GC B cells and the development and differentiation of plasma cells are processes orchestrated by the alternate programs of gene expression regulated the reciprocal negative feedback of BCL6 and BLIMP1. PC differentiation is mainly regulated by the zinc finger transcription factor BLIMP1 and consists of a huge expansion of endoplasmic reticulum and increased protein synthesis. Moreover, BLIMP1 reduces GC program by lowering BCL6 and AID expression and represses the expression of PAX5, leading to derepression of XBP1 which induces the transcription of many genes encoding chaperones and enzymes necessary to the correct functionality of secretory apparatus. In addition, BLIMP1 regulates the mechanism of processing of heavy chain pre-mRNA to generate a transcript encoding secreted immunoglobulins.
+
+IRF4 is required for initiation but not maintenance of GC, by inducing BCL6 expression. Furthermore, IRF4 is required for generation of plasma cells, acting in coordinated manner with the transcriptional repressor BLIMP1, upstream of XBP1 (Fig. 1). Transgenic mice with conditional deletion of IRF4 in germinal center B cells do not form post-germinal center plasma cells. Moreover, IRF4-deficient B cells had reduced expression of AID and showed impairment in CSR [28]. In fact, IRF4−/− cells stimulated with CD40 and IL4 to induce CSR do not generate IgG1 + cells due to the low level of AID expression.Fig. 1 IRF4-graded expression in mature B cells during germinal center reaction. After antigen engagement, mature B cells initiate the germinal center reaction where proliferating centroblasts are regulated by high expression levels of BCL6, PAX5 and AID but low levels of IRF4. Reduced IRF4 levels also favor the localization of cycling B cells in the dark zone by regulating CXCR4 expression. Following antigen affinity maturation, IRF4 levels are progressively increased favoring class-switch recombination (CSR), mobilization of B cells to the light zone, and plasma cell (PC) differentiation. High IRF4 levels activate the PC transcriptional program by lowering BCL6 and inducing BLIMP1 and XBP1. Abbreviations: SHM, somatic hypermutation; CSR, class-switch recombination; FDC, follicular dendritic cells; PC, plasma cells; GC, germinal center; Tfh: follicular helper T cells
+
+However, fluctuations of IRF4 concentration in B cells underlie the generation of alternative fate, also known as “kinetic control model” (Fig. 2). According to this model, the rate of IRF4 expression upon BCR stimulation regulates the duration of AID expression, leading to CSR and SHM. Whether IRF4 accumulates in B cells above a critical threshold, it can activate Prdm1 gene (encoding BLIMP1) promoting plasma cell differentiation. Increased antigen affinity enhances BCR-mediated expression of IRF4 [29, 30]. High IRF4 concentration, allowing homodimerization, results in DNA binding at interferon sequence response motifs (ISRE) enriched in genes involved in PC differentiation.Fig. 2 IRF4 “kinetic control model”. Fluctuation of IRF4 concentration inside germinal center controls the establishment of specific genetic programs governing germinal center reaction vs. plasma cell differentiation. Low IRF4 levels allow the maintenance of high expression of BCL6, PAX5, AID and CXCR4 allowing GC formation and class-switch recombination. Upon affinity maturation, cycling B cells increase IRF4 expression above an “on–off threshold” which conversely induces BCL6 down-regulation together with BLIMP1 over-expression promoting plasma cell differentiation. Abbreviations: GC, germinal center
+
+Mechanisms of CSR and PC differentiation are strictly coordinated by the ability of IRF4 to control the expression of both Aicda and Prdm1 genes, encoding AID and BLIMP1, respectively. IRF4 is expressed in a graded manner with higher concentrations of IRF4 inducing BLIMP1 expression and transition of B cells from a GC program to that of plasma cell, whereas lower levels of IRF4 activating isotype switching/CSR and SHM by inducing AID expression [31]. Genome-wide analyses demonstrated that IRF4 regulates the entire BLIMP1-dependent plasma cell program and is involved in isotype switching process by inducing AID. Acting in a stepwise manner, IRF4 can regulate two antagonist developmental states. When B cells are stimulated by LPS- or CD40/IL4 to promote B cell activation, IRF4 expression is rapidly induced throughout several cell division, but just the appearance of an IRF4-high expressing sub-population is associated with plasma cell program.
+
+Increased IRF4 expression: addiction to IRF4-regulated genetic program in multiple myeloma and diffuse large B cell lymphoma
+
+IRF4 is highly expressed in MM cells, often as a result of activating mutations or translocations and is strictly required for MM survival. IRF4 mRNA expression is an independent risk factor for poor survival, particularly in cases without 13q deletion [32]. About 20% of cases harbor the chromosomal translocation t(6;14)(p25;q32), which juxtaposes the immunoglobulin heavy chain locus to IRF4 [33, 34]. In addition, mutations in the DNA-binding domain of IRF4 gene were reported in MM cells, particularly in recurrent “hot-spots” L116R and K123R [35]. However, most MM do not have genetic lesions in the IRF4 locus but are nonetheless addicted to the aberrant genetic program regulated by IRF4 [36]. Using a loss-of-function, RNA-interference-based genetic screen, IRF4 inhibition was reported to interfere with survival of several myeloma cell lines. The IRF4-regulated network in MM cells comprises the up-regulation of over 100 genes that are quiescent in healthy plasma cells, generating an abnormal transcriptional profile more closely similar to the genetic program of antigen-stimulated B cells. The direct IRF4 targets MYC, SCD, SQLE, CCNC and CDK6 are not highly expressed in normal plasma cells but are induced in mature B cells on antigen receptor signaling activation. The pleiotropic program regulated by IRF4 in MM cells also comprises genes influencing metabolic control, membrane biogenesis, cell cycle progression, and plasmacytic differentiation.
+
+A noteworthy target gene of IRF4 is MYC. IRF4 binds to MYC promoter region inducing its expression. A positive autoregulatory feedback loop is created when MYC up-regulates IRF4 by interacting to its intronic region. MYC expression in MM plasma cells is unusual since normal plasma cells do not express MYC due to the repression by BLIMP1. An alternative form of BLIMP1, called BLIMP1β, was reported to be over-expressed in MM cell lines. BLIMP1β is a truncated form lacking the first 101 amino-terminal residues, showing a reduced capacity to repress MYC. The expression of the truncated form of BLIMP1 can explain the inability of BLIMP1 to repress MYC in MM cells [37]. As a consequence, MYC over-expression promotes B cell activation and sustains MM survival. Furthermore, enforced expression of miR-125b-5p promotes IRF4 downregulation and impairment of its downstream effectors, reducing the growth of primary MM cells and MM cell lines [38]. Loss of IRF4 through CRISP-Cas9-mediated deletion affects MM viability and proliferation. Moreover, IRF4-regulated genes implicated in cell survival (KLF2, BCMA, MYB and MYC) were downmodulated upon IRF4 deletion, whereas pro-apoptotic factors BCL2-modifying factor (BMF) and BCL2L11 (encoding BIM) were upregulated. It implies that IRF4 affects MM apoptotic cell death by reducing the expression of pro-apoptotic factors regulating BCL2 [39]. Using a patient-derived xenograft model (PDX) of high-risk MM disease, IRF4 was reported to be highly expressed in MM progenitors and to be active in induction of several target genes involved in cell cycle progression. IRF4 down-regulation via IRF4 antisense oligonucleotide (ASO) ION251 reduced tumor formation and myeloma dissemination, eradicated myeloma progenitors and improved survival and sensitivity to myeloma drugs [40]. A phase I clinical trial of ION251 in patients with relapsed/refractory MM (NCT04398485) is ongoing.
+
+Diffuse large B cell lymphoma (DLBCL), the most common subtype of non-Hodgkin lymphoma, is clinically and biologically heterogeneous. This heterogeneity depends on the stage of B cell development from which the disease derives (COO, cell of origin) and the activity of different biological pathways. The classification of DLBCL based on gene-expression profile related to the cell-of-origin defines 2 broad categories, the germinal center B cell (GCB)-like DLBCL and the activated B cell (ABC)-like DLBCL, with about 15% of DLBCL in the “unclassified” category [41–44]. More recently, a genetic classification based on mutations, copy-number variation and structural variants dissects DLBCL into seven genetically defined categories [45–47].
+
+The hallmarks of ABC-DLBCL are aberrant NF-κB activation and IRF4 over-expression [48]. Similar to MM, ABC-DLBCL cells are addicted to IRF4 for survival, by activating BCR-dependent NF-κB cascade. Then, a positive-feedback loop allows the aberrant BCR signaling to sustain IRF4 over-expression in ABC-DLBCL [49]. Lenalidomide inhibits ABC-DLBCL cell proliferation, by reducing BCR-dependent NF-kB activation throughout IRF4 down-regulation. Accordingly, the knockdown of IRF4 mimics lenalidomide-mediated downregulation of NF-κB activity, whereas forced induction of IRF4 expression confers resistance to lenalidomide. Inhibition of BCR signaling with ibrutinib synergizes with lenalidomide to block IRF4 and kill ABC-DLBCL cells. In 2020, the combination of lenalidomide with the cytolytic CD19 targeting monoclonal antibody tafasitamab was approved for the treatment of relapsed/refractory DLBCL [50].
+
+The 5th edition of WHO classification recognizes as definitive entity large B cell lymphoma with IRF4 rearrangement (LBCL-IRF4). LBCL-IRF4, despite a GCB transcriptional program, is characterized by mutations in IRF4 and NF-kB-related genes, such as CARD11, CD79B and MYD88, losses of 17p13 and gains of chromosome 7 [51]. In addition, a strong expression of IRF4 is detected in LBCL-IRF4, probably contributing to NF-kB activation. However, further studies are needed to define the potential functional effect of IRF4 in this subtype of lymphoma.
+
+Reduced IRF4 expression: regulating activation and immune escape in chronic lymphocytic leukemia cells
+
+Several studies suggest a possible role of IRF4 in the pathogenesis of CLL (Fig. 3). A genome-wide single-nucleotide polymorphism (SNP) association study in 517 CLL patients from the UK and 1438 British1958 Birth Cohort controls identified IRF4 as a major susceptible gene for CLL, identifying rs872071 SNP within the 3′ untranslated region (UTR) and rs9378805 SNP 10-kb centromeric to the 3′UTR of IRF4 gene as variants with the strongest association with risk to develop CLL. These findings were confirmed through two internal validation cohorts [52]. Then, rs9378805 near IRF4 and rs735665 near GRAMD1B were validated as associated with CLL risk in an independent cohort of 438 non-Hispanic Caucasian CLL [53]. Fine-scale mapping analysis identified association with CLL in 4 SNPs mapped to a 3-kb region in the 3’-UTR of the IRF4 gene [54]. Of note, reduced IRF4 expression was associated with risk alleles, suggesting a model in which it could favor CLL development by arresting transition of memory B cells into PCs [52].Fig. 3 IRF4 functions in chronic lymphocytic leukemia (CLL). CLL cells show low expression of IRF4 in comparison with normal B cells. Low IRF4 level promotes the expression of molecules involved in CLL adhesion and migration such as VLA-4 and CXCR4, thus controlling leukemic cells positioning inside lymph nodes. Moreover, reduced expression of IRF4 enforces BCR signaling by regulating the expression of IKAROS and SYK. Lastly, the interaction between CLL cells and T cells is regulated by the IRF4Low, which decreases the expression of CD80 and CD86, thus favoring immune evasion. Abbreviations: CLL, chronic lymphocytic leukemia; NLC, nurse-like cells; TCR, T cell receptor; MHC, major histocompatibility complex; BCR, B cell receptor; MSC, mesenchymal stromal cells
+
+A recurrent heterozygous somatic mutation in the DNA-binding domain (DBD) of IRF4, consisting of a substitution of a leucine with an arginine at the position 116 of the amino acid sequence (p.L116R, c.347T > G), was detected in 1.2–2% of CLL patients [55–58]. Patients harboring IRF4 mutation had unmutated immunoglobulin heavy chain variable gene (IGHV) status, which is associated with adverse clinical outcome in CLL [59]. Whole-genome sequencing (WGS) and whole-exome sequencing (WES) studies by next-generation sequencing (NGS) reported recurrently mutated genes in CLL patients, including the IRF4 gene with L116R variant at a frequency ranging from 0.7 to 1.6% [60–62]. Of note, Puente et al. reported IRF4 gene mutations among novel prognostic drivers in CLL, finding association with shorter time to first treatment, independently from clinical stage and immunoglobulin mutational status [63]. IRF4 L116R mutation seems to accumulate in treated CLL patients and in CLL experiencing Richter transformation (RT) [61, 64]. IRF4 L116R mutation was found in 11% of ibrutinib-relapsed patients who had experienced RT [65]. In addition, the genomic characterization of the patient-derived tumor xenograft models of Richter syndrome revealed the L116R IRF4 mutation in the mutational profile [66]. A recent study demonstrated that IRF4 L116R mutation is functionally active conferring a proliferative advantage to CLL cells [67]. The leucine 116 is positioned in the highly conserved DNA-binding domain of IRF4 gene, and its substitution with an arginine may affect IRF4 DNA-binding properties. The L116R mutation determines a more robust binding of IRF4 to all DNA targets (ISRE, EICE, AICE), suggesting a gain-of-function mechanism. Additional analyses are required to define the specific DNA‐binding properties of IRF4 L116R protein and the oncogenic role of this missense variant in CLL transformation.
+
+IRF4 L116R mutation is rare in untreated CLL patients. In the majority of CLL, IRF4 expression is significantly downregulated as compared with healthy individuals [68]. Moreover, patients showing low IRF4 expression had significantly decreased time to first treatment (51.3 month) compared with IRF4high CLL patients (79.4 months). The negative prognostic impact of decreased IRF4 expression was also validated in 2 independent CLL patient cohorts. Furthermore, low IRF4 expression, defined by immunohistochemical stains as less than 20% CD20+ B cells positive for MUM1/IRF4, was reported to be associated with advanced clinical stage, diffuse marrow involvement and reduced time to first treatment (TTFT) in CLL patients. High IRF4 expression is more frequent in CLL with mutated IGHV gene and better outcome [68, 69].
+
+The maintenance and evolution of CLL clone rely on leukemic cell positioning inside “proliferation centers” and on the efficient transmission of BCR-mediated intracellular cascade. Blocking the transmission at different nodal points leads to an effective reduction of CLL survival and exiles cells from the protective tissue microenvironment. B cells deficient of IRF4 show an enrichment of genes involved in cell migration and homing, in particular of VLA-4 [70]. In CLL cells harboring trisomy 12 aberration, low levels of IRF4 mediate VLA-4 expression throughout the regulation of IKAROS [71]. Low IRF4 levels enforce BCR signaling by inducing SYK expression and promoting the accumulation of IKAROS protein, which reduces the expression of the BCR negative regulator SHIP1 [72].
+
+A causal relationship between low levels of IRF4 and the development of CLL was also demonstrated in mouse models [73–75]. In New Zealand Black (NZB) IRF4+/− mouse model, CLL development is dramatically accelerated and IRF4+/− CLL cells showed hyper-responsiveness to BCR stimulation [74]. Shukla et al. backcrossed Vh11 mice, which have expanded B1 cell population, into IRF4-deficient mice and found that 100% of IRF4−/− Vh11 mice developed CLL within 10 months [73]. Enhanced CLL disease progression was observed in IRF4-deficient TCL1 transgenic mice, finding a severe downregulation of genes involved in T cell activation such as MHC molecules and CD80 and CD86 [68]. This study demonstrates that IRF4 is involved in regulating the CLL/T cell interaction. Lack of IRF4 in murine CLL contributes to tumor immune evasion by reducing the numbers of antigen-experienced, potentially tumor-specific T cells and is associated with a more aggressive disease.
+
+Overall, reduced level of IRF4 seems to improve CLL homing to lymph nodal compartment, BCR activation and tumor immune evasion, but it may also potentially contribute to differentiation arrest. However, when CLL cells acquire IRF4 mutations, rarely occurring in untreated patients, a different genetic program might be activated, conferring the trajectory to a transformed phenotype. Further studies are needed to unravel the complexity of IRF4 function in CLL cells and its contribution to CLL and Richter transformation.
+
+Future considerations
+
+The dynamics of IRF4 expression influence the cell fate of B cell from the early B cell development, thought the germline formation, the transition from centroblast to centrocyte, until plasma cell differentiation. The fluid behavior of IRF4 is mediated by complex mechanisms related to different DNA-binding affinity, multiple IRF4-specific target DNA motif, and complex interactions with several transcriptional partners. IRF4 is an attractive therapeutic target in B cell malignancies, particularly in MM and CLL settings. Classical strategies involving the use of immunomodulatory drugs (IMIDs) such as lenalidomide or novel approaches comprising next-generation class of IRF4 antisense oligonucleotides (ASOs), that employ constrained ethyl residues that mediate RNase H-dependent degradation of IRF4 mRNA, mediate IRF4 down-modulation, interfering with the IRF4-regulated transcriptional program and IRF4-MYC feedback loop in MM. Conversely, over-expression of IRF4 in CLL seems to interfere with survival signals mediated by BCR activation and leukemic cell homing inside “proliferation centers”, counteracting key signals of CLL progression and clonal evolution. In this setting, exploiting the inverse effect of lenalidomide on IRF4 in CLL cells or testing all-trans retinoic acid (ATRA) to increase IRF4 expression need further investigation.
+
+Acknowledgements
+
+This work was supported by Grants from the Associazione Italiana per la Ricerca sul Cancro (AIRC) to M.L. (AIRC IG project #20624), to R.M. (AIRC IG project#21436) and to R.Maf. (AIRC TRIDEO project#16923). Figures were created with BioRender.com (accessed on 30 June 2022).
+
+Author contributions
+
+R.Maf. reviewed the literature and wrote the manuscript; other authors collaborate to R.Maf. to review the literature, contribute to the discussion and critically revise the manuscript. All authors have read and agreed to the published version of the manuscript.
+
+Funding
+
+This research received no external funding.
+
+Declarations
+
+Conflict of interest
+
+The authors declare that they have no conflict of interest.
+
+Publisher's Note
+
+Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
+==== Refs
+References
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+38. Morelli E Leone E Cantafio MEG Selective targeting of IRF4 by synthetic microRNA-125b-5p mimics induces anti-multiple myeloma activity in vitro and in vivo Leukemia 2015 29 11 2173 2183 10.1038/leu.2015.124 25987254
+39. Fedele PL Liao Y Gong JN The transcription factor IRF4 represses proapoptotic BMF and BIM to licence multiple myeloma survival Leukemia 2021 35 7 2114 2118 10.1038/s41375-020-01078-0 33149265
+40. Mondala PK Vora AA Zhou T Selective antisense oligonucleotide inhibition of human IRF4 prevents malignant myeloma regeneration via cell cycle disruption Cell Stem Cell 2021 28 4 623 636.e9 10.1016/j.stem.2020.12.017 33476575
+41. Alizadeh AA Eisen MB Davis RE Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling Nature 2000 403 6769 503 511 10.1038/35000501 10676951
+42. Wright G Tan B Rosenwald A Hurt EH Wiestner A Staudt LM A gene expression-based method to diagnose clinically distinct subgroups of diffuse large B cell lymphoma Proc Natl Acad Sci U S A 2003 100 17 9991 9996 10.1073/pnas.1732008100 12900505
+43. Rosenwald A Wright G Chan WC The use of molecular profiling to predict survival after chemotherapy for diffuse large-B-cell lymphoma N Engl J Med 2002 346 25 1937 1947 10.1056/NEJMoa012914 12075054
+44. Pasqualucci L The genetic basis of diffuse large B-cell lymphoma Curr Opin Hematol 2013 20 4 336 344 10.1097/MOH.0b013e3283623d7f 23673341
+45. Chapuy B Stewart C Dunford AJ Molecular subtypes of diffuse large B cell lymphoma are associated with distinct pathogenic mechanisms and outcomes Nat Med 2018 24 5 679 690 10.1038/s41591-018-0016-8 29713087
+46. Schmitz R Wright GW Huang DW Genetics and pathogenesis of diffuse large B-cell lymphoma N Engl J Med 2018 378 15 1396 1407 10.1056/NEJMoa1801445 29641966
+47. Lacy SE Barrans SL Beer PA Targeted sequencing in DLBCL, molecular subtypes, and outcomes: a Haematological Malignancy Research Network report Blood 2020 135 20 1759 1771 10.1182/blood.2019003535 32187361
+48. Bea S Zettl A Wright G Diffuse large B-cell lymphoma subgroups have distinct genetic profiles that influence tumor biology and improve gene-expression-based survival prediction Blood 2005 106 9 3183 3190 10.1182/blood-2005-04-1399 16046532
+49. Yang Y Shaffer AL Emre NCT Exploiting synthetic lethality for the therapy of ABC diffuse large B cell lymphoma Cancer Cell 2012 21 6 723 737 10.1016/j.ccr.2012.05.024 22698399
+50. Salles G Duell J González Barca E Tafasitamab plus lenalidomide in relapsed or refractory diffuse large B-cell lymphoma (L-MIND): a multicentre, prospective, single-arm, phase 2 study Lancet Oncol 2020 21 7 978 988 10.1016/S1470-2045(20)30225-4 32511983
+51. Ramis-Zaldivar JE Gonzalez-Farré B Balagué O Distinct molecular profile of IRF4-rearranged large B-cell lymphoma Blood 2020 135 4 274 286 10.1182/blood.2019002699 31738823
+52. Di Bernardo MC Crowther-Swanepoel D Broderick P A genome-wide association study identifies six susceptibility loci for chronic lymphocytic leukemia Nat Genet 2008 40 10 1204 1210 10.1038/ng.219 18758461
+53. Slager SL Goldin LR Strom SS Genetic susceptibility variants for chronic lymphocytic leukemia Cancer Epidemiol Biomark Prev 2010 19 4 1098 1102 10.1158/1055-9965.EPI-09-1217
+54. Crowther-Swanepoel D Broderick P Ma Y Fine-scale mapping of the 6p25.3 chronic lymphocytic leukaemia susceptibility locus Hum Mol Genet 2010 19 9 1840 1845 10.1093/hmg/ddq044 20123861
+55. Havelange V Pekarsky Y Nakamura T IRF4 mutations in chronic lymphocytic leukemia Blood 2011 118 10 2827 2829 10.1182/blood-2011-04-350579 21791429
+56. Landau DA Tausch E Taylor-Weiner AN Mutations driving CLL and their evolution in progression and relapse Nature 2015 526 7574 525 530 10.1038/nature15395 26466571
+57. Puente XS Beà S Valdés-Mas R Non-coding recurrent mutations in chronic lymphocytic leukaemia Nature 2015 526 7574 519 524 10.1038/nature14666 26200345
+58. Nadeu F Clot G Delgado J Clinical impact of the subclonal architecture and mutational complexity in chronic lymphocytic leukemia Leukemia 2018 32 3 645 653 10.1038/leu.2017.291 28924241
+59. Havelange V Pekarsky Y Nakamura T IRF4 mutations in chronic lymphocytic leukemia Blood 2011 118 10 2827 2829 10.1182/blood-2011-04-350579 21791429
+60. Landau DA Tausch E Taylor-Weiner AN Mutations driving CLL and their evolution in progression and relapse Nature 2015 526 7574 525 530 10.1038/nature15395 26466571
+61. Amin NA Seymour E Saiya-Cork K Parkin B Shedden K Malek SN A quantitative analysis of subclonal and clonal gene mutations before and after therapy in chronic lymphocytic leukemia Clin Cancer Res 2016 22 17 4525 4535 10.1158/1078-0432.CCR-15-3103 27060156
+62. Nadeu F Clot G Delgado J Clinical impact of the subclonal architecture and mutational complexity in chronic lymphocytic leukemia Leukemia 2018 32 3 645 653 10.1038/leu.2017.291 28924241
+63. Puente XS Beà S Valdés-Mas R Non-coding recurrent mutations in chronic lymphocytic leukaemia Nature 2015 526 7574 519 524 10.1038/nature14666 26200345
+64. Nadeu F Royo R Massoni-Badosa R Detection of early seeding of Richter transformation in chronic lymphocytic leukemia Nat Med 2022 28 8 1662 1671 10.1038/s41591-022-01927-8 35953718
+65. Kadri S Lee J Fitzpatrick C Clonal evolution underlying leukemia progression and Richter transformation in patients with ibrutinib-relapsed CLL Blood Adv 2017 1 12 715 727 10.1182/bloodadvances.2016003632 29296715
+66. Vaisitti T Braggio E Allan JN Novel Richter syndrome xenograft models to study genetic architecture, biology, and therapy responses Cancer Res 2018 78 13 3413 3420 10.1158/0008-5472.CAN-17-4004 29735551
+67. Benatti S Atene CG Fiorcari S IRF4 L116R mutation promotes proliferation of chronic lymphocytic leukemia B cells inducing MYC Hematol Oncol 2021 39 5 707 711 10.1002/hon.2915 34431535
+68. Asslaber D Qi Y Maeding N B-cell specific IRF4 deletion accelerates Chronic Lymphocytic Leukemia development by enhanced tumor immune evasion Blood 2019 134 20 1717 1729 10.1182/blood.2019000973 31537531
+69. Chang CC Lorek J Sabath DE Expression of MUM1/IRF4 correlates with clinical outcome in patients with B-cell chronic lymphocytic leukemia Blood 2002 100 13 4671 4675 10.1182/blood-2002-01-0104 12393648
+70. Simonetti G Carette A Silva K IRF4 controls the positioning of mature B cells in the lymphoid microenvironments by regulating NOTCH2 expression and activity J Exp Med 2013 210 13 2887 2902 10.1084/jem.20131026 24323359
+71. Fiorcari S Benatti S Zucchetto A Overexpression of CD49d in trisomy 12 chronic lymphocytic leukemia patients is mediated by IRF4 through induction of IKAROS Leukemia 2019 33 5 1278 1302 10.1038/s41375-018-0296-5 30659236
+72. Maffei R Fiorcari S Benatti S IRF4 modulates the response to BCR activation in chronic lymphocytic leukemia regulating IKAROS and SYK Leukemia 2021 35 5 1330 1343 10.1038/s41375-021-01178-5 33623139
+73. Shukla V Ma S Hardy RR Joshi SS Lu R A role for IRF4 in the development of CLL Blood 2013 122 16 2848 2855 10.1182/blood-2013-03-492769 23926303
+74. Ma S Shukla V Fang L Gould KA Joshi SS Lu R Accelerated development of chronic lymphocytic leukemia in New Zealand Black mice expressing a low level of interferon regulatory factor 4 J Biol Chem 2013 288 37 26430 26440 10.1074/jbc.M113.475913 23897826
+75. Zhong Y Byrd JC IRF4(−/−)Vh11 mice: a novel mouse model of CLL Blood 2013 122 16 2769 2770 10.1182/blood-2013-08-521120 24136077
+

PMC10391002.txt

@@ -0,0 +1,178 @@
+
+==== Front
+J Manag Care Spec Pharm
+J Manag Care Spec Pharm
+jmcsp
+Journal of Managed Care & Specialty Pharmacy
+2376-0540
+2376-1032
+Academy of Managed Care Pharmacy
+
+34464215
+10.18553/jmcp.2021.27.9.1315
+Perspectives on Value
+Anti B-cell maturation antigen CAR T-cell and antibody drug conjugate therapy for heavily pretreated relapsed and refractory multiple myeloma
+A summary from the Institute for Clinical and Economic Review’s Midwest Comparative Effectiveness Public Advisory Council
+Beinfeld Molly MPH 1 *
+Lee Sei MD, MAS 2
+McQueen Brett PhD 3
+Fluetsch Noemi MPH 1
+Pearson Steven D MD, MSc 1
+Ollendorf Daniel A PhD 4
+1 Institute for Clinical and Economic Review, Boston, MA.
+2 University of California, San Francisco.
+3 Skaggs School of Pharmacy and Pharmaceutical Sciences, University of Colorado Anschutz Medical Campus, Aurora.
+4 Center for the Evaluation of Value and Risk in Health, Tufts Medical Center, Boston, MA.
+* AUTHOR CORRESPONDENCE: Molly Beinfeld, mbeinfeld@icer.org
+Funding for this summary was contributed by Arnold Ventures, California Health Care Foundation, The Donaghue Foundation, Harvard Pilgrim Health Care, and Kaiser Foundation Health Plan to the Institute for Clinical and Economic Review (ICER), an independent organization that evaluates the evidence on the value of health care interventions.
+
+ICER’s annual policy summit is supported by dues from AbbVie, Aetna, America’s Health Insurance Plans, Anthem, Alnylam, AstraZeneca, Biogen, Blue Shield of CA, Boehringer-Ingelheim, Cambia Health Services, CVS, Editas, Evolve Pharmacy, Express Scripts, Genentech/Roche, GlaxoSmithKline, Harvard Pilgrim, Health Care Service Corporation, HealthFirst, Health Partners, Humana, Johnson & Johnson (Janssen), Kaiser Permanente, LEO Pharma, Mallinckrodt, Merck, Novartis, National Pharmaceutical Council, Pfizer, Premera, Prime Therapeutics, Regeneron, Sanofi, Spark Therapeutics, uniQure, and United Healthcare.
+
+Beinfeld, Fluetsch, and Pearson are employed by ICER. Ollendorf received funding from ICER for work on this summary and reports consulting and other personal fees from EMD Serono, Amgen, Analysis Group, Aspen Institute/University of Southern California, GalbraithWight, Cytokinetics, Executive Insight, Sunovion, University of Colorado, World Health Organization, and Eli Lilly, unrelated to this work. Lee and McQueen received funding from ICER for work on this summary.
+
+9 2021
+27 9 10.18553/jmcp.2021.27.9.1315Copyright © 2021, Academy of Managed Care Pharmacy. All rights reserved.
+2021
+https://creativecommons.org/licenses/by/4.0/ This article is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use and redistribution provided that the original author and source are credited.
+==== Body
+pmcMultiple myeloma (MM) is a hematologic cancer of plasma cells, primarily affecting older individuals. Approximately 32,000 cases are diagnosed each year, and 150,000 people are living with MM in the United States.1 Black Americans appear to have approximately twice the risk of developing MM compared with White Americans.2 Direct medical costs of treating MM are substantial; in a recent claims analysis, average costs exceeded $250,000 over a 21-month period, and the majority of these costs were medication related.3
+
+Current treatment of MM typically includes 3 general classes of drugs: immunomodulatory agents, proteasome inhibitors, and anti-CD38 monoclonal antibodies.4 Unfortunately, most patients eventually relapse; patients with relapsed or refractory MM (RRMM) often cycle through different combinations of available agents. When a patient’s disease is no longer responding to agents in each of these classes, the disease is referred to as “triple-class refractory” MM (TCRMM).4 Response rates to existing, later-line therapies are relatively low, underscoring the need for new treatment options in this heavily pretreated population.
+
+The Institute for Clinical and Economic Review (ICER) conducted a systematic literature review and cost-effectiveness analysis to evaluate the health and economic outcomes of 3 new treatments targeting the B-cell maturation antigen (BCMA) for heavily pretreated patients with RRMM. BCMA is preferentially expressed on plasma cells, making it an attractive therapeutic target for MM. Belantamab mafodotin blmf (Blenrep, GlaxoSmithKline) is an antibody drug conjugate, with a monoclonal antibody to BCMA linked to a cytotoxic drug. Belantamab was studied in patients with heavily pretreated (6-7 previous lines of therapy) TCRMM (majority quad- and penta-refractory, usually defined as refractory to 4 or 5 agents across all 3 drug classes previously mentioned). Idecabtagene vicleucel (ide-cel, Abecma, Bristol Myers Squibb and bluebird bio) and ciltacabtagene autoleucel (cilta-cel, Janssen and Legend biotech) are chimeric antigen receptor (CAR) T-cell therapies, involving engineering a patient’s own T cells to target BCMA.
+
+Ide-cel and cilta-cel were studied in patients who were mostly TCRMM (majority triple- or quad-refractory patients). Belantamab was approved by the US Food and Drug Administration (FDA) for RRMM in August 2020; idecel was approved in March 2021; and cilta-cel was granted fast track designation and has a Prescription Drug User Fee Act (PDUFA) target action date of November 29, 2021.
+
+Complete details of ICER’s systematic literature search and protocol, as well as the methodology and model structure for the economic evaluation, are available on ICER’s website at https://icer.org/assessment/multiple-myeloma-2021/. In this review, we present the summary of our findings and highlights of the policy discussion with key stakeholders held at a public meeting of the Midwest Comparative Effectiveness Public Advisory Council on April 16, 2021. In addition, new data for cilta-cel has become available since that meeting and is highlighted here.
+
+Summary of Findings
+
+CLINICAL EFFECTIVENESS
+
+The systematic literature review identified only 2 open-label, single arm phase 1/2 studies of the CAR-Ts (one each for ide-cel and cilta-cel) and 1 open-label phase 2 study of belantamab, making indirect comparisons of relative effectiveness impossible. At the time of the review, only the pivotal trials of ide-cel and belantamab had been published.5,6 Our review also evaluated information from conference abstracts, regulatory documents, and data submitted by the manufacturers.
+
+The pivotal trials of ide-cel and cilta-cel included similar distributions of baseline characteristics (such as age and previous lines of therapy); however, the ide-cel trial included more patients with extramedullary disease (a marker of more aggressive disease) and high-risk cytogenetics.3,5 The pivotal trial of belantamab included patients who were older, had undergone more pretreatment, and were more likely to have high-risk cytogenetics compared with the patient populations in the CAR-T trials.6 A key difference between the pivotal CAR-T therapy trials and the belantamab trial was the approach for inclusion in the outcomes analysis. The CAR-T trials only included patients who were infused in the analysis in an “as-treated” approach, whereas the belantamab trial reports on the full intention-to-treat (ITT) population. At the time of this publication, only interim, unpublished data on cilta-cel was available.3,5,6
+
+In the pivotal trial of ide-cel, with a median follow-up of 13.3 months, as-treated median progression-free survival (PFS) and overall survival (OS) were 8.8 months and 19.4 months, respectively. Using an ITT approach, 94 of 149 enrolled patients (63%) achieved an overall response to treatment (Table 1). Twelve-month OS and PFS were 78% and 38%, respectively.5,7
+
+TABLE 1 Key Trial Results of Ide-Cel, Cilta-Cel, and Belantamab
+
+Intervention	Trial	Median follow-up duration	As-treated PFS, median months (95% CI)	As-treated OS, median months (95% CI)	ITT ORR, n (%); [95% CI]	
+Ide-cel	KarMMa5,7 (N = 149)a	13.3 monthsb	8.8 (5.6-11.6)b	19.4 (18.2, NE)b	94 (63.0); [NR]	
+Cilta-cel	CARTITUDE-13,8,9(N = 126)a	18 months5	Not reached at 18 monthsb	Not reached at 18 monthsb	95 (75.0); [NR]	
+Belantamab	DREAMM-26 (N = 97)a,c	13 months	2.8 (1.6-3.6)b	13.7 (9.9, not reached)a	31 (32.0); [97.5% CI: 21.7-43.6]	
+a Intention to treat.
+
+b Median follow-up duration, PFS and OS for KarMMa and CARTITUDE-1 are based on the as-treated population.
+
+c 2.5 mg/kg arm only.
+
+ITT = intention to treat; NE = not estimable; NR = not reported; ORR = overall response rate; OS = overall survival; PFS = progression-free survival.
+
+Subsequent to the publication of the final ICER report, updated data from the pivotal trial of cilta-cel were released.8 With a median follow-up of 18 months, median PFS and OS had not been reached. Ninety-five of 126 enrolled patients (75%) achieved an overall response (Table 1).9 Eighteen-month OS and PFS were 81% and 66%, respectively, a decline from 89% and 77% at 12 months.3
+
+In the pivotal trial of belantamab, with a median follow-up of 13 months, median PFS and OS in the enrolled population (in an ITT approach) were 2.8 and 13.7 months, respectively. Thirty-one of 97 enrolled patients (32%) achieved an overall response (Table 1). Twelve-month OS was 57%.6
+
+In the pivotal trials of ide-cel and cilta-cel, the most important adverse events (AEs) included cytokine release syndrome (CRS), neurotoxicity, and thrombocytopenia. CRS of any grade was reported by 84% and 95% of patients in the ide-cel and cilta-cel trials, respectively.3,5,10 The median duration of CRS symptoms was 4 to 5 days and were most likely to be managed with tocilizumab or corticosteroids. In the pivotal ide-cel trial, 3 deaths (2%) were treatment related (due to CRS, pneumonia, and gastrointestinal hemorrhage) within 8 weeks of infusion, and an additional death from pneumonia was reported within 6 months of infusion. In the pivotal cilta-cel trial, 6 deaths (6%) were related to treatment (due to sepsis, CRS, lung abscess, respiratory failure, and neurotoxicity).
+
+In the pivotal trial of belantamab, AEs were reported by 98% of patients, of which 88% were related to treatment. Nine patients (10%) discontinued due to AEs. The most important treatment-related AE was keratopathy (a disease of the cornea), reported by 72% of patients; however, at 13 months follow-up, the majority had recovered. A decline in vision in their better-seeing eye to 20/50 or worse was reported by 18%, but no patients had permanent vision loss. One death (1%) was treatment related (due to sepsis).6
+
+LIMITATIONS OF THE CLINICAL EVIDENCE
+
+The primary limitations in the clinical evidence include the lack of data from randomized controlled trials and limited follow-up time. The lack of head-to-head comparative data near the time of launch is not unexpected but makes even indirect comparisons needed to guide judgments of relative effectiveness highly uncertain.
+
+Differences in study populations between the single arm trials and in assessing and reporting outcomes further complicate the comparability of the trial results. Although outcomes of ide-cel and cilta-cel show response rates that are better than currently available treatments, there continues to be considerable uncertainty about their long-term efficacy and concerns about potential safety issues. One potential advantage of the CAR-T therapies is the ability for patients to be off treatment for extended periods; however, long-term data are needed to confirm if this holds true over time. The pivotal trial of ide-cel included retreatment, but it is unclear if standard of care for CAR-Ts will include retreatment. For belantamab, the trial data suggest similar response rates to existing therapies, but its use is associated with potentially serious ocular side effects.
+
+LONG-TERM COST-EFFECTIVENESS
+
+We developed a de novo decision analytic model for the evaluation of treatments for heavily pretreated MM patients. Our model was informed by key clinical trials, previous relevant economic models, as well as by stakeholder input.11,12 Belantamab, cilta-cel, and ide-cel were compared with a triple- or quad-refractory MM comparator market basket that was derived from a contemporaneous observational study of similar patient populations.13 For the CAR-T therapies, an initial decision tree was used to calculate the costs and consequences from treatment initiation (ie, leukapheresis) to T-cell infusion.
+
+Outcomes of interest included total life-years (LYs) gained, quality-adjusted life-years (QALYs) gained, equal value life-years gained (evLYG), time spent in progression free state/responding to treatment, and total costs for each intervention over a lifetime time horizon discounted at 3% per annum. Full details on ICER’s cost-effectiveness analysis and model are available on ICER’s website at https://icer.org/wp-content/uploads/2020/10/ICER_Multiple-Myeloma_Final-Report_Update_070921.pdf.
+
+The base-case findings suggest that CAR-T therapies provide clinical benefit for this population, with gains in both QALYs and OS over current treatment options. Model findings across all interventions were sensitive to the cost of comparators, PFS and OS estimates, quality of life related to PFS (on and off treatment), and overall health care costs for MM patients.
+
+The incremental cost-effectiveness ratios for ide-cel vs the MM comparator market basket were approximately $319,000 per QALY gained, $250,000 per LY gained, $280,000 per evLYG gained, and $35,000 per additional PFS month gained. Incremental cost-effectiveness ratios for cilta-cel vs the MM comparator market basket were updated with newly released 18-month trial data and found approximately $253,000 per QALY gained, $207,000 per LY gained, and $228,000 per evLYG gained.
+
+Base-case findings for belantamab suggest that current list pricing is within commonly cited cost-effectiveness thresholds when compared with a triple-, quad-, or penta-refractory MM market basket. However, small changes in any of the key model inputs changed the belantamab model findings to a significant extent, so our results should be viewed with caution. Table 2 summarizes the full results for all incremental cost-effectiveness outcomes evaluated. A modified societal perspective was explored for all treatments as a scenario analysis and included productivity losses and transportation time to and from health care appointments. Results were not notably different from those of the base-case analysis.
+
+TABLE 2 Incremental Cost-Effectiveness Ratios for the Base Case
+
+Intervention	Comparator	Cost per QALY gained	Cost per LY gained	Cost per evLYG	Cost per additional PFS month gained	
+Ide-cel	CAR-T comparator market basket	$319,000	$250,000	$280,000	$35,000	
+Cilta-cel (preliminary)a	CAR-T comparator market basket	$253,000	$207,000	$228,000	$17,000	
+Belantamab	Belantamab comparator market basket	$98,000	$70,000	$93,000	$18,000	
+a Using placeholder price for cilta-cel.
+
+CAR-T = chimeric antigen receptor T-cell; evLYG = equal value life-year gained; LY = life-year; PFS = progression-free survival; QALY = quality-adjusted life-year.
+
+The ICER health benefit price benchmark (HBPB) is a suggested price range based on cost-effectiveness thresholds at the $100,000 and $150,000 per QALY and per evLYG. The annual HBPB for ide-cel ranged from $192,000 to $265,000. The annual HBPB for cilta cel ranged from $230,000 to $312,000. The HBPB for belantamab ranged from $8,300 to $9,500 per vial. Full results are available on ICER’s website at https://icer.org/wp-content/uploads/2020/10/ICER_Multiple-Myeloma_Final-Report_Update_070921.pdf.
+
+LIMITATIONS OF THE COST-EFFECTIVENESS MODEL
+
+Considering that evidence was abstracted from single-arm studies, it was difficult to obtain data for a suitable comparator, and significant uncertainty remains regarding how these treatments would compare head-to-head to current best usual care. Furthermore, given that the treatment landscape changes dramatically over short time periods in RRMM and the lack of a quantitative indirect treatment comparison to inform these analyses, caution should be used when interpreting cost-effectiveness estimates, particularly for the cilta-cel results.
+
+Policy Discussion
+
+The Midwest Comparative Effectiveness Public Advisory Council (CEPAC) is one of the independent appraisal committees convened by ICER to engage in the public deliberation of the evidence on clinical and cost-effectiveness of health care interventions. The Midwest CEPAC is composed of medical evidence experts, including practicing clinicians, methodologists, and leaders in patient engagement and advocacy. Their deliberation includes input from clinical experts and patient representatives specific to the condition under review, as well as formal comment from manufacturers and the public. A policy roundtable concludes each meeting during which representatives from insurers and manufacturers join clinical experts and patient representatives to discuss how best to apply the findings of the evidence to clinical practice, insurance coverage, and pricing negotiations.
+
+The ICER report on treatments for heavily pretreated RRMM was the subject of a Midwest CEPAC meeting on April 16, 2021. Following the discussion, the Midwest CEPAC panel members deliberated on key questions raised by ICER’s report. The results of their votes on the clinical evidence were as follows: (1) the panel voted 10-5 that the evidence was not adequate to demonstrate that the net health benefit of belantamab mafodotin is superior to that provided by usual care; (2) the panel voted 15-0 that the evidence was adequate to demonstrate that the net health benefit of ide-cel is superior to usual care; (3) the panel voted 13-2 that the evidence was adequate to demonstrate that the net health benefit of cilta-cel is superior to usual care; and (4) the panel voted 15-0 that the evidence was not adequate to distinguish the net health benefit of ide-cel from cilta-cel.
+
+The CEPAC panel also voted on “other potential benefits” and “contextual considerations” as part of a process intended to signal to policymakers whether there are important considerations when making judgments about long-term value for money not adequately captured in analyses of clinical and/or cost-effectiveness. The results of these votes are shown in Tables 3-5.
+
+TABLE 3 Votes on Other Benefits and Contextual Considerations for Belantamab
+
+	Major negative	Minor negative	No difference	Minor positive	Major positive	
+What are the relative effects of belantamab vs usual care on patients’ ability to achieve life goals related to education, work, or family life?	0	1	11	3	0	
+What are the relative effects of belantamab vs usual care on caregivers’ quality of life and/or ability to achieve major life goals relative to education, work, or family life?	0	6	8	1	0	
+What are the relative effects of belantamab vs usual care on society’s goal of reducing health inequalities?	2	5	8	0	0	
+
+TABLE 4 Votes on Other Benefits and Contextual Considerations for Ide-Cel and Cilta-Cel
+
+What are the relative effects of ide-cel and cilta-cel vs usual care on the following outcomes?	
+	Major negative effect	Minor negative effect	No difference	Minor positive effect	Major positive effect	
+Patients’ ability to achieve major life goals related to education, work, or family life	0	0	1	8	6	
+Caregivers’ quality of life and/or ability to achieve major life goals related to education, work, or family life	0	0	0	13	2	
+Patients’ ability to manage and sustain treatment given the complexity of regimen	0	0	1	12	2	
+Society’s goal of reducing health inequities	5	9	1	0	0	
+
+TABLE 5 Votes on Long-Term Value for Money of Belantamab and Ide-Cel
+
+Given the available evidence on comparative effectiveness and incremental cost-effectiveness and considering other benefits, disadvantages, and contextual considerations, what is the long-term value for money of treatment at current pricing with belantamab or ide-cel vs usual care?	
+	Low long-term value for money	Intermediate long-term value for money	High long-term value for money	
+Belantamab	8	6	1	
+Ide-cel	9	5	0	
+
+Following its deliberation on the evidence, the Midwest CEPAC panel engaged in a moderated discussion with a policy roundtable about how best to apply the evidence on CAR-T therapies and belantamab for TCRMM to policy and practice. The full set of policy recommendations can be found in the Final Evidence Report on the ICER website: https://icer.org/wp-content/uploads/2020/10/ICER_Multiple-Myeloma_Final-Report_Update_070921.pdf. Several key policy recommendations follow:
+
+All stakeholders have a responsibility and an important role to play in ensuring that effective new treatment options for patients with MM are introduced in a way that will help reduce health inequities.Manufacturers should engage with a variety of people from diverse communities to help inform the design and implementation of clinical trials and to ensure that people of color and those from less advantaged backgrounds are adequately represented in the trials. In addition, manufacturers should moderate new treatment pricing. Even with insurance coverage, cost is a tremendous driver of health inequities.
+
+Payers should recognize that, in addition to often steep out-of-pocket costs for the treatments, there are often ancillary costs that can become real barriers to care and exacerbate inequities. Payers should develop coverage that creates a broader package of benefits so that patients who face financial or logistical hurdles can have equal access to specialized care at Centers of Excellence, if desired.
+
+Clinicians and clinical societies should conduct (or continue to conduct) active outreach and education to underserved communities and the general oncologists and other members of the health care team serving those communities to get new, effective treatments to those patients who would benefit most.
+
+The clinical research community should move rapidly to address key gaps in evidence for treatments for MM. These gaps include whether patients can stop therapy while in response (which could decrease side effects for patients and decrease costs for payers), how well the clinical trial populations reflect the target populations for treatment, data on preferences and patient-reported outcomes in historically disadvantaged populations, and the clinical characteristics of the disease and its affected populations that may be predictive of response.
+
+Manufacturers should seek to set prices that will foster affordability and good access for all patients by aligning prices with the patient-centered therapeutic value of their treatments. In the setting of these new interventions for MM, while there is considerable hope associated with the promise of the therapies, there also remains substantial uncertainty regarding their longer-term safety and effectiveness, and the platform on which they are based has been funded in part with taxpayer money. Manufacturer pricing should also reflect these considerations in moderating launch pricing.
+
+Payers should use the FDA label as the guide to coverage policy and engage clinical experts and diverse patient representatives in considering how to address coverage issues for which there is limited or no evidence at the current time.
+
+ACKNOWLEDGMENTS
+
+The authors thank Mel Wittington, Eric Gutierrez, Sue Kwon, Belen Herce-Hagiwara, Liis Shea, and Monica Fredrick for their contributions to this report.
+==== Refs
+REFERENCES
+
+1. Mikhael J. Treatment options for tripleclass refractory multiple myeloma. Clin Lymphoma Myeloma Leuk. 2020;20 (1 ):1-7.31767529
+2. National Cancer Institute. Cancer stat facts: myeloma. Accessed July 24, 2021. https://seer.cancer.gov/statfacts/html/mulmy.html
+3. Berdeja JG, Madduri D, Usmani SZ, et al. Ciltacabtagene autoleucel, a B-cell maturation antigen-directed chimeric antigen receptor T-cell therapy in patients with relapsed or refractory multiple myeloma (CARTITUDE-1): a phase 1b/2 open-label study. Lancet. 398 (10297 ):314-24. doi:10.1016/S0140-6736(21)00933-8
+4. Rajkumar SV. Multiple myeloma: 2020 update on diagnosis, risk-stratification and management. Am J Hematol. 2020;95 (5 ):548-67.32212178
+5. Munshi NC, Anderson LD Jr., Shah N, et al. Idecabtagene vicleucel in relapsed and refractory multiple myeloma. N Engl J Med. 2021;384 (8 ):705-16.33626253
+6. Lonial S, Lee HC, Badros A, et al. Belantamab mafodotin for relapsed or refractory multiple myeloma (DREAMM-2): a two-arm, randomised, open-label, phase 2 study. Lancet Oncol. 2020;21 (2 ):207-21.31859245
+7. Celgene. Efficacy and safety study of bb2121 in subjects with relapsed and refractory multiple myeloma (KarMMa). ClinicalTrials.gov Identifier: NCT03361748. 2020. Accessed. https://clinicaltrials.gov/ct2/show/record/NCT03361748
+8. Harris J, Dobkowski D. Durable responses observed at 18 months with cilta-cel to treat relapsed/refractory multiple myeloma. Cancer Network. June 8, 2021. Accessed June 24, 2021. https://www.cancernetwork.com/view/durable-responses-observed-at-18-months-with-cilta-cel-to-treat-relapsed-refractory-multiple-myeloma
+9. Janssen Research & Development. A study of JNJ-68284528, a chimeric antigen receptor T cell (CAR-T) therapy directed against B-cell maturation antigen (BCMA) in participants with relapsed or refractory multiple myeloma (CARTITUDE-1). ClinicalTrials.gov Identifier: NCT03548207. 2020. Accessed August 5, 2021. https://www.clinicaltrials.gov/ct2/show/NCT03548207
+10. Munshi NC, Anderson JLD, Shah N, et al. 8503: Idecabtagene vicleucel (ide-cel; bb2121), a BCMA-targeted CAR T-cell therapy, in patients with relapsed and refractory multiple myeloma (RRMM): initial KarMMa results [abstract]. J Clin Oncol. 2020;39 (15 Suppl ):8503. doi:10.1200/JC0.2020.38.15_suppl.8503
+11. Asrar MM, Lad DP, Prinja S, Bansal D. A systematic review of economic evaluations of treatment regimens in multiple myeloma. Expert Rev Pharmacoecon Outcomes Res. June 27, 2020. Online ahead of print. Accessed August 5, 2021. 10.1080/14737167.2020.1779064.
+12. Carlson JJ, Guzauskas GF, Chapman RH, et al. Cost-effectiveness of drugs to treat relapsed/refractory multiple myeloma in the United States. J Manag Care Spec Pharm. 2018;24 (1 ):29-38. doi:10.18553/jmcp.2018.24.1.2929290170
+13. Gandhi UH, Cornell RF, Lakshman A, et al. Outcomes of patients with multiple myeloma refractory to CD38-targeted monoclonal antibody therapy. Leukemia. 2019;33 (9 ):2266-75.30858549
+

PMC10391122.txt

@@ -0,0 +1,346 @@
+
+==== Front
+J Manag Care Spec Pharm
+J Manag Care Spec Pharm
+jmcsp
+Journal of Managed Care & Specialty Pharmacy
+2376-0540
+2376-1032
+Academy of Managed Care Pharmacy
+
+34595957
+10.18553/jmcp.2021.27.10.1457
+Research
+Treatment utilization patterns of newly initiated oral anticancer agents in a national sample of Medicare beneficiaries
+Doshi Jalpa A PhD 1 *
+Jahnke Jordan MS 2
+Raman Swathi MPH 2
+Puckett Justin T BA 2
+Brown Victoria T PharmD, BCOP 3
+Ward Melea Anne PhD, PharmD, BCPS 3
+Li Pengxiang PhD 1
+Manz Christopher R MD, MSHP 4
+1 Division of General Internal Medicine, Perelman School of Medicine, and Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia.
+2 Division of General Internal Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia.
+3 Humana, Inc., Louisville, KY.
+4 Division of Hematology and Oncology, Perelman School of Medicine, and Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia.
+* AUTHOR CORRESPONDENCE: Jalpa A Doshi, 215.898.7989; jdoshi@pennmedicine.upenn.edu
+This study was supported by Humana, Inc. (Louisville, KY). The sponsor played a role in the development of the study protocol, interpretation of results, and revisions of the manuscript. The sponsor was not involved in data analysis.
+
+Brown is employed by Humana, Inc., and Ward was employed by Humana, Inc., from research inception through initial drafts. Doshi has served as an advisory board member or consultant for Allergan, Ironwood Pharmaceuticals, Janssen, Kite Pharma, Merck, Otsuka, Regeneron, Sarepta, Sage Therapeutics, Sanofi, and Vertex and has received research funding from AbbVie, Biogen, Humana, Janssen, Novartis, PhRMA, Regeneron, Sanofi, and Valeant. Her spouse holds stock in Merck and Pfizer. All other authors have no financial conflicts of interest to report.
+
+10 2021
+27 10 10.18553/jmcp.2021.27.10.1457Copyright © 2021, Academy of Managed Care Pharmacy. All rights reserved.
+2021
+https://creativecommons.org/licenses/by/4.0/ This article is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use and redistribution provided that the original author and source are credited.
+BACKGROUND:
+
+Few studies have examined oral anticancer treatment utilization patterns among Medicare beneficiaries.
+
+OBJECTIVE:
+
+To assess treatment utilization patterns of newly initiated oral anticancer agents across national samples of Medicare beneficiaries for 5 cancer types: chronic myeloid leukemia (CML), multiple myeloma (MM), metastatic prostate cancer (mPC), metastatic renal cell carcinoma (mRCC), and metastatic breast cancer (mBC).
+
+METHODS:
+
+This retrospective claims analysis used 100% Medicare Chronic Condition Data Warehouse (CCW) Parts A, B, and D files from 2011 to 2014 (for CML, MM, mPC, and mRCC patients) and a 5% random fee-for-service sample from 2011 to 2013 (for mBC patients). Outcomes of interest were the number of 30-day supply prescriptions, adherence, and discontinuation of newly initiated (ie, index) oral anticancer agents indicated for each of the cancers. Adherence was calculated with both the “traditional” proportion of days covered (PDC) approach, measured over a fixed 1-year period or until hospice/death, and a “modified” PDC approach, measured over the time between the first and last fill of the index oral anticancer agent. Patients with PDC of at least 0.80 were deemed as being adherent. Discontinuation was defined as the presence of a continuous 90-day gap in the availability of days supply of the index oral anticancer agent.
+
+RESULTS:
+
+Our study included 1,650, 7,461, 6,998, 2,553, and 79 patients for CML, MM, mPC, mRCC, and mBC, respectively. Patients with mRCC had the highest proportion of patients with only 1 fill of their index anticancer agent (28%) followed by mBC (17%), MM (17%), mPC (12%), and CML (12%). Patients with CML had the highest mean (SD) number of 30-day supply equivalent prescriptions (8.3 [4.6]), followed by patients with mPC (6.5 [4.2]), MM (5.7 [4.1]), mBC (4.7 [3.2]), and mRCC (4.5 [3.9]). Using the modified PDC measured between the first and last fills, approximately three-quarters of patients with CML (74%), mRCC (71%), and mBC (70%) were adherent to the index oral anticancer agent. Adherence was highest for patients with mPC (87%) and lowest for patients with MM (58%). The percentage of patients defined as adherent to the index oral anticancer agent decreased for all cancers when using the traditional PDC measure over a fixed 1-year period: CML (54%), MM (35%), mPC (48%), mRCC (37%), and mBC (22%). Rates of discontinuation for patients in our sample were 32% (CML), 38% (mPC), 42% (mRCC), 48% (MM), and 58% (mBC).
+
+CONCLUSIONS:
+
+Between 13% and 42% of Medicare patients were nonadherent between the first and last fill of their newly initiated oral anticancer therapies across a range of cancers. This study provides a valuable benchmark for stakeholders seeking to measure and improve adherence to oral anticancer agents in Medicare patients.
+==== Body
+pmc What is already known about this subject
+
+New oral cancer therapies offer greater convenience and fewer side effects, but the responsibility for appropriate administration and monitoring is shifted to patients.
+
+Medicare patients in particular face unique circumstances that may make adherence a much greater challenge, but most studies that have examined adherence to oral cancer therapies have focused on the commercially insured population.
+
+What this study adds
+
+This is the first study that offers a wide-ranging assessment of treatment utilization patterns with newly initiated oral anticancer agents across multiple cancers and therapies in a national sample of US Medicare beneficiaries.
+
+Between 13% and 42% of Medicare patients were nonadherent between the first and last fill of their newly initiated oral anticancer therapies across a range of cancers.
+
+Our findings provide a valuable benchmark for stakeholders seeking to measure and improve adherence to oral anticancer agents in Medicare patients.
+
+The past decades have seen an explosion of new oral therapeutic options for cancer patients that may improve survival and outcomes across a wide variety of cancers, including blood cancers, breast cancer, prostate cancer, and kidney cancer. For example, oral tyrosine kinase inhibitors (TKIs) have enabled patients with chronic myeloid leukemia to achieve near-normal life expectancy.1,2 In addition to improved outcomes, oral anticancer agents do not require infusion visits and often have better tolerability profiles.3 However, realizing the full benefits of these breakthrough treatments shifts much of the burden for appropriate administration and monitoring from health care professionals to the patient. Hence, a careful examination of adherence to oral anticancer treatments in real-world clinical practice is critical.
+
+The existing literature suggests that measuring adherence among patients receiving oral anticancer agents is a multifaceted problem: rates of adherence vary widely based on adherence assessment methodology, type of cancer and medication, and insurance type.4 Medicare patients in particular face unique circumstances such as polypharmacy, high out-of-pocket costs, and multiple comorbidities that may make adherence a much greater challenge.5-8 While cancer prevalence and incidence are highest among the elderly,9-13 they are often under-represented in cancer clinical trials, and thus adherence to anticancer therapies among Medicare beneficiaries has implications for real-world effectiveness.14
+
+Despite these facts, most studies have examined adherence within the commercially insured population.15-17 Previous studies that have examined adherence to oral anticancer agents among Medicare beneficiaries have significant drawbacks.18,19 They often use differing measures of adherence, such as proportion of days covered (PDC) or medication possession ratio (MPR), which may either under- or overestimate adherence rates. Further, many studies have only examined adherence for a single cancer or use differing time frames to measure adherence, complicating meaningful comparisons across cancer types. Given these limitations, a broader analysis is warranted.
+
+The objective of this descriptive study was to comprehensively assess treatment utilization patterns of newly initiated oral anticancer agents across national samples of Medicare beneficiaries for the following 5 cancer types: chronic myeloid leukemia (CML), multiple myeloma (MM), metastatic prostate cancer (mPC), metastatic renal cell carcinoma (mRCC), and metastatic breast cancer (mBC).
+
+Methods
+
+DATA SOURCE
+
+This study was performed using the Medicare Chronic Conditions Data Warehouse (CCW) files based on the most recent data available to the authors at the time of the analysis. These files contained data on all fee-for-service (FFS) Medicare beneficiaries, including Medicare Parts A and B medical claims for inpatient care, skilled nursing facility care, home health services, outpatient services, durable medical equipment, and hospice services and Part D prescription claims for outpatient prescription drug events. For patients being treated for CML, MM, mPC, and mRCC, we had access to CCW data for the 100% (ie, national) FFS sample from 2011 to 2014. For patients being treated for mBC, we only had access to CCW data for a 5% random FFS sample from 2011 to 2013.
+
+STUDY DESIGN AND SAMPLE SELECTION
+
+This was a retrospective claims database analysis using Medicare data. Patients were included in the study sampling frame if they had a prescription claim for an oral anticancer drug indicated for CML (bosutinib, dasatinib, imatinib, nilotinib, ponatinib), MM (lenalidomide, pomalidomide, thalidomide), mPC (abiraterone, enzalutamide), or mRCC (axitinib, pazopanib, sorafenib, sunitinib) between January 1, 2012, and December 31, 2013. For the sample selection of patients receiving oral anticancer treatment for mBC (everolimus, lapatinib), the identification window was between January 1, 2012, and December 31, 2012. Oral anticancer agents approved after December 31, 2012, for mBC and those approved after December 31, 2013, for the remaining cancers were not included given the years of the data available for the study. Of note, this study also did not examine adherence to oral endocrine therapies (eg, tamoxifen) used in breast cancer, as these are older oral therapies wherein there is already extensive adherence literature.
+
+The first claim for the oral anticancer drug was assigned as the index date. Patients were required to have no claims for their index treatment in the 12-month pre-index period to ensure that they were new users of the index anticancer agent. For all cancers of interest, we required patients to have at least 1 claim with a diagnosis for the selected cancer (International Classification of Diseases, Ninth Revision, Clinical Modification [ICD-9-CM] codes for CML [205.1], MM [203.0], mPC [185], mRCC [189.0], mBC [174]). In addition, for solid tumor malignancies of mPC, mRCC, and mBC, we required at least 1 claim with a diagnosis for cancer metastases (ICD-9-CM codes: 196, 197, 198, 199) in the 12 months before the index date. We required patients to have continuous FFS Medicare Parts A, B, and D (stand-alone prescription drug plan) eligibility in the 12-month pre-index period and the 12-month post-index period (or until a patient died or entered hospice during the post-index period).
+
+KEY OUTCOME MEASURES
+
+We used multiple measures to assess utilization patterns of the index oral anticancer agent. First, we examined the number of prescription fills for the index oral anticancer agent in the post-index period, overall and stratified by whether the patient died or entered hospice during the post-index period or was still alive at the end of the period. The counts for all prescriptions were standardized into 30-day supply equivalents by dividing the days supply on each prescription claim by 30. This measure provided a conceptually simpler measure of treatment utilization via the number of months a patient had the index cancer drug on hand over the 12-month follow-up. For example, a count of twelve 30-day supply prescriptions indicated that the patient had the whole year with the drug covered. In addition to measuring the mean number of prescriptions filled over the 12-month follow-up, we also examined the proportion of patients filling only 1 prescription of the index oral anticancer agent.
+
+Next, we examined adherence to the index oral anticancer agent, defined using the proportion of days covered (PDC), measured as the number of days covered with the index oral anticancer agent during a fixed time interval divided by the fixed time interval from date of index treatment initiation. We measured PDC using 2 different definitions for the fixed time interval. Under the first definition, adherence to the index oral anticancer agent was measured between index date and the earlier of 365 days post-index date or until a patient died or entered hospice (“traditional” PDC approach). Under the second definition, adherence to the index oral anticancer agent was measured between the dates of the first fill (ie, index date) and last fill of the index oral anticancer treatment during follow-up (“modified” PDC approach).
+
+The 2 approaches provide a lower bound and upper bound for the adherence estimates. They help with sensitivity analyses to address the key limitation of administrative claims data, wherein we do not know the reasons for the observed refill patterns and whether the individual was truly nonadherent or whether they stopped taking the index anticancer oral agent due to clinical reasons (eg, disease remission, progression, or adverse effects) and/or per the advice of their oncologist.
+
+The traditional PDC approach is more conservative and assumes all patients should have continued their index oral anticancer agent for the 365 days post-index (or at least until they died or entered hospice). The modified PDC approach is more generous and assessed adherence only between the first and last fill of the index oral anticancer agent, permitting the assumption that all patients may have stopped taking the index agent after its last observed fill based on their oncologist’s advice. The latter modified PDC approach was also used to allow for differences in how cancer patients utilize oral cancer therapy based on their cancer type. For example, a fixed interval is well suited for CML patients who require lifelong treatment; however, a 12-month interval is not as clinically meaningful for mRCC or mPC, where progression may occur and require a switch in treatment in less than a year. In these instances, the time interval between the first and the last refill date for the index agent may be more appropriate.
+
+Under the modified PDC approach using the first to last fill date as the fixed time interval, we excluded patients who only had a single fill of their index treatment from our adherence calculation. Under both PDC approaches, patients with a PDC greater than or equal to 0.80 were defined as adherent. In sensitivity analyses, we defined adherence using alternative thresholds as PDC of at least 0.70 and PDC of at least 0.90.
+
+Our third set of outcomes measured discontinuation and persistence. Discontinuation was a dichotomous measure indicating the presence of a 90-consecutive-day period without availability of days supply of the index oral anticancer agent during the 365 days post-index or until a patient died or entered hospice in the post-index period. Among patients who discontinued their oral anticancer agent, we measured the time to discontinuation, defined as the time from the index date to the beginning date of the 90-consecutive-day gap.
+
+For mapping out the measurement of outcomes of adherence, discontinuation, and persistence, we relied on the days supply reported on the prescription claim for the index oral anticancer agent for all except 3 study drugs. The 3 exceptions included 2 of the MM drugs (ie, lenalidomide and pomalidomide) and an mRCC drug (ie, sunitinib). For these drugs, dosing strengths may vary, and patients are on treatment for a defined number of weeks and then off treatment for 1 or 2 weeks. Hence, we manually adjusted the days supply on the prescription based on the quantity of pills reported for the prescription and common dosing regimens. This method of adjusting the days supply of the prescription to better reflect the dosing schedule of the anticancer agent has been used in previously published studies of oral cancer therapy treatment patterns.20,21 Additional details on these adjustments to the days supply for lenalidomide, pomalidomide, and sunitinib are available in Supplementary Table 1 (available in online article).
+
+ANALYSIS
+
+Descriptive characteristics such as age, gender, race, region, reason for Medicare eligibility, and National Cancer Institute comorbidity index were generated for each of the cancer populations. Descriptive analyses were conducted to estimate the number of 30-day supply prescriptions, adherence, and discontinuation during the follow-up period. Median time to discontinuation was also estimated. Statistical testing was not conducted because this study was planned as a purely descriptive analysis with no a priori hypotheses. Any estimate based on a cell size less than 11 (or that allowed back-calculation of a cell size to less than 11) was omitted to comply with the Centers for Medicare & Medicaid Services cell size suppression policy.
+
+Results
+
+SAMPLE CHARACTERISTICS
+
+Our study sample selection criteria for identifying new users of oral anticancer agents for the 2 blood cancers resulted in 1,650 patients with CML and 7,461 patients with MM (Supplementary Table 2, available in online article). Similarly, the study samples for new users of oral anticancer agents for the 3 solid tumors included 6,998 patients with mPC, 2,553 patients with mRCC, and 79 (corresponding to a 100% sample of 1,580) patients with mBC. Patient characteristics for these 5 cancer samples are presented in Table 1. The samples were predominantly White (77%-84%). There was a roughly even split between male and female patients for CML and MM, but patients with mRCC were more likely to be male (64%). Additionally, between one quarter and one half of patients qualified for part or full low-income subsidy (Table 1).
+
+TABLE 1 Sample Characteristics
+
+	Patients who newly initiated an oral anticancer agent indicated for each of the following cancersa	
+CML	MM	mPC	mRCC	mBC	
+n	%	n	%	n	%	n	%	n	%	
+Sample size	1,650	100	7,461	100	6,998	100	2,553	100	79	100	
+Age, years	
+  < 65	427	26	839	11	342	5	400	16	16	20	
+  65-69	290	18	1,511	20	1,099	16	612	24	25	32	
+  70-74	320	19	1,907	26	1,713	24	660	26	19	24	
+  75-79	265	16	1,516	20	1,551	22	488	19	b	b	
+  > 79	348	21	1,688	23	2,293	33	393	15	b	b	
+Disability as the original reason for Medicare entitlement	632	38	1,696	23	935	13	689	27	25	32	
+Sex	
+  Female	881	53	3,794	51	0	0	930	36	79	100	
+  Male	769	47	3,667	49	6,998	100	1,623	64	0	0	
+Race	
+  White	1,348	82	5,764	77	5,731	82	2,137	84	66	84	
+  Black	177	11	1,251	17	809	12	279	11	b	b	
+  Latino or other race	125	8	446	6	458	6	137	5	b	b	
+Region	
+  Northeast	275	17	1,321	18	1,288	18	379	15	12	15	
+  Midwest	420	25	1,830	25	1,661	24	667	26	22	28	
+  West	284	17	1,246	17	1,411	20	443	17	19	24	
+  South	671	41	3,064	41	2,638	38	1,064	42	26	33	
+Part D LIS status	
+Part or Full LIS	723	44	2,371	32	1,885	27	971	38	37	47	
+Non-LIS	927	56	5,090	68	5,113	73	1,582	62	42	53	
+Any other oral anticancer treatment in pre-index periodc	90	5	140	2	50	1	123	5	b	b	
+RxHCC, mean (SD)	3.89 (0.58)	–	2.85 (0.47)	–	1.10 (0.45)	–	1.28 (0.48)	–	0.95 (0.38)	–	
+Follow-up status	
+  Hospitalization during follow-up	697	42	4,623	62	3,618	52	1542	60	39	49	
+  Died or entered hospice during follow-up	210	13	1973	26	2,941	42	1216	48	37	47	
+aNew users of an oral anticancer agent (ie, index agent) indicated for each cancer (CML: bosutinib, dasatinib, imatinib, nilotinib, ponatinib; MM: lenalidomide, pomalidomide, thalidomide; mPC: abiraterone, enzalutamide; mRCC: axitinib, pazopanib, sorafenib, sunitinib; mBC: everolimus, lapatinib). Oral anticancer agents approved after December 31, 2012, for mBC and those approved after December 31, 2013, for the remaining cancers were not included given the years of the data available for the study.
+
+bCell sizes with n < 11 omitted to comply with the Centers for Medicare & Medicaid Services’ cell size suppression policy.
+
+cAny use of nonindex oral anticancer agent indicated for the specific cancer in the 12-month pre-index period (CML: bosutinib, dasatinib, imatinib, nilotinib, ponatinib; MM: lenalidomide, pomalidomide, thalidomide; mPC: abiraterone, enzalutamide; mRCC: axitinib, pazopanib, sorafenib, sunitinib; mBC: everolimus, lapatinib).
+
+CML = chronic myeloid leukemia; LIS = low-income subsidy; mBC = metastatic breast cancer; MM = multiple myeloma; mPC = metastatic prostate cancer; mRCC = metastatic renal cell carcinoma; NCI = National Cancer Institute; RxHCC = prescription drug hierarchical condition category risk score.
+
+NUMBER OF PRESCRIPTION FILLS
+
+Table 2 displays the count of prescription fills for the index oral anticancer agents. We found that the percentage of patients filling only a single prescription varied across cancers (CML = 12%; MM = 17%; mPC = 12%; mRCC = 28%; mBC = 17%). Patients with CML had the highest mean (SD) number of 30-day supply prescriptions (8.3 [4.6]), followed by patients with mPC (6.5 [4.2]). Patients with MM filled 5.7 (4.1) 30-day supply prescriptions; the fewest prescriptions were filled by patients with mBC (4.7 [3.2]) and mRCC (4.5 [3.9]). Among patients who were alive during the post-index period, the number (SD) of 30-day supply prescriptions was slightly higher for all cancers: 9.0 (4.4) for the CML sample, 8.5 (4.0) for the mPC sample, 6.8 (4.0) for the MM sample, 6.2 (4.4) for the mRCC sample, and 5.6 (3.5) for the mBC sample.
+
+TABLE 2 Number of Prescription Fills for Index Oral Anticancer Agents in the Post-Index Period
+
+Type of cancer and oral anticancer agent	All patients n	Patients with only 1 fill of index oral anticancer agent %	Number of 30-day supply equivalent prescriptions for index oral anticancer agent, mean (SD)	
+All patients	Alive during post-index period	Died or entered hospice during post-index period	
+CML	1,650	191 (12)	8.3 (4.6)	9.0 (4.4)	3.7 (3.1)	
+  Bosutinib	a	a	5.6 (5.9)	5.6 (5.9)	a	
+  Dasatinib	429	66 (15)	7.8 (4.7)	8.5 (4.5)	3.1 (3.3)	
+  Imatinib	846	82 (10)	8.8 (4.6)	9.5 (4.3)	3.9 (3.1)	
+  Nilotinib	346	38 (11)	8.1 (4.4)	8.7 (4.2)	3.9 (3.2)	
+  Ponatinib	a	a	5.8 (2.9)	5.9 (3.1)	5.3 (1.2)	
+MM	7,461	1,279 (17)	5.7 (4.1)	6.8 (4.0)	2.7 (2.3)	
+  Lenalidomide	6,518	1,065 (16)	5.9 (4.1)	6.9 (4.0)	2.7 (2.3)	
+  Pomalidomide	368	70 (19)	5.5 (3.9)	7.4 (3.7)	3.0 (2.4)	
+  Thalidomide	575	144 (25)	4.4 (3.7)	5.5 (4.0)	2.6 (2.2)	
+mPC	6,998	871 (12)	6.5 (4.2)	8.5 (4.0)	3.6 (2.6)	
+  Abiraterone	5,585	708 (13)	6.5 (4.2)	8.4 (4.0)	3.7 (2.6)	
+  Enzalutamide	1,413	163 (12)	6.4 (4.2)	8.9 (3.8)	3.6 (2.4)	
+mRCC	2,553	718 (28)	4.5 (3.9)	6.2 (4.4)	2.6 (2.1)	
+  Axitinib	313	71 (23)	5.3 (4.3)	7.5 (4.5)	3.1 (2.7)	
+  Pazopanib	851	243 (29)	4.7 (4.0)	6.3 (4.4)	2.7 (2.2)	
+  Sorafenib	175	57 (33)	3.8 (3.3)	5.4 (4.1)	2.6 (2.0)	
+  Sunitinib	1,214	347 (28.5)	4.3 (3.8)	5.9 (4.3)	2.4 (1.8)	
+mBC	79	13 (17)	4.7 (3.2)	5.6 (3.5)	3.6 (2.3)	
+  Lapatinib	a	a	4.0 (3.0)	4.6 (3.6)	3.2 (2.0)	
+  Everolimus	a	a	5.0 (3.2)	6.0 (3.5)	3.8 (2.4)	
+aCell sizes with n < 11 were omitted to comply with the Centers for Medicare & Medicaid Services’ cell size suppression policy.
+
+CML = chronic myeloid leukemia; mBC = metastatic breast cancer; MM = multiple myeloma; mPC = metastatic prostate cancer; mRCC = metastatic renal cell carcinoma.
+
+ADHERENCE
+
+Table 3 presents the results of our adherence outcomes using 2 separate definitions of the fixed time interval. Using the traditional PDC measure, the mean (SD) PDC for the index oral anticancer agent was 0.69 (0.32) for patients with CML, 0.69 (0.29) for patients with mPC, 0.61 (0.33) for patients with mRCC, 0.58 (0.31) for patients with MM, and 0.52 (0.29) for patients with mBC. Approximately half of patients with CML (54%) and patients with mPC (48%) were deemed adherent to their index oral anticancer treatment based on a PDC of at least 0.80; a little more than one-third of patients with MM (35%) and mRCC (38%) were adherent. Patients with breast cancer had the lowest adherence rate (22%).
+
+TABLE 3 Adherence to Index Oral Anticancer Agents in the Post-Index Period
+
+Type of cancer and oral anticancer agent	All patients n	PDC measured over 365 days post-index or until death/hospice for index oral anticancer agent	Patients with > 1 fill of index oral anticancer agent	PDC measured between first fill to last fill among those with > 1 fill for index oral anticancer agent	
+PDC mean (SD)	PDC ≥ 0.80 %	n	PDC mean (SD)	PDC ≥ 0.80 %	
+CML	1,650	0.69 (0.32)	884 (54)	1,459	0.85 (0.19)	1,074 (74)	
+  Bosutinib	a	0.41 (0.41)	a	a	0.90 (0.14)	a	
+  Dasatinib	429	0.66 (0.34)	216 (50)	363	0.86 (0.20)	274 (75)	
+  Imatinib	846	0.73 (0.30)	493 (58)	764	0.86 (0.18)	577 (76)	
+  Nilotinib	344	0.67 (0.32)	172 (50)	306	0.82 (0.21)	208 (68)	
+  Ponatinib	a	0.49 (0.24)	a	a	0.80 (0.17)	a	
+MM	7,461	0.58 (0.31)	2,585 (35)	6,182	0.79 (0.20)	3,600 (58)	
+  Lenalidomide	6,518	0.59 (0.31)	2312 (35)	5,453	0.79 (0.20)	3,154 (58)	
+  Pomalidomide	368	0.62 (0.29)	126 (34)	298	0.79 (0.19)	159 (53)	
+  Thalidomide	575	0.50 (0.32)	147 (26)	431	0.82 (0.22)	287 (67)	
+mPC	6,998	0.69 (0.29)	3,347 (48)	6,127	0.92 (0.15)	5,302 (87)	
+  Abiraterone	5,585	0.69 (0.30)	2631 (47)	4,877	0.92 (0.15)	4,232 (87)	
+  Enzalutamide	1,413	0.71 (0.28)	716 (51)	1,250	0.91 (0.15)	1,070 (86)	
+mRCC	2,553	0.61 (0.33)	981 (38)	1,835	0.87 (0.18)	1,365 (74)	
+  Axitinib	313	0.65 (0.30)	130 (42)	242	0.86 (0.18)	174 (72)	
+  Pazopanib	851	0.54 (0.34)	262 (31)	608	0.85 (0.20)	431 (71)	
+  Sorafenib	175	0.57 (0.33)	56 (32)	118	0.82 (0.19)	75 (64)	
+  Sunitinib	1,214	0.65 (0.32)	484 (40)	867	0.86 (0.19)	685 (79)	
+mBC	79	0.52 (0.29)	17 (22)	46	0.86 (0.16)	46 (70)	
+  Lapatinib	a	0.43 (0.28)	a	a	0.85 (0.17)	12 (75)	
+  Everolimus	a	0.56 (0.28)	a	a	0.86 (0.16)	34 (68)	
+aCell sizes with n < 11 omitted to comply with the Centers for Medicare and Medicaid Services’ cell size suppression policy.
+
+CML = chronic myeloid leukemia; mBC = metastatic breast cancer; MM = multiple myeloma; mPC = metastatic prostate cancer; mRCC = metastatic renal cell carcinoma; PDC = proportion of days covered.
+
+When the modified PDC was measured between first and last prescription fills among those with more than 1 fill of the index oral anticancer treatment as a measure of adherence, the mean (SD) PDC was higher for all cancers: CML (0.85 [0.19]), MM (0.79 [0.20]), mPC (0.92 [0.15]), mRCC (0.85 [0.19]), and mBC (0.86 [0.16]). The percentage of patients defined as adherent to their index oral anticancer agent based on PDC of at least 0.80 also increased to 74% (CML), 58% (MM), 87% (mPC), 74% (mRCC), and 70% (mBC).
+
+Sensitivity analyses using alternate thresholds for defining adherence showed results as expected. More patients were adherent if the threshold was PDC of at least 0.70 (instead of at least 0.80), and fewer patients were adherent if the cut-off was PDC of at least 0.90 (Figure 1).
+
+FIGURE 1 Sensitivity Analysis Using Alternative Cut-Offs to Define Adherence to Index Oral Anticancer Agents in the Post-Index Period
+
+DISCONTINUATION
+
+Rates of discontinuation of the index oral anticancer agent are displayed in Table 4. Patients with CML had the lowest rates of discontinuation (32%), followed by patients with mPC (38%) and mRCC (42%). Discontinuation rates were higher for patients with MM (48%) and mBC (58%). Median time to discontinuation of index oral anticancer agent among patients who discontinued was between 84 days for patients with CML, 98 days for patients with MM, 120 days for patients with mPC, 84 days for patients with mRCC, and 103 days for patients with mBC. There was substantial variation in these outcomes across the individual index oral anticancer agents for some of the cancers (Table 4).
+
+TABLE 4 Discontinuation of Index Oral Anticancer Agents in the Post-Index Period
+
+Type of cancer and oral anticancer agent	All patients n	Discontinuation of index oral anticancer agent n (%)	Time to discontinuation (days) among those who discontinued, median	
+CML	1,650	525 (32)	84	
+  Bosutinib	a	a	60	
+  Dasatinib	429	156 (36)	64	
+  Imatinib	846	238 (28)	90	
+  Nilotinib	344	110 (32)	79	
+  Ponatinib	a	14 (67)	154	
+MM	7,461	3,544 (48)	98	
+  Lenalidomide	6,518	3,093 (47)	102	
+  Pomalidomide	368	140 (38)	105	
+  Thalidomide	575	311 (54)	84	
+mPC	6,998	2,638 (38)	120	
+  Abiraterone	5,585	2,169 (39)	120	
+  Enzalutamide	1,413	469 (33)	120	
+mRCC	2,553	1,080 (42)	84	
+  Axitinib	313	110 (35)	96	
+  Pazopanib	851	401 (47)	61	
+  Sorafenib	175	71 (41)	90	
+  Sunitinib	1,214	498 (41)	88	
+mBC	79	46 (58)	103	
+  Lapatinib	a	16 (73)	112	
+  Everolimus	a	30 (53)	101	
+aCell sizes with n < 11 were omitted to comply with the Centers for Medicare & Medicaid Services’ cell size suppression policy.
+
+CML = chronic myeloid leukemia; mBC = metastatic breast cancer; MM = multiple myeloma; mPC = metastatic prostate cancer; mRCC = metastatic renal cell carcinoma.
+
+Discussion
+
+To our knowledge, this is the first study that offers a wide-ranging assessment of treatment utilization patterns with newly initiated oral anticancer agents across multiple cancers and therapies in a national sample of US Medicare beneficiaries. As such, it offers several insights that add to our understanding of utilization patterns in this population and provides a valuable benchmark for stakeholders seeking to measure and improve adherence to oral anticancer agents.
+
+Our study is among the first to report on the nontrivial proportion of cancer patients who filled only a single prescription for their index oral anticancer agent. More than 1 in 5 patients with mRCC, 1 in 6 patients with MM, and 1 in 10 patients with mPC and CML had only a single fill of their index agent. Our findings on the number of 30-day supply prescriptions suggested that even among those who were alive during the entire 12 months of follow-up, the average number of 30-day prescriptions ranged from as low as 6 (mRCC and mBC) to almost 9 (CML and mPC).
+
+In some cases, the high rates of single fills and low number of prescription fills could be due to toxicities of certain oral agents, such as those for mBC and mRCC, wherein the latter have been shown to lead to discontinuation or therapy switching in the first month of treatment.22,23 Other factors such as social determinants of health may also be at play. As toxicities and disease progression will always necessitate that some patients stop medications after 1 fill, the optimal number of patients with only 1 fill will vary by disease and therapy type and will never be zero. However, payers and pharmacies may find these data to be useful benchmarks in determining whether quality issues like toxicity management may be suboptimal in their patient populations.
+
+Clinicians may also find these data useful as they initiate patients on new oral anticancer treatments, directing clinical attention to adherence and toxicity management. Refill behavior for oral anticancer agents in clinical practice is significantly more difficult to track relative to traditional intravenous cancer treatments, in which missed clinic or office visits can immediately signal an issue to providers. Our findings also inform and have implications for budget impact modeling and economic analyses wherein clinical trial-based assumptions on the number of prescriptions to be filled (and hence total treatment costs) for a new oral anticancer agent over a 12-month period may grossly overestimate treatment utilization patterns in real-world practice.
+
+Our evaluation of adherence using the PDC measure revealed several important findings. From a methodological perspective, calculating adherence using a modified PDC measured between first and last fill of the oral anticancer agent yielded higher adherence rates than a traditional PDC calculated based on a fixed period. As the median progression-free survival for many of the drugs in our study is less than a year (ie, most patients will need to change treatment within 12 months), our modified PDC measure based on first and last fills is likely a better representation of patient adherence. The first and last fill date may also better capture provider intention to change treatment than a fixed time interval: a physician may have tried a therapy for several months before deciding a patient was a better candidate for another therapy, perhaps due to toxicities or disease progression. This finding has obvious methodological implications for future studies or interventions using claims data to track adherence to oral anticancer medications.
+
+From a clinical perspective, it was concerning to find that even with this modified PDC measure, adherence to the oral anticancer agent was still suboptimal. Even for a cancer like CML, wherein daily oral TKI use has been shown to permit near-normal life expectancy, between 25% (or 1 in 4) and 32% (or 1 in 3) of patients were not adherent.24 Adherence rates based on the modified PDC were the highest for oral anticancer agents for mPC, likely reflecting the relatively benign side effect profile of these agents compared to the other medications in this study, but even then, 1 in 7 patients were not adherent. On the other hand, adherence was poorest for MM, with almost half to one third of patients not being adherent between their first and last fill. However, dose reductions and treatment breaks are common with these myeloma therapies but not readily perceptible in claims data. Thus, the apparently lower adherence may partially reflect patients taking fewer pills or longer breaks at the direction of their oncologist. Nevertheless, these findings provide a helpful benchmark for comparison in other studies that use a similar methodology. While a PDC threshold of 0.80 for deeming patients as being adherent may make sense for many chronic diseases and certain cancers like CML, the variable dosing regimens, toxicities, and methodologic challenges of measuring adherence to anticancer therapies may warrant using different thresholds for other cancers such as MM and mRCC. Future studies should identify disease-specific adherence thresholds that reflect these realities and use those thresholds as targets for quality improvement.
+
+Finally, we also found high discontinuation rates of the index oral anticancer agent across most cancers. Anticancer drugs may be discontinued for a number of reasons. A longer length of treatment-free interval may be a sign of cancer remission due to a good response to the index agent. However, patients may also discontinue the index agent and switch to another cancer treatment due to toxicity or disease progression. While examination of switching to another treatment among those who discontinued their index oral anticancer agent was beyond the scope of this study, especially given the prevalence of off-label use in this setting, we conducted post hoc exploratory analyses using a simplistic approach to identify discontinuers who received any subsequent anticancer drug (oral, injectable, or infusible), regardless of the approved indication of the agent or when it was initiated.
+
+Our additional exploratory analyses found many of the patients who discontinued their index oral anticancer agent also had evidence of use of a different anticancer agent during the post-index period (79% CML, 71% MM, 95% mPC, 65% mRCC, 98% mBC; data not shown). In the case of the treatments for MM, mBC, and mPC, the use of other agents is typically concomitant with the index agent; in the other cancers that we studied, it is likely that some of the patients who discontinued the oral anticancer agent may have switched to another treatment due to progression and/or toxicity with the index agent. For instance, we found that all patients with CML who started and subsequently discontinued imatinib took a different oral cancer agent during the post-index period (100%), which likely represents switching to a newer generation of oral CML therapies.
+
+Among patients with mRCC who discontinued their medication, 53% received another oral cancer agent, while 18% received an IV agent during the post-index period, likely representing therapy change, as combination therapy was not used widely during the study period. Nevertheless, the findings raise concerns that suboptimal adherence to the initial (index) oral anticancer agent as measured by the modified PDC between the first and last fill may have contributed to disease progression and the resultant discontinuation of the initial oral agent and switch to a different treatment, as some studies have demonstrated.24
+
+Although there is no consensus on what constitutes ideal adherence to these oral oncology therapies, our study may provide useful real-world adherence benchmarks for interventions being pursued to improve treatment adherence given the association of lower adherence with poor cancer outcomes. Clinicians, pharmacists, and payers may use our study findings to further direct efforts to identify and target low adherence in their patient populations and tailor strategic interventions given limited resources. For instance, oncology practices should ask their patients about adherence to oral anticancer therapies and do so in a nonjudgmental fashion, with particular emphasis on barriers to adherence, such as cost and side effects. Early and aggressive toxicity management (eg, hand foot syndrome with sorafenib) may also help improve adherence and persistence to effective medications.25,26
+
+Physician practices could also take a more proactive role in helping patients navigate the costs of cancer care through the use of an office financial counselor to assist in obtaining financial assistance.27 Similarly, pharmacistled interventions may include follow-up evaluations with patients shortly after starting therapy and reminders to refill medication after their first cycle—a particularly important consideration given that many patients in our study only filled a single prescription.28 In fact, the recently published best practices for the management of oral anticancer therapies from the Hematology/Oncology Pharmacist Association (HOPA) could lead to an expanded role for the pharmacist at several points along the care continuum.29
+
+Payers may also consider the possibility of more comprehensive solutions such as a cycle management program, which focuses on “providing specialized counseling and monitoring for patients to improve their therapy experience.”30 Such programs are increasingly being pursued by payers through their specialty pharmacies, and these programs offer services such as prescription refill reminders, advice on potential side effects, and alerts for physicians when a patient is not picking up their medication.31-34
+
+Specialty pharmacies can inquire about barriers to adherence when coordinating a refill with the patient, particularly if the refill has been delayed. They may also offer synchronization services, where patients taking multiple oral medications have all of their medications filled at the same time each month, which have also demonstrated some promise at improving adherence.35 Specialty pharmacies can also play a role in alleviating the financial burden placed on patients by high drug costs through programs to help patients identify and apply for financial assistance.36
+
+Finally, there are also potential policy solutions to improve adherence by limiting the out-of-pocket costs of oral anticancer therapies under Medicare Part D. Several stakeholders have called for the institution of an annual out-of-pocket maximum and “smoothing out” of the cost sharing under Part D37; however, these proposals have only recently been introduced in Congress, and their future remains uncertain.
+
+LIMITATIONS
+
+Our study had several limitations that deserve mention. First, as a retrospective study using insurance claims data, reasons for nonadherence and discontinuation were unavailable (eg, disease progression, treatment response, side effects). Thus, we are unable to comment on the appropriateness of nonadherence and discontinuation. Relatedly, our descriptive analysis results should not be taken as a guide to what optimal adherence should be. There likely isn’t a “one-size-fits-all” adherence target (eg, PDC ≥ 0.80), and a more comprehensive view would determine the role of “thoughtful nonadherence,” which might also include a range of alternative treatment options, including palliative care.
+
+Second, although our adherence and discontinuation outcomes were based on widely accepted measurement methods with administrative claims, they are unable to capture whether a medication was actually taken as prescribed or not.
+
+Third, we adjusted the days supply for some drugs for mRCC and MM given the dosing schedules of these drugs. While this was done to avoid misestimation of our outcome measures, we acknowledge that we had to make assumptions in the absence of how the drugs had been actually prescribed and cannot account for further dosing adjustments made by providers that are not captured in claims data.
+
+Fourth, as with all claims analyses, data may be subject to coding errors. Fifth, claims data are only available for the Medicare FFS patients and hence our findings may not be generalizable to the Medicare Advantage population. Sixth, the scope of this study did not include assessing the factors associated with oral anticancer treatment utilization patterns and their impact on clinical outcomes, health resource use, and costs.
+
+Finally, the national Medicare claims data available to the team at the time of the analysis was only until 2014. Several new oral anticancer agents have entered the market since then for some of the cancers (eg, breast cancer) examined in our study and should be evaluated in future studies using more recent data.
+
+Conclusions
+
+Our study provides a comprehensive assessment of treatment utilization patterns in US Medicare patients initiating oral anticancer treatments with direct implications for clinical practice and policy. As the proliferation of innovative oral anticancer therapies is likely to continue unabated, further research is urgently needed to examine factors associated with nonadherence to oral anticancer agents in Medicare patients and develop and test interventions to address these barriers.
+==== Refs
+REFERENCES
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+21. Vogelzang NJ, Pal SK, Ghate SR, et al. Real-world economic outcomes during time on treatment among patients who initiated sunitinib or pazopanib as first targeted therapy for advanced renal cell carcinoma: a retrospective analysis of Medicare claims data. J Manag Care Spec Pharm. 2018;24 (6 ):525-33. doi:10.18553/jmcp.2018.24.6.52529799328
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+35. Girdish C, Shrank W, Freytag S, et al. The impact of a retail prescription synchronization program on medication adherence. J Am Pharm Assoc. 2017:57 (5 ):579-84.
+36. James D. The importance of financial counseling services for patients with cancer. 2019. Accessed August 16 , 2021 . https://www.pharmacytimes.com/publications/directions-in-pharmacy/2019/september2019/the-importance-of-financial-counseling-services-for-patients-with-cancer
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+

PMC10391124.txt

@@ -0,0 +1,273 @@
+
+==== Front
+J Manag Care Spec Pharm
+J Manag Care Spec Pharm
+jmcsp
+Journal of Managed Care & Specialty Pharmacy
+2376-0540
+2376-1032
+Academy of Managed Care Pharmacy
+
+34818089
+10.18553/jmcp.2021.27.12.1691
+Research
+Cost-effectiveness of adding daratumumab or bortezomib to lenalidomide plus dexamethasone for newly diagnosed multiple myeloma
+Narsipur Nihal PharmD 1
+Bulla Sabrina BA 2
+Yoo Connie BA 2
+Do Brenda BS 2
+Tran Kyle BS 2
+Gu Dian PhD 3
+Zhong Lixian PhD 5
+Wilson Leslie PhD 4 *
+1 UCSF-Actelion Clinical Research and Medical Communications Fellow, University of California, San Francisco.
+2 PharmD Candidates 2021, University of California, San Francisco.
+3 Institute for Health and Aging, University of California, San Francisco.
+4 Department of Clinical Pharmacy, University of California, San Francisco.
+5 College of Pharmacy, Texas A&M University.
+* AUTHOR CORRESPONDENCE: Leslie Wilson, 415.990.1012; leslie.wilson@ucsf.edu
+No funding was received for this study. At the time of this study, Narsipur was a UCSF-Actelion Clinical Research and Medical Communications Fellow, unrelated to this study. The other authors have nothing to disclose.
+
+12 2021
+27 12 10.18553/jmcp.2021.27.12.1691Copyright © 2021, Academy of Managed Care Pharmacy. All rights reserved.
+2021
+https://creativecommons.org/licenses/by/4.0/ This article is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use and redistribution provided that the original author and source are credited.
+BACKGROUND:
+
+Multiple myeloma survival rates are steadily increasing due to availability of new drug classes used in combination with corticosteroids and chemotherapy. The latest treatments are daratumumab or bortezomib in combination therapy with lenalidomide and dexamethasone (Rd). Daratumumab, a CD38-targeted, human IgG1k monoclonal antibody, and bortezomib, a proteasome inhibitor, are both approved as regimens for transplant-ineligible relapsed/refractory multiple myeloma (RRMM). There have been cost-effectiveness analyses for daratumumab and bortezomib use in RRMM, but there are limited data regarding cost-effectiveness for daratumumab or bortezomib use in newly diagnosed multiple myeloma patients who are ineligible for stem cell transplantation.
+
+OBJECTIVE:
+
+To compare the cost-effectiveness of 3 separate regimens—(1) daratumumab, lenalidomide, and dexamethasone triple therapy (DRd); (2) bortezomib and lenalidomide plus dexamethasone triple therapy (VRd); and (3) lenalidomide plus dexamethasone (Rd)—in patients with multiple myeloma ineligible for autologous stem cell transplant.
+
+METHODS:
+
+A 2-state Markov model was developed using a US health system perspective and lifetime time horizon. Transition probabilities were calculated from the latest progression-free survival data reported in two phase 3 randomized controlled trials—MAIA and SWOG S0777—and extrapolated using a Weibull distribution based on the Hoyle Henley method. National data sources were used to obtain costs in 2019 US dollars, discounted by 3%. Health state utilities from available literature were applied to each health state. Utility decrements for adverse events were individualized in each choice branch with utility decrement weighted by the percentage of patients who experienced the adverse event in the MAIA and SWOG S0777 trials. We assumed a treatment would be cost-effective at a willingness to pay (WTP) of $150,000 per progression-free quality-adjusted life-year ($/PFQALY). One-way and probabilistic sensitivity analyses were conducted.
+
+RESULTS:
+
+Rd standard therapy had the lowest overall cost at $329,867, followed by VRd at $385,434 and DRd with the highest overall total cost at $626,900. Rd was estimated to result in the least amount (1.24) of PFQALYs, followed by VRd at 1.35 PFQALYs and DRd at 1.52 PFQALYs. With a WTP threshold of $150,000 per PFQALY, VRd was not cost-effective compared with Rd standard therapy, with an incremental cost-effectiveness ratio (ICER) of $530,256 per PFQALY. DRd was not cost-effective compared with VRd (ICER = $1,396,318 per PFQALY), nor as compared with Rd standard therapy (ICER = $1060,832). One-way sensitivity analysis showed that our model was sensitive to cost of DRd, VRd, and Rd drugs. Probabilistic sensitivity analysis showed that only at a WTP threshold of $550,000 was VRd cost-effective for 40% of iterations. There were no reasonable WTP thresholds, up to $800,00, where DRd became more cost-effective than VRd.
+
+CONCLUSIONS:
+
+This study is the first analysis to directly compare the cost-effectiveness of 3 acceptable chemotherapy treatment regimens for patients with multiple myeloma ineligible for autologous stem cell transplant. Neither DRd nor VRd triple therapy were found to be cost-effective vs Rd. Further cost-effectiveness analyses that include overall survival data for daratumumab and bortezomib triple therapies are needed to demonstrate an ICER in QALYs.
+==== Body
+pmc What is already known about this subject
+
+Daratumumab added to lenalidomide and dexamethasone significantly improves progression-free survival for newly diagnosed multiple myeloma patients compared with lenalidomide and dexamethasone alone.
+
+The National Comprehensive Cancer Network recommends triple therapy regimens of bortezomib added to lenalidomide and dexamethasone, triple therapy of daratumumab, lenalidomide, and dexamethasone, or dual therapy lenalidomide and dexamethasone as preferred treatment options for the treatment of newly diagnosed multiple myeloma patients ineligible for autologous stem cell transplantation.
+
+From previous cost-effectiveness studies, the triple therapy regimen of daratumumab added to lenalidomide and dexamethasone has not been shown to be cost-effective compared with lenalidomide and dexamethasone in patients with relapsed, refractory multiple myeloma.
+
+What this study adds
+
+This study evaluates the cost-effectiveness of both daratumumab and bortezomib in combination with lenalidomide and dexamethasone for the treatment of newly diagnosed multiple myeloma patients ineligible for autologous stem cell transplantation.
+
+This study compares the 3 treatment regimens recommended by the National Comprehensive Cancer Network guidelines for newly diagnosed multiple myeloma patients ineligible for stem cell transplantation.
+
+The direct comparison between multiple regimens provides essential guidance to payers, clinicians, and formulary decision makers, regarding which regimen is most cost-effective.
+
+Multiple myeloma is a hematologic malignancy that is characterized by the proliferation of malignant plasma cells in the bone marrow and accounts for approximately 13% of hematologic cancers.1,2 In the United States, there were an estimated 32,270 new cases of multiple myeloma and 12,830 deaths related to multiple myeloma in 2020.3 Within the past decade, there has been a 35% decline in mortality, but treatment costs for multiple myeloma have increased by 26%.4 Despite recent advances in the treatment of multiple myeloma, it remains largely incurable with a 5-year survival rate of only 52.2%.5
+
+Based on the National Comprehensive Cancer Network Multiple Myeloma Guidelines, preferred treatment for multiple myeloma patients is high-dose chemotherapy followed by 2 autologous stem cell transplantations (ASCT).6,7 However, more than half of patients with newly diagnosed multiple myeloma (NDMM) are deemed ineligible for ASCT because of common comorbidities and complications of multiple myeloma.8 Standard-risk patients with NDMM ineligible for ASCT are generally recommended for treatment initiation of a 2-drug regimen of lenalidomide and dexamethasone (Rd).9
+
+However, a triple therapy with bortezomib (V) or daratumumab (D), combined with the lenalidomide and dexamethasone (Rd) backbone (VRd and DRd, respectively) can now also be considered for patients with NDMM, who are ineligible for ASCT, with category 1 recommendations (grade based on high-level evidence).9 In 2010, VRd was shown to be an effective treatment for patients with NDMM compared with Rd.10 In 2018, 2 separate landmark clinical trials demonstrated the effectiveness of daratumumab therapy added to bortezomib, melphalan, and prednisone and daratumumab therapy added to Rd in patients with NDMM.11-13
+
+Currently, there is no head-to-head clinical trial and no cost-effectiveness analysis across all the newly recommended treatment regimens for patients with NDMM.14 Our study goal was to determine if DRd is cost-effective compared with guideline-recommended treatments, VRd and Rd in patients with NDMM ineligible for ASCT, by combining data from the 2 available trials that compare VRd with Rd and DRd with Rd in this sample.9,14 This study will help to understand the comparative value of these treatments in order to provide information that can guide treatment decisions by health care providers for this patient population.
+
+Methods
+
+STUDY POPULATION
+
+Our Markov model analysis is based on two phase 3 randomized, open-label trials, MAIA and SWOG S0777, both of which contained a study population of patients with NDMM ineligible for ASCT. The MAIA and SWOG S0777 trials had no planned differences in the inclusion and exclusion criteria across studies. However, there are slight differences in demographics between the final study populations in the MAIA (DRd vs Rd comparison) and SWOG S0777 (VRd vs Rd comparison) trials. The MAIA trial had more ISS stage II and fewer ISS stage III patients. The median age in the SWOG S077 and MAIA trials was 63 and 73 years, respectively, giving the average age of entry for patients into the Markov model of 68 years.13,15
+
+The study population in the MAIA trial was randomized to receive either DRd, the triple therapy arm, or Rd, the standard of care arm. Patients in the DRd arm received 28-day cycles of intravenous daratumumab 16 mg/kg once weekly for cycles 1 and 2, every 2 weeks for cycles 3 through 6, and every 4 weeks thereafter; lenalidomide 25 mg for 21 days; and dexamethasone 40 mg for 4 days, in addition to pre-infusion and post-infusion medications.13
+
+The study population in the SWOG S077 trial was randomized to receive either 21-day cycles of VRd, the triple therapy arm, or 28-day cycles of Rd, the standard of care arm. Patients in the VRd arm received intravenous bortezomib 1.3 mg/m2 4 times per cycle; oral lenalidomide 25 mg for 14 days; and oral dexamethasone 20 mg for 8 days, in addition to herpes simplex virus prophylaxis and oral aspirin 325 mg to reduce the risk of thromboembolic events.15
+
+Both studies had a common comparative arm receiving Rd therapy consisting of oral lenalidomide 25 mg for 21 days and oral dexamethasone 40 mg for 4 days per cycle (Supplementary Table 1, available in online article). However, patients in the Rd arm of the SWOG S077 additionally received 325 mg oral aspirin once a day.13
+
+MARKOV MODEL
+
+We developed a 2-state Markov model with 3 choice branches at the decision node—DRd, VRd, or Rd—using TreeAge Pro 2020 (TreeAge Software). The patient populations, treatment, and dosing regimens included in the model reflect the protocol used in the MAIA and SWOG S077 trials and from the products package inserts.16,17 A cycle of 28 days was used in the model, since Rd-based treatments are administered in 28-day cycles. The 21-day cycle of the VRd regimen was converted to a 28-day cycle by multiplying the values going into the model by 1.33 (28 days over 21 days) to account for the 7 fewer days in the cycle.
+
+All patients began in the progression-free (PF) health state and either stayed in PF or transitioned to the progressed or dead health state (Supplementary Figure 1, available in online article).4,15,18 A half-cycle correction was implemented to correct for overestimations. We determined the incremental cost-effectiveness ratio (ICER) of the 3 comparisons using the formula ICER = change in cost/change in effect. Our main outcome was PF quality-adjusted life-years (PFQALY) saved over the lifetime horizon. Our model took a health system perspective and costs were in US dollars.
+
+Cost-effectiveness was assessed at a willingness-to-pay (WTP) threshold of $150,000, using the World Health Organization’s recommendation to set the WTP threshold at 3 times the per capital annual income of a country (approximately $50,000 in the United States).19 Although this recommendation is for QALYs rather than PFQALYs, there is no recommendation yet for a WTP for PFQALYs. However, estimates of the probability of new cancer drug comparisons remaining significantly positive when moving from PF survival (PFS) outcomes to overall survival (OS) outcomes is over 60%. In addition, when moving from PFS to OS, we expected the survival difference among treatment and control drugs to narrow, whereas cost differences would remain similar. Given these 2 expectations, the WTP level should remain the same for PFS as OS, or even be more generous. Therefore, we used the standard WTP in this study.
+
+We conducted a 1-way sensitivity analysis on key costs and utilities to examine their effects on our model outcomes (Table 1). Probabilistic sensitivity analysis (PSA) using a Monte Carlo simulation with 10,000 iterations was conducted to obtain a cost-effectiveness acceptability curve. We also looked at PF life-years (PFLY) saved over the lifetime horizon (Supplementary Table 3, available in online article).
+
+TABLE 1 Model Input Parameters Including Base Case, Ranges for Sensitivity Analysis, and Distributions for Probabilistic Sensitivity Analysis
+
+Parameter	Base case	Rangea	Source	
+Low value	High value	
+Drug costs (γ distribution), $	
+  DRd triple therapy (cycle 1-6)	62,223	46,667	77,779	23-24, RED BOOK	
+  DRd triple therapy (cycle 7 or more)	22,352	16,764	27,941	
+  Rd doublet therapy	16,191	12,143	20,239	
+  VRd triple therapy	17,243	12,932	21,554	
+  VRd triple therapy HSV prophylaxis	612	459	765	
+Cost of managing AEs (γ distribution), $	
+  Anemia for DRd	1,061	796	1,326	16, 19, HCUP, UPMC Lab Fee Schedule, AMA RBRVS Data Manager	
+  Neutropenia for DRd	9,248	6,936	11,560	
+  Lymphopenia for DRd	1,388	1,041	1,735	
+  Nonhematologic AEs for DRd	3,066	2,299	3,832	
+  Anemia for Rd	1,605	1,204	2,006	
+  Neutropenia for Rd	5,206	3,905	6,508	
+  Lymphopenia for Rd	1,591	1,193	1,989	
+  Nonhematologic AEs for Rd	6,324	4,743	7,905	
+  Anemia for VRd	1,169	877	1,461	
+  Neutropenia for VRd	3,514	2,635	4,393	
+  Lymphopenia for VRd	2,114	1,586	2,643	
+  Nonhematologic for VRd	4,207	3,155	5,259	
+Cost of progression (γ distribution), $	30,481	22,861	38,101	12, 25	
+Cost of supportive care (γ distribution), $	2,199	1,650	2,749	12, 25	
+Utilities and AE disutilitiesb (β distribution)	
+  Baseline alive with no progression for DRd, Rd, or VRd	0.81	0.61	1.01	18, 26-31	
+  Progressed or dead for DRd, Rd, or VRd	0.701	0.53	0.88	
+  Disutility for DRd AEs	0.12	0.09	0.15	
+  Disutility for Rd AEs	0.07	0.05	0.09	
+  Disutility for VRd AEs	0.09	0.07	0.11	
+a ±25% of baseline was used to calculate low and high values for sensitivity analysis.
+
+b Calculated by multiplying the expected disutility for each AE by the proportion of participants who reported grade 3 or 4 AEs and then summing all of the respective AEs for each treatment group.
+
+AE = adverse event; AMA RBRVS = American Medical Association’s Resource-Based Relative Value Scale; DRd = daratumumab/lenalidomide/dexamethasone; HCUP = Healthcare Utilization and Cost Project; HSV = herpes simplex virus; Rd = lenalidomide/dexamethasone; UPMC = University of Pittsburgh Medical Center; VRd = bortezomib/lenalidomide/dexamethasone.
+
+TRANSITION PROBABILITIES
+
+To calculate transition probabilities, the PFS Kaplan-Meier (KM) curves for the treatment and active control arms in the MAIA and SWOG S0777 trials were extracted using a graphical digitizer (Engauge Digitizer, version 12; Mark Mitchell, Baurzhan Muftakhidinov and Tobias Winchen et al). The spreadsheet developed by Martin Hoyle and William Henley was used to estimate individual patient data from the KM curves.20 The estimated individual data points were then used to generate the scale and shape parameters for the most suitable model of 4 options: log-logistic, Weibull, lognormal, and logistic. The most suitable model for each PFS curve was chosen based on the lowest Bayesian information criterion and Akaike information criterion.
+
+The modeled PFS curves were adjusted to account for the different trial lengths and rates of censoring in the MAIA and SWOG S0777 trials, by averaging the scale and shape parameters of the common comparator (Figure 1). The adjusted Rd survival curve scale parameter (0.0197) was derived by averaging the original scale parameters from the MAIA and SWOG S077 trials. The adjusted Rd survival curve shape parameter (1.0465) was derived by averaging the original shape parameters from the MAIA and SWOG S0777 trials.13,15
+
+FIGURE 1 Adjusted and Unadjusted PFS Curves for DRd, VRd, and Rd
+
+The adjusted scale and shape parameters of the DRd and VRd survival curves were derived by multiplying the original parameters by a conversion ratio. The conversion ratio was the adjusted Rd parameter divided by the original Rd parameter from its respective trial (Supplementary Table 2, available in online article). These distributions were then used to extend the PFS curves to lifetime curves and used to calculate transition probabilities for each monthly cycle of the Markov model.
+
+COSTS AND UTILITIES
+
+All costs were updated to 2019 US dollars using the medical care component of the Consumer Price Index. Average wholesale price (AWP) drug costs in 2019 US dollars were taken from RED BOOK (IBM), with a 16% discount applied to reflect contract pricing and to be consistent with estimates for Medicare reimbursement.21 All costs and health outcomes were discounted 3% annually.22 Drug treatment protocols were taken from MAIA for the DRd regimen and from SWOG S0777 for the VRd regimen. Any weight-based costs were calculated based on the number of vials needed to dose a standard patient with a weight of 70 kg and body surface area of 1.7m2. Drug costs per cycle were calculated as the sum of drug therapies, pre-infusion medications, and necessary prophylaxis medications (Table 1).
+
+We used the adverse event (AE) data from the MAIA and SWOG S0777 trials, which reported grade 3 or 4 AEs.13,15 The AEs for the Rd arm were derived from AEs in the Rd groups that occurred with a frequency greater than 10% in the MAIA and SWOG S077 trials. The length of treatment for AEs was derived mainly from previous cost-effectiveness studies with similar side effects and treatment guidelines (Table 1). The main AEs were lymphopenia and sensory neuropathy for VRd and neutropenia for Rd. Utilization and costs for grade 3 or 4 AE management included the use of drugs to treat side effects, laboratory tests to diagnose and monitor the AEs, hospitalization, outpatient visits, and physician fees (Table 1) and were based on treatment guidelines, literature, and expert opinion.
+
+Current Procedural Terminology codes for laboratory tests and outpatient visits were used to identify costs to Medicare from the 2019 University of Pittsburgh Medical Center Lab Fee Schedule and American Medical Association’s Resource-Based Relative Value Scale Data Manager and discounted 3% annually.23 Hospitalization visits for all grade 3 or 4 AEs were determined to be high severity. The mean cost of hospitalization and mean length of stay were determined using the Healthcare Utilization and Cost Project (HCUP) and International Classification of Diseases, Tenth Revision, Clinical Modification codes for each AE.24 The cost was then adjusted for inflation using the Consumer Price Index.25 Hospitalizations were followed by outpatient visits at every 2 weeks.
+
+In addition, we included a one-time progression cost that included supportive care for all the patients that moved to the progression or dead branch. Best supportive care included tests for diagnosis of progression, palliative pain treatment using radiation therapy, and bisphosphonate therapy (Table 1).9,26
+
+Health state utilities based on the EuroQol-5 Dimensions Questionnaire were derived from publicly available literature. The baseline PF utility and all patients within the PF state in the model were assumed to have a utility of 0.81 based on a previous cost-effectiveness analysis (CEA) of therapies for patients with NDMM ineligible for ASCT.14 Disease progression utility of 0.701 was derived from averaged progression utilities of past CEA literature for each treatment.14,27 These utilities ranged from 0.27 to 0.73 and were averaged across studies for the base-case estimates (Table 1). These utilities were varied in the PSA.
+
+Disutilities for each treatment arm were found by multiplying the disutility of an AE by the proportion of patients reporting the AE and summing them based on the respective AE for each treatment arm. The health state utility for the 3 treatment arms were subtracted by the disutility of the AE experienced by the patients receiving those treatments. Each disutility was weighted based on the percentage of patients who had each AE in the MAIA and SWOG S0777 trials. The percentage of each AE in the Rd group was derived from averaging the AE prevalence in both trials (Table 1).14,27-32
+
+Results
+
+COST-EFFECTIVENESS ANALYSIS
+
+In the base-case analysis, the total treatment costs of Rd standard therapy had the lowest overall cost at $329,867, followed by the VRd triple therapy arm at $385,434 and DRd triple therapy with the highest overall total cost at $626,900 (Table 2). The cost of DRd triple therapy was nearly 90% higher than Rd standard therapy and almost 62% more expensive than VRd triple therapy. However, Rd had the least (1.24) PFQALYs, and DRd had the most (1.52) PFQALYs. VRd had 1.35 PFQALYs, which was more than Rd but less than DRd.
+
+TABLE 2 Summary of Cost-Effectiveness of Lenalidomide and Dexamethasone Double Therapy; Bortezomib, Lenalidomide, and Dexamethasone Triple Therapy; and Daratumumab, Lenalidomide, and Dexamethasone Triple Therapy
+
+	Total cost ($)	Incremental cost ($)	Effectiveness (PFQALY)	Incremental effectiveness	ICERa ($/PFQALY)	
+Rd standard therapy	329,867	–	1.24	–	–	
+VRd triple therapy	385,434	55,567	1.35	0.10	530,256b	
+DRd triple therapy	626,900	241,466	1.52	0.17	1,396,318b	
+DRd triple therapy	626,900	297,033	1.52	0.28	1,060,832c	
+a ICERs cannot be replicated based on disaggregated results due to rounding.
+
+b Compared with the next lowest cost alternative.
+
+c Compared with the lowest cost alternative.
+
+DRd = daratumumab/lenalidomide/dexamethasone; ICER = incremental cost-effectiveness ratio; PFQALY = progression-free quality-adjusted life-year; Rd = lenalidomide/dexamethasone; VRd = bortezomib/lenalidomide/dexamethasone.
+
+Our model demonstrated that Rd standard therapy is the most cost-effective treatment choice. However, DRd triple therapy and VRd triple therapy were more effective than Rd therapy in reducing progression or death for patients with NDMM ineligible for ASCT. VRd triple therapy was not cost-effective compared with Rd therapy, with an ICER of $530,256 per PFQALY. In addition, DRd triple therapy was not cost-effective when compared with VRd triple therapy, with an ICER of $1,396,318, nor was it cost-effective compared with Rd standard therapy (ICER = $1,060,832 per PFQALYs). These were not cost-effective choices over Rd, when compared against a WTP threshold of $150,000 per PFQALY.
+
+SENSITIVITY ANALYSIS
+
+One-way sensitivity and threshold analysis were conducted on all costs, utilities, and disutilities included in our model, in order to determine which parameters were the most sensitive in our base-case model (Figure 2). When comparing DRd to VRd treatment choices, the overall drug cost per cycle for DRd, the overall drug cost per cycle of VRd, and the utility of PFS were the most sensitive variables, and they accounted for more than 83% of the variability of the cost-effectiveness model. Reducing the cost per cycle of DRd, increasing the cost per cycle of VRd, and increasing the utility of PFS decreased the ICER of DRd. We found that the cost of DRd triple therapy was the most sensitive in the model, accounting for 41% of the variability of the cost-effectiveness result, followed by the cost of VRd triple therapy, which accounted for 29%.
+
+FIGURE 2 Tornado Diagrams for 1-Way Sensitivity Analysis
+
+When the overall cost per cycle of daratumumab triple therapy was decreased by 20% from the base-case cost of $22,352 to $17,882, there was approximately a 55% decrease in its ICER, from $1,396,318 per PFQALY to $767,619 per PFQALY, but this is still not cost-effective at a $150,000 WTP threshold. In order for DRd to be cost-effective at a WTP threshold of $150,000, the overall cost per cycle of daratumumab triple therapy would need to be discounted to $11,851, a 53% total discount from the base-case cost of $22,352 compared with Rd alone. In contrast, VRd triple therapy would be cost-effective compared with Rd alone, at a WTP of $150,000 when the overall cost of therapy is reduced to $15,373, an 11% discount from base-case cost.
+
+We also performed a PSA (Figure 3). The acceptability curve from this analysis confirmed our findings that neither VRd nor DRd triple therapies were cost-effective options compared with Rd. At a WTP of $150,000, only Rd was cost-effective for the majority, 65% of all iterations. Although, as WTP thresholds increased during the sensitivity analysis, the probability that both DRd and VRd triple therapy became more cost-effective relative to Rd increased, VRd was not cost-effective compared with Rd until a WTP threshold of $550,000 was reached. At this increased threshold, VRd became more cost-effective than Rd for 40% of iterations. DRd was not cost-effective compared with Rd until a WTP threshold of $750,000 was reached. At this threshold, DRd became more cost-effective than Rd for 25% of iterations, while VRd, was still more cost-effective than DRd. There were no reasonable WTP thresholds, up to $800,00, where DRd became more cost-effective than VRd.
+
+FIGURE 3 Cost-Effective Acceptability Curve
+
+In our PFLY analysis, the total treatment costs for the 3 groups remained the same (Supplementary Table 3, available in online article). The PFLY saved for DRd triple therapy was longest (2.03 years), whereas for Rd standard therapy PFLY saved was shortest (1.62 years). The overall ICERs for DRd and VRd decreased to $963,815 per PFLY and $360,673 per PFLY, respectively, but remained above the $150,000 WTP threshold.
+
+Discussion
+
+Our study is the first to date to compare 3 clinically relevant treatments for patients with NDMM ineligible for ASCT, showing that neither DRd nor VRd is cost-effective compared with standard therapy, Rd, and that VRd is more cost-effective than DRd. Rd is a viable treatment regimen based on National Comprehensive Cancer Network guidelines, and it is the therapy shown to be the most cost-effective choice.9 The high-cost of daratumumab and bortezomib had the most contribution to the lack of cost-effectiveness compared with our other treatment alternatives.33
+
+Our results were based on PFQALY rather than QALY, because of the availability of only PFS data for these drugs. Current WTP estimates are based on OS rather than PFS. Therefore, once OS data becomes available across all studied treatments, our model can be restructured to calculate QALYs and allow for comparison with WTP thresholds commonly cited across diseases.
+
+Currently, there is no accepted recommendation for WTP estimates for PF CEA outcomes. However, for nonsmall-cell lung cancer, 1 study showed that access granted to drugs with PFS benefit between 3 and 3.5 months were robustly beneficial across all model parameters, whereas access for drugs with any PFS benefit was usually not beneficial.34 Therefore, given that the average PFS length is 4.9 months and 14.9 months in nonsmall-cell lung cancer and multiple myeloma, respectively, we would expect the CEA relative comparison to remain largely unchanged due to the PFS benefit being 3.4 months and 1.3 months for DRd and VRd, respectively.35,36 Nevertheless, we recommend conducting an additional CEA when OS data becomes available.
+
+In our analysis, we found an ICER for DRd vs VRd of $1,396,318 per PFQALY and vs Rd of $530,256 per PFQALY. These results are similar to previous CEA findings that looked at DRd vs Rd in patients with relapsed/refractory multiple myeloma (RRMM). Patients who progressed to RRMM were similar to patients with NDMM ineligible for ASCT because the traditional regimen for both is Rd. In RRMM, the ICER was $1,369,062 per QALY for DRd vs Rd. In addition, DRd was found not to be cost-effective under any discount level.14 Another CEA looking at DRd vs Rd for second-line therapy in RRMM found an ICER of $187,728 per QALY.37 DRd as second-line therapy had a lower ICER due to spending less time in the PF health state, therefore, spending less time on treatment. However, even when DRd was prescribed as second-line therapy for RRMM, the ICER did not meet the $150,000 WTP threshold.
+
+In order to elucidate the effects of the high cost of drugs, 1-way sensitivity analyses are recommended to provide additional cost-effectiveness information. The cost of DRd triple therapy followed by the cost of VRd triple therapy had the most impact on the overall ICER when comparing DRd with VRd (Figure 2). This is understandable considering that the cost of 1 cycle of DRd and VRd after loading is $22,352.68 and $17,243.13, respectively (Table 1). In addition, the 3.5 mg vial size for bortezomib is higher than the average dose prescribed, leading to 36.9% of the dose being wasted for a person with a body surface area of 1.7m2.38 This amount of wastage was included in our model and was a factor leading to the high ICER for VRd.
+
+Our acceptability curve (Figure 3) was generated by varying drug costs, costs of AE management, costs of progression, costs of supportive care, utilities of health states, and AE disutilities. Previous acceptability curves for DRd vs Rd in RRMM showed that after the WTP increased to $1,500,000 per QALY, DRd had a greater than 50% probability of being cost-effective compared with the Rd regimen.14 This is similar to our findings that DRd and VRd were not cost-effective compared with Rd until a WTP threshold of $750,000 per PFQALY and $550,000 per PFQALY was reached (Figure 3). Both analyses confirm that the probability of DRd or VRd being cost-effective compared with Rd at a WTP threshold of $150,000 per PFQALY was very low.
+
+This CEA analysis can play a role in the decision-making process of clinicians when determining the most cost-effective choice for patients with NDMM ineligible for ASCT. However, formulary considerations and channels of distribution for each product also heavily influence treatment choices. Although the addition of daratumumab or bortezomib to Rd may be recommended by the National Comprehensive Cancer Network for “non-frail” patients with NDMM ineligible for ASCT, the results of our CEA do not support the addition of daratumumab or bortezomib.9 Therefore, clinicians need to strongly consider the benefits vs risks and costs of adding these agents for patients with NDMM ineligible for ASCT.
+
+LIMITATIONS
+
+This study has some limitations to consider. Given that our model was developed using the MAIA and SWOG S0777 trials, our results are generalizable to patients with similar characteristics as those enrolled in the trials. Also, only the costs for managing grade 3 and 4 AEs, defined as severe/life threatening adverse events that would require hospitalization as well as treatment intervention to resolve, were included.39 We chose to exclude the cost of grade 1 and 2 AEs because they were unlikely to contribute significantly to the model due to the low severity. Systematic reviews of immune checkpoint inhibitors, which are a main treatment for many oncologic diseases, show that grade 1-2 AEs are commonly excluded in CEAs.40
+
+Without data for the OS curves in the MAIA trial, we did not have the data to distinguish between progression and death health states, which limited our analysis. In addition, each patient incurred the same average progression cost when they entered the progressed or dead health state. The cost of progression included a hospitalization cost due to exacerbation of multiple myeloma symptoms. Considering the multiple therapy options available after progression, we assumed that the choice of treatment after progression was the same between groups.
+
+In addition, our WTP thresholds were based on QALYs, not PFQALYs, for which there are no estimates for comparisons. Therefore, this study should be followed up with an additional analysis once the OS data are available.
+
+Overall, the inclusion of daratumumab or bortezomib with Rd therapy increased the PFS of patients with NDMM ineligible for ASCT. However, the costs of adding these therapies proved to be not cost-effective compared with using Rd alone. Our analysis provides important considerations when choosing value-based treatments for this patient population.
+
+Conclusions
+
+Despite the limitations, our study was the first to compare daratumumab and bortezomib to current standard of care Rd. The cost-effectiveness for these 2 drugs is mainly inhibited by the high monthly costs of the treatments. Without significant discounts or rebates, Rd is the more cost-effective option for this patient population. However, because of the promising clinical efficacy of both treatments shown in the MAIA and SWOG S0777 trials, CEAs of these 2 therapies are necessary, since they may have broader clinical uses in other disease states.
+
+Because the AWP of daratumumab and bortezomib may change in the near future due to market competition and possible FDA approval of additional indications, our study could be used to help support the formulary decision making of a health plan when determining the health value of these triple therapies. Our study is the first to compare 3 viable treatment options for patients with NDMM who are ineligible for ASCT, and the added information our study offers can guide health care professionals in choice of treatment regimens.
+==== Refs
+REFERENCES
+
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+2. Zweegman S, Palumbo A, Bringhen S, Sonneveld P. Age and aging in blood disorders: multiple myeloma. Haematologica. 2014;99 (7 ):1133-37. doi:10.3324/haematol.2014.11029624986875
+3. American Cancer Society. Key statistics for multiple myeloma. 2020. Updated January 12, 2021. Accessed October 4, 2020. https://www.cancer.org/cancer/multiple-myeloma/about/key-statistics.html
+4. Maiese EM, Evans KA, Chu B-C, Irwin DE. Temporal trends in survival and healthcare costs in patients with multiple myeloma in the United States. Am Health Drug Benefits. 2018;11 (1 ):39-46.29692879
+5. National Cancer Institute, SEER. Cancer stat facts: myeloma. Accessed May 7, 2020. https://seer.cancer.gov/statfacts/html/mulmy.html
+6. Attal M, Harousseau J-L, Facon T, et al. Single versus double autologous stem-cell transplantation for multiple myeloma. N Engl J Med. 2003;349 (26 ):2495-502. doi:10.1056/NEJMoa03229014695409
+7. Child JA, Morgan GJ, Davies FE, et al. High-dose chemotherapy with hematopoietic stem-cell rescue for multiple myeloma. N Engl J Med. 2003;348 (19 ): 1875-83. doi:10.1056/NEJMoa02234012736280
+8. Atrash S, Bhutani M, Paul B, Voorhees PM, Usmani SZ. Management of newly diagnosed transplant ineligible multiple myeloma. Leuk Lymphoma. 2020;61 (11 ):2549-60. doi:10.1080/10428194. 2020.178655832623918
+9. National Comprehensive Cancer Network. NCCN clinical practice guidelines in oncology - multiple myeloma. Version 1.2022. Accessed August 23, 2020. https://www.nccn.org/guidelines/guidelines-detail?category=1&id=1445
+10. Richardson PG, Weller E, Lonial S, et al. Lenalidomide, bortezomib, and dexamethasone combination therapy in patients with newly diagnosed multiple myeloma. Blood. 2010;116 (5 ):679-86. doi:10.1182/blood-2010-02-26886220385792
+11. Orlowski RZ, Stinchcombe TE, Mitchell BS, et al. Phase I trial of the proteasome inhibitor ps-341 in patients with refractory hematologic malignancies. J Clin Oncol. 2002;20 (22 ):4420-27. doi:10.1200/JCO.2002.01.13312431963
+12. Mateos M-V, Dimopoulos MA, Cavo M, et al. Daratumumab plus bortezomib, melphalan, and prednisone for untreated myeloma. N Engl J Med. 2018;378 (6 ):518-28. doi: 10.1056/NEJMoa171467829231133
+13. Facon T, Kumar S, Plesner T, et al. Daratumumab plus lenalidomide and dexamethasone for untreated myeloma. N Engl J Med. 2019;380 (22 ):2104-15. doi:10.1056/NEJMoa181724931141632
+14. Zhang T-T, Wang S, Wan N, Zhang L, Zhang Z, Jiang J. Cost-effectiveness of daratumumab-based triplet therapies in patients with relapsed or refractory multiple myeloma. Clin Ther. 2018;40 (7 ):1122-39. doi:10.1016/j.clinthera.2018.05.01230006069
+15. Durie BGM, Hoering A, Abidi MH, et al. Bortezomib with lenalidomide and dexamethasone versus lenalidomide and dexamethasone alone in patients with newly diagnosed myeloma without intent for immediate autologous stem-cell transplant (SWOG S0777): a randomised, open-label, phase 3 trial. Lancet. 2017;389 (10068 ):519-27. doi:10.1016/S0140-6736(16)31594-X28017406
+16. Darzalex. Prescribing information. Janssen Biotech; 2021. Accessed July 30, 2021. http://www.janssenlabels.com/package-insert/product-monograph/prescribing-information/DARZALEX-pi.pdf
+17. Velcade. Prescribing information. Millennium Pharmaceuticals; 2014. Accessed December 7, 2020. https://www.accessdata.fda.gov/drugsatfda_docs/label/2014/021602s040lbl.pdf
+18. Pelligra CG, Parikh K, Guo S, et al. Cost-effectiveness of pomalidomide, carfilzomib, and daratumumab for the treatment of patients with heavily pretreated relapsed-refractory multiple myeloma in the United States. Clin Ther. 2017;39 (10 ):1986-2005.e5. doi: 10.1016/j.clinthera.2017.08.010.28967482
+19. Neumann PJ, Cohen JT, Weinstein MC. Updating cost-effectiveness—the curious resilience of the $50,000-per-QALY threshold. N Engl J Med. 2014;371 (9 ):796-97. doi:10.1056/NEJMp140515825162885
+20. Hoyle MW, Henley W. Improved curve fits to summary survival data: application to economic evaluation of health technologies. BMC Med Res Methodol. 2011;11 (1 ):139. doi:10.1186/1471-2288-11-13921985358
+21. Congressional Budget Office. Prescription drug pricing in the private sector. January 2007. Accessed October 13, 2021. https://www.cbo.gov/sites/default/files/110th-congress-2007-2008/reports/01-03-prescriptiondrug.pdf
+22. Sanders GD, Neumann PJ, Basu A, et al. Recommendations for conduct, methodological practices, and reporting of cost-effectiveness analyses: Second Panel on Cost-Effectiveness in Health and Medicine. JAMA. 2016;316 (10 ):1093-103. doi:10.1001/jama.2016.1219527623463
+23. UPMC. Billing terms and definitions. Accessed July 29, 2021. https://www.upmc.com/patients-visitors/paying-bill/glossary
+24. Healthcare Cost and Utilization Project. SID Overview. Agency for Healthcare Research and Quality. Accessed July 29, 2021. https://www.hcup-us.ahrq.gov/sidoverview.jsp
+25. U.S. Bureau of Labor Statistics. How to use the Consumer Price Index for escalation. Revised November 25, 2020. Accessed July 29, 2021. https://www.bls.gov/cpi/factsheets/escalation.htm
+26. Cömert M, Güneş AE, Şahin F, Saydam G. Quality of life and supportive care in multiple myeloma. Turk J Hematol. 2013;30 (3 ):234-46. doi:10.4274/Tjh.2012.0192
+27. Kouroukis T, White D, Kruse M, Lawrence D, Trambitas C, Cheung MC. Cost-utility of bortezomib in induction treatment prior to autologous stem-cell transplantation (ASCT) in previously untreated multiple myeloma patients in Canada. Blood. 2013;122 (21 ):1735-35. doi:10.1182/blood.V122.21.1735.1735
+28. Ossa DF, Briggs A, McIntosh E, Cowell W, Littlewood T, Sculpher M. Recombinant erythropoietin for chemotherapy-related anaemia: economic value and health-related quality-of-life assessment using direct utility elicitation and discrete choice experiment methods. PharmacoEconomics. 2007;25 (3 ):223-37. doi:10.2165/00019053-200725030-0000517335308
+29. Büyükkaramikli NC, de Groot S, Fayter D, et al. Pomalidomide with dexamethasone for treating relapsed and refractory multiple myeloma previously treated with lenalidomide and bortezomib: an evidence review group perspective of an NICE single technology appraisal. Pharmacoeconomics. 2018;36 (2 ):145-59. doi:10.1007/s40273-017-0581-629086363
+30. Doyle S, Lloyd A, Walker M. Health state utility scores in advanced nonsmall cell lung cancer. Lung Cancer Amst Neth. 2008;62 (3 ):374-80. doi:10.1016/j. lungcan.2008.03.019
+31. Franic DM, Pathak DS, Gafni A. Are health states “timeless”? A case study of an acute condition: postchemotherapy nausea and vomiting. J Eval Clin Pract. 2003;9 (1 ):69-82. doi:10.1046/j.1365-2753.2003.0 0381.x12558704
+32. Beusterien KM, Szabo SM, Kotapati S, et al. Societal preference values for advanced melanoma health states in the United Kingdom and Australia. Br J Cancer. 2009;101 (3 ):387-89. doi:10.1038/sj.bjc.660518719603025
+33. Institute for Clinical and Economic Review. Value assessment framework. Accessed January 23, 2021. https://icer.org/our-approach/methods-process/value-assessment-framework/
+34. Lakdawalla DN, Chou JW, Linthicum MT, MacEwan JP, Zhang J, Goldman DP. Evaluating expected costs and benefits of granting access to new treatments on the basis of progressionfree survival in non-small-cell lung cancer. JAMA Oncol. 2015;1 (2 ):196-202. doi:10.1001/jamaoncol.2015.020326181023
+35. Hayashi H, Okamoto I, Morita S, Taguri M, Nakagawa K. Postprogression survival for first-line chemotherapy of patients with advanced non-small-cell lung cancer. Ann Oncol Off J Eur Soc Med Oncol. 2012;23 (6 ):1537-41. doi:10.1093/annonc/mdr487
+36. Verelst SGR, Blommestein HM, De Groot S, et al. Long-term outcomes in patients with multiple myeloma: a retrospective analysis of the Dutch Population-based HAematological Registry for Observational Studies (PHAROS). HemaSphere. 2018;2 (4 ):e45. doi:10.1097/HS9.000000000000004531723779
+37. Carlson JJ, Guzauskas GF, Chapman RH, et al. Cost-effectiveness of drugs to treat relapsed/refractory multiple myeloma in the United States. J Manag Care Spec Pharm. 2018;24 (1 ):29-38. doi:10.18553/jmcp.2018.24.1.2929290170
+38. Clark L, Castro AP, Fortes AF, et al. Ideal vial size for bortezomib: real-world data on waste and cost reduction in treatment of multiple myeloma in Brazil. Value Health. 2011;14 (5 ):S82-S84. doi:10.1016/j.jval.2011.05.01321839906
+39. Russell JS, Colevas AD. Adverse event monitoring in oncology clinical trials. Clin Investig. 2013;3 (12 ):1157-65. doi:10.4155/cli.13.111
+40. Verma V, Sprave T, Haque W, et al. A systematic review of the cost and cost-effectiveness studies of immune checkpoint inhibitors. J Immunother Cancer. 2018;6 (1 ):128. doi:10.1186/s40425-018-0442-730470252
+

PMC10391134.txt

@@ -0,0 +1,376 @@
+
+==== Front
+J Manag Care Spec Pharm
+J Manag Care Spec Pharm
+jmcsp
+Journal of Managed Care & Specialty Pharmacy
+2376-0540
+2376-1032
+Academy of Managed Care Pharmacy
+
+32011965
+10.18553/jmcp.2020.26.2.186
+Research
+Medication Adherence, Health Care Utilization, and Costs Among Patients Initiating Oral Oncolytics for Multiple Myeloma or Chronic Lymphocytic Leukemia/Small Lymphocytic Lymphoma
+Dashputre Ankur A. MS 1 *
+Gatwood Katie S. PharmD 2
+Gatwood Justin PhD 3
+1 Institute for Health Outcomes and Policy, College of Graduate Health Sciences, University of Tennessee Health Science Center, Memphis
+2 Vanderbilt University Medical Center, Nashville, Tennessee
+3 College of Pharmacy, University of Tennessee Health Science Center, Nashville.
+* AUTHOR CORRESPONDENCE: Ankur A. Dashputre, MS, Institute for Health Outcomes and Policy, College of Graduate Health Sciences, University of Tennessee Health Science Center, 956 Court Ave., Coleman B212, Memphis, TN 38163. Tel.: 901.448.5358; E-mail: adashput@uthsc.edu.
+This study received no outside funding. Dashputre was recently employed by Novartis; K. Gatwood has received speaker fees from Jazz Pharmaceuticals; and J. Gatwood has received research funding from Merck & Co. and GlaxoSmithKline, unrelated to this study..
+
+2 2020
+26 2 10.18553/jmcp.2020.26.2.186Copyright © 2020, Academy of Managed Care Pharmacy. All rights reserved.
+2020
+https://creativecommons.org/licenses/by/4.0/ This article is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use and redistribution provided that the original author and source are credited.
+BACKGROUND:
+
+Oral oncolytic therapies have improved survival in hematologic cancers, such as chronic lymphocytic leukemia/small lymphocytic lymphoma (CLL/SLL) and multiple myeloma (MM), which are now being managed like chronic conditions. However, compared with other cancers, there is a lack of studies assessing adherence, health care resource utilization, and costs in patients with these cancers.
+
+OBJECTIVE:
+
+To assess factors associated with adherence to oral oncolytic therapies, health care utilization, and costs in patients with CLL/SLL or MM.
+
+METHODS:
+
+A retrospective database study was conducted using the IBM MarketScan Commercial Claims and Medicare Supplement databases. Adults (aged ≥ 18 years) diagnosed with and prescribed an oral oncolytic for CLL/SLL (ibrutinib or idelalisib) or MM (thalidomide, lenalidomide, or pomalidomide) between 2013 and 2016 and with continuous eligibility 6 months before and 12 months after oral oncolytic initiation were identified. Adherence to oral oncolytics was measured using the proportion of days covered (PDC) metric. Multiple linear regression and multivariable logistic regression were used to identify adherence predictors. Count models assessed the relationship between adherence and resource utilization, and generalized linear models assessed the relationship between adherence and health care costs.
+
+RESULTS:
+
+A total of 701 and 2,385 patients were identified with CLL/SLL or MM, respectively. Mean PDC (SD) for CLL/SLL and MM patients was 75.3 (22.5) and 57.6 (26.5), respectively. For CLL/SLL patients, those aged ≥ 65 years (beta [B] = −4.00) had lower medication use. Among MM patients, multiple predictors of higher medication use emerged: aged ≥ 65 years (B = 3.44), higher than average outpatient resource utilization (B = 3.53), insurance plan other than preferred provider organization (PPO; B = −2.58), previous cancer therapy (B = −2.81), higher number of concurrent unique therapeutic classes (B = −0.35), and higher comorbidity burden (B = −2.55). Patients with CLL/SLL and enrolled in plans other than a PPO were more likely to be adherent (OR = 1.41, 95% CI = 1.01-1.98), whereas patients who were aged ≥ 65 years, were residents of the southern United States, and had visited the emergency department in the baseline period were less likely to be adherent. For MM patients, those aged ≥ 65 years (OR = 1.68, 95% CI = 1.38-2.04) and with higher than average outpatient services utilization (OR = 1.24, 95% CI = 1.01-1.52) were more likely to be adherent, whereas those enrolled in plans other than a PPO, previously treated with cancer therapy, and with higher comorbidity burden were less likely to be adherent. In both cohorts, adherent patients had significantly lower odds of health care utilization and incurred lower medical costs, but higher prescription costs, following oncolytic initiation; however, total costs were not significantly lower in those adherent.
+
+CONCLUSIONS:
+
+Factors were identified that influenced adherence at the patient, treatment, and health system levels. These factors can be used to identify patients requiring interventions for improving medication-taking behavior and associated health care burden.
+==== Body
+pmc What is already known about this subject
+
+Treatment paradigms have changed in chronic lymphocytic leukemia/small lymphocytic lymphoma (CLL/SLL) and multiple myeloma (MM) following approval of novel oral oncolytics.
+
+Adherence to oral oncolytics is critical to successful cancer outcomes, and poor adherence to oral oncolytics has been consistently identified as a driver of increased health care burden and costs and poor survival.
+
+Research is limited on adherence to oral oncolytics for MM and CLL/SLL and its effect on health care utilization and costs.
+
+What this study adds
+
+This study identified factors associated with medication adherence and the effect of medication adherence on health care utilization and costs in CLL/SLL or MM, an area that has been understudied in these cancers.
+
+Patient, treatment, and health system factors, such as older age, increased medication and comorbidity burden, previous cancer therapy, health insurance type, and higher outpatient visits, were identified as factors influencing adherence to oral therapies for CLL/SLL or MM.
+
+Adherent patients had a significantly lower risk of health care utilization and incurred lower medical but higher prescription costs.
+
+Multiple myeloma (MM) and chronic lymphocytic leukemia/small lymphocytic lymphoma (CLL/SLL) are hematologic cancers that affect the plasma cells and lymphocytes, respectively.1,2 Both cancers are commonly diagnosed between ages 65 and 74 years, slightly predominant in males, with MM being more common in blacks, and CLL/SLL being more common in whites.3 As of 2016, the current incidence of CLL/SLL and MM are 4.9 and 6.9 per 100,000 cases with a 5-year survival rate of 84.2% and 50.7%, respectively.3
+
+The introduction of novel targeted oral therapies such as lenalidomide, pomalidomide, panobinostat, and ixazomib for MM and ibrutinib and idelalisib for CLL/SLL has changed the treatment paradigm for these malignancies. These oral targeted agents have contributed significantly to increased survival in addition to improving the ease of administration.4-7 The improved survival benefit offered by oral therapies has changed the treatment strategies for these malignancies, which are now more commonly managed as chronic conditions. Consequently, the chronic management strategy and mode of drug administration have shifted the burden of treatment and adherence to the patient. Similar to chronic conditions, nonadherence to oral oncolytics is associated with cancer progression and worse survival outcomes.8 Additionally, nonadherence to oral therapies also leads to increased health care utilization and costs.8,9 Consequently, adherence to oral oncolytics and identification of key drivers (e.g., patient, health care, environmental factors) of medication use are critical to the success of cancer management and devising adherence-promoting strategies to achieve desirable patient outcomes. For example, numerous studies in chronic myeloid leukemia (CML), another type of hematologic cancer mainly treated by oral therapies (tyrosine kinase inhibitors), have identified age, concurrent medications, comorbidity burden, cost sharing, and insurance subsidy as some of the drivers of adherence, with adherence associated with lower health care utilization, lower costs, and better survival.10-18 Similarly, it is critical to study and identify drivers of adherence and associated outcomes in patients with MM or CLL/SLL initiating oral oncolytics.
+
+Although some studies have assessed medication adherence, health care utilization, and costs in MM, there is a dearth of real-world studies assessing factors associated with adherence to oral therapies, health care utilization, and costs in MM and CLL/SLL. The studies on MM therapies focused on treatment patterns and adherence,19-21 health care utilization and costs,22 and association of cost-sharing and adherence23; however, these studies did not assess factors associated with adherence and the association of adherence with health care utilization and costs.
+
+Specifically, in MM, adherence to oral therapies can be burdensome to the patient due to the complex treatment regimens and associated high therapy costs, side effects, and variable dosing patterns for oncolytics within a treatment regimen, making identifying factors influencing adherence even more critical.24-26 Considering the current gaps in understanding medication use in MM and CLL/SLL, this study sought to determine factors associated with adherence to oral therapies, and the association of adherence with health care utilization and costs for patients with MM or CLL/SLL initiating oral oncolytics.
+
+Methods
+
+Data Source
+
+This study used data from the January 1, 2013-December 31, 2016 IBM MarketScan Commercial Claims and Medicare Supplement databases. These databases provide a nationally representative convenience sample of individuals (and their dependents) covered by employer-sponsored or Medicare supplemental health insurance, which include patient demographics, enrollment information, and adjudicated claims for health care experiences (outpatient medical, inpatient medical, and outpatient prescription drugs) across the continuum of care. The databases are deidentified and fully compliant with the Health Insurance Portability and Accountability Act of 1996. This study was determined to be exempt by the University of Tennessee Health Science Center Institutional Review Board.
+
+Patient Selection
+
+Patients were included in the analysis if they met the following criteria for study years 2013 through 2016: (a) at least 1 diagnosis claim using the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM before October 2015) and ICD-10-CM (from October 2015) for MM (ICD-9-CM: 203.0x; ICD-10-CM: C90.0x) or CLL/SLL (ICD-9-CM: 204.1x, 200.8x; ICD-10-CM: C91.1x, C83.0x); (b) at least 2 prescription claims for immunomodulating agents (thalidomide, lenalidomide, or pomalidomide) for MM or any kinase inhibitor (ibrutinib or idelalisib) for CLL/SLL; (c) continuous eligibility 6 months pre-index (baseline) through 12 months post-index (follow-up); and (d) at least aged 18 years at index date, which was defined as the date of first prescription for selected oncolytic agents (Appendix A, available in online article). Additionally, as an exploratory supplementary analysis, patients prescribed panobinostat (a histone deacetylase inhibitor) or ixazomib (a proteasome inhibitor) for MM were included. However, for these agents, a shorter follow-up period of 6 months was chosen as they were approved in 2015.
+
+Patient Characteristics
+
+Patient demographics extracted at index included age, sex, region of residence (Northeast, North Central, South, West), urbanicity (metropolitan statistical area [MSA] or non-MSA), and health plan type and primary payer. Comorbidity burden was measured using the National Cancer Institute Charlson Comorbidity Index (NCI CCI), calculated based on contributing comorbidities in the baseline period, excluding cancer.27 Index prescription characteristics included fill location and patient cost sharing, calculated as the sum of reported coinsurance and copay amounts. Other characteristics measured during the baseline period included counts of outpatient medical, inpatient medical, emergency department visits, the number of concurrent unique therapeutic classes, and receipt of cancer therapy.
+
+Adherence
+
+The proportion of days covered (PDC) metric measured adherence to oral oncolytics and was calculated as a percentage (0%-100%) of the ratio of the sum of days supplied to the number of days of follow-up (365 days).28 Additionally, we also assessed adherence over 90 days, 180 days, and 270 days. Patients with a PDC ≥ 80% were considered adherent. As an established, acceptable adherence threshold does not exist for hematologic cancers, PDC ≥ 85% and PDC ≥ 90% as adherent were used as sensitivity analysis. The adherence threshold (medication possession ratio [MPR] or PDC) of ≥ 85% has been previously used in claims-based studies assessing adherence to therapies for chronic myeloid leukemia.29-32 A fixed follow-up of 180 days was used to assess adherence to panobinostat and ixazomib.
+
+Health Care Utilization and Costs
+
+All-cause health care utilization was evaluated for outpatient, inpatient, and emergency department visits in the follow-up period. Costs associated with use were reported as medical costs (sum of costs for each visit type), and those associated with outpatient pharmacy utilization were reported as pharmacy costs. All costs were inflated to 2019 dollars using the medical care component of the Bureau of Labor Statistics Consumer Price Index.33
+
+Statistical Analysis
+
+Descriptive statistics were used for patient characteristics with means (standard deviation [SD]) reported for continuous variables and counts (plus proportions) reported for categorical variables. Multiple linear regression and multivariable logistic regression were used to identify factors associated with adherence as a continuous measure and binary measure, respectively. Logistic regression models used both the base case PDC ≥ 80% threshold and the sensitivity thresholds (PDC ≥ 85% and PDC ≥ 90%). Negative binomial regression assessed association of adherence with outpatient visits, whereas inpatient and emergency department visits were collapsed together, and zero-inflated Poisson regression was used for these resource utilization categories. Multivariable generalized linear models with a gamma distribution and log link assessed the association of adherence to oncolytic use with total medical and pharmacy costs, separately and as total costs (total medical cost + prescription cost). All multivariable models were adjusted for baseline characteristics such as age; sex; region of residence; urbanicity; health plan type; NCI CCI; index prescription fill location; index copay; baseline outpatient, inpatient, and emergency department visits; number of concurrent unique therapeutic classes; and prior receipt of cancer therapy. Index prescription fill location was excluded in the models for CLL/SLL due to significantly low numbers in the mail order category. A 2-sided P value of < 0.05 was considered statistically significant, and all analyses were conducted using SAS Enterprise Guide 6.1 (SAS Institute, Cary, NC).
+
+Results
+
+A total of 2,385 and 701 patients with MM or CLL/SLL and at least 2 oral oncolytic fills were identified, respectively. The CLL/SLL cohort was older (67.1 [SD 11.5] years) than the MM cohort (63.5 [SD 11.1] years). Both cohorts were > 50% male (CLL/SLL [65%], MM [55.9%]); had comparatively more residents in the southern United States and urban areas; and primarily insured by a preferred provider organization (PPO; Table 1). Mean PDC (SD) for CLL/SLL and MM cohorts over the 365 days follow-up period were 75.3 (22.5) and 57.6 (26.5), respectively. Mean PDC (SD) over 90 days, 180 days, and 270 days for CLL/SLL was 87.9 (16.7), 81.8 (20.9), and 78.2 (23.5), respectively, and for MM was 83.3 (18.3), 69.2 (24.0), and 60.9 (26.2), respectively. Using a PDC of 80% as the adherence threshold, 62.7% (CLL/SLL) and 28.9% (MM) of patients were adherent to their oral oncolytic. Using the sensitivity adherence thresholds, percentage of adherent patients dropped to 56.1% (CLL/SLL) and 23.8% (MM) for 85% threshold and to 46.5% (CLL/SLL) and 17.5% (MM) for the 90% threshold. For the exploratory analysis, mean PDC (SD) for panobinostat (n = 41) and ixazomib (n = 168) over 6 months following initiation was 52.6 (22.3) and 71.3 (23.5), respectively.
+
+TABLE 1 Characteristics of Patients Initiating Oral Oncolytic
+
+Characteristic	CLL/SLLa (n = 701)	MMb (n = 2,385)	
+Age at index, years, mean (SD)	67.1 (11.5)	63.5 (11.1)	
+Age group at index, years, n (%)	
+  18-64	348 (49.6)	1,412 (59.2)	
+  ≥ 65	353 (50.4)	973 (40.8)	
+Sex, n (%)	
+  Male	456 (65.0)	1,333 (55.9)	
+  Female	245 (35.0)	1,052 (44.1)	
+Region, n (%)c	
+  Northeast	188 (26.8)	584 (24.5)	
+  North Central	158 (22.5)	538 (22.6)	
+  South	260 (37.1)	977 (40.9)	
+  West	95 (13.5)	260 (10.9)	
+Metropolitan statistical area, n (%)	600 (85.6)	2,036 (85.3)	
+Benefit plan, n (%)c	
+  Comprehensive	133 (18.9)	385 (16.1)	
+  EPO/HMO	83 (11.8)	265 (11.1)	
+  POS/POS with capitation	46 (6.6)	173 (7.2)	
+  PPO	362 (51.6)	1,321 (55.4)	
+  CDHP/HDHP	66 (9.4)	214 (8.9)	
+Cancer therapy in baseline period, n (%)d	317 (45.2)	1,584 (66.4)	
+Unique therapeutic classes in baseline period, mean (SD)	7.4 (4.8)	8.5 (4.6)	
+Index prescription place, n (%)c	
+  Retail	628 (89.6)	1,737 (72.8)	
+  Mail order	5 (0.7)	561 (23.5)	
+Index copay, n (%)	
+  $0	162 (23.1)	523 (21.9)	
+  $1-$50	261 (37.2)	1,056 (44.3)	
+  $51-$100	127 (18.1)	432 (18.1)	
+  > $100	151 (21.5)	374 (15.7)	
+Baseline outpatient visits, mean (SD)	17.8 (13.1)	22.3 (13.1)	
+Baseline inpatient visits, mean (SD)	0.3 (0.8)	0.4 (0.7)	
+Baseline emergency room visits, mean (SD)	0.3 (0.8)	0.3 (0.8)	
+NCI Charlson Comorbidity Index, mean (SD)	0.4 (0.8)	0.7 (1.1)	
+Primary payer, n (%)	
+  Commercial	346 (49.4)	1,401 (58.7)	
+  Medicare	355 (50.6)	984 (41.3)	
+aIbrutinib or idelalisib.
+
+bThalidomide, lenalidomide, or pomalidomide.
+
+cIndicates missingness (n) for region (MM [26]); benefit plan (CLL/SLL [11], MM [27]); and index prescription place (CLL/SLL [68]; MM [87]).
+
+dIndicates whether patients received any recommended therapy for MM or CLL/SLL before initiating oral oncolytic.
+
+CDHP = consumer-driven health plan; CLL = chronic lymphocytic leukemia; EPO = exclusive provider organization; HDHP = high deductible health plan; HMO = health maintenance organization; MM = multiple myeloma; NCI = National Cancer Institute; POS = point of service; PPO = preferred provider organization; SD = standard deviation; SLL = small lymphocytic lymphoma.
+
+The multiple linear regression (adherence as continuous outcome) results revealed that being aged 65 years and older (beta [B] = −4.00, 95% confidence interval [CI] = −7.95, −0.05) was negatively associated with adherence among CLL/SLL patients. Among MM patients, being aged 65 years and older (B = 3.44, 95% CI = 1.13, 5.76) and having higher than average (> 22 visits) outpatient utilization (B = 3.53, 95% CI = 1.12-5.94) was positively associated with adherence, whereas being on a benefit plan other than a PPO (B = −2.58, 95% CI = −4.86, −0.31), previous cancer therapy (B = −2.81, 95% CI = −5.28, −0.35), a higher number of concurrent unique therapeutic classes (B = −0.35, 95% CI = −0.63, −0.08), and higher NCI CCI (B = −2.55, 95% CI = −3.61, −1.49) were negatively associated with adherence (Table 2).
+
+TABLE 2 Factors Associated with Adherence (Continuous Outcome) to Oral Oncolytics
+
+Characteristic	Reference	Parameter Estimate (95% CI)a	
+CLL/SLLb (n = 686)	MMc (n = 2,242)	
+Age group at index, years (range)	
+≥ 65	18-64	−4.00 (−7.95-−0.05)d	3.44 (1.13-5.76)d	
+Sex	
+Male	Female	3.06 (−0.96-7.08)	−0.93 (−3.13-1.27)	
+Region	
+North Central	Northeast	1.60 (−4.32-7.52)	−0.02 (−3.34-3.30)	
+South	−5.21 (−10.4--0.07)	0.77 (−2.18-3.73)	
+West	−3.1 (−9.77-3.62)	2.80 (−1.26-6.86)	
+Metropolitan statistical area	
+Yes	No	−3.85 (−9.34-1.63)	−0.46 (−3.65-2.72)	
+Benefit plan	
+Othere	PPO	2.98 (−1.03-6.99)	−2.58 (−4.86-−0.31)d	
+Cancer therapy in baseline periodf	
+Yes	No	0.51 (−3.65-4.67)	−2.81 (-5.28-−0.35)d	
+Unique therapeutic classes in baseline period		−0.45 (−0.96-0.05)	-0.35 (−0.63-−0.08)d	
+Index prescription place, retail	Mail order	N/A	0.05 (−2.52-2.62)	
+Index copay	
+$1-$50	$0	0.99 (−4.26-6.24)	−1.86 (−4.80-1.09)	
+$51-$100	2.71 (−3.47-8.89)	−0.45 (−4.03-3.11)	
+> $100	2.63 (−3.41-8.67)	−2.46 (−6.23-1.30)	
+Baseline outpatient visits > mean baseline visitsg	≤ mean baseline visits	0.32 (−4.24-4.89)	3.53 (1.12-5.94)d	
+Baseline inpatient visits	
+Yes	No	0.96 (−4.22-6.14)	1.80 (−0.74-4.35)	
+Baseline emergency room visits	
+Yes	No	−5.08 (−10.1-0.01)	−0.29 (−2.97-2.38)	
+NCI Charlson Comorbidity Index		−1.27 (−3.62-1.07)	−2.55 (−3.61-−1.49)d	
+aParameter estimate and 95% CI estimated by multiple linear regression assessing factors associated with adherence to oral oncolytics for patients with CLL/SLL or MM.
+
+bIbrutinib or idelalisib.
+
+cThalidomide, lenalidomide, or pomalidomide.
+
+dP < 0.05.
+
+eOther category includes comprehensive, consumer-directed health plan, exclusive provider organization, health maintenance organization, high-deductible health plan, point of service with/without capitation.
+
+fIndicates whether patients received any recommended therapy for MM or CLL/SLL before initiating oral oncolytic.
+
+gMean baseline outpatient visit: CLL/SLL (mean = 18 visits); MM (mean = 22 visits).
+
+CI = confidence interval; CLL = chronic lymphocytic leukemia; MM = multiple myeloma; NCI = National Cancer Institute; N/A = not applicable; PPO = preferred provider organization; SLL = small lymphocytic lymphoma.
+
+Using a PDC ≥ 80%, patients with CLL/SLL who were aged 65 years and older (vs. aged 18-64 years), resided in the southern United States (vs. the Northeast), and had emergency department visits in the baseline period were less likely to be adherent. Whereas, those enrolled in plans other than a PPO were more likely to be adherent (Figure 1). However, for the PDC ≥ 85% threshold, only residence in the southern United States was associated with lower adherence, whereas no factor was significantly associated with the PDC ≥ 90% threshold (Appendix B, available in online article). For MM patients, the logistic regression using a PDC ≥ 80% revealed that those aged 65 years and older (vs. aged 18-64 years) and with higher than average (> 22 visits) outpatient utilization were more likely to be adherent, whereas those enrolled in a plan other than a PPO, previously treated with cancer therapy, and with a higher NCI CCI were less likely to be adherent (Figure 2). The sensitivity models using the PDC ≥ 85% and ≥ 90% thresholds for MM showed similar patterns with those aged 65 years and older and having a higher than average (> 22 visits) outpatient utilization having higher odds of adherence, whereas those covered by insurance other than a PPO and with higher NCI CCI having lower odds of adherence. Additionally, for the PDC ≥ 85% threshold, a greater number of unique concurrent therapeutic classes in the baseline was associated with lower adherence odds (Appendix B).
+
+FIGURE 1 Factors Associated with Adherence to Oral Oncolytics for CLL/SLL
+
+FIGURE 2 Factors Associated with Adherence to Oral Oncolytics for Multiple Myeloma
+
+Adherence was associated with a reduced risk of all-cause health care utilization for both cohorts. Among CLL/SLL patients, those adherent (PDC ≥ 80%) to oncolytic therapy had lower outpatient utilization (incidence rate ratio [IRR] = 0.80, 95% CI = 0.75-0.86) and inpatient/emergency utilization (IRR = 0.73, 95% CI = 0.62-0.87). Similarly, in the MM cohort, being adherent was associated with lower risk of outpatient (IRR = 0.78, 95% CI = 0.74-0.81) and inpatient (IRR = 0.64, 95% CI = 0.57-0.72) visits. Accordingly, those adherent to oral oncolytics incurred significantly lower all-cause total medical costs. However, prescription costs were significantly higher among adherent patients (Table 3). Total costs (medical + prescription) were not significantly lower for CLL/SLL or MM patients. Adjustments to the threshold for adherence did not yield appreciable differences in results.
+
+TABLE 3 Association of Adherence to Oral Oncolytics with Health Care Utilization and Costs
+
+	CLL/SLLa	MMb	
+	Adherent	Nonadherent	Adherent	Nonadherent	
+Health care utilizationc	
+Outpatient used	0.80 (0.75-0.86)	Reference	0.78 (0.74-0.81)	Reference	
+Inpatient/emergency used	0.73 (0.62-0.87)	Reference	0.64 (0.57-0.72)	Reference	
+Health care costse	
+Total medical costs ($)d,f	55,141	127,198	81,762	191,541	
+Prescription costs ($)d	128,561	74,627	151,442	79,769	
+aIbrutinib or idelalisib.
+
+bThalidomide, lenalidomide, or pomalidomide.
+
+cAdjusted incidence rate ratios and 95% CI for the association between adherence (PDC ≥80%) and outpatient utilization (negative binomial regression) and inpatient/emergency utilization (zero-inflated Poisson regression), adjusted for age; sex; region of residence; urbanicity; health plan type; NCI CCI; index prescription fill location (MM model only); index copay; baseline outpatient, inpatient, and emergency department visits; number of concurrent unique therapeutic classes; and prior receipt of cancer therapy.
+
+dP < 0.05.
+
+eAdjusted costs estimated by multivariable generalized linear models with a gamma distribution and log link adjusted for age; sex; region of residence; urbanicity; health plan type; NCI CCI; index prescription fill location (MM model only); index copay; baseline outpatient, inpatient, and emergency department visits; number of concurrent unique therapeutic classes; and prior receipt of cancer therapy.
+
+fTotal medical costs included costs associated with outpatient, inpatient, and emergency health care utilization.
+
+CCI = Charlson Comorbidity Index; CI = confidence interval; CLL = chronic lymphocytic leukemia; MM = multiple myeloma; NCI = National Cancer Institute; PDC = proportion of days covered; SLL = small lymphocytic lymphoma.
+
+Discussion
+
+Using administrative claims from the IBM MarketScan Commercial Claims and Medicare Supplement databases, we examined factors associated with medication adherence among patients with CLL/SLL or MM initiating oral oncolytic therapy. Our results highlight that adherence to oral oncolytics is higher over the initial period after initiating oral oncolytic but reduces over time. Furthermore, we used these data to relate adhering to oral oncolytics with health care utilization and costs. For CLL/SLL patients, we found that being enrolled in a plan other than a PPO was associated with higher odds of adherence; conversely older age, residing in the southern United States, emergency department visits before oncolytic initiation were associated with a lower odds of adherence. Among patients with MM, older age and higher outpatient utilization were associated with an increased likelihood of being adherent, whereas being enrolled in a plan other than a PPO, previous cancer treatment, and higher comorbidity burden were associated with a lower likelihood of adherence. Being adherent was also associated with a reduced risk of all-cause health care utilization, lower overall medical costs, and higher prescription costs compared with those less adherent to oral oncolytic therapy.
+
+Compared with existing research on MM, we observed comparatively lower adherence rates to the immunomodulating agents thalidomide, lenalidomide, and pomalidomide. Using specialty pharmacy data, Lee et al. (2016) observed an MPR of 83%, compared with a PDC of 58% in the current analysis.23 The lower adherence in our study may be attributable to the difference in study population and measure used for adherence assessment (MPR double counts prescription fills) and may inflate the measured adherence level.34 Similarly, for our exploratory assessment of adherence to panobinostat, we observed lower adherence (PDC 52%) compared with adherence (MPR 90%) observed by Chari et al. (2018).19 In our study, adherence to oral therapies for MM ranged from 52% to 71%, whereas higher adherence was observed for the CLL/SLL patients. A systematic review by Greer et al. (2016) found that adherence to oral oncolytics ranged from 46% to 100% depending on the type of cancer, adherence measure, patient sample, medication type, and follow-up period.8 Although our estimates for adherence lie within the upper and lower estimates in the systematic review, it can be said that adherence to the selected agents for MM and CLL/SLL was suboptimal, especially for the MM agents.
+
+Ruddy et al. (2009) proposed a biophysical model that identified personal factors, treatment factors, and interaction with the health care system as potential influencers on medication adherence and persistence.35 In the context of this model, we identified that personal factors such as age, residence, and comorbidity burden; treatment factors, such as exposure to previous cancer therapies and burden of multiple medications; and system factors, such as a greater number of outpatient visits, contributed to adherence. Systematic reviews reported mixed results on the influence of age (older vs. younger), geographic area of residence, comorbidity burden (higher vs. lower), medication burden, exposure to previous cancer therapies, and health care utilization as facilitators/barriers for adherence to oral oncolytics.8,35,36 For example, there is no consensus on whether factors such as age or comorbidity burden positively or negatively affect adherence to oral oncolytics.8,36 Patients with CLL/SLL and MM are more likely to be diagnosed around age 70 years, thus it is critical for older patients with these cancers to be adherent to their oral therapies to get the survival benefit offered by these agents.3
+
+Although adherence in comparatively younger adults with these malignancies is as important as in older adults, older adults might be more likely to suffer from multimorbidity and thus require more specialized disease management.37 An increased comorbidity and therapy burden are known to negatively affect oral medication adherence.38-41 Specifically, for patients with MM, it is critical to be adherent to not only their oral therapies but also their entire regimen, given the complex regimens for MM.24 We observed a negative association between previous exposure to MM-specific cancer therapies and adherence to MM oral oncolytics. Management of MM typically includes multiple therapeutic agents (2 or more) such as targeted therapies, chemotherapy, or corticosteroids that can be administered by different routes, thus increasing regimen complexity, a treatment factor affecting medication adherence.24,42,43
+
+We also observed lower odds of adherence among patients with CLL/SLL residing in the southern United States. Among commercial and Medicare patients taking chronic medications, residence in the southern United States has been found to be associated with lower odds of medication adherence.44,45
+
+In our study, we found that adherent patients had reduced risk of all-cause health care utilization and lower all-cause medical costs and is in line with previous literature indicating reduced utilization and costs with increased adherence.8,9 Promoting better adherence to oral oncolytics may help reduce future health resource and related financial burden on the patient. Conversely, adherent patients experienced higher prescription costs. The costs of cancer therapies have been increasing over the years, and adherent patients might incur more costs due to medication-taking behavior.46 Additionally, it can be hypothesized that a positive medication-taking behavior for oral oncolytics may influence their medication- taking behaviors for concurrent medications, leading to higher incurred prescription costs.
+
+Limitations
+
+This study was limited in several ways. First, we used a convenience sample of patients with commercial or Medicare supplemental insurance; thus, the results might not be generalizable to a broader, more heterogeneous population, such as Medicare Advantage and fee-for-service populations, which might offer a better representation of patients with these cancers, as majority cases are diagnosed in and after age 60 years. Interestingly, our MM and CLL/SLL cohorts were younger than the median age of diagnosis for these cancers. However, previous literature has used administrative claims like MarketScan to study outcomes in these cancers.3,19,20,47-49
+
+Second, we used prescription fills to assess medication adherence, which do not corroborate whether patients actually took the oncolytic; however, such indirect measures have been extensively used in epidemiologic research as a reliable medication adherence metric.
+
+Third, we used administrative claims data, which have inherent limitations such as potential miscoding and missing data, lack of data on cancer severity/staging, year of diagnosis/length of cancer, number of prior lines of therapy, and reasons for medication nonadherence/discontinuation.
+
+Fourth, we studied patients who had continuous enrollment for 12 months following oncolytic initiation, thus, our results might not be applicable to those who might die within a year of initiation. However, with improved survival rates with oral oncolytics, it might be unlikely that patients will die within a year of oral therapy initiation.
+
+Fifth, we found mixed results on factors influencing adherence such as age and insurance type. However, previous studies on cancers have also found inconsistent results on various factors affecting adherence depending on the characteristics of the data source and patients being studied.
+
+Finally, we wanted to assess adherence to recently approved multiple myeloma therapies ixazomib and panobinostat over a longer period of time as a part of the main analysis but could not because they were only approved in 2015, and hence, there was a lack of sufficient follow-up. However, we assessed adherence to these therapies as a supplementary analysis over a shorter time period.
+
+Conclusions
+
+Adherence to oral oncolytics for MM and CLL/SLL was low. Adherence to oral oncolytics for hematologic cancers may be influenced by patient-, treatment-, and health system-related factors, such as increased outpatient utilization. Adherence to oral oncolytics may reduce health care utilization and costs and eventually reduce the economic burden faced by the patients, health care system and the payer. Health care professionals should consider the identified predictors when promoting adherence to oral oncolytics for hematologic cancers. These factors may help guide health system efforts such as pharmacist-/physician-based counseling, medication reminders from physician’s offices, and incentives for proper medication taking, lowering health care utilization and costs, and, eventually, improving patient outcomes in patients with MM or CLL/SLL using oral oncolytics.
+
+APPENDIX A Patient Selection Flow Criteria
+
+APPENDIX B Factors Associated with Adherence to Oral Oncolytics (Sensitivity Analyses)
+
+Characteristic	OR (95% CI)a	
+PDC >85%	PDC >90%	
+CLL/SLLb	MMc	CLL/SLLb	MMc	
+Age group at index, years	
+  55-64	Reference	Reference	Reference	Reference	
+  ≥ 65	0.81 (0.59-1.12)	1.62 (1.31-1.99)d	0.89 (0.65-1.23)	1.59 (1.26-2.00)d	
+Sex	
+  Female	Reference	Reference	Reference	Reference	
+  Male	0.99 (0.72-1.38)	0.88 (0.72-1.08)	0.99 (0.72-1.38)	0.98 (0.79-1.23)	
+Region	
+  Northeast	Reference	Reference	Reference	Reference	
+  North Central	0.91 (0.56-1.45)	1.03 (0.76-1.39)	1.11 (0.69-1.79)	1.02 (0.72-1.43)	
+  South	0.57 (0.38-0.88)d	1.06 (0.81-1.39)	0.68 (0.45-1.03)	1.14 (0.84-1.56)	
+  West	0.82 (0.47-1.42)	1.19 (0.83-1.71)	0.81 (0.47-1.39)	1.11 (0.74-1.67)	
+Metropolitan statistical area	
+  No	Reference	Reference	Reference	Reference	
+  Yes	0.99 (0.64-1.56)	1.10 (0.83-1.48)	0.97 (0.62-1.52)	0.86 (0.63-1.18)	
+Benefit plan	
+  PPO	Reference	Reference	Reference	Reference	
+  Othere	1.29 (0.93-1.79)	0.77 (0.63-0.95)d	1.28 (0.92-1.77)	0.79 (0.62-0.99)d	
+Cancer therapy in baseline periodf	
+  No	Reference	Reference	Reference	Reference	
+  Yes	0.78 (0.56-1.09)	0.83 (0.66-1.03)	0.83 (0.59-1.17)	0.91 (0.71-1.17)	
+  Unique therapeutic classes in baseline period	0.97 (0.93-1.01)	0.97 (0.95-0.99)d	0.97 (0.93-1.01)	0.97 (0.94-1.00)	
+Index prescription place	
+  Mail order	N/A	Reference	N/A	Reference	
+  Retail	0.99 (0.78-1.26)	0.92 (0.70-1.20)	
+Index copay	
+  $0	Reference	Reference	Reference	Reference	
+  $1-$50	1.04 (0.67-1.59)	1.02 (0.78-1.33)	1.07 (0.70-1.64)	1.03 (0.76-1.39)	
+  $51-$100	1.21 (0.73-2.00)	1.09 (0.79-1.51)	0.77 (0.76-2.02)	1.04 (0.72-1.49)	
+  > $100	1.18 (0.72-1.93)	0.90 (0.64-1.27)	1.24 (0.76-1.17)	0.91 (0.62-1.35)	
+Baseline outpatient visits	
+  ≤ mean baseline visits	Reference	Reference	Reference	Reference	
+  > mean baseline visitsg	0.93 (0.64-1.34)	1.26 (1.01-1.56)d	0.81 (0.56-1.17)	1.33 (1.04-1.70)d	
+Baseline inpatient visits	
+  No	Reference	Reference	Reference	Reference	
+  Yes	1.06 (0.69-1.62)	1.04 (0.82-1.31)	0.99 (0.65-1.51)	0.98 (0.75-1.27)	
+Baseline emergency room visits	
+  No	Reference	Reference	Reference	Reference	
+  Yes	0.67 (0.46-1.01)	1.01 (0.79-1.29)	0.74 (0.49-1.13)	0.92 (0.69-1.22)	
+NCI Charlson Comorbidity Index	0.91 (0.75-1.09)	0.85 (0.77-0.95)d	0.96 (0.79-1.17)	0.80 (0.70-0.91)d	
+aAdjusted OR and 95% CI estimated by multivariable logistic regression to assess factors associated with adherence (defined as PDC ≥ 85% and PDC ≥ 90%) to oral oncolytics for patients with CLL/SLL or MM.
+
+bIbrutinib or idelalisib.
+
+cThalidomide, lenalidomide, or pomalidomide.
+
+dP < 0.05.
+
+eOther category includes comprehensive, consumer-directed health plan, exclusive provider organization, health maintenance organization, high-deductible health plan, point of service with/without capitation.
+
+fIndicates whether patients received any recommended therapy for MM or CLL/SLL before initiating oral oncolytic.
+
+gMean baseline outpatient visit: CLL/SLL (mean = 18 visits); MM (mean = 22 visits).
+
+CI = confidence interval; CLL = chronic lymphocytic leukemia; MM = multiple myeloma; NCI = National Cancer Institute; N/A = not applicable; OR = odds ratio; PDC = proportion of days covered; PPO = preferred provider organization; SLL = small lymphocytic lymphoma.
+==== Refs
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+45. Tan E, Yang W, Pang B, Dai M, Loh FE, Hogan P. Geographic variation in antidiabetic agent adherence and glycemic control among patients with type 2 diabetes. J Manag Care Spec Pharm. 2015;21 (12 ):1195-202. Available at: https://www.jmcp.org/doi/10.18553/jmcp.2015.21.12.1195.26679968
+46. Shih YC, Smieliauskas F, Geynisman DM, Kelly RJ, Smith TJ. Trends in the cost and use of targeted cancer therapies for the privately insured non-elderly: 2001 to 2011. J Clin Oncol. 2015;33 (19 ):2190-26.25987701
+47. Hagiwara M, Panjabi S, Sharma A, Delea TE. Healthcare utilization and costs among relapsed or refractory multiple myeloma patients on carfilzomib or pomalidomide as monotherapy or in combination with dexamethasone. J Med Econ. 2019;22 (8 ):818-29.31046501
+48. Maiese EM, Evans KA, Chu BC, Irwin DE. Temporal trends in survival and healthcare costs in patients with multiple myeloma in the United States. Am Health Drug Benefits. 2018;11 (1 ):39-46.29692879
+49. Reyes C, Engel-Nitz NM, DaCosta Byfield S, et al. Cost of disease progression in patients with chronic lymphocytic leukemia, acute myeloid leukemia, and non-Hodgkin’s lymphoma. Oncologist. 2019;24 (9 ):1219-28.30808814
+

PMC10391150.txt

@@ -0,0 +1,102 @@
+
+==== Front
+J Manag Care Spec Pharm
+J Manag Care Spec Pharm
+jmcsp
+Journal of Managed Care & Specialty Pharmacy
+2376-0540
+2376-1032
+Academy of Managed Care Pharmacy
+
+34464211
+10.18553/jmcp.2021.27.9.1321
+Perspectives on Value
+The high cost burden of third- to fifth-line treatments for multiple myeloma: unsustainable and unaffordable
+Jensen Chelsee PharmD 1 *
+1 Instructor of Pharmacy, Pharmaceutical Formulary Manager, Department of Finance, Mayo Clinic, Rochester, MN.
+* AUTHOR CORRESPONDENCE: Chelsee Jensen, jensen.chelsee@mayo.edu
+No funding was provided for the writing of this commentary. In this commentary, the author refers to CivicaRx, for which Mayo Clinic is a founding member. As an employee of Mayo Clinic, the author does not have any direct financial relationship with or support from CivicaRx.
+
+9 2021
+27 9 10.18553/jmcp.2021.27.9.1321Copyright © 2021, Academy of Managed Care Pharmacy. All rights reserved.
+2021
+https://creativecommons.org/licenses/by/4.0/ This article is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use and redistribution provided that the original author and source are credited.
+==== Body
+pmcCOMMENTARY
+
+The cost of multiple myeloma (MM) therapy continues to increase because of the approval of high-cost medications by the US Food and Drug Administration (FDA). The Institute of Clinical and Economic Review (ICER) Midwest Comparative Effectiveness Public Advisory Council recently evaluated the cost-effectiveness of 3 new immunotherapy treatments for MM: belantamab mafo-dotin-blmf (Blenrep), chimeric antigen receptor T-cell (CAR-T) therapy ide-cabtagene vicleucel (ide-cel; Abecma), and ciltacabtagene autoleucel (cilta-cel).1 According to the ICER evaluation, CAR-T therapies prevail in the multirefractory setting based on superior overall response rates, but it recommended a 50% discount to the current list price of ide-cel to meet the $100,000 per quality-adjusted life-year gained threshold.1 Belantamab is within the cost-effectiveness threshold compared with other triple- or penta-refractory comparators, but confirmation of overall survival data and management of visual side effects must be better understood.1 To interpret the recommendations, it is important to consider them in the context of cumulative financial burden on MM patients and payers for drug therapy and toxicity management, once they reach later line therapies.
+
+Table 1 outlines Medicare and patient costs for common first- through fifth-line therapies for nontransplant eligible MM patients, with emphasis on the highest cost drug within the regimen and excludes manufacturer discounts or rebates.2 Using the Medicare Part D 2020 standard drug benefit design, patients prescribed oral therapies billed through Part D would enter the “doughnut hole” by the second month of therapy. Medicare Part D patients prescribed high-cost medications would reach their out-of-pocket (OOP) maximums, with Medicare covering the remainder.3-5
+
+TABLE 1 Medicare and Patient Costs for Common First- Through Fifth-Line Therapies for Nontransplant-Eligible Multiple Myeloma
+
+Patient type	Stage 2	Regimen a	Most expensive drugs in regimen	Annual cost to Medicare (M) 5,18,19,b	Estimated patient medical expenses c	Annual cost to Medicare (P) 18,19,b,d	Annual OOP pharmacy costs (standard Medicare Part D) 3	Annual cumulative cost to Medicare for regimen (M & P)	Annual cumulative patient OOP expenses for regimen (M & P)	
+Non transplant eligible: Traditional Medicare insurance	First line	VRd	Bortezomib (M) Lenalidomide (P)	$33,523	$6,705	$231,397	$9,719	$248,496	$16,424	
+First line	DRd	Daratumumab SC (M) Lenalidomide (p)	$183,645	$36,729	$231,397	$9,719	$368,594	$46,448	
+First line	KRd	Carfilzomib (M) Lenalidomide (P)	$34,667	$6,933	$231,397	$9,719	$249,412	$16,652	
+Second line	DVd20	Bortezomib (M) Daratumumab Sc (M)	$206,681	$41,336	N/A	N/A	$165,345	$41,336	
+Second line	SVd	Bortezomib (M) Selinexor (P)	$51,792	$10,358	$332,323	$9,719	$364,038	$20,077	
+Fifth line	N/A	Melphalan flufenamide (M)	$234,840	$46,968	N/A	N/A	$187,872	$46,968	
+Fifth line	N/A	Ide-cel (M)	$432,085	$86,417	N/A	N/A	$345,668	$86,417	
+Stage 2	Regimen a	Most expensive drugs in regimen	Cost to Medicare (based on mDOT) 8,9,18	Estimated medical expenses	Cost to Medicare (P) 18	OOP pharmacy costs (standard Medicare Part D)	Cumulative cost to Medicare for regimen (M & P)	Cumulative patient OOP expenses for regimen (M & P)	
+Fifth line8	N/A	Belantamab (M)	$54,658	$10,932	N/A	N/A	$43,726	$10,932	
+Fifth line9	Sd	Selinexor (P)	N/A	N/A	$110,744	$9,719	$110,744	$9,719	
+a Regimens containing daratumumab can use intravenous daratumumab (Darzalex) or subcutaneous daratumumab (Darzalex Faspro); Subcutaneous daratumumab costs were used in this table, since the WAC for subcutaneous therapy is only marginally more than the intravenous therapy and offers shorter infusion times, which is convenient for patients and offers efficiency for cancer infusion centers. 8
+
+b Usual recommended dosing without accounting for dose reductions and standard 80 kg, 2.00 m2 body surface area were used for the cost calculations. Medicare Part B reimburses based on ASP plus 6% or WAC plus 3% when ASP is not yet established.
+
+c Estimated patient medical expenses are 20% of annual cost to Medicare (M) column.
+
+d The mean point of sale price per 1-month fill of lenalidomide filled through Medicare Part D in 2018 was $21,412.18.5
+
+ASP = average sales price; AWP = average wholesale price; M = medical benefit administered by a health care professional billed to Medicare Part B; mDOT = median duration of treatment from clinical trials; N/A = not applicable; OOP = out of pocket; P = pharmacy benefit filled at an outpatient pharmacy and billed to Medicare Part D; SC = subcutaneous; WAC = wholesale acquisition cost.
+
+Patients with Medicare are not eligible for copay coupons; many are on a fixed income and most likely are not able to sustainably pay these costs when advancing through multiple lines of treatment. There is no OOP maximum for Medicare Part B, but recognizing these high costs, it is presumed that most patients will have a Medicare supplement insurance (Medigap), which is not accounted for in Table 1.6 Fortunately, there may be some relief for patients and payers when patent exclusivity expires and generic competition can occur for lenalidomide (Revlimid) in 2022, carfilzomib (Kyprolis) in 2027, daratumumab (Darzalex; intravenous) in 2029, and daratumumab and hyaluronidase-fihj (Darzalex Faspro; subcutaneous) in 2036.7
+
+ICER notes that belantamab has comparable or slightly superior overall survival with fifth-line comparators at 13.8 months vs 9.2 months and 5.6 months for triple-, quad-, and penta-refractory patients, respectively.1 However, belantamab’s ocular toxicity risk and resultant payer and patient costs for Risk Evaluation and Mitigation Strategy (REMS) program requirements cannot not be over-looked.1,8 Patients may prefer selinexor plus dexamethasone in the fifth-line setting because of oral therapy, less time traveling to an infusion center, no ocular side effects other than the risk of cataract associated with dexamethasone, and possibly lower OOP costs (Table 1).9 Payers must weigh overall response rates, progression-free survival, ancillary costs from administration and toxicity management, and adherence when considering formulary status for these 2 fifth-line agents.
+
+While ide-cel and cilta-cel have impressive response rates compared with usual care and belantamab at 63%, 75%, 31%, and 32%, respectively, and comparable median progression-free survival at 8.6 months and 12.4 months for ide-cel and cilta-cel, respectively, the cumulative financial burden already absorbed by the patient and payer from earlier lines of therapy is substantial.1 While cilta-cel is not yet approved by the FDA, launching at the ICER recommended threshold of $200,000 would improve access and pressure ide-cel’s manufacturer to lower its list price. Presumably most payers will only cover 1 infusion/dose of CAR-T for cost containment purposes. Yet payers should pursue value-based contracts for CAR-T therapies and ensure that the costs for cytokine release syndrome management (eg, tocilizumab and intensive care days) are negotiated within the value-based arrangement. Close monitoring of the uptake of CAR-T therapy by prescribers, patients, and payers and clinical trials of CAR-T in earlier lines of therapy will be informative.
+
+While not mentioned in the ICER review, melphalan flufenamide (Pepaxto) was approved by the FDA in February 2021 for relapsed/refractory MM after 4 or more lines of therapy (Table 1).10 Unfortunately, there are no trials comparing melphalan flufenamide with more inexpensive conventional alkylating agents such as cyclophosphamide and melphalan. I would challenge payers to encourage clinical trial participation for MM patients, particularly at low-income referral centers and for underserved populations, that compare outcomes, safety, and total cost of care for melphalan flufenamide vs melphalan or cyclophosphamide. Furthermore, outcomes, toxicity management, and total cost of care comparisons between melphalan flufenamide and belantamab in the fifth-line setting should be studied.
+
+As ICER recommends, all stakeholders need to increase efforts to enroll African American patients in clinical trials and moderate new treatment pricing to improve affordability.1 Patients who are African American are twice as likely to have MM compared with patients who are White. Enrollment of African American patients are low in recent trials for later line therapies, accounting for 17% of patients in the DREAMM-2 trial for belantamab, 16% of patients in the STORM trial for selinexor (Xpovio), 6% of patients enrolled in the ide-cel trials, and 6% in the HORIZON study for melphalan flufenamide.1,8-11 Recognizing that historical mistreatment of patients who are African American may be a barrier to clinical trial participation, providers need to educate patients about human subjects research protections.12
+
+Future policy should focus on manufacturer-sponsored postmarketing trials in minority groups that were underrepresented within preapproval trials. Adequate reimbursement to the center performing the clinical trial should be an area of focus for health care policy reform to promote clinical trial programs at centers that serve low-income and high-risk communities to close racial and socioeconomic gaps.
+
+With respect to ICER’s recommendations to Medicare, value-based payment structures, including outcomes-based measures and real-world evidence, must unequivocally be considered for future policy.1 The costs of treatments to manage toxicities should be factored into the value-based payment structures, since these costs are substantial and affect quality of life. For incurable diseases such as MM, the FDA and Centers for Medicare & Medicaid Services should be able to negotiate sale prices based on the incremental value provided by the drug.13 This concept should also be applied to medications granted accelerated approval by the FDA. If such negotiations are allowed, Medicare patients should not be excluded from using copay coupons. The clinical research community should consider studies funded by the National Institutes of Health (NIH) to evaluate treatment holidays, effect on outcomes, costs, and patient-reported quality of life.1 Drugs that have been funded in part by the NIH should be reevaluated and priced at a discount at minimum to government-funded programs and potentially commercial programs.
+
+As ICER also recommended, prescribing tools, algorithms, and the development of cost-effective care pathways can help guide prescribers and financial counselors when having treatment discussions with patients, in addition to increased availability and affordability of cytogenetic testing, which facilitates treatment selection.1 Payers should team with the National Comprehensive Cancer Network (NCCN) and other institutions to create care pathways for NCCN-endorsed category 1 treatment pathways, incentivizing patients to these pathways by lowering direct costs and having a set, fair market, reimbursement structure for institutions.
+
+Although Table 1 focuses primarily on Medicare, average wholesale price was used to demonstrate the highly inflated price tag that makes coinsurance and deductibles unaffordable, especially for uninsured and self-pay patients. Private insurers pay 141%-259% of Medicare rates for all hospital services; patients with employer-sponsored insurance were 44% more likely to report not taking a medication because of the cost and 192% more likely to have medical debt compared with Medicare patients.14,15
+
+ICER recommends that manufacturers price novel treatments that align with the patient-centered therapeutic value.1 Novel therapies should be priced low at product launch, and then the price can increase as real-world evidence of benefit is gained or, in the case of medications granted accelerated approval by the FDA, until stage 4 confirmatory trials are complete. Costs to manage toxicities must be factored in when pricing novel therapies, since they contribute to the cumulative treatment cost. Adding cost-effectiveness as a secondary endpoint for trials with later line therapies should become standard, especially as it relates to incurable diseases.13
+
+Future health care reform policies should address “ever-greening” and other patent-extending tactics by manufacturers that go beyond the 20 years intended by US patent law. Federally funded insurance programs should receive the product at a cost that is discounted in a similar trend to drugs that experience generic competition from multiple manufacturers.16,17 In this framework, it is pertinent that facilities administering these therapies are reimbursed sufficiently across the continuum of care.
+
+If the federal government would incentivize companies to produce products using the supplemental biologic license application (sBLA) and abbreviated new drug application (ANDA) pathways, the availability of cheaper drugs could increase, and pay-for-delay strategies would become less advantageous to follow-on companies.15 Encouraging nonprofit companies, such as CivicaRx, to develop these cheaper alternatives would help mitigate drug shortages, improve access to care, and lower costs to patients and payers.
+
+In conclusion, third- to fifth-line treatments for MM are unaffordable and unsustainable for patients and payers. Efforts need to be made to moderate list prices for new medications, create cost-effective care pathways, enroll at-risk populations in clinical trials, and increase market competition to improve US health care sustainability.
+==== Refs
+REFERENCES
+
+1. Lee SJ, McQueen RB, Beinfeld M, et al. Anti B-cell maturation antigen CAR T-cell and antibody drug conjugate therapy for heavily pre-treated relapsed and refractory multiple myeloma. Final evidence report. Institute for Clinical and Economic Review. May 11, 2021. Accessed May 12, 2021. https://icer.org/wp-content/uploads/2020/10/ICER_Multiple-Myeloma_Final-Report_Update_070921.pdf
+2. National Comprehensive Cancer Network. Multiple myeloma (version 7.2021). 2021. Accessed June 14, 2021. https://www.nccn.org/professionals/physician_gls/pdf/myeloma.pdf
+3. Desai R, Kraus A, Gurley K. USA. In: Pricing & Reimbursement 2020. Global Legal Group; 2020. Accessed May 19, 2021. https://www.globallegalinsights.com/practice-areas/pricing-and-reimbursement-laws-and-regulations/usa
+4. Wolters Kluwer Health. Revlimid, Darzalex Faspro, Kyprolis, Xpovio, Pepaxto, Abecma, Blenrep. Medi-Span Price Rx. Database. Updated May 19, 2021. Accessed May 19, 2021. 4. https://pricerx.medispan.com/Refresh/Login.aspx
+5. Dusetzina SB. Specialty drug pricing and out-of-pocket spending on orally administered anticancer drugs in Medicare Part D, 2010 to 2019. JAMA. 2019;321 (20 ):2025-27.31135837
+6. Healthline Media. Understanding Medicare out-of-pocket maximums. 2020. Accessed June 14, 2021. https://www.healthline.com/health/medicare/medi-care-out-of-pocket-maximum#takeaway
+7. IPD Analytics. Life-cycle insights. March 2021. Accessed May 19, 2021. https://www.ipdanalytics.com
+8. Lonial S, Lee H, Badros A, et al. Belantamab mafodotin fir relapsed or refractory multiple myeloma (DREAMM-2): a two-arm, randomised, open-label, phase 2 study. Lancet Oncol. 2020;21 (2 ):207-21. doi:10.1016/S1470-2045(19)30788-031859245
+9. Xpovio (selinexor). Package insert. Karyopharm Therapeutics Inc; 2021. Accessed August 17, 2021. https://www.karyopharm.com/wp-content/uploads/2019/07/NDA-212306-SN-0071-Prescribing-Information-01July2019.pdf
+10. Pepaxto (melphalan flufenamide). Package insert. Oncopeptides Inc; 2021. Accessed August 17, 2021. https://www.accessdata.fda.goy/drugsatfdadocs/label/2021/214383s000lbl.pdf
+11. Abecma (idecabtagene vicleucel). Package insert. Celgene Co; 2021. Accessed August 17, 2021. https://packageinserts.bms.com/pi/piabecma.pdf
+12. Washington, Harriet A. Medical Apartheid: The Dark History of Medical Experimentation on Black Americans from Colonial Times to the Present. Doubleday; 2006.
+13. Rajkumar SV, Harousseau JL. Next-generation multiple myeloma treatment: a pharmacoeconomic perspective. Blood. 2016;128 (4 ):2757-64.27742709
+14. Lopez E, Neuman T, Jacobson G, Levitt L. How much more than Medicare do private insurers pay? A review of the literature. Kaiser Family Foundation. April 2020. Accessed June 17, 2021. https://www.kff.org/medicare/issue-brief/how-much-more-than-medicare-do-private-insurers-pay-a-review-of-the-literature/
+15. Wray CM, Khare M, Keyhani S. Access to care, cost of care, and satisfaction with care among adults with private and public health insurance in the US. JAMA Netw. 2021;4 (6 ):e2110275.
+16. Rogers DL. Double patenting: follow-on pharmaceutical patents that suppress competition. Northwest J Technol Intellect Prop. 2017;14 (3 ):317-80. Accessed June 17. 2021. https://scholarlycommons.law.northwestern.edu/njtip/vol14/iss3/3
+17. I-MAK. Overpatented, overpriced: how excessive pharmaceutical patenting is extending monopolies and driving up drug prices. 2018. Accessed June 17, 2021.https://www.i-mak.org/wp-content/uploads/2018/08/I-MAK-Overpatented-Overpriced-Report.pdf
+18. Wolters Kluwer. Medi-Span PriceRx Pro Online. Database. Accessed May 19, 2021. https://www.wolterskluwer.com/en/solutions/medi-span/price-rx
+19. IPD Analytics. Codesource. Accessed June 14, 2021. https://www.ipdanalytics.com/coding-reimbursement
+20. Palumbo A, Chanan-Khan A, Weisel K, et al. Daratumumab, bortezomib, and dexamethasone for multiple myeloma. N Engl J Med. 2016;375 (8 ):754-66. doi:10.1056/NEJMoa160603827557302
+

PMC10397654.txt

@@ -0,0 +1,51 @@
+
+==== Front
+J Manag Care Spec Pharm
+J Manag Care Spec Pharm
+jmcsp
+Journal of Managed Care & Specialty Pharmacy
+2376-0540
+2376-1032
+Academy of Managed Care Pharmacy
+
+10.18553/jmcp.2018.24.7.711
+Letters
+Reframing the Value of Treatments for Relapsed/Refractory Multiple Myeloma
+Ailawadhi Sikander MD 1
+Panjabi Sumeet PhD 2
+Campioni Marco PhD 3
+Majer Istvan PhD 3
+Jakubowiak Andrzej MD, PhD 4
+1 Division of Hematology/Oncology, Department of Medicine, Mayo Clinic, Jacksonville, Florida
+2 Amgen, South San Francisco, California
+3 Amgen, Zug, Switzerland
+4 Myeloma Program, University of Chicago, Chicago, Illinois
+Ailawadhi reports research support from Pharmacyclics and consulting relationships with Takeda, Amgen, and Celgene. Jakubowiak reports consulting and advisory board relationships with AbbVie, Amgen, BMS, Celgene, Karyopharm, SkylineDX, and Takeda. Panjabi, Campioni, and Majer are employees of and stockholders in Amgen.
+
+7 2018
+24 7 10.18553/jmcp.2018.24.7.711Copyright © 2018, Academy of Managed Care Pharmacy. All rights reserved.
+2018
+https://creativecommons.org/licenses/by/4.0/ This article is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use and redistribution provided that the original author and source are credited.
+==== Body
+pmcIn the article “Cost-effectiveness of Drugs to Treat Relapsed/Refractory Multiple Myeloma in the United States,” published in the JMCP January 2018 issue, Carlson et al. seek to identify the most cost-effective therapy for relapsed/refractory multiple myeloma (R/RMM).1 Instead, for MM, a chronic, progressive, and heterogenous disease, the decision problem is to identify the regimen sequences that maximize benefit for patients longitudinally over their disease course. Optimal treatment varies by patient at each relapse, since stakeholders need to consider prognostic factors, previous therapies, and associated response (duration/depth); residual adverse events; and, of course, individual patient preferences. In this letter, we discuss limitations of the Carlson et al. analysis that significantly affect the results and conclusions. Inaccurate statements that warrant correction are also identified.
+
+A major limitation stems from the network meta-analysis (NMA), which yields estimates of relative efficacy that are not reliable and valid and lack face validity: lenalidomide (LEN) plus dexamethasone (DEX) is estimated to be less effective than bortezomib (BOR) + DEX, even though all listed trials (Appendix A) show that LEN + DEX achieved double the progression-free survival (PFS) than BOR + DEX1; panobinostat (PAN) + BOR + DEX is estimated to be more effective than LEN + DEX (PFS hazard ratio of 0.54), even though the appraisal committee for the National Institute of Clinical Excellence (NICE) concluded that there is no difference in effectiveness between PAN + BOR + DEX and LEN + DEX in third-line treatment.2
+
+Trials included in the NMA are not comparable due to differences in clinical trial designs, populations, and end-points. In CASTOR,3 BOR + DEX duration was capped at 6 months and in ENDEAVOR was used to treat-to-progression (TTP),4 and a majority of patients (> 50%) received BOR + DEX beyond 6 months in ENDEAVOR. In its appraisal, the NICE committee concluded that capping treatment duration instead of TTP would reduce BOR + DEX efficacy.5
+
+Carfilzomib (CFZ) + DEX, an NCCN preferred category 1 regimen for previously treated MM with proven PFS and overall survival (OS) superiority over BOR + DEX, was not even considered in the Carlson et al. analysis. The cost-effectiveness of CFZ + DEX versus BOR + DEX has been unequivocally established.6 For CFZ + LEN + DEX, Carlson et al. appear to overestimate treatment costs by possibly not capping CFZ dosing at 18 cycles per the ASPIRE trial and label, not considering discontinuation of treatments due to reasons other than progression, and by overestimating wastage. Accounting for these corrections would confirm that CFZ + LEN + DEX is cost-effective versus LEN + DEX.7
+
+The Jakubowiak et al. (2017) study,4 which concluded that CFZ + LEN + DEX is cost-effective versus LEN + DEX, was cited inaccurately in several instances. Carlson et al. misreported an odds ratio as a hazards ratio. In Jakubowiak et al., PFS curves for CFZ + LEN + DEX generated by the model aligned very well with the PFS curves observed in the ASPIRE trial (Figure 2),4 yet Carlson et al. stated that there is a mismatch between “modeled” and “ASPIRE trial-observed” results probably because they compare a “mean” estimate from the model to the “median” from the ASPIRE trial. Jakubowiak et al. report postprogression survival and costs to be greater for CFZ + LEN + DEX (Table 8),4 yet Carlson et al. inaccurately state the opposite.
+
+We strongly recommend that Carlson et al. undertake a reanalysis to address cited limitations and withdraw inaccurate statements. By publishing this letter, JMCP will serve patients well in its mission to present balanced information that contributes to improving the quality of care for patients.
+==== Refs
+REFERENCES
+
+1. Carlson JJ, Guzauskas GF, Chapman RH, et al . Cost-effectiveness of drugs to treat relapsed/refractory multiple myeloma in the United States. J Manag Care Spec Pharm. 2018;24 (1 ):29-38. Available at: https://www.jmcp.org/doi/10.18553/jmcp.2018.24.1.29.29290170
+2. National Institute for Health and Care Excellence. Panobinostat for treating multiple myeloma for a least 2 previous treatments. Technology Appraisal Guidance 380. January 27, 2016. Available at: www.nice.org.uk/guidance/ta380. Accessed June 6, 2018.
+3. Palumbo A, Chanan-Khan A, Weisel K, et al . Daratumumab, bortezomib, and dexamethasone for multiple myeloma. N Engl J Med. 2016;375 (8 ):754-66.27557302
+4. Dimopoulos MA, Goldschmidt H, Niesvizky R, et al . Carfilzomib or bortezomib in relapsed or refractory multiple myeloma (ENDEAVOR): an interim overall survival analysis of an open-label, randomised, phase 3 trial. Lancet Oncol. 2017;18 (10 ):1327-37.28843768
+5. National Institute for Health for Care Excellence. Carfilzomib for previously treated multiple myeloma. June 2017. Available at: https://www.nice.org.uk/guidance/ta457/documents/final-appraisal-determination-document. Accessed June 6, 2018.
+6. Jakubowiak AJ, Houisse I, Májer I, et al . Cost-effectiveness of carfilzomib plus dexamethasone compared with bortezomib plus dexamethasone for patients with relapsed or refractory multiple myeloma in the United States. Expert Rev Hematol. 2017;10 (12 ):1107-19.29027825
+7. Jakubowiak AJ, Campioni M, Benedict Á, et al . Cost-effectiveness of adding carfilzomib to lenalidomide and dexamethasone in relapsed multiple myeloma from a U.S. perspective. J Med Econ. 2016;19(11 ):1061-74.27471948
+

PMC10397794.txt

@@ -0,0 +1,254 @@
+
+==== Front
+J Manag Care Spec Pharm
+J Manag Care Spec Pharm
+jmcsp
+Journal of Managed Care & Specialty Pharmacy
+2376-0540
+2376-1032
+Academy of Managed Care Pharmacy
+
+29290169
+10.18553/jmcp.2018.24.1.39
+Research
+Evaluating Oncology Value-Based Frameworks in the U.S. Marketplace and Challenges in Real-World Application: A Multiple Myeloma Test Case
+Djatche Laurence M. PharmD 1 *
+Goble Joseph A. PharmD 2
+Chun Grace PharmD 3
+Varga Stefan PharmD 1
+1 Thomas Jefferson University, Philadelphia, Pennsylvania.
+2 University of Texas Austin and Baylor Scott & White Health, Temple, Texas.
+3 Rutgers University, New Brunswick, New Jersey.
+* AUTHOR CORRESPONDENCE: Laurence Djatche, PharmD, College of Population Health, Thomas Jefferson University, 901 Walnut St., Philadelphia, PA 19107. Tel.: 215.955.0107; E-mail address: Laurence.djatche@jefferson.edu.
+No outside funding supported this study. The authors have nothing to disclose.
+
+All authors contributed to study concept and design, as well data collection and interpretation. Djatche and Goble wrote and revised the manuscript, along with Chun and Varga.
+
+Portions of this work have previously been presented at the AMCP Managed Care and Specialty Pharmacy Annual Meeting 2017 in Denver, Colorado, March 27-30, 2017, and at the ISPOR 22nd Annual International Meeting in Boston, Massachusetts, May 20-24, 2017.
+
+1 2018
+24 1 10.18553/jmcp.2018.24.1.39Copyright © 2018, Academy of Managed Care Pharmacy. All rights reserved.
+2018
+https://creativecommons.org/licenses/by/4.0/ This article is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use and redistribution provided that the original author and source are credited.
+
+BACKGROUND:
+
+With the continuous rise in costs for oncology drugs, the American Society of Clinical Oncology (ASCO), the Institute for Clinical and Economic Review (ICER), the Memorial Sloan Kettering Cancer Center’s Drug Abacus (DrugAbacus), and the National Comprehensive Cancer Network (NCCN) have developed value-based frameworks (VBFs) to assist stakeholders in formulary and treatment decision-making processes. Since emerging VBFs have the potential to affect available treatment options for patients, it is important to understand the differences associated with these VBFs within various therapeutic areas.
+
+OBJECTIVES:
+
+To (a) compare VBFs across 3 therapeutic options for relapsed or refractory multiple myeloma (RRMM) and (b) identify challenges and limitations associated with real-world decision making using VBFs in the U.S. marketplace.
+
+METHODS:
+
+The values of regimens carfilzomib (CFZ), elotuzumab (ELO), and ixazomib (IX) were generated using the ASCO, NCCN, ICER, and DrugAbacus VBFs. These regimens, used for second- or third-line treatment of RRMM, shared a common comparator in clinical trials: lenalidomide + dexamethasone (LEN + DEX). ASCO’s 2016 VBF, which incorporated clinical benefit, toxicity, and bonus points, was used to generate a net health benefit score, along with the drug wholesale acquisition cost, for each regimen compared with LEN + DEX. Results of the 2016 NCCN Evidence Blocks for multiple myeloma and the ICER 2016 report of treatment options for RRMM were extracted to generate the value of CFZ, ELO, and IX. No output was generated from DrugAbacus because of the lack of regimens included in the test case. Shortcomings associated with running the test case in RRMM for each VBF were also identified.
+
+RESULTS:
+
+Among the 3 therapeutic agents, CFZ, in combination with LEN + DEX, was the most valued. ASCO and ICER VBFs suggested that CFZ + LEN + DEX may be the most valued, followed by ELO + LEN + DEX and IX + LEN + DEX. NCCN suggested that LEN + DEX may be the most valued followed by CFZ + LEN + DEX, IX + LEN + DEX, and ELO + LEN + DEX. A number of shortcomings were noted across each VBF, such as complexities of drug evidence evaluation with the ASCO VBF, the inability to adjust the ICER and NCCN VBFs to specific populations, and subjectivity associated with the NCCN VBF and DrugAbacus.
+
+CONCLUSIONS:
+
+Although the test case provided some consensus on treatment decisions, there is much nuance and limitations with the VBFs available for RRMM. Clearer objectivity and better adaptability to specific treatment decisions are warranted.
+==== Body
+pmc What is already known about this subject
+
+Value-based frameworks (VBFs) evaluate clinical and/or economic evidence to inform health care decision making across payer, physician, and patient perspectives.
+
+The American Society of Clinical Oncology, the National Comprehensive Cancer Network, Memorial Sloan Kettering Cancer Center’s Drug Abacus, and the Institute for Clinical and Economic Review have developed respective VBFs with the intent to justifiably evaluate therapies available for a variety of disease states.
+
+What this study adds
+
+This study evaluated all VBFs available in the U.S. marketplace using oncology therapeutic options and identified shortcomings of the process.
+
+A unique comparison of 3 oncology regimens that share a common comparator was provided, enabling an indirect valuation comparison of all frameworks.
+
+In order to increase VBF usability and effect in real-world decision making, recommendations are suggested for future VBF versions so that the entirety of the valuation process can be captured.
+
+Amidst the uncertainty over the impending fate of U.S. health care reform, health economists may find consensus on the following notion: the continuous rise in health care spending is unsustainable. Much emphasis on the resolution of this problem has been placed on curbing the amount spent on prescription medications, given that pharmaceutical drugs continue to be the fastest growing aspect of U.S. health care spending, which reached $325 billion in 2015.1 Within the health sector, 11.5% of U.S. total drug costs are derived from oncology treatments, which amounted to nearly $37.8 billion in 2015 and was largely driven by the increased use of new medications.2 Experts predict that annual costs for oncology care will continue to rise 7.5%-10.5% each year through 2020, accounting for over $140 billion.2
+
+Major increases in oncology drug spending are associated with increased drug prices and use of emerging targeted oncology therapies, especially immunotherapies.3 The factors that dictate oncology drug prices include cancer pharmaceutical research and development, manufacturing costs for complex compounds, and the economic principles surrounding oncology drug pricing.3 Cost sharing for patients has increased, as well. Annual patient out-of-pocket payments for intravenous and oral medications now soar to over $7,000 and $3,000, respectively.2 Strategies to control costs have been proposed, and some have already been implemented to mitigate cancer drug costs, such as the use of evidence-based clinical treatment pathways and tools to facilitate cost discussions with patients to establish value of treatments.3,4 These strategies highlight a key role for health technology assessment (HTA) in pharmaceutical valuation moving forward.
+
+The United States persistently lacks meaningful use of cost-effectiveness in health economic evaluations, which is largely because of a diverse health care landscape that is riddled with numerous gaps in care and the complexity of a multipayer system.5 A recent survey of private payers discovered that all respondents had used at least 1 external HTA organization to help influence coverage decision making with regards to personalized medicine in oncology.6 Nonetheless, the survey responders reported lack of availability, timeliness and redundancy of reviews, and inadequate incorporation of cost-effectiveness as main shortcomings of HTA assessments, highlighting cost-effectiveness as a primary nonclinical factor needed in HTA assessments.6
+
+Today, an emerging HTA model known as the value-based framework (VBF) evaluates evidence from clinical and economic data to inform health care decision making across payer, physician, and patient perspectives. The American Society of Clinical Oncology (ASCO), the National Comprehensive Cancer Network (NCCN), Memorial Sloan Kettering Cancer Center’s Drug Abacus (DrugAbacus), and the Institute for Clinical and Economic Review (ICER) have developed VBFs with the collective mission to justifiably valuate therapies available for a variety of disease states.7 With these VBFs, the use of weights to reflect user preference varies tremendously, barring the economic principle of trade-off, which ultimately convolutes the utility of these assessment models. Furthermore, these frameworks seem to continually advance towards multiple criteria decision analysis, which many health economists believe to be the next step in value assessment.5 Yet, all existing VBFs have received criticism and challenges because of the minimal influence from health economists in their creation.5
+
+Despite the proliferation of recent literature assessing the validity, reliability, and practicality of VBFs, few studies have critically evaluated all available models for oncology and their potential effect on real-world decision making.7-11 There is no study that has compared the value of cancer regimens for a specific disease state across all available U.S. oncology VBFs. Thus, the purpose of this study was to (a) describe and assess VBFs used for relapsed or refractory multiple myeloma (RRMM) regimens and (b) identify challenges and limitations associated with real-world decision making using VBFs in the U.S. marketplace.
+
+Methods
+
+Study Overview
+
+A literature review was performed to identify all U.S. VBFs used to assess the value of oncology drugs. Four VBFs were identified and included in this study: ASCO and ICER VBFs, NCCN Evidence Blocks, and DrugAbacus. To determine the RRMM treatment of greatest value, a test case analysis was performed for each VBF. Once the test case analysis was completed, results for each VBF were compared to evaluate whether value was equally measured across all frameworks. As the evaluation was performed, shortcomings of each VBF were reported.
+
+Test Case: Multiple Myeloma Drugs
+
+For our test case, we defined inclusion criteria for cancer drugs used within the same disease state. Four inclusion criteria were considered in the selection of oncology drugs for the test case: (1) oncology therapy approved between 2015 and 2016 by the U.S. Food and Drug Administration (FDA) for multiple myeloma; (2) oncology therapy with phase III clinical trial results available through databases such as PubMed and Scopus; (3) oncology therapy that used the same comparator in clinical trials; and (4) oncology therapy evaluated and included in the ICER VBF report and NCCN Evidence Blocks. Based on these criteria, RRMM was chosen for the test case analysis.
+
+Since 2015, the FDA has approved 6 drugs for RRMM: carfilzomib (CFZ), elotuzumab (ELO), ixazomib (IX), daratumumab (DARA), panobistat (PAN), and pomalidomide (POM). DARA was excluded from the analysis because it did not have a phase III clinical trial, and PAN and POM were excluded because they had different comparators in their clinical trials, making it inappropriate to compare using the ASCO VBF. So, for the test case, we used CFZ, ELO, and IX because they fulfilled the predefined inclusion criteria and used the same standard of care as the comparator, lenalidomide + dexamethasone (LEN + DEX).
+
+Oncology Value Frameworks and Usability in Test Case
+
+ASCO Value-Based Framework.
+
+The updated 2016 ASCO framework was used to generate the value of CFZ, ELO, and IX. This VBF allows users to generate a net health benefit (NHB) score for a regimen assessed against its comparator using the available prospective randomized controlled trial data. The NHB of the regimen is derived from the clinical benefit (calculated using hazard ratio [HR], overall survival, or progression-free survival); toxicity (calculated using frequency of side effects and grade); and bonus points (calculated by evaluating tail of the curve, palliation, health-related quality of life, and treatment-free interval).12,13 The NHB score serves as an indicator of the clinical effect of a regimen as compared with a control regimen.
+
+Each regimen was independently scored by 2 of the authors who followed the directions provided in the revised ASCO framework tool for advanced disease developed by Schnipper et al. (2016).13 After each author independently scored the RRMM treatment, the scoring results were compared by the 2 authors for similarities or discrepancies. Any discrepancies were further discussed among all 4 authors to establish a final NHB score and drug cost for that regimen. Results from the phase III randomized clinical trials ASPIRE, ELOQUENT, and TOURMALINE were used for CFZ, ELO, and IX to generate the regimen’s NHB score, respectively.14-17 The cost of each therapy was included using the drug acquisition cost or the patient cost, depending on patient health insurance status.13 The wholesale acquisition cost (WAC)—the list price from a manufacturer to a wholesaler—was used to generate the regimen cost. WAC prices were calculated using a standard weight-based dosing of 70 kg and height of 170 cm and were obtained from Medi-Span Price Rx and RED BOOK for pricing references.18
+
+NCCN Evidence Blocks.
+
+The 2016 multiple myeloma NCCN Evidence Blocks VBF was used to obtain the value of CFZ, ELO, and IX for second- or third-line therapy in RRMM compared with LEN + DEX.19 The NCCN Evidence Blocks has an assessment of different factors that are used to evaluate a regimen. These factors include 5 evidence blocks: efficacy, safety, quality, consistency of evidence supporting the recommended therapy, and affordability.20 The score ranges from 1 to 5, with 5 as the most favorable.20
+
+ICER Value-Based Framework.
+
+ICER publishes reports that include clinical and economic evaluations of therapies of certain disease states. The ICER evidence-based medicine matrix rates comparative clinical effectiveness with 3 levels of certainty: low certainty (I); moderate certainty (B+, C+, P/I, and I); and high certainty (A, B, C, and D).21 The cost-effectiveness analysis results are reported as cost per quality-adjusted life-years (QALYs), and the budget impact analysis results are reported in average budget impact cost per year. The ICER 2016 (May 5, 2016) report on treatment options for RRMM was used to extract results on the value of CFZ, ELO, and IX.22 This report incorporates comparative clinical effectiveness, incremental costs per outcome achieved, potential budgetary impact, and value-based price benchmarks. In the test case, results of comparative effectiveness analysis, cost-effectiveness analysis, and budget impact analysis were used to estimate the value of RRMM agents selected.
+
+DrugAbacus.
+
+The DrugAbacus price is calculated based on the measure of 1 domain and the weight defining the importance of that domain according to the user.23 This tool contains 8 domains: efficacy, tolerability, novelty, rarity, population burden, research and development, costs, unmet need, and prognosis.23 DrugAbacus determines a theoretical price—the Abacus price—for cancer drugs based on opinions of experts regarding possible domains of a drug’s value.23 The authors sent a questionnaire by e-mail to 6 pharmacists and physicians who specialized in oncology. The questionnaire was composed of the standard 8 questions included in DrugAbacus. Because of the lack of multiple myeloma drug options from DrugsAbacus, we were unable to use this tool for the test case.
+
+Results
+
+The results were obtained after generating the value of CFZ, ELO, and IX using the ASCO VBF and extracted from the 2016 RRMM ICER report and the 2016 NCCN Evidence Blocks.
+
+ASCO Value-Based Framework
+
+Scoring from the ASCO VBF suggested CFZ as the preferred option, with a cost slightly higher than ELO (Appendix, available in online article). CFZ, in combination with LEN + DEX, had an NHB of 28.8 obtained from the clinical benefit score, clinical toxicity score, and bonus points (Figure 1). The clinical benefit score of CFZ represented an HR of 0.79 for death, or 21% reduction in risk of death, when compared with the control LEN + DEX. The toxicity score was 29.5 versus 26.5 for LEN + DEX. Because of the statistically significant improvement in quality of life reported from patients taking CFZ in the ASPIRE clinical trial, 10 bonus points were awarded in NHB.
+
+FIGURE 1 ASCO Score Results for Clinical Benefit, Toxicity, NHB, and Cost
+
+IX, in combination with LEN + DEX, had an NHB of 23.0, resulting from the clinical benefit and toxicity score (Figure 1). The IX clinical benefit score represented an HR of 0.77 for death, or 23% reduction in risk of death, when compared with LEN + DEX. IX + LEN + DEX and LEX + DEX had a clinical toxicity score of 50.5, and no bonus points were awarded to the regimen.
+
+ELO, in combination with LEN + DEX, had an NHB of 23.7 resulting from the clinical benefit score and toxicity score (Figure 1). Because of lack of information on the HR for death or disease progression, the median progression-free survival was used to determine the clinical benefit score for ELO. The addition of ELO to LEN + DEX provided a 30.2% increase in median progression-free survival compared with LEN + DEX alone, which resulted in a clinical benefit score of 24.2 (Appendix). The toxicity score for ELO + LEN + DEX was 38.5 versus 37.5 for LEN + DEX. No bonus points were awarded to the regimen because the tail of the curve could not be reliably interpreted. The monthly WAC or WAC per cycle for LEN + DEX was $11,616. The addition of CFZ, ELO, and IX to LEN + DEX provided a monthly drug cost of $17,364, $16,032, and $20,607, respectively (Figure 1).
+
+NCCN Evidence Blocks
+
+The NCCN Evidence Blocks report suggested CFZ + LEN + DEX as a preferred regimen in terms of efficacy, but LEN + DEX had a preferred safety and cost profile. As the baseline comparator, LEN + DEX achieved a score of 4, 4, 4, 4, and 2 in the efficacy, safety, quality, consistency, and affordability domains, respectively (Figure 2).19 Efficacy was the only differentiating drug assessment factor, with scores of 5, 3, and 4 for CFZ, ELO, and IX regimens, respectively. Safety, quality, consistency, and affordability were the same across regimens with scores of 3, 4, 4, and 1, respectively (Figure 2).19 Based on the efficacy score from the NCCN Evidence Blocks, CFZ, in addition to LEN + DEX, was the preferred option among all 3 regimens.
+
+FIGURE 2 NCCN Evidence Block Multiple Myeloma Reporta,b
+
+ICER Value-Based Framework
+
+Results from the ICER VBF report suggested CFZ as the preferred agent from all 3 agents in terms of cost-effectiveness and budget impact. The ICER VBF report assigned a “B+” rating, or moderate certainty, for the comparative clinical effectiveness of CFZ, ELO, and IX, in combination with LEN + DEX.22 These 3 regimens provided a better NHB for second-line and third-line therapy in patients with RRMM compared with the standard therapy LEN + DEX. The comparative clinical effectiveness rating was based on the progression-free survival benefit observed with each regimen and the positive balance of benefits and harms of each regimen. In the ICER report, costs per QALYs for second-line therapy were $199,982, $427,607, and $433,794 for CFZ, ELO, and IX, in combination with LEN + DEX (Table 1A).22 Third-line treatment costs per QALY for CFZ, ELO, and IX, in combination with LEN + DEX were $238,560, $481,244, and $484,582, respectively (Table 1B).22 These results suggest that no regimen would be a good value in the long term. Thus, significant list price reductions for each regimen would be required to achieve acceptable long-term cost-effectiveness between $100,000 and $150,000 per QALY for second-line and third-line use (Table 1). None of the 3 regimens exceeded the ICER budget impact threshold of $904 million per year for a new drug. The average potential budget impact per year for second-line therapy was $226 million per year for CFZ + LEN + DEX, $395 million per year for ELO + LEN + DEX, and $330 million for IX + LEN + DEX (Figure 3).22 The budget impact was lower for third-line regimens because of smaller patient populations.
+
+TABLE 1 Long-term Cost-effectiveness Results and List Price Discounta
+
+Drug	ICER (Costs/QALY), $	Discount from List Price, %b	
+A. Second-Line Treatment	
+Carfilzomib	199,982	32-64	
+Elotuzumab	427,607	75-89	
+Ixazomib	433,794	80-94	
+B. Third-Line Treatment	
+Carfilzomib	238,560	48-77	
+Elotuzumab	481,244	80-93	
+Ixazomib	484,582	85-97	
+aData were extracted from the ICER VBF report.22
+
+bDiscount from wholesale acquisition cost to achieve acceptable long-term costeffectiveness between $100,000 to $150,000 per QALY for second-line and third-line use.
+
+ICER = incremental cost-effectiveness ratio; QALY = quality-adjusted life-year.
+
+FIGURE 3 Budget Impact Analysis Resultsa
+
+VBF Shortcomings
+
+Because of the structure of the ASCO VBF and DrugAbacus, the results, respectively, rely on understanding and interpretation of randomized controlled trial results and user weights that are used as input, which could cause subjectivity bias. However, the ICER VBF and NCCN Evidence Blocks are published reports and cannot be edited or adjusted for a specific situation or population. Overall, the clinical application of the 4 VBFs can be challenging, since they do not produce patient-specific outcomes. In addition, the ASCO evaluation is not practical for everyday practice, since input information is not always readily available and its collection can be time consuming. Likewise, the ICER report is extensive and not completely clear its guidance for clinical practice and physician prescribing. Based on ease of implementation, NCCN Evidence Blocks have the highest potential for uptake in clinical practice, yet they could be subject to bias, since the categories are based on the NCCN panel’s perspective.
+
+Discussion
+
+Novel value frameworks target different audiences—clinician, patient, or payer—which makes it challenging when trying to determine the true value of an oncology regimen. This study evaluated all oncology VBFs that can be used for RRMM in the U.S. health care system. The ASCO VBF, which targets clinicians, and the ICER report, which targets payers, suggested CFZ in combination with LEN + DEX as the preferred treatment regimen in the test case. The NCCN Evidence Blocks, which target clinicians and patients, suggested that LEN + DEX was the best regimen, followed by CFZ in combination with LEN + DEX. Because of the lack of availability of some of the cancer drugs in DrugAbacus, no output was generated from that VBF.
+
+Despite different targeted audiences, these VBFs recommended the same regimen among all 3 novel cancer drugs. Each audience has different preferences and attributes weights of value that differ from one audience to another, which can be problematic when determining if each VBF captures the true value for its audience.8,24 Our findings indicate that value may be measured the same across all frameworks regardless of the audience targeted. This finding may imply that attributable weights incorporated in each VBF (ASCO, ICER, or NCCN) converge in their assessment of value or that each VBF captures shared components of value across all stakeholders.
+
+Although some VBFs highlighted the same medication as the most valued therapy option, it is unclear whether this will be the case across different therapeutic areas. At the time of this study, we were limited to 2 cancers because published ICER reports were only available for multiple myeloma and advanced non-small cell lung cancer. This study focused on multiple myeloma, since it is the second most common blood cancer, with the majority of patients experiencing intermittent relapse and remission throughout the course of the disease.25 For patients with advanced RRMM disease, there is no standard of care despite several approved novel agents, 3 of which were included in the test case.26,27 This lack of a standard of care highlights the need for VBFs that can aid in treatment and formulary decision making.
+
+In the test case, we identified limitations specific to VBFs that can prevent their implementation into real-world practice. For instance, we identified complexities of drug evaluation as a limitation for ASCO, which led to discrepancies among the authors’ results. The ASCO framework quantifies values in terms of points awarded for clinical benefit, toxicity, and bonus points. These points can be problematic to calculate and to determine the right value for the regimen.8 Moreover, previous studies have concluded that there are mixed results on interrater reliability when using the ASCO VBF.9,11 Wilson et al. (2017) suggest a low inter-reliability because of the confusion among clinicians about the calculation of the toxicity score and uncertainty about bonus points scoring.9
+
+The lack of available data on bonus points, such as palliation, quality of life, and treatment-free survival, were also identified in the literature as a limitation associated with ASCO that affects the NHB score used to establish the value of a regimen.9 With the 2016 passage of the 21st Century Cures Act, a particular focus on patient experience data may encourage standard reporting of these endpoints in future studies.28
+
+A study that evaluated the validity of the ASCO, ICER and NCCN VBFs for advanced lung cancer drugs showed a high convergent validity between ASCO and NCCN and a low convergent validity between ICER and NCCN.11 However, the results of our test case demonstrated that ICER and ASCO outcomes were the same with regards to the most valued regimen (CFZ in combination with LEN + DEN), while NCCN results differed from ASCO and ICER. A unique inherent design component of the NCCN framework enabled the baseline comparator (LEN + DEX) to be considered the most valued treatment among our considered treatments. It is important to note that Bentley et al. (2017) used transparent descriptions of comparative clinical effectiveness11—the evidence-rating matrix provided by ICER in its analysis—while we used all of the components of the ICER RRMM report (comparative clinical effectiveness, cost-effectiveness analysis, and budget impact analysis) to obtain the value of the regimens.
+
+There was novelty in the test case approach. We intended to evaluate multiple therapies within a disease state that had a common comparator (i.e., LEN + DEX) using different available U.S. VBFs. In essence, our study provides a unique example of an indirect comparison of value within different VBFs. Meaningful comparisons across treatment regimens are challenging when dealing with multiple comparators in clinical trial evidence in certain VBFs, such as ASCO. For example, we considered including POM in our study, the pivotal phase III trial for which was compared with high-dose DEX. Any comparison of POM to other agents in our test case would have been biased in the ASCO framework, since each NHB score is derived from the differential advantage of the novel agent over the comparator. This example highlights a major challenge for future versions of available VBFs: how to address discrepancies when comparators are not consistent across therapies within a treatment space.
+
+We also intended to include all available U.S. VBFs in the test case. To our knowledge, no single study has used all 4 available oncology frameworks. While no single stakeholder is anticipated to use all frameworks to assist in decision making, a comprehensive insight into the output of each VBF for a common test case would help to elucidate further the role each plays in this intricate process. The ability to evaluate any set of treatments across these 4 oncology VBFs for a common indication remains largely limited by gaps in evidence assessment.
+
+Limitations
+
+There are some methodological limitations associated with the test case used in this study. The pharmacologic treatment pathways (and their development) in RRMM, like many cancer types, are constantly adapting to new available therapies. This study was mainly limited to 3 recently approved therapies (CFZ, ELO, and IX); however, we recognize that these therapies do not encompass the totality of the RRMM treatment paradigm. For example, NCCN guidelines designate several “preferred” treatment regimens other than the 3 drugs included in the test case. While the test case provided a meaningful comparison of 3 add-on agents to LEN + DEX for patients with RRMM, this was the only disease state that we identified to fit our inclusion criteria. However, although DrugAbacus includes 52 cancer drugs that were approved by the FDA between 2001 and 2015, only PAN, POM, and bortezomib are included as RRMM drugs.23 Consequently, our ability to generate a value output from DrugAbacus and effectively compare it with the ICER, NCCN, and ASCO VBFs was limited.
+
+Implications
+
+A divergent aspect among the oncology VBFs is their level of customizability. NCCN and ICER publish reports, which eliminates a user’s ability to adjust weights or designate preferences of certain criteria over others. In the ASCO framework, users at least have complete transparency in the NHB derivation, but they cannot adjust the weighting of the tool. Navigation of the DrugAbacus tool is completely based on user-weighted preferences, yet it succumbs to scrutiny for its lack of explanation of domain input ranges and scarcity of drugs for evaluation.
+
+Conversely, there is a convergent aspect among the oncology VBFs with regard to their sources of evidence. It is clear that the frameworks mainly rely on evidence from randomized clinical trials. If VBFs gain considerable tracking for value assessment, manufacturers may be compelled to incorporate certain endpoints, especially “bonus” domains in the ASCO framework, as standard drug performance metrics in future study designs.
+
+Our recommendation for future versions of VBFs includes incorporation of explicit weighting systems, such that end users may have a clear understanding of implicit trade-offs created by their decisions when certain criteria are weighted higher than others. In addition, all relevant costs should be presented relative to the perspective of the VBF. Some of the VBFs do not adhere to this stipulation and may underestimate the value of drugs, the costs of which may be offset by lesser medical expenditures. Conceivably, a framework that could comprehensively capture costs, health outcomes, toxicity, quality of life, and other value criteria (e.g., ease of use) could be customizable to multiple targeted stakeholder perspectives.
+
+Conclusions
+
+Even in their early beginnings, VBFs presented the opportunity to establish meaningful drug evaluation to help inform key stakeholders when making health care decisions. While consistency and reliability remain to be established, oncology VBFs have opened the door to multiple criteria decision analysis, a valuation method that enables user preferences to navigate through conflicting criteria such as quality and costs. The potential usefulness of this methodology has prompted ICER to incorporate a modified multiple criteria decision analysis process into the 2.0 version of its value framework report. As value for service and products continue to drive U.S. health care reform, VBFs may be a potential tool in the value-based care toolbox.
+
+APPENDIX Calculations for Clinical Benefit, Toxicity, Bonus Points, NHB, and Cost Using ASCO Framework
+
+	CFZ + LEN + DEX vs. DEN + DEX for RRMM: Calculations for Clinical Benefit, Toxicity, Bonus Points, NHB, and Cost	ELO + LEN + DEX vs. LEN + DEX for RRMM: Calculations for Clinical Benefit, Toxicity, Bonus Points, NHB, and Cost	IX + LEN + DEX vs. LEN + DEX for RRMM: Calculations for Clinical Benefit, Toxicity, Bonus Points, NHB, and Cost	
+Measure		Result/Score		Result/Score		Result/Score	
+Clinical benefit score	
+HR (death) or PFS	HR = 0.79		PFS = (19.4-14.9)/14.9 = 0.302		HR = 0.77		
+Clinical benefit	(1-0.79) × 100 = 21	21	0.302 × 100 × 0.8	24.2	(1-0.77) × 100 = 23	23	
+Toxicity score	([26.5 ÷ 29.5] - 1) × -20	-2.2	(38.5/37.5-1) × -20	-0.53	([50.5 ÷ 50.5] -1) × -20	0	
+LEN + DEX in combination with the agent	29.5		38.5		50.5		
+  Grade 1 to 2 < 10% (0.5)	1.5		0		2		
+  Grade 1 to 2 > 10% (1)	9		14		17		
+  Grade 3 to 4 < 5% (1.5)	15		10.5		19.5		
+  Grade 3 to 4 > 5% (2)	4		14		12		
+LEN + DEX	26.5		37.5		50.5		
+  Grade 1 to 2 < 10% (0.5)	2.5		0		2		
+  Grade 1 to 2 > 10% (1)	7		14		17		
+  Grade 3 to 4 < 5% (1.5)	15		13.5		25.5		
+  Grade 3 to 4 > 5% (2)	2		10		6		
+Bonus points total		10		0	0	0	
+Tail of the curve	0		0		0		
+Palliation	0		0		0		
+Treatment-free interval	0		0		0		
+Health-related QoL	10		0		0		
+NHB	31-2.2	28.8	24.2-0.53	23.67	23-0	23	
+Regimen cost	
+LEN + DEX in combination with the agent	
+  AWP (per month/cyle)		17,364.13		16,031.85		$20,607.00	
+  AWP out-of-pocket cost (20% coinsurance, per month/cycle)		$3,472.82		$5,971.13		$4,121.43	
+Placebo + LEN + DEX	
+  AWP (per month/cycle)		$11,616.13		$11,616.13		$11,616.13	
+  AWP out-of-pocket cost (20% coinsurance, per month/cycle)		$2,323.22		$2,323.22		$2,323.23	
+ASCO = American Society of Clinical Oncology; AWP = acquisition wholesale price; CFZ = carfilzomib; ELO = elotuzumab; HR=hazard ratio; IX = ixazomib; LEN + DEX = lenalidomide + dexamethasone; NHB = net health benefit; QoL = quality of life; RRMM = relapsed and refractory multiple myeloma..
+==== Refs
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+7. Schnipper LE, Bastian A. New frameworks to assess value of cancer care: strengths and limitations. Oncologist. 2016;21 (6 ):654-58.27245568
+8. Cohen JT, Anderson JE, Neumann PJ. Three sets of case studies suggest logic and consistency challenges with value frameworks. Value Health. 2017;20 (2 ):193-99.28237194
+9. Wilson L, Lin T, Wang L, et al. Evaluation of the ASCO value framework for anticancer drugs at an academic medical center. J Manag Care Spec Pharm. 2017;23 (2 ):163-69. Available at: http://www.jmcp.org/doi/10.18553/jmcp.2017.23.2.163.28125363
+10. Mandelblatt JS, Ramsey SD, Lieu TA, Phelps CE. Evaluating frameworks that provide value measures for health care interventions. Value Health. 2017;20 (2 ):185-92.28237193
+11. Bentley TG, Cohen JT, Elkin EB, et al. Validity and reliability of value assessment frameworks for new cancer drugs. Value Health. 2017;20 (2 ):200-05.28237195
+12. Schnipper LE, Davidson NE, Wollins DS, et al. American Society of Clinical Oncology statement: a conceptual framework to assess the value of cancer treatment options. J Clin Oncol. 2015;33 (23 ):2563-77.26101248
+13. Schnipper LE, Davidson NE, Wollins DS, et al. Updating the American Society of Clinical Oncology value framework: revisions and reflections in response to comments received. J Clin Oncol. 2016;34 (24 ):2925-34.27247218
+14. Stewart AK, Rajkumar SV, Dimopoulos MA, et al. Carfilzomib, lenalidomide, and dexamethasone for relapsed multiple myeloma. N Engl J Med. 2015;372 (2 ):142-52.25482145
+15. Stewart AK, Dimopoulos MA, Masszi T, et al. Health-related quality of life results from the open-label, randomized, phase III ASPIRE trial evaluating carfilzomib, lenalidomide, and dexamethasone versus lenalidomide and dexamethasone in patients with relapsed multiple myeloma. J Clin Oncol. 2016;34 (32 ):3921-30. Available at: http://ascopubs.org/doi/abs/10.1200/JCO.2016.66.9648?url_ver=Z39.88-2003&rfr_id=ori:rid:crossref.org&rfr_dat=cr_pub%3dpubmed. Accessed November 30, 2017.27601539
+16. Moreau P, Masszi T, Grzasko N, et al. Oral ixazomib, lenalidomide, and dexamethasone for multiple myeloma. N Engl J Med. 2016;374 (17 ):1621-34.27119237
+17. Lonial S, Dimopoulos M, Palumbo A, et al. Elotuzumab therapy for relapsed or refractory multiple myeloma. N Engl J Med. 2015;373 (7 ):621-31.26035255
+18. Wolters Kluwer. Medi-Span Price Rx. Updated 2016. Available at: http://www.wolterskluwercdi.com/drug-data/why-medispan/. Accessed November 30, 2017.
+19. National Comprehensive Cancer Network. NCCN Evidence Blocks. Multiple myeloma. Version 3.2016. Available at: https://www.nccn.org/evidenceblocks/default.aspx. Accessed November 30, 2017.
+20. Carlson RW, Jonasch E. NCCN evidence blocks. J Natl Compr Canc Netw. 2016;14 (5 Suppl ):616-19.27226499
+21. Institute for Clinical and Economic Review. Methodology: ICER integrated evidence rating. Updated 2013. Available at: http://icer-review.org/wp-content/uploads/2016/01/ICER-Rating-System-Apr-2013-Update-FINAL. pdf. Accessed November 30, 2016.
+22. Institute for Clinical and Economic Review. Treatment for relapsed and refractory multiple myeloma: effectiveness, value and value-based price benchmarks. Evidence report. May 5, 2016. Available at: http://icer-review.org/wp-content/uploads/2016/05/MWCEPAC_MM_Evidence_Report_050516-002.pdf. Accessed November 30, 2017.
+23. DrugPricingLab. DrugAbacus. Memorial Sloan Kettering Cancer Center. Available at: https://drugpricinglab.org/tools/drug-abacus/. Accessed November 30, 2017.
+24. Chandra A, Shafrin J, Dhawan R. Utility of cancer value frameworks for patients, payers, and physicians. JAMA. 2016;315 (19 ):2069-70.27187295
+25. Siegel RL, Miller KD, Jemal A. Cancer statistics, 2016. CA Cancer J Clin. 2016;66 (1 ):7-30.26742998
+26. Brenner H, Gondos A, Pulte D. Recent major improvement in long-term survival of younger patients with multiple myeloma. Blood. 2008;111 (5 ):2521-26.17901246
+27. Palumbo A, Anderson K. Multiple myeloma. N Engl J Med. 2011;364 (11 ):1046-60.21410373
+28. U.S. House of Representatives, Energy and Commerce Committee. 21st Century Cures. Updated 2016. Available at: https://energycommerce.house. gov/cures. Accessed November 30, 2017.
+

PMC10397815.txt

@@ -0,0 +1,283 @@
+
+==== Front
+J Manag Care Spec Pharm
+J Manag Care Spec Pharm
+jmcsp
+Journal of Managed Care & Specialty Pharmacy
+2376-0540
+2376-1032
+Academy of Managed Care Pharmacy
+
+27459662
+10.18553/jmcp.2016.22.8.991
+Research
+Estimating the Economic Impact of Adding Panobinostat to a U.S. Formulary for Relapsed and/or Refractory Multiple Myeloma: A Budget Impact and Cost-Benefit Model
+Bloudek Lisa PharmD, MS 1 *
+Roy Anuja PhD, MBA 2
+Kish Jonathan K. PhD, MPH 1
+Siegel David S. MD, PhD 3
+Jagannath Sundar MD 4
+Globe Denise PhD 2
+Orloski Laurie PharmD 1
+Kuriakose Emil T. MD 2
+1 Xcenda, Palm Harbor, Florida.
+2 Novartis, East Hanover, New Jersey.
+3 Myeloma Division, Hackensack University Medical Center, Hackensack, New Jersey.
+4 Multiple Myeloma Program and Hematology and Medical Oncology, The Tisch Cancer Institute, Mount Sinai Hospital, New York, New York.
+* AUTHOR CORRESPONDENCE: Lisa Bloudek, PharmD, MS, Assistant Director, Global Health Economics & Outcomes Research, Xcenda, 4114 Woodlands Pkwy., Ste. 500, Palm Harbor, FL 34685. Tel.: 727.771.4100 x 203 4073; E-mail: Lisa.Bloudek@xcenda.com.
+Funding for this study was sponsored by Novartis, East Hanover, New Jersey. Bloudek and Kish are employees of Xcenda, a consulting company contracted by Novartis to conduct this analysis. Roy, Globe, and Kuriakose are employees of Novartis. Siegel is on the advisory boards and speaker’s bureau of Celgene, Onyx/Amgen, Millennium/Takeda, and Novartis and is on the advisory boards of Merck. Jagannath is a consultant to Sanofi, Bristol-Meyers Squibb, and Celgene. Orloski is a contractor to Xcenda and provided medical writing support, which was funded by Novartis.
+
+Study design and concept were contributed by Bloudek, Roy, and Kish, assisted by Globe. Bloukek took the lead in data collection, along with Kish, and data interpretation was performed by Siegal, Jagannath, Globe, and Kuriakose. The manuscript was written primarily by Orloski, along with Roy and Kish, and revised by Roy, along with Siegal, Jagannath, Globe, Orloski, and Kuriakose.
+
+8 2016
+22 8 10.18553/jmcp.2016.22.8.991© 2016, Academy of Managed Care Pharmacy. All rights reserved.
+2016
+https://creativecommons.org/licenses/by/4.0/ This article is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use and redistribution provided that the original author and source are credited.
+
+BACKGROUND:
+
+Multiple myeloma is an incurable B-cell malignancy with a natural history that involves alternating periods of remission and subsequent relapse. For relapsed and/or refractory multiple myeloma (RRMM), the typical patient currently receives more lines of therapy than has been feasible in the past, translating into longer progression-free survival (PFS). Consequently, cost issues have become more prominent because patients may be offered newer and more expensive therapies during a more prolonged overall treatment course.
+
+OBJECTIVE:
+
+To estimate the economic impact of adding panobinostat to a U.S. health plan formulary as a treatment option with bortezomib and dexamethasone for patients with RRMM previously treated with a proteasome inhibitor (PI) and immunomodulatory drug (IMiD), using a budget impact and cost-benefit model.
+
+METHODS:
+
+Total costs of commonly used salvage therapy regimens were combined with market share data and population prevalence estimates of RRMM to yield the total cost of treatment, from the perspective of a U.S. third-party payer (commercial or Medicare) with a time horizon of 1 year. Comparator treatment regimens included bortezomib-dexamethasone, lenalidomide-dexamethasone, lenalidomide-bortezomib-dexamethasone, carfilzomib monotherapy, carfilzomib-lenalidomide-dexamethasone, and pomalidomide-dexamethasone. Costs (2015 U.S. dollars) included drug costs for oral oncology agents, medical and administration costs for injectable oncology agents, costs of adverse event (AE) prophylaxis and monitoring, and costs of grade 3/4 AEs.
+
+RESULTS:
+
+In a hypothetical health plan with 1 million members, the annual number of RRMM patients with previous PI and IMiD treatments was estimated at 16 and 118 for a commercial and Medicare plan, respectively. Introduction of panobinostat as part of the panobinostat-bortezomib-dexamethasone regimen was not expected to result in a substantial budget impact to either commercial or Medicare plans, with an incremental cost < $0.01 per member per month. Panobinostat-bortezomib-dexamethasone had a low cost per treated patient per month without progression, owing to the minimal increase in expenditure over existing bortezomib-based regimens and long median PFS, compared with median duration of treatment.
+
+CONCLUSIONS:
+
+Adding panobinostat to a plan formulary as a treatment option is expected to be cost neutral (and potentially cost saving in the context of new and more expensive treatment regimens). With a low cost per month without progression, panobinostat-bortezomib-dexamethasone represents good value for the money.
+==== Body
+pmc What is already known about this subject
+
+The introduction of second-generation therapies has significantly lengthened progression-free and overall survival for relapsed and/or refractory multiple myeloma (RRMM) patients.
+
+Literature searches show only estimates of the costs of therapies before introduction of the second-generation proteasome inhibitor carfilzomib or the immunomodulatory drugs lenalidomide or pomalidomide.
+
+What this study adds
+
+This budget impact model estimates the incremental cost after the introduction of panobinostat, including comparison with the recommended and most widely used treatments for patients suffering from RRMM.
+
+Results suggest that the addition of panobinostat to the formulary is cost neutral or cost saving in comparison with other currently used therapies.
+
+The driving factor in the costs of treating patients with RRMM rests on the difference between the duration of treatment and the duration of progression-free survival.
+
+Multiple myeloma is an incurable B-cell malignancy resulting in the accumulation of terminally differentiated plasma cells that not only infiltrate the bone marrow but also have a propensity for damaging adjacent bone and marrow.1,2 It accounts for 10% of all blood cancers and has a natural history that typically involves alternating periods of remission and subsequent relapse.1,3 For relapsed and/or refractory multiple myeloma (RRMM)—with relapsed defined as response to therapy with subsequent progression beyond 60 days of the last therapy; refractory defined as disease that is nonresponsive while on primary or salvage therapy, or progresses within 60 days of last therapy; and relapsed/refractory defined as progression of disease while on or within 60 days of discontinuing therapy4—therapeutic advances have conferred prolonged overall survival from a median of 4.6 years in 2001-2005 to 6.1 years in 2006-2010.5 The typical RRMM patient receives more lines of therapy than has been feasible in the
+
+past, translating into longer progression-free survival (PFS), a primary goal of therapy. Consequently, cost concerns have become more prominent, since patients may be offered newer and more expensive therapies during a more prolonged overall treatment course.6 However, it is also appreciated that disease complications characteristic of multiple myeloma are significant in the context of myeloma-related health care costs, requiring inpatient hospitalizations, readmissions, and procedures and a particularly long duration of hospitalization.7-10 Prolonging PFS, that is, delaying progression, may therefore lead to reduced hos-pitalizations and costs savings, depending in part on the cost of therapy required for such PFS prolongation.11
+
+There are several approved novel agents (proteasome inhibitors [PI] bortezomib and carfilzomib [second-generation], immunomodulatory drugs [IMiDs] lenalidomide and pomalidomide, and, most recently, the histone deacetylase [HDAC] inhibitor panobinostat) but no formal standard of care, since the National Comprehensive Cancer Network (NCCN) clinical practice guidelines assign multiple regimens a category 1 recommendation.2 This lack of a formal standard of care results in various real-world practices regarding treatment regimens and their sequencing. Strategies for prolonging PFS in RRMM include retreatment with bortezomib or an IMiD after initial relapse and the addition of new drugs to these established agents.11-13 Overall, lower clinical response rates and shorter PFS are anticipated with each subsequent relapse.5,11
+
+Results from the pivotal placebo-controlled phase 3 study of the HDAC inhibitor panobinostat plus bortezomib and dexamethasone for the treatment of patients who received previous treatment with up to 3 previous lines of therapy demonstrated significantly longer PFS compared with bortezomib and dexamethasone alone.2,14,15 Panobinostat increased median PFS from 8.1 months in the control arm to 12 months (hazard ratio [HR] = 0.63; 95% confidence interval [CI] = 0.52-0.76) in the panobinostat arm for the overall study population (N = 768) and from 5.8 months to 10.6 months (HR = 0.52; 95% CI = 0.36-0.76) in the subset of patients who had previously received bortezomib plus an iMiD and a median of 2 previous therapies.15,16 Accelerated approval from the U.S. Food and Drug Administration (FDA) was based on this latter subset,14and panobinostat has since been incorporated into the NCCN clinical practice guidelines as a category 1 option for this same population.2 As highlighted in the FDA-approved product labeling, panobinostat has the propensity to increase the rates of certain grade 3/4 adverse events (AEs), most notably diarrhea and cardiac events over bortezomib and dexamethasone alone.14
+
+As with any new drug under consideration for formulary placement, the addition of panobinostat is expected to add certain costs while offsetting other costs. Therefore, a Microsoft Excel-based budget impact and cost-effectiveness model was constructed to estimate the economic impact of adding panobinostat to the formulary as a treatment option for these patients and to estimate the value for the money, both from the perspective of a U.S. third-party payer.
+
+Methods
+
+Model Structure Overview
+
+The budget impact model structure is illustrated in Figure 1. The model was developed to assess the pharmacy and medical budget impact of panobinostat over a 1-year time horizon, while also assessing value for the money spent in terms of cost per patient for 1 month without progression. The target patient population was composed of adults aged ≥ 18 years who were initiating salvage therapy for RRMM, having previously been treated with ≥ 2 regimens that must have included a PI and an IMiD.
+
+FIGURE 1 Budget Impact Model Structure
+
+Modeling Technique
+
+Inputs for disease prevalence were used to estimate the size of the target population in a hypothetical health plan of 1,000,000 members, using default values derived from the 2012 U.S. Census data, Medicare demographic data, information from the Surveillance, Epidemiology, and End Results (SEER) database, and published literature.
+
+Comparator treatment regimens, based on NCCN-recommended regimens for salvage therapy for RRMM and FDA-approved product labeling, included bortezomib-dexamethasone, lenalidomide-dexamethasone, lenalidomide-bortezomib-dexamethasone, carfilzomib monotherapy, carfilzomib-lenalidomide-dexamethasone, and pomalidomide-dexamethasone.
+
+Total costs to a third-party payer (commercial or Medicare) were compared in the scenario before the introduction of panobinostat versus after the introduction of panobinostat. Cost per patient for each treatment regimen was calculated based on the drug price and cost of administration, AE prophylaxis and monitoring, and grade 3/4 AEs. All patients were assumed to be treated for the median duration of treatment (DOT) reported in product labeling or clinical trials. PFS for each regimen was based on the median PFS observed in product labeling or clinical trials, corresponding to the median DOT in the model. Detailed descriptions of the costs per treatment component have been previously published.17 These data were combined with market share estimates to simulate the cost of treating RRMM patients with previous bortezomib and IMiD exposure. Current market shares were assumptions derived from Novartis market research. Total cost of RRMM treatment to the plan was calculated by multiplying the cost per treatment regimen by the size of the target patient population and respective proportion of patients. The total, per-member-per-year (PMPY), and per-member-per-month (PMPM) costs to the plan in the scenario after the introduction of panobinostat were subtracted from the total cost in the scenario before panobinostat to estimate the incremental costs resulting from adding panobinostat to the plan formulary.
+
+The cost-effectiveness of each treatment regimen was calculated by considering the cost per month without progression, with low cost per month without progression indicative of good value for the money. Since PFS was an outcome reported across all comparator regimens, cost per month of PFS was deemed to be a representative way of comparing outcomes and assessing value.18 To assess the relative impact of key parameters on the model results, a one-way sensitivity analysis was performed, whereby each model parameter was lowered or raised (default of ± 10%). Model results after each iteration of low and high value for each parameter were tested in the model were recorded and presented in tabular format and as a tornado chart in order to assess which parameters had the greatest impact on model results of incremental cost, as well as cost per month of PFS for each regimen.
+
+Model Inputs
+
+Target Population.
+
+In a hypothetical commercial plan (1,000,000 covered lives), 72.3% of the population were estimated to be aged ≥ 18 years, and 0% were aged ≥ 65 years.19 In a hypothetical Medicare plan, 17.0% of the population were estimated to be aged ≥ 18 years, and 83% were aged ≥ 65 years.20Prevalence of multiple myeloma (MM) in people aged < 65 and ≥ 65 years was derived from age-specific prevalence rates in the SEER database and weighted by age groups reported in the 2012 U.S. Census.19,21 Because the SEER database does not present prevalence data using cutoffs of ≥ 18 years or ≥ 65 years (as would be relevant to a Medicare plan), age groups of 20-59 and 60+ were used as a proxy for 18-64 and 65+ years. Among patients with MM, 56.5% were assumed to be relapsed or relapsed/refractory at any given time, an input derived from approximating the area under the PFS survival curve for the pooled study population of nonbortezomib-based and bortezomib-based treatment arms of a meta-analysis of phase 3 trials.22 Patients entered the model at any point in MM treatment. By taking the average proportion of patients who progressed over each time point, the proportion of patients in a progressed (relapsed and/or refractory) state was estimated to be over 60 months.
+
+Among RRMM patients, 25.1% were expected to have been pretreated with a PI and an IMiD based on the subgroup of 193 of 768 randomized patients in the PANORAMA-1 phase 3 trial of panobinostat.14 According to these prevalence estimates, 16 and 118 patients in a commercial plan and Medicare plan, respectively, made up the target patient population of RRMM patients with previous use of a PI and IMiD who would receive treatment with any second-line regimen.
+
+Proportion of Patients Treated.
+
+It was assumed that 10% of patients currently treated with existing regimens would be prescribed panobinostat-bortezomib-dexamethasone upon panobinostat availability, with gain taken from each comparator regimen in proportion to the current market share. An example calculation is as follows: lenalidomide-dexamethasone future proportion of patients (28%) = current proportion of patients (32%) – (panobinostat-bortezomib-dexamethasone proportion of patients [10%] × current proportion of patients [32%]).
+
+Drug Utilization and Cost Inputs.
+
+The cost of each treatment regimen was calculated by the sum of each individual treatment component (cost of drug, administration, and AE prophylaxis) and cost of grade 3/4 AEs observed for that treatment regimen. The unit cost of each grade 3/4 AE was based on published literature and inflated to 2015 U.S. dollars using the medical care component of the Consumer Price Index.23Any AE occurring in ≥ 5% of the treatment arm in any regimen was included in the model; additionally, the cost of cardiac arrhythmias was included because of the black box warning for cardiac toxicity observed in patients treated with panobinostat. Cardiac arrhythmias have also been reported in trials of carfilzomib.24 Methods used to standardize AE rates (to account for different median durations of exposures), and values used to estimate the pharmacy or medical net cost per dose of the individual components of each treatment regimen have been previously published.17 Of note, the cost of intravenous medications used in this model differ from those in the published table for intravenous medications. Intravenous drug cost for commercial and Medicare plans were based on average sales price plus 6% without inflation for commercial costs (whereas the previous model inflated commercial intravenous drug costs to 123.5% of the Medicare rate). Inflation of commercial cost of medical services, such as physician office visits for infusion, was maintained. Additionally, all costs included in the previous model were updated to the most recent Medicare average sales price (applicable to July 1, 2015-September 30, 2015) or RED BOOK pricing.25,26
+
+The model assumed perfect adherence to treatment, with no discontinuations or dose reductions.
+
+DOT and PFS.
+
+According to the PANORAMA-1 phase 3 trial, the median PFS of panobinostat-bortezomib-dexamethasone in the overall study population was 12.0 months, with a median DOT of 5.8 months, compared with a median PFS of 8.1 months and a median DOT of 6.1 months for bortezomib-dexamethasone.14,15 DOT and PFS data for comparator regimens were extracted from clinical trials in similar RRMM populations, although the median total number of previous regimens may have differed (range 1-4) from the PANORAMA-1 population. Based on these trials in a similar RRMM population, lenalidomide-dexamethasone had a median PFS of 11.1 months and median DOT of 10.1 months.27 For lenalidomide-bortezomib-dexamethasone, median PFS and DOT were reportedly 9.5 and 8.0 months, respectively.28 For carfilzomib-dexamethasone, median PFS and DOT were 3.7 months and 3.0 months, respectively, in a phase 2 trial in which 82% of patients had ≥ 4 lines of therapy, and 95% were refractory to their last line.29 A similar phase 2 trial of carfilzomib-lenalidomide-dexamethasone in RRMM patients (median of 3 previous treatments) found a PFS of 15.4 months.30 The median numbers of 28-day cycles in this study were 9.5 for carfilzomib, 8.5 for lenalidomide, and 9 for dexamethasone. For the model, this was approximated as nine 28-day cycles of carfilzomib-lenalidomide-dexamethasone (252 days or 8.4 months). Finally, pomalidomide-dexamethasone patients received a median of 4.7 months of treatment and obtained a median of 3.6 months of PFS among patients who had received a median of 5 previous therapies.31 A scenario analysis was also undertaken to model the subpopulation of patients in the PANORAMA-1 phase 3 trial who had previouly used a PI and an IMiD, with a DOT and corresponding PFS of 4.6 months and 10.6 months for the panobinostat-bortezomib-dexamethasone arm versus 5.0 months and 5.8 months for the bortezomib-dexamethasone arm.
+
+Under default settings for the base-case analysis, after completing a course of therapy, it was assumed that patients remained progression free for the median PFS reported in the literature and returned to therapy upon progression, with subsequent cycles of therapy assumed to provide equal PFS benefit. In any typical 12-month period, some patients would be beginning therapy, while others would be mid-regimen or carried over from the previous year. To provide a fair comparison across regimens, median time on therapy corresponding to 12 months of PFS (using DOT/PFS) was calculated.
+
+Results
+
+Base-Case Analysis
+
+The total costs per treatment regimen per year for a commercial plan and a Medicare plan are presented in Table 1. Over a 1-year time horizon, assuming patients resumed treatment upon progression to achieve 12 months of PFS using the DOT to PFS ratio, the bortezomib-dexamethasone regimen was associated with the lowest total cost to the plans (commercial and Medicare), and the panobinostat-bortezomib-dexamethasone regimen had increased overall monthly cost for therapy but not total cost over 1 year.
+
+TABLE 1 Cost per Treatment Regimen per Year in Commercial and Medicare Plansa
+
+	Drug and Administration ($)	Prophylaxis and Monitoring ($)	Grade 3/4 AEs ($)	Total ($)	
+Pharmacy	Medical	Hydration	CBC	Oral prophyb	DVT/PE	ECG	Pharmacyc	Medicald	Total	
+Commercial plan	
+    PAN-BTZ-Dex	50,704	46,226	536	1,308	117	0	155	10,118	51,362	57,804	109,166	
+    BTZ-Dex	6	64,717	751	2,319	208	0	0	7,081	764	74,317	75,081	
+    LEN-Dex	120,617	0	0	931	0	259	0	7,893	121,075	8,625	129,701	
+    LEN-BTZ-Dex	100,510	70,880	823	2,616	235	242	0	2,246	101,199	76,353	177,552	
+    CFZ	0	113,913	4,578	832	324	0	222	15,631	1,379	129,544	130,923	
+    CFZ-LEN-Dex	103,866	120,516	4,435	812	309	226	217	1,783	104,517	127,647	232,164	
+    POM-Dex	151,540	0	0	1,086	0	286	0	26,055	152,699	26,268	178,967	
+Medicare plan	
+    PAN-BTZ-Dex	50,704	45,351	434	1,059	117	0	126	10,118	51,362	56,549	107,911	
+    BTZ-Dex	6	63,492	608	1,877	208	0	0	7,081	764	72,508	73,272	
+    LEN-Dex	120,617	0	0	754	0	259	0	7,893	121,075	8,448	129,523	
+    LEN-BTZ-Dex	100,510	69,539	666	2,118	235	242	0	2,246	101,199	74,357	175,556	
+    CFZ	0	107,468	3,707	674	324	0	180	15,631	1,379	126,606	127,985	
+    CFZ-LEN-Dex	103,866	120,516	3,591	658	309	226	176	1,783	104,517	126,607	231,124	
+    POM-Dex	151,540	0	0	879	0	286	0	26,055	152,699	26,061	178,760	
+a Assuming a duration on therapy needed to yield 12 months of PFS using the ratio of median duration of treatment to median PFS.
+
+b Includes acyclovir for herpes zoster prophylaxis, dexamethasone for infusion reaction prophylaxis, and allopurinol for prophylaxis of renal toxicity and tumor lysis syndrome.
+
+c Pharmacy costs include oral chemotherapy agents; DVT/PE prophylaxis; herpes zoster prophylaxis; renal toxicity and tumor lysis syndrome prophylaxis; and grade 3/4 anemia, hyponatremia, hypophosphatemia, leukopenia, thrombocytopenia, neutropenia, and lymphopenia.
+
+d Medical costs include intravenous chemotherapy agents; intravenous hydration; CBC laboratory tests; ECGs, and all grade 3/4 AEs except those listed in pharmacy costs.
+
+AE = adverse event; BTZ = bortezomib; CBC = complete blood count; CFZ = carfilzomib; Dex = dexamethasone; DVT = deep vein thrombosis; ECG = electrocardiogram; HZ = herpes zoster; LEN = lenalidomide; PAN = panobinostat; PE = pumonary embolism; PFS = progression-free survival; POM = pomalidomide; prophy = prophylaxis.
+
+In a commercial hypothetical plan of 1,000,000 members, the introduction of panobinostat was not associated with substantial budget impact to the plan and is expected to be budget neutral (Table 2). Under default assumptions for proportion of patients who will receive panobinostat-bortezomib-dexamethasone in lieu of other used regimens, DOT, PFS, and rate of grade 3/4 AEs, addition of the panobinostat-bortezomib-dexamethasone was associated with a net savings of $46,450 (corresponding to -$0.05 PMPY or less than -$0.004 PMPM). In addition, panobinostat-bortezomib-dexamethasone demonstrated good value for the money, with a cost per month without progression under $10,000 per month (Figure 2A).
+
+FIGURE 2 Total Cost of Treatment Regimen Over 1 Year, Cost per Month on Treatment,a and Cost per Month Without Progressionb in Commercial and Medicare Plans
+
+TABLE 2 Budget Impact of Panobinostat in Commercial and Medicare Plans: Base-Case Analysis
+
+	Total Annual Cost ($)	PMPY ($)	PMPM ($)	
+Current	Future	Current	Future	Current	Future	
+Commercial plan	
+    PAN-BTZ-Dex	0	174,921	0.00	0.17	0.00	0.01	
+    BTZ-Dex	281,514	253,363	0.28	0.25	0.02	0.02	
+    LEN-Dex	556,967	501,270	0.56	0.50	0.05	0.04	
+    LEN-BTZ-Dex	406,832	366,149	0.41	0.37	0.03	0.03	
+    CFZ	282,253	254,028	0.28	0.25	0.02	0.02	
+    CFZ-LEN-Dex	178,562	160,706	0.18	0.16	0.01	0.01	
+    POM-Dex	507,573	456,816	0.51	0.46	0.04	0.04	
+Total	2,213,703	2,167,253	2.21	2.17	0.184	0.181	
+Incremental change		-46,450		-0.05		-0.004	
+Medicare plan	
+    PAN-BTZ-Dex	0	1,277359	0.00	1.28	0.00	0.11	
+    BTZ-Dex	2,029,570	1,826,613	2.03	1.83	0.17	0.15	
+    LEN-Dex	4,108,955	3,698,060	4.11	3.70	0.34	0.31	
+    LEN-BTZ-Dex	2,971,674	2,674,507	2.97	2.67	0.25	0.22	
+    CFZ	2,026,515	1,823,864	2.03	1.82	0.17	0.15	
+    CFZ-LEN-Dex	1,313,214	1,181,893	1.31	1.18	0.11	0.10	
+    POM-Dex	3,745,354	3,370,818	3.75	3.37	0.31	0.28	
+Total	16,195,283	15,853,113	16.20	15.85	1.350	1.321	
+Incremental change		-342,169		-0.34		-0.029	
+BTZ = bortezomib; CFZ = carfilzomib; Dex = dexamethasone; LEN = lenalidomide; PAN = panobinostat; PMPM = per member per month; PMPY = per member per year; POM = pomalidomide.
+
+The addition of panobinostat-bortezomib-dexamethasone is also expected to be neutral or cost saving for a Medicare plan (Table 2). In a hypothetical Medicare plan of 1,000,000 members, the introduction of panobinostat is expected to result in cost savings of $342,169. This corresponds to a PMPY net savings of $0.34 ($0.029 PMPM). Cost per month without progression for panobinostat-bortezomib-dexamethasone was below $10,000 at $8,993 and lower than lenalidomide-dexamethasone ($10,794) and carfilzomib-dexamethasone but higher than bortezomib-dexamethasone ($6,106; Figure 2B).
+
+Scenario Analysis: Subpopulation of PANORAMA-1 Which Received Previous Treatment with a PI and an IMiD
+
+Based on the subgroup of patients in PANORAMA-1 which had received previous PI and IMiD therapy, 5.2 treatment-months are expected to yield 12.0 months of PFS for the panobinostat-bortezomib-dexamethasone regimen, using the ratio of duration of treatment to PFS (4.6 months: 10.6 months). For bortezomib-dexamethasone, 10.3 treatment-months are expected to yield 12.0 months of PFS (5.0 months: 5.8 months). The DOT and PFS remained the same as those in the base-case analysis for all other regimens. The total costs per treatment regimen per year for panobinostat-bortezomib-dexamethasone were $116,196 and $114,862 for a commercial plan and a Medicare plan, respectively; corresponding values for bortezomib-dexamethasone were $82,795 and $80,788, respectively.
+
+In assessing the incremental budget impact, panobinostat remained favorable in this scenario for commercial and Medicare plans (Table 3). In a commercial plan under this scenario, the total budget impact over 1 year is estimated at -$38,078 ($0.04 PMPY; $0.003 PMPM). For a Medicare plan, the anticipated budget impact is -$280,697 over 1 year ($0.28 PMPY; -$0.023 PMPM). The model predicts the introduction of the panobinostat-bortezomib-dexamethasone regimen to be potentially cost saving to the plan through the reduction in the proportion of patients treated with regimens more costly than panobinostat-bortezomib-dexamethasone for this subpopulation of patients.
+
+TABLE 3 Budget Impact of Panobinostat in Commercial and Medicare Plans: Scenario Analysis (PANORAMA-1 Subset with Previous Treatment with PI and IMiD)
+
+	Total Annual Cost ($)	PMPY ($)	PMPM ($)	
+Current	Future	Current	Future	Current	Future	
+Commercial plan	
+    PAN-BTZ-Dex	0	186,184	0.00	0.19	0.00	0.02	
+    BTZ-Dex	310,437	279,393	0.31	0.28	0.03	0.02	
+    LEN-Dex	556,967	501,270	0.56	0.50	0.05	0.04	
+    LEN-BTZ-Dex	406,832	366,149	0.41	0.37	0.03	0.03	
+    CFZ	282,253	254,028	0.28	0.25	0.02	0.02	
+    CFZ-LEN-Dex	178,562	160,706	0.18	0.16	0.01	0.01	
+    POM-Dex	507,573	456,816	0.51	0.46	0.04	0.04	
+Total	2,242,625	2,204,547	2.24	2.20	0.187	0.184	
+Incremental change		-38,078		-0.04		-0.003	
+Medicare plan	
+    PAN-BTZ-Dex	0	1,359,649	0.00	1.36	0.00	0.11	
+    BTZ-Dex	2,237,743	2,013,968	2.24	2.01	0.19	0.17	
+    LEN-Dex	4,108,955	3,698,060	4.11	3.70	0.34	0.31	
+    LEN-BTZ-Dex	2,971,674	2,674,507	2.97	2.67	0.25	0.22	
+    CFZ	2,026,515	1,823,864	2.03	1.82	0.17	0.15	
+    CFZ-LEN-Dex	1,313,214	1,181,893	1.31	1.18	0.11	0.10	
+    POM-Dex	3,745,354	3,370,818	3.75	3.37	0.31	0.28	
+Total	16,403,456	16,122,759	16.40	16.12	1.367	1.344	
+Incremental change		-280,697		-0.28		-0.023	
+BTZ = bortezomib; CFZ = carfilzomib; Dex = dexamethasone; IMiD = immunomodulatory drug; LEN = lenalidomide; PAN = panobinostat; PI = proteasome inhibitor; PMPM = per member per month; PMPY = per member per year; POM = pomalidomide.
+
+One-Way Sensitivity Analysis
+
+For the outcome of total incremental budget impact (base-case model output = -$46,450 for a commercial plan), median PFS and median DOT for the panobinostat-bortezomib-dexamethasone regimen were the most influential parameters, varying the incremental budget from -$34,334 to -$58,565. The next most influential parameters were cost of panobinostat and lenalidomide 25 mg tablets. The most influential 20 parameters are displayed in Figure 3A. Under no scenario did the one-way sensitivity analysis show increased cost associated with the addition of panobinostat to the formulary.
+
+FIGURE 3 One-Way Sensitivity Analysis Tornado Chart for the Outcomes of Total Incremental Cost and Cost Per Month of PFS for Each Regimen
+
+For the outcome of cost per month of PFS for each regimen, median PFS was the most influential factor on model results followed by median DOT (Figure 3B). After these 2 parameters, drug cost was typically the most influential.
+
+Discussion
+
+Often, an immediate result of the introduction of new drugs into payer formularies is the associated budget impact. Therefore, an economic model was created to quantify the budget impact of the introduction of panobinostat into a typical payer formulary for patients experiencing their second or later relapse who have been previously treated with bortezomib and an IMiD. This model demonstrated that the incremental cost per month associated with the addition of panobinostat is balanced by the treatment benefit of months of PFS gained with panobinostat. In the PANORAMA-1 trial, although the median DOT was shorter with panobinostat versus placebo (5.0 vs. 6.1 months), it significantly extended the median PFS to 12 months (vs. 8.1 months for placebo).15 Because of this ability to prolong PFS beyond the DOT (demonstrated in patients who had received previous treatment with a PI and an IMiD) and the low rates of cost-intensive AEs (e.g., venous thromboembolic events, included as black box warnings for lenalidomide and pomalidomide), panobinostat represents better value compared with other regimens indicated in the RRMM population (with the exception of bortezomib-dexamethasone). Additionally, panobinostat is associated with an acceptable budget impact from the perspective of a health plan formulary, with a projected small incremental PMPM cost of less than $0.01, and it may be cost saving overall because of the low cost per month of PFS gained on panobinostat-bortezomib-dexamethasone, compared with alternative regimens in this population.
+
+RRMM exacts a heavy humanistic and economic burden on patients. Although RRMM remains an incurable disease at present, this population is benefiting from a growing list of approved agents with the ability to prolong PFS. When the PFS benefits extend beyond the DOT, this provides time off of therapy that not only brings about clinical benefit but also humanistic and economic benefit by reducing disease-related symptoms and exposure to treatment-related toxicity, which provides patients with a chance to return to a more normal daily experience.18While the availability of options is welcome for relapsing-remitting disease, clinical decision making is becoming increasingly complex, warranting not only further studies comparing the efficacy of various combinations and sequences but also cost-effectiveness analyses to guide choice of a given therapy at time of relapse and retreatment. There has been a paucity of published cost analyses for novel therapies in the RRMM setting, although data have been emerging in recent years.6,32,33 More specifically, an economic model by Durie et al. (2013) compared total treatment costs along with cost per month without progression for lenalidomide-dexamethasone with bortezomib-dexamethasone, demonstrating substantially higher drug and medical costs (translating into an annual increase of $17,000) with the latter combination.6 This analysis was limited by the comparison involving only 2 regimens and its consideration of only selected AEs, prompting the development of a more comprehensive treatment cost estimator, which established the framework on which this budget impact analysis is based.17
+
+The current model focused on calculating the economic impact of adding panobinostat to the health plan formulary, including pharmacy and medical budget impacts. It was comprehensive, characterizing total annual incremental budget impact, incremental budget impact PMPY, incremental budget impact PMPM, and cost per month without progression. While the monthly cost of panobinostat-bortezomib-dexamethasone was relatively high compared with the alternative treatment regimens (other than carfilzomib-lenalidomide-dexamethasone), the total cost of treating an RRMM patient using the panobinostat-bortezomib-dexamethasone regimen was less expensive than the alternative regimens of lenalidomide-bortezomib-dexamethasone and carfilzomib-lenalidomide-dexamethasone (and lenalidomide-dexamethasone in a Medicare plan) because of the more favorable ratio of median DOT to PFS benefit over the entire year. While offering a competitively priced treatment option for RRMM patients, panobinostat-bortezomib-dexamethasone also offers superior value compared with alternative treatment regimens, with a PFS cost per month of $9,097 in a commercial plan and $8,993 in a Medicare plan.
+
+Limitations
+
+We acknowledge that this modeling study and its results have limitations. The model took the perspective of a third-party payer, so it only included costs relevant to this audience. Patient out-of-pocket costs (copays and coinsurance) are only considered to the extent that they offset payer costs. Also, indirect costs of lost productivity are not considered in the model. There are other limitations inherent to modeling studies based on data from published sources of clinical trial data and pricing information. In real-world practice, the DOT, adherence to treatment, and dosing schedules may differ from clinical trial experiences. The model is highly sensitive to assumptions of baseline proportions of patients on alternative treatment regimens, which are based on the market share of these products derived from market research data. The budget impact of panobinostat-bortezomib-dexamethasone for any specific health plan will be highly dependent on the most common RRMM salvage therapy regimens used within that plan. Not all possible RRMM salvage therapies used in clinical practice were included in the model. To maintain simplicity and transparency, the model assumed that patients returned to their original treatment regimens upon disease progression, as is recommended by some physicians and some clinical practice guidelines.11,34 This assumption may not reflect individualized real-world patient treatment pathways; however, the ability to model detailed treatment pathways is limited by data availability. Finally, it is important to emphasize that the purpose of the framework was to compare regimens with respect to cost per month of therapy and costs for 12 months of PFS—not to compare the efficacy of the different treatment regimens, which would be influenced by the variability of the study populations across the clinical trials and prescribing practices in the real-world setting.
+
+Conclusions
+
+Adding panobinostat to the plan formulary for the treatment of PI- and IMiD-pretreated RRMM, in combination with bortezomib and dexamethasone, is not associated with significant budget impact for a health plan. The neutral or cost-saving budgetary impact is driven by the favorable DOT/PFS ratio in the panobinostat regimen, its comparatively low incidence of costly AEs, and the proportionate reduction in market share for costly alternative regimens.
+==== Refs
+References
+
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+22. Sonneveld P, Goldschmidt H, Rosinõl L, et al. Bortezomib-based versus nonbortezomib-based induction treatment before autologous stem-cell transplantation in patients with previously untreated multiple myeloma: a meta-analysis of phase III randomized, controlled trials. J Clin Oncol. 2013;31 (26 ):3279-87. Available at: http://jco.ascopubs.org/content/31/26/3279.long. Accessed June 30, 2016.23897961
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+

PMC10397851.txt

@@ -0,0 +1,363 @@
+
+==== Front
+J Manag Care Spec Pharm
+J Manag Care Spec Pharm
+jmcsp
+Journal of Managed Care & Specialty Pharmacy
+2376-0540
+2376-1032
+Academy of Managed Care Pharmacy
+
+29799330
+10.18553/jmcp.2018.24.6.504
+Research
+Greater Spending Associated with Improved Survival for Some Cancers in OCM-Defined Episodes
+Baumgardner James PhD 1 *
+Shahabi Ahva PhD 1
+Linthicum Mark MPP 1
+Vine Seanna MPH 1
+Zacker Christopher RPh, PhD 2
+Lakdawalla Darius PhD 3
+1 Precision Health Economics, Los Angeles, California.
+2 Novartis Pharmaceuticals, East Hanover, New Jersey.
+3 Leonard D. Schaeffer Center for Health Policy & Economics, University of Southern California, Los Angeles.
+* AUTHOR CORRESPONDENCE: James Baumgardner, PhD, Precision Health Economics, 11100 Santa Monica Blvd., Ste. 500, Los Angeles, CA 90025. Tel.: 310.984.7781; E-mail: james.baumgardner@PrecisionHealthEconomics.com.
+Funding for this research was provided by Novartis Pharmaceuticals to Precision Health Economics in support of research design, analysis, and technical writing services. The funder provided input on study design and comments on the draft report. Baumgardner, Shahabi, and Linthicum are employees of Precision Health Economics (PHE), a health care consultancy to the insurance and life science industries, including firms that market oncology therapies. Vine was an employee of PHE at the time of this research. Zacker is an employee of and shareholder in Novartis Pharmaceuticals. Lakdawalla is a consultant to PHE and holds equity in its parent company, Precision Medicine Group.
+
+6 2018
+24 6 10.18553/jmcp.2018.24.6.504Copyright © 2018, Academy of Managed Care Pharmacy. All rights reserved.
+2018
+https://creativecommons.org/licenses/by/4.0/ This article is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use and redistribution provided that the original author and source are credited.
+
+BACKGROUND:
+
+Previous research finds significant variation in spending and utilization across regions, with little evidence of differences in outcomes. While such findings have been interpreted as evidence that spending can be reduced without compromising patient outcomes, the link between spending variation and outcomes remains a critical question.
+
+OBJECTIVE:
+
+To use evidence from geographic variations in spending and an individual-level survival analysis to test whether spending within oncology care episodes is associated with survival, where episodes are defined as in the Center for Medicare and Medicaid Innovation’s Oncology Care Model (OCM).
+
+METHODS:
+
+In this retrospective cohort analysis, patient data from the Surveillance, Epidemiology and End Results Medicare (SEER-Medicare) database for 2007-2013 were linked to hospital referral regions (HRRs) using ZIP codes. Patients in the SEER program are a part of selected population-based cancer registries throughout the United States whose records are linked to Medicare enrollment and claims data (93% of elderly registry patients were successfully linked to Medicare data). Episodes of cancer care were defined as in the OCM: 6 months following a triggering chemotherapy claim. We analyzed episodes of care for 5 tumor types: advanced breast cancer (BC), non-small cell lung cancer (NSCLC), renal cell carcinoma (RCC), multiple myeloma (MM), and chronic myeloid leukemia (CML). We removed the effects of differentials in Medicare payment rates, which were mostly geographic. Regression analysis was then used to calculate standardized spending levels for each HRR, that is, spending adjusted for differences in patient and episode characteristics. To examine the effect of spending during OCM-defined episodes on individual-level survival, we used Cox regression with patient characteristics and standardized HRR spending per episode as covariates. To address concerns that may arise from multiple comparisons across the 5 tumor types, we used the Benjamini-Hochberg procedure to control the false discovery rate.
+
+RESULTS:
+
+Our analysis showed significant differences in standardized spending across HRRs. Compared with spending at the 20th percentile episode, spending at the 80th percentile ranged from 25% higher ($57,392 vs. $45,995 for MM) to 47% higher ($36,920 vs. $24,127 for RCC), indicating practice style variation across regions. The hazard of dying for patients with NSCLC and MM statistically significantly decreased by 7% (HR = 0.93, P = 0.006) and 13% (HR = 0.87, P = 0.019), respectively, for a $10,000 increase in standardized spending (in 2013 U.S. dollars). For the 3 other cancers, spending effects were not statistically significant. After using the Benjamini-Hochberg procedure with a 5% false discovery rate, the effects of increased spending on improved survival for NSCLC and MM remained statistically significant.
+
+CONCLUSIONS:
+
+The association we found between spending and survival suggests caution may be warranted for physicians, pharmacists, other health care professionals, and policymakers involved in efforts to reduce across-the-board spending within OCM-defined episodes for at least 2 of the 5 cancers studied.
+==== Body
+pmc What is already known about this subject
+
+Some innovative payment models such as the Center for Medicare and Medicaid Innovation’s Oncology Care Model (OCM) aim to reduce overall costs for cancer care while improving the quality of care.
+
+Past research on geographic variations in health care generally suggests that spending variation is attributable to local area differences in providers’ practice styles, with conflicting results as to whether greater spending is associated with better or worse outcomes for patients.
+
+What this study adds
+
+Significant variation was documented across regions in spending per episode for 5 cancer types after adjustment for differences in patient and episode characteristics and in Medicare payment rates.
+
+The association between spending and survival differed across cancer types; for example, the hazard of dying for patients with non-small cell lung cancer (NSCLC) and multiple myeloma (MM) decreased by 7% (HR = 0.93, P = 0.006) and 13% (HR = 0.87, P = 0.019), respectively, for a $10,000 increase in standardized spending, whereas spending effects were not statistically significant for the other 3 cancer types studied.
+
+Study results suggest that health care providers should consider exercising caution when attempting to reduce spending in response to new payment models, especially in cases of NSCLC and MM.
+
+Payers are experimenting with alternative payment models (APMs) that include incentives to reduce spending on the treatment of cancer patients.1 For example, the Center for Medicare and Medicaid Innovation initiated a demonstration project called the Oncology Care Model (OCM) in 2016.2-4 Shared savings models such as the OCM and other APMs seek to reduce spending by moving away from payment for volume of services provided and toward payment for value or better patient outcomes, with the assumption that significant unnecessary spending currently exists.
+
+In the case of the OCM, an oncology practice receives a performance-based payment dependent on a practice’s performance on several measures of quality and the extent to which spending per episode is kept below a target set to yield savings to the Medicare program. The OCM defines an episode as the 6-month period initiated by outpatient chemotherapy, either infused or oral.3 Because the OCM creates incentives to reduce spending in episodes so defined, a crucial question is whether reduced spending in those episodes would adversely affect patient outcomes despite the potential effect of the quality improvement incentives.
+
+The general spirit of findings in the well-established literature on geographic variations in health care has been of significant variation in spending across regions, often attributed to differences in the practice styles of physicians in different areas, with little and conflicting evidence of differences in outcomes.5-7 Such findings have been interpreted as evidence that spending can be reduced without compromising patient outcomes, if, for instance, the higher spenders in the health care system were incentivized to behave like the lower spenders.6
+
+In this analysis, for 5 cancer types we measured the geographic variation in spending, which may be indicative of inefficient spending, and the association between spending and survival, which may indicate potential consequences of general spending reductions on outcomes unless they are offset by quality improvement incentives. We measured spending per OCM-defined episode, since that is the unit over which practices are incentivized to reduce spending within the OCM. Our analysis can inform policymakers and health care professionals of potential concerns in pursuing APMs such as the OCM, which imbed incentives for general spending reductions along with incentives to improve quality of care. Our analysis made no attempt to evaluate the effect of the OCM methodology as a whole or, more specifically, the effects of its quality metrics.
+
+Methods
+
+We used a geographic variations approach to (a) assess the amount of interregional variation in spending on cancer treatment, which has typically been interpreted as evidence of differences in physicians’ practice styles across regions, and (b) examine the association between spending and patient survival. An advantage of our approach is that we estimated survival functions using individual-level data, which allowed us to control for individual characteristics and to make use of complete and incomplete survival spells with the Cox survival model.
+
+Our approach also avoided the spurious correlation that would exist in a survival analysis conducted entirely at the individual level. In particular, if one simply regressed individual survival on patient-level spending and other covariates, unobserved differences in severity would likely cause higher spending to be associated with lower survival because treatment of patients with poorer prognoses tends to be more resource intensive. This approach would lead to the incorrect inference that higher spending had harmed the patients, when instead, their earlier deaths owed to the greater severity of their illnesses at baseline.
+
+To avoid confounding by a patient’s unmeasured disease severity, we regressed patient survival on the local region’s spending per episode on a standardized patient and on other individual-level covariates. In essence, we use the local area’s level of spending on a standardized patient as a means of measuring variation in spending that is unrelated to the individual’s disease severity. We analyzed whether patients who live in areas where practice styles incorporate higher spending have significantly better survival. Our estimate removed the effects of observable differences in patients and episodes and of payment rate differentials across areas.
+
+Data Source
+
+Our study used data from the Surveillance, Epidemiology and End Results-Medicare (SEER-Medicare) database for patients who received chemotherapy treatment in the 2007-2013 period. The SEER-Medicare database links cancer registry data with longitudinal Medicare claims for Parts A, B, and D. We also used data from 2006 in order to create the Charlson index of comorbidities based on diagnoses observed in the 12 months before a cancer treatment episode. The data allowed us to follow patients during the study period from Medicare enrollment to death or December 31, 2013, which was the most recent date of availability for the SEER-Medicare files at the time we conducted the analysis. The Medicare data also include date of death, obtained from the Social Security Administration, which allows for reliable measurement of survival duration.
+
+Cancer Types Included
+
+We identified patients diagnosed with 1 of the following tumor types as the primary cancer: advanced breast cancer (BC), non-small cell lung cancer (NSCLC), renal cell carcinoma (RCC), multiple myeloma (MM), and chronic myeloid leukemia (CML). These tumor types were chosen to provide variety in terms of incidence and prevalence in the Medicare population, the innovation and resource mix used in treatment, and a mix of solid and hematological cancers. In the case of BC, we focused on advanced stage (stages 3 and 4) diagnoses because the longer survival times of earlier stage patients would have resulted in few deaths occurring within our data window, leaving little variation to measure. Our patient cohort included patients newly diagnosed with 1 of the 5 selected tumors between January 1, 2007, and December 31, 2011. Recoded International Classification of Diseases for Oncology, Third Revision (ICD-O-3) codes were used to identify patients with these 5 tumor types (BC: 26000, NSCLC: 22030, RCC: 29020, MM: 34000, and CML: 35022). Details on additional histologic codes used appear in Appendix A (available in online article).
+
+We restricted the study cohort to patients enrolled in Parts A, B, and D throughout their time in our data window. Since the OCM excludes patients enrolled in Medicare Advantage, those with end-stage renal disease, and those with a primary payer other than Medicare, we excluded those groups. We restricted the sample to patients treated with at least 1 OCM-eligible chemotherapy drug in an outpatient setting (including Part D chemotherapy claims). This restriction allowed us to define an episode in the same way as the OCM.
+
+Episode Definition
+
+We followed the OCM definition of an episode of care: 6 months (180 days) triggered by initiation of outpatient chemotherapy, either infused/injected or oral. If a patient continued or resumed outpatient chemotherapy after the end of the episode, then a new 6-month episode began. This was continued through the end of the data or their death; a final episode could be less than 6 months, as in the OCM.
+
+Standardization for Medicare Payment Rate Differentials
+
+Using the Medicare claims, we calculated the base cost for each claim. In general, the base cost was the sum of the payment amount from Medicare and deductible and coinsurance amounts. Each claim payment was standardized to remove the effect of differentials across geographic areas (and in the case of inpatient hospital payments, across hospitals in the same region) in Medicare payment rates for the same service. This analytic step eliminated variation in spending due to pricing or payment rate differences. Because our spending data covered several years, we also corrected for changes in payment rates over time (including the sequester), with a normalization to (presequester) 2013 U.S. dollars. Details on our standardization methods appear in Appendix A. As a result of our standardization, when we measured higher spending, it reflected either greater utilization or greater use of goods and services having higher average costs.
+
+Survival
+
+We measured survival as duration between diagnosis date and date of death and used a Cox survival model (see the “Statistical Analysis” section) for estimating the effects of covariates. For patients who did not die during the study period, we treated their survival duration as right-censored. The Cox model used information from both censored and noncensored cases in estimating the parameters of the survival function. Patients who were diagnosed at the beginning of our sample frame had the potential to remain alive and in the sample for up to 7 years (January 2007-December 2013).
+
+Defining the Geographic Market
+
+Hospital referral regions (HRRs) developed by the Dartmouth Atlas of Health Care were used to define regional health care markets.8 The HRRs have been widely used to define geographic markets for use in studies of the variation in use of medical services. Medicare beneficiaries receive over 80% of their care within their HRRs.9 Patient ZIP code of residence, obtained through an additional application process with SEER-Medicare, was used to map patients to their respective HRRs.
+
+Spending and Covariates Used in Regression Analysis
+
+We calculated spending per OCM-defined episode by first identifying use of outpatient chemotherapy and then applying the OCM definition of an episode. We aggregated all claims within each episode to calculate spending per episode. We included all spending on Parts A, B, and D services.
+
+For the spending regressions, covariates included age, sex, eligibility for Medicaid, disability, Charlson Comorbidity Index (CCI) score, disease stage at diagnosis, number of previous episodes for the patient, death during the episode, and the geographic region of residence. The survival analysis included indicator variables for race and ethnicity, as well as other patient characteristics.
+
+Statistical Analysis
+
+Step 1: Estimation of Spending per Episode Regressions.
+
+A generalized linear model with a gamma distribution and log-link function was used to regress spending per episode on characteristics of the patient, other control variables, and an indicator for the patient’s geographic region (HRR). To account for the correlation of episodes for each patient, we clustered at the patient level, which allowed for estimation of cluster robust standard errors. Separate regression models were run for each cancer type.
+
+Step 2: Calculation of Standardized Spending per Episode for Each Region.
+
+The regression results from step 1 were used to calculate a value of spending per episode for each HRR for a patient with a standardized set of characteristics, which were defined using mean values of the covariates over the entire sample for each respective cancer. The distribution of standardized spending per episode across HRRs can be interpreted as reflecting practice style differences across the providers in different regions.
+
+Step 3: Estimation of Individual Survival Regressions.
+
+To examine the effect of spending on survival, we used a Cox regression model with individual patients as the unit of observation. Regressors included patient characteristics, and the key variable of interest was the standardized spending per episode for the patient’s HRR. Because the latter variable was calculated using the estimated coefficients from the spending regression, bootstrap standard errors were calculated where the spending regression, calculation of HRR standardized spending, and the Cox regression were estimated together in each round of the bootstrap. The Schoenfeld’s residuals test was used to test the proportional hazards assumption of the Cox model. Because this analysis was performed on all 5 cancers to determine if spending affects survival, concern about multiple comparisons exists. Therefore, the Benjamini-Hochberg procedure10 to control the false discovery rate (FDR) was used to assess statistical significance among our 5 results with an FDR of 5% (i.e., among significant results, 5% are acceptable as false positives).
+
+Results
+
+Summary statistics appear in Table 1. Because of differences in disease prevalence, our sample contained more NSCLC and advanced BC episodes and patients and relatively fewer RCC and CML episodes and patients. HRRs were included in the analysis for a particular cancer if there were at least 20 episodes from patients in the HRR. Hence, our number of included HRRs ranged from 64 for CML to 112 for NSCLC. Average age at diagnosis was similar across our cancer types, ranging from 72.4 for NSCLC to 74.7 for MM. NSCLC was shown to have the lowest median survival (20 months) in a Kaplan-Meier analysis, with the longest survival for advanced BC and CML. Average total spending per episode (unadjusted for differences in patient characteristics) ranged from just under $21,000 for advanced BC to over $52,000 for MM.
+
+TABLE 1 Characteristics of Adult Cancer Patients Diagnosed 2007-2011 and Who Received Treatment 2007-2013 Within 6-Month Episodes of Care as Defined by the OCM
+
+Level	Cancer Type	
+BC	NSCLC	RCC	MM	CML	
+Disease	
+  Total number of 6-month episodes	16,039	25,697	2,711	7,246	2,163	
+  Total number of patients	3,593	14,380	1,224	2,218	466	
+  Number of included HRRs with ≥ 20 episodes in each HRR	102	112	69	91	64	
+Patient	
+  Mean age at diagnosis (SD)	73.0 (10.6)	72.4 (8.10)	73.1 (8.8)	74.7 (8.3)	72.6 (11.2)	
+  Mean CCI score at first episode (SD)	1.0 (1.3)	1.5 (1.4)	1.7 (1.7)	1.4 (1.6)	1.5 (1.7)	
+  Mean months of follow-up time (SD)	35.9 (21.3)	19.7 (18.3)	31.8 (22.6)	32.2 (20.8)	34.4 (22.6)	
+  Median survival, monthsa	Not reached	20	62	70	Not reached	
+  Percentage male (n)	Excluded	49.8 (7,157)	55.6 (681)	45.9 (1,017)	50.9 (237)	
+  Race/ethnicity	
+    Percentage white (n)	80.4 (2,887)	79.5 (11,427)	77.3 (946)	73.4 (1,627)	79.6 (371)	
+    Percentage black (n)	12.6 (452)	10.5 (1,511)	8.9 (109)	14.8 (329)	9.0 (42)	
+    Percentage other (n)	7.0 (254)	10.0 (1,442)	13.8 (169)	11.8 (262)	11.4 (53)	
+  Stage at diagnosisb	
+    Percentage stage 1 (n)	NA	17.1 (2,299)	36.9 (426)	NA	NA	
+    Percentage stage 2 (n)	NA	6.3 (855)	6.1 (70)	NA	NA	
+    Percentage stage 3 (n)	65.2 (2,342)	32.0 (4,309)	15.2 (176)	NA	NA	
+    Percentage stage 4 (n)	34.8 (1,251)	44.6 (6,017)	41.9 (484)	NA	NA	
+  Percentage entitled to Medicare through disability (n)	23.3 (836)	24.2 (3,487)	21.7 (266)	15.6 (346)	26.6 (124)	
+  Percentage Medicaid eligible (n)	40.8 (1,467)	38.7 (5,565)	42.3 (518)	33.2 (737)	39.9 (186)	
+Episode	
+  Mean number of previous episodes (SD)	2.7 (2.5)	0.9 (1.5)	1.4 (1.9)	2.0 (2.1)	3.1 (2.8)	
+  Mean unadjusted total spending per episode in USD (SD)c	20,887.3 (22,600.6)	39,544.1 (27,435.3)	33,553.3 (27,186.2)	52,489.3 (29,452.9)	40,452.1 (24,704.3)	
+  Percentage of episodes ended due to death (n)	7.7 (1,242)	32.2 (8,273)	19.8 (538)	11.5 (830)	7.5 (163)	
+aMedian survival “not reached” indicates that more than 50% of the study population was still alive by the end of the study follow-up (median survival could not be calculated).
+
+bPatients with missing stage at diagnosis: NSCLC, n = 900 (~6%) and RCC, n = 68 (~5.5%).
+
+cMean episode spending was adjusted for Medicare payment rate differentials but not for patient characteristics.
+
+BC = advanced breast cancer (stages 3 and 4); CCI = Charlson Comorbidity Index; CML = chronic myeloid leukemia; HRR = hospital referral region; MM = multiple myeloma; NA = not available; NSCLC = non-small cell lung cancer; OCM = Oncology Care Model; RCC = renal cell carcinoma; SD = standard deviation; USD = U.S. dollars.
+
+Although some qualitative differences exist across the cancer types, the effects of covariates on spending per episode were similar when we controlled for regional variations in practice styles (Table 2). Spending per episode decreased with age for all 5 cancer types when evaluated at mean age (all P < 0.001). More advanced disease stages at diagnosis were associated with statistically significant increases in spending when compared with stage 1, ranging from 5.6% for stage 2 NSCLC (coefficient = 0.056, P = 0.05) to 67.9% for stage 4 RCC (coefficient = 0.679, P < 0.001), although spending did not increase monotonically with stage in all cancers. Similarly, comorbidities (CCI) were associated with statistically significant increases in spending across cancers at mean CCI except for MM, where the effect is not statistically significant. The relationship between other covariates and episode-level spending is more varied. For example, dual eligibility for Medicaid was associated with 6.7% (coefficient = 0.067, P = 0.027) higher spending in advanced BC but 2.6% lower spending in NSCLC (coefficient = -0.026, P = 0.046).
+
+TABLE 2 Regression Results at the Episode Level with Total Cost per Episode as the Outcomea
+
+	Cancer Type	
+BC	NSCLC	RCC	MM	CML	
+Coefficient (95% CI)	P Value	Coefficient (95% CI)	P Value	Coefficient (95% CI)	P Value	Coefficient (95% CI)	P Value	Coefficient (95% CI)	P Value	
+Age	0.016 (-0.006–0.038)	0.153	0.036 (0.021–0.051)	< 0.001	0.028 (-0.026–0.082)	0.312	0.037 (0.019–0.055)	< 0.001	0.054 (0.022–0.086)	< 0.001	
+Age-squared	-0.00021 (-0.00036– -0.00006)	0.006	-0.00034 (-0.00045– -0.00023)	< 0.001	-0.00026 (-0.00064–0.00012)	0.180	-0.00036 (-0.00048– -0.00022)	< 0.001	-0.00050 (-0.00073– -0.00026)	< 0.001	
+Maleb	NA	0.032 (0.010–0.054)	0.004	0.125 (0.022–0.229)	0.017	0.048 (0.011–0.085)	0.012	0.029 (-0.064–0.121)	0.547	
+CCI score	0.123 (0.085–0.161)	< 0.001	0.031 (0.015–0.047)	< 0.001	0.041 (-0.021–0.102)	0.198	0.006 (-0.017–0.030)	0.590	0.077 (0.021–0.133)	0.007	
+CCI score squared	-0.0044 (-0.01130–0.00263)	0.222	-0.00222 (-0.00511–0.00067)	0.133	0.00392 (-0.00771–0.01560)	0.509	-0.00013 (-0.00428–0.00402)	0.951	-0.00559 (-0.01460–0.00346)	0.226	
+Dual eligible for Medicaidd	0.067 (0.008–0.127)	0.027	-0.026 (-0.051–0.000)	0.046	0.011 (-0.104–0.126)	0.852	-0.012 (-0.055–0.030)	0.574	0.055 (-0.059–0.169)	0.343	
+Disability entitlement for Medicared	0.018 (-0.067–0.102)	0.682	-0.021 (-0.054–0.012)	0.218	-0.001 (-0.155–0.153)	0.989	-0.040 (-0.097–0.018)	0.176	-0.053 (-0.189–0.083)	0.445	
+Died during episodeb	0.317 (0.258–0.376)	< 0.001	-0.208 (-0.229– -0.187)	< 0.001	-0.026 (-0.110–0.058)	0.540	-0.163 (-0.214– -0.111)	< 0.001	0.000 (-0.129–0.128)	0.998	
+Number of previous episodes	-0.117 (-0.128– -0.106)	< 0.001	-0.022 (-0.032– -0.012)	< 0.001	-0.058 (-0.086– -0.030)	< 0.001	-0.017 (-0.024– -0.010)	< 0.001	-0.002 (-0.014–0.010)	0.782	
+HRRs	Fixed effects of HRRs included in all modelse	
+Stage of diseasec	
+  Stage 1	NA	Reference	Reference	NA	NA	
+  Stage 2	NA	0.056 (0.000–0.111)	0.050	0.448 (0.245–0.651)	< 0.001	NA	NA	
+  Stage 3	Reference	0.209 (0.171–0.247)	< 0.001	0.394 (0.232–0.557)	< 0.001	NA	NA	
+  Stage 4	0.420 (0.362–0.478)	< 0.001	0.271 (0.233–0.308)	<0.001	0.679 (0.567–0.791)	< 0.001	NA	NA	
+aCoefficients were acquired from generalized linear models with a log link and gamma distribution with total cost per episode (adjusted for Medicare payment rate differentials and rate changes over time) as the outcome. Indicator variables were included for each HRR.
+
+bReference group for analysis was “female.”
+
+cFor BC, only stages 3 and 4 were included in the study. For MM and CML, standard American Joint Committee on Cancer staging does not apply and was not acquired from the data. For stage variables, “reference” indicates that a given stage was the reference group for analysis.
+
+dReference group for analysis is “no.”
+
+eWald test P < 0.001 for all cancer types, rejecting the null hypothesis that all HRR coefficients = 0 and indicating significance in improving model fit.
+
+BC = advanced breast cancer (stages 3 and 4); CCI = Charlson Comorbidity Index; CI = confidence interval; CML = chronic myeloid leukemia; HRR = hospital referral region; MM = multiple myeloma; NA = not available; NSCLC = non-small cell lung cancer; RCC = renal cell carcinoma.
+
+Our analysis showed that, after controlling for patient characteristics, there were significant differences in standardized spending per episode across HRRs for each of the 5 cancers (Figure 1). A Wald test determined that including regional effects created a statistically significant improvement in the model fit for each cancer and indicated that the region coefficients were significantly different from one another and simultaneously not equal to zero (Wald test P < 0.001 for all cancers). Compared with spending at the 20th percentile episode, spending at the 80th percentile ranged from 25% higher ($57,392 vs. $45,995 for MM) to 47% higher ($36,920 vs. $25,127 for RCC), indicating that the amount of variation in spending was the least for MM and the greatest for RCC. The interquartile ranges varied from approximately $5,400 for advanced BC to about $11,000 for CML.
+
+FIGURE 1 Regional Variation in Standardized Spending for 5 Types of Cancer (2013 USD)
+
+Next, we examined the relationship between the patient-level hazard of death—that is, the probability of death at any time conditional on survival up to that time—and regional per episode standardized spending and other covariates that described the patient. The estimated coefficients appear in Table 3. Our specifications passed the Schoenfeld residuals test for proportional hazards. The effects of covariates generally accorded with expectations. As one might expect, risk of death increased with more advanced stages of cancer at time of diagnosis, but that increased risk declined slightly over time. Interestingly, dual eligibility for Medicaid was also associated with a statistically significant increased probability of death, ranging from a 14% increase for NSCLC patients (hazard ratio (HR) = 1.14, P < 0.001) to a 34% increase for MM patients (HR = 1.34, P = 0.001) in all cancer types except CML (HR = 0.82, P = 0.317).
+
+TABLE 3 Hazard Ratios for Deatha,b
+
+	Cancer Type	
+BC	NSCLC	RCC	MM	CML	
+HR (95% CI)	P Value	HR (95% CI)	P Value	HR (95% CI)	P Value	HR (95% CI)	P Value	HR (95% CI)	P Value	
+Standardized spending for a region (in $10,000s USD)	1.06 (0.91–1.24)	0.442	0.93 (0.88–0.98)	0.006	0.94 (0.81–1.10)	0.455	0.87 (0.77–0.98)	0.019	0.87 (0.69–1.09)	0.216	
+Age at diagnosis	0.91 (0.87–0.96)	< 0.001	0.94 (0.91–0.97)	< 0.001	0.86 (0.77–0.96)	0.009	0.95 (0.87–1.04)	0.272	1.01 (0.86–1.19)	0.882	
+Age at diagnosis squared	1.0009 (1.0005–1.0012)	< 0.001	1.0006 (1.0003–1.0008)	< 0.001	1.0012 (1.0004–1.0020)	0.002	1.0007 (1.0001–1.0013)	0.031	1.0004 (0.9993–1.0014)	0.519	
+Malec	Omitted	1.32 (1.26–1.38)	< 0.001	1.20 (0.99–1.45)	0.062	1.08 (0.94–1.24)	0.258	1.21 (0.86–1.71)	0.277	
+Medicaid eligibled	1.26 (1.10–1.44)	0.001	1.14 (1.08–1.20)	< 0.001	1.28 (1.02–1.59)	0.030	1.34 (1.13–1.58)	0.001	0.82 (0.57–1.20)	0.317	
+Medicare entitlement through disabilityd	1.41 (1.19–1.67)	< 0.001	1.11 (1.04–1.19)	0.001	0.92 (0.67–1.28)	0.632	1.15 (0.92–1.45)	0.227	1.19 (0.72–1.97)	0.490	
+CCI score	1.21 (1.11–1.32)	< 0.001	1.07 (1.03–1.11)	< 0.001	1.07 (0.94–1.22)	0.305	1.10 (0.99–1.21)	0.076	1.10 (0.88–1.37)	0.415	
+CCI score squared	0.9790 (0.9640–0.9950)	0.010	0.9957 (0.9891–1.0024)	0.212	0.9948 (0.9726–1.0176)	0.655	0.9977 (0.9800–1.0156)	0.797	1.0010 (0.9710–1.0318)	0.951	
+Stage at diagnosis	
+  Stage 1 (fixed)	NA	Reference	Reference	NA	NA	
+  Stage 1 (time varying)	NA	Reference	Reference	NA	NA	
+  Stage 2 (fixed)	NA	1.31 (1.09–1.57)	0.004	2.15 (1.08–4.31)	0.028	NA	NA	
+  Stage 2 (time varying)	NA	1.00 (0.83–1.20)	0.960	1.01 (0.05–2.02)	0.983	NA	NA	
+  Stage 3 (fixed)	Reference	3.06 (2.71–3.45)	< 0.001	2.47 (1.48–4.12)	0.001	NA	NA	
+  Stage 3 (time varying)	Reference	0.98 (0.87–1.10)	0.739	1.00 (0.60–1.67)	0.990	NA	NA	
+  Stage 4 (fixed)	3.76 (3.14–4.51)	< 0.001	6.72 (5.97–7.58)	< 0.001	12.50 (8.09–19.33)	< 0.001	NA	NA	
+  Stage 4 (time varying)	0.99 (0.82–1.19)	0.906	0.97 (0.86–1.09)	0.561	0.97 (0.63–1.50)	0.887	NA	NA	
+Race/ethnicity	
+  White	Reference	Reference	Reference	Reference	Reference	
+  Black	1.10 (0.92–1.30)	0.301	1.00 (0.92–1.07)	0.930	1.17 (0.82–1.67)	0.380	0.99 (0.80–1.22)	0.890	0.97 (0.57–1.64)	0.914	
+  Hispanic	0.83 (0.59–1.17)	0.294	1.00 (0.85–1.18)	0.964	0.86 (0.53–1.40)	0.540	0.93 (0.64–1.36)	0.714	2.64 (0.07–100.09)	0.601	
+  Asian	0.93 (0.67–1.30)	0.670	0.90 (0.82–1.00)	0.040	1.03 (0.69–1.54)	0.873	0.98 (0.72–1.34)	0.922	0.88 (0.42–1.83)	0.728	
+  Native American	NC	1.10 (0.68–1.78)	0.710	2.46 (1.23–4.94)	0.011	NC	NC	
+  Other	1.03 (0.64–1.67)	0.888	0.90 (0.77–1.04)	0.151	1.15 (0.65–2.04)	0.634	0.96 (0.55–1.67)	0.884	NC	
+  Unknown	NC	0.97 (0.56–1.69)	0.926	NC	NC	NC	
+aConfidence intervals and P values reflect bootstrapped standard errors.
+
+bSome values were not calculable due to insufficient sample size and are indicated by “NC”.
+
+cReference group for analysis is “female.”
+
+dReference group for analysis is “no.”
+
+BC = advanced breast cancer (stages 3 and 4); CI = confidence interval; CML = chronic myeloid leukemia; HR = hazard ratio; MM = multiple myeloma; NA = not available; NC = not calculable; NSCLC = non-small cell lung cancer; RCC = renal cell carcinoma; SE = standard error; USD = U.S. dollars.
+
+Figure 2 illustrates the estimated effect of spending per episode on the hazard of death. We found a statistically significant reduction in the hazard of death for NSCLC and MM when there was greater spending per episode. Specifically, the hazard rate of dying for patients with NSCLC and MM, respectively, decreased by 7% (HR = 0.93, P = 0.006) and 13% (HR = 0.87, P = 0.019) for a $10,000 increase in standardized episode spending. For the 3 remaining cancer sites, results suggested a reduction in hazard of death in RCC and CML and an increase in advanced BC as standardized spending increased, but none of these results was statistically significant at the 5% level. After multiple comparisons adjustment using the Benjamini-Hochberg procedure with an FDR of 5%, the effects of increased spending on improved survival for NSCLC and MM remained statistically significant.
+
+FIGURE 2 Hazard Ratios for a $10,000 Increase in HRR Standardized Spending
+
+Discussion
+
+Our results on standardized spending per episode across HRRs bear a similarity to other analyses of geographic variations in health care by demonstrating that such variations exist and are significant. When coupled with findings of no systematic variation in outcomes, previous analyses have been interpreted as evidence of waste in the system. Brooks et al. (2013) examined the extent of regional variation in cancer spending in advanced cancer and found substantial regional variation in Medicare spending categories for advanced non-small cell lung, colorectal, pancreatic, breast, and prostate cancers.11 The authors also examined the relationship between spending and cohort-level survival outcomes, finding no consistent or statistically significant relationship. In contrast, Keating et al. (2012), as with our study, found greater survival among lung cancer patients in higher-spending regions.12 Hassett et al. (2014) focused on breast cancer and, similar to Brooks et al. and our study, found no correlation for that cancer type between survival and either spending level or quality of care.13
+
+Our study differs in several ways from earlier studies of geographic variation in cancer care.11-17 Unlike previous work, we used the OCM definition of an episode. Thus, our analysis can more readily speak to the potential effects of reducing spending during chemotherapy-centered episodes such as those used by the OCM. We also differ from other studies in some of the cancer types that we included—to our knowledge, we are the first to consider RCC, MM, and CML. We also used more recent data and included Medicare Part D chemotherapy costs (and Part D drug costs more generally).
+
+For 2 of the cancer types we studied (NSCLC and MM), we found a beneficial and statistically significant effect of spending on survival, with greater standardized spending per episode at the HRR level associated with lower hazard of death. While these results do not rule out the existence of inefficiencies, they do indicate that greater spending is associated with improved survival outcomes. Unless further studies are able to separate out effective from ineffective spending, the implication is that across-the-board reductions in spending may reduce survival, on average, for some cancer types.
+
+One might ask whether the additional survival is worth the cost. Given the estimated HRs, one can approximate the additional life expectancy, compared with the median, from a $10,000 increase in standardized spending per episode. For NSCLC, an additional $10,000 in standardized episode spending was associated with a 1.53 month increase in median survival. After adjusting for number of episodes per patient, that translates to about $107,000 per additional life-year. Utility adjustments for reduced quality of life for patients with this cancer vary across the literature, but depending on the utility value chosen, the range of cost per quality-adjusted life-year (QALY) is about $160,000-$228,000.18-20 For MM, an additional $10,000 in spending is associated with an increase in median survival estimated at 11 months, or after adjusting for multiple episodes per patient, a cost of $36,000 per additional life-year. Applying a utility adjustment for a myeloma patient yields a cost per added QALY number of $69,000.21 Details on these calculations are in Appendix B (available in online article). Neumann et al. (2014) recommended that analysts use a range of thresholds between $50,000 and $200,000 per QALY for defining cost-effective care.22 Hence, the additional spending is near the higher end of the suggested cost-effectiveness range of cost per QALY thresholds for NSCLC and near the lower, more favorable, end for MM.
+
+With regard to the OCM specifically, 2 important points should be recognized. First, a key policy lever in the OCM is the setting of the spending thresholds, below which a practice receives a performance-based payment (2.75% or 4% below the otherwise calculated target for a practice depending on whether two-sided or one-sided risk is used). Such thresholds are incentives to reduce spending, and our results suggest that, in setting these thresholds for specific types of cancer, policymakers should consider the potential for blunt spending reductions to affect survival. Second, in setting its targets for practices, the OCM does allow differences based on the HRR of the practice. Thus, the OCM allows the current amount of geographic variation to continue but encourages reductions in spending from those baseline differentials. Our analysis simply used the existing geographic variation as a tool for measuring the effect of episode spending on survival.
+
+Our results are relevant to the general set of policies that aim to provide cancer care at lower spending levels than otherwise. Although we defined episodes in the same way as the OCM, we defined spending slightly differently. Our study included spending on patient copays and deductibles and all spending on Part D drugs, although the OCM model excludes some of these sources. We took this approach so that our results would have more general applicability and could answer the broader questions of whether and how systemwide spending is associated with survival. This also has the effect of providing a broader, societal perspective on total spending, as is currently recommended for economic evaluations in health care.23
+
+While we did find evidence of an association between improved survival outcomes and higher episode spending in some cases, our analysis did not identify the specific differences in treatment choices that explain the survival differences. As in the OCM, our measure of episode spending included all types of services, although chemotherapy costs were likely a substantial fraction of costs in these episodes. Several expensive therapy options existed in the time frame of our analysis for all the cancer types we studied (in the case of NSCLC, these included crizotinib, erlotinib, and gefitinib, and for MM, these included bortezomib, lenalidomide, and carfilzomib). Future research using real-world evidence may be able to distinguish differences in practice patterns in chemotherapy choice and document survival differences.
+
+Use of quality measures may be one strategy for encouraging more efficient spending in APMs and related strategies (e.g., clinical pathways) that also aim to limit spending. The OCM payment methodology bases the amount of a performance-based payment on a practice’s performance on several quality measures. It is not yet known how the use of the particular quality measures in the OCM payment formula affects outcomes. Our analysis suggests that physicians, pharmacists, and other health professionals involved in the OCM and in other APMs should carefully consider the areas in which they reduce spending and the potential effect on outcomes, especially in NSCLC and MM.
+
+Limitations
+
+As with all analyses, our study has limitations. First, our data were limited to regions available in the SEER registries, which cover only about 28% of the U.S. population, and to the Medicare population. Hence, the analysis may not be representative of the nation as a whole or to APMs outside of Medicare; however, because the OCM is a Medicare initiative for the traditional fee-for-service population, a Medicare sample such as ours is appropriate to the OCM.
+
+In addition, the SEER-Medicare database, which is maintained by the National Cancer Institute, provided us with well-regarded measures of diagnosis, staging, and diagnosis dates and also covered a wide variety of geographic regions. Because we studied patients enrolled in Medicare Parts A, B, and D, our results may not be representative of the entire Medicare-eligible population, if those who opt into Part D are systematically different from others.
+
+Also, while we structured episodes of cancer care to reflect the definition used in the OCM, our data were drawn from a period ending in 2013. Therefore, any recent changes in treatment patterns for the included cancers are not reflected here.
+
+Finally, although survival is clearly an important outcome for assessing cancer treatment, another limitation is that we restricted our study to only that measure. It is possible that other endpoints or patient-reported outcomes, such as quality of life and satisfaction, are also affected by spending differences.
+
+Conclusions
+
+After controlling for differences caused by Medicare payment differentials and patient characteristics, we found statistically significant differences in spending per episode across geographic regions in 5 different types of cancer. While such differences suggest the possibility that inefficient or wasteful spending is occurring, we also found evidence that greater spending is associated with improved survival in NSCLC and MM, with a similar but statistically insignificant association in 2 other cancer types. The OCM and other future payment models that move away from fee-for-service represent a move toward a new variant of Medicare managed care in which practices are faced with a new set of incentives within “traditional” Medicare. Physicians, pharmacists, other health care professionals, and policymakers should maintain awareness of how curbing resource use in particular areas may adversely affect outcomes and build incentive and management mechanisms accordingly.
+
+ACKNOWLEDGMENTS
+
+This study used the linked SEER-Medicare database. The interpretation and reporting of these data are the sole responsibility of the authors. The authors acknowledge the efforts of the National Cancer Institute; the Office of Research, Development and Information, CMS; Information Management Services; and the Surveillance, Epidemiology, and End Results (SEER) Program tumor registries in the creation of the SEER-Medicare database. The authors also thank Amitabh Chandra, Sarah Beers, Michelle Brauer, Sarah Green, Caroline Huber, Aubrey Love, Joanna MacEwan, and Lara Yoon for their advice and assistance.
+
+APPENDIX A Further Details: Histology Codes and Standardization for Differences in Medicare Prices
+
+Study Population: Histology Codes for RCC and NSCLC	
+In addition to the ICD-O-3 codes reported in the text, the RCC and NSCLC populations were further refined using histology codes (RCC: 8260, 8310, 8312, 8316, 8317, 8318, 8319, 8320, 8510, 8959. NSCLC: 8012, 8013, 8014, 8022, 8031, 8032, 8033, 8046, 8052, 8070, 8071, 8072, 8073, 8082, 8083, 8084, 8123, 8140, 8250, 8251, 8252, 8253, 8254, 8255, 8260, 8310, 8333, 8430, 8470, 8480, 8481, 8490, 8550, 8560, 8972, 8980).	
+Standardization for Differences in Medicare Payment Rates	
+Because we fundamentally care about differences in utilization, we adjusted spending for the different prices that Medicare pays for the same services in different regions. For each claim, we adjusted the spending value to a standardized amount so that the same service will be priced at the same dollar value regardless of region or price markups received by particular providers, such as the indirect medical education adjustments or disproportionate share adjustment received by certain hospitals for inpatient care, or adjustments to payment rates to reflect varying wage rates or input costs across different regions.
+The standardization method varied based on claim type. For inpatient hospital costs, we proceeded as follows, using LOS and DRG.	
+Spending Calculation for Inpatient Hospitalizations	
+Costs for extra short inpatient hospital stays accompanied by a transfer to another hospital, SNF, or IRF were calculated as:
+{2 × (Daily Base Amount)Unadjusted + (LOS-1) × (Daily Base Amount)Unadjusted} × DRGWeight + (Outlier Payment)
+where weights and geometric mean LOS values were available from CMS by claim year and DRG. The cost of all other inpatient hospitalizations was calculated as:
+(Standard Base Amount)Unadjusted × DRGWeight + (Outlier Payment)
+(These methods for obtaining a standardized spending amount for inpatient claims follow Schousboe et al., 2014.)24
+Part B drug claims did not need to be adjusted because of uniform payment nationwide. All other Part A and Part B claims were assigned to a cost-adjustment category to match up with tables developed by CMS to adjust for payment differences at the county level (categories are long-term care hospital, SNF, HHA, hospice, federally qualified health center/rural health center, hospital outpatient, ambulatory surgery center, E + M, procedures, imaging, DME, tests, general outpatient, and ambulance).25 The base costs were multiplied by the CMS ratio of standardized to actual cost for the appropriate cost category, FIPS county, and claim year. If the value of the actual or standardized amount was zero or missing for the county, we used the state average. Part D claims were not adjusted because drugs are commodities with negotiations occurring over pricing between competing drug plans and manufacturers.
+Because our spending data covered several years, and Medicare fee schedules increase year to year, we also corrected for changes in prices over time. All claims were set to 2013 U.S. dollars. Inpatient hospitalization claims were adjusted based on the standardized base amount. All hospital outpatient and ambulatory claims were adjusted based on the standard OPPS conversion factor for the calendar year. Physician services, E + M, procedure and imaging claims were adjusted based on Hanh (2014).26 SNF, HHA, hospice, long-term care, inpatient psychiatric, and inpatient rehab were adjusted based on the appropriate CMS MarketBasket data. The payment amount for outpatient, carrier, HHA, hospice, and DME claims on or after April 1, 2013, were adjusted to account for sequester. Part D drugs were adjusted using the Consumer Price Index for prescription drugs, and Part B drugs were adjusted using the PPI for pharmaceutical preparations. Clinical labs were adjusted based on the Consumer Price Index for medical care services. Ambulance services were adjusted based on the CMS Ambulance Fee Schedule. DME claims were adjusted based on the MEI.	
+CMS = Centers for Medicare & Medicaid Services; DME = durable medical equipment; DRG = diagnosis-related group; E + M = evaluation and management; FIPS = Federal Information Processing Standards; HHA = home health agency; ICD-O-3 = International Classification of Diseases for Oncology, Third Revision; IRF = inpatient rehabilitation facility; LOS = length of stay; MEI = Medicare Economic Index; NSCLC = non-small cell lung cancer; OPPS = outpatient prospective payment system; PPI = Producer Price Index; RCC = renal cell carcinoma; SNF = skilled nursing facility.
+
+APPENDIX B Cost per Additional Life-Year
+
+For the 2 cancer types that showed a statistically significant improvement in survival for a $10,000 increase in standardized spending, we calculated the cost per additional life-year as follows.	
+NSCLC	
+In general, with proportional hazards the following relationship holds:
+S1(t)=S0(t)r (1)
+where, in our context, r is the hazard ratio for a $10 thousand increase in standardized spending, S1(t) is survival at time t with $10 thousand more of standardized spending, and S0(t) is survival at time t at the base value of standardized spending.
+Our method was to calculate the value of S0(t) at the life expectancy of a patient with $10 thousand more in spending where, by definition, S1(t) equals 0.5. We then used the Kaplan-Meier (K-M) curve that we generated from our NSCLC data as the base case survival curve and found that value of t associated with the life expectancy at $10 thousand more in spending. We took the difference between that value and the life expectancy of someone with base level spending, which by definition was the value of t at which S0(t) equals 0.5, which we also read from the K-M curve, to get the additional amount of life expectancy at $10 thousand more in standardized episode spending.
+Applying the above method:
+S0(t)=(0.5)1/0.929=0.4742. (2)
+Reading from the K-M survival curve, the value derived in (2) was associated with 22 months of survival since diagnosis. The difference between that and the value from the K-M curve where S0 is 0.5 equaled a 2-month increase in survival.
+Multiplying by 6 to translate to 1-year of survival gave $60 thousand per episode. One must also account for multiple episodes per patient. After adjusting for 1.787 episodes per patient, we concluded that our survival estimates imply a cost of $107 thousand per additional life-year in the case of NSCLC.
+In order to convert to a cost per QALY measure, we had to adjust for quality of life for an NSCLC patient. There are several estimates in the literature.18-20 Nafees et al. (2008) provides a representative range from 0.67 to 0.47, yielding cost per QALY values ranging from about $160 thousand to about $228 thousand.19	
+MM	
+Analogous calculations were done to evaluate the additional life expectancy for a $10 thousand increase in spending for MM. Using the HR from the Cox regression for MM:
+S0(t)=(0.5)1/0.866=0.4491. (3)
+From the K-M curve for MM, the value calculated in (3) was associated with 78 months of survival, while S0(t) was 0.5 at 69 months of survival for an increase of 11 months in life expectancy for $10 thousand more in standardized spending per episode. Converting to a full-year basis by multiplying by 12/11 and adjusting for 3.267 episodes per patient, our survival estimates imply a cost of $36 thousand per additional life-year. Using the quality adjustment for myeloma patients of 0.52 reported in Kharroubi et al. (2015) gave a cost per QALY of $69 thousand.21	
+Alternative Method	
+An alternative method of calculating the implied cost per additional life-year yields very similar results. If one assumes an exponential distribution rather than using the empirical K-M curve to approximate the survival function, the following relationship holds:
+r = m0/m1, (4)
+where m0 is median survival in the base case and m1 is median survival at $10 thousand more in standardized spending. Using equation (4) with our NSCLC results and using our K-M curve at survival equal to 0.5 to yield m0 equal to 20 months, we obtain
+m1=m0/r=20/0.929=21.53 months (5)
+or a 1.53-month gain in life expectancy for $10 thousand more in spending per episode. Translating to a 12-month gain and correcting for multiple episodes per patient, we obtained $140 thousand as the cost of an additional life-year.
+Applying equation (4) to the case of MM, analogous calculations show
+m1=m0/r=69/0.866=79.68 months (6)
+or a 10.68-month gain in life expectancy for $10 thousand more in spending per episode. That translates to $37 thousand as the cost of an additional life-year.
+We see that both methods yield similar results. We prefer the first method because it uses the K-M curve from the data as the approximation of the survival function and does not require the assumption of an exponentially distributed survival curve.	
+MM = multiple myeloma; NSCLC = non-small cell lung cancer; QALY = quality-adjusted life-year.
+==== Refs
+REFERENCES
+
+1. Newcomer LN, Gould B, Page RD, Donelan SA, Perkins M. Changing physician incentives for affordable, quality cancer care: results of an episode payment model. J Oncol Pract. 2014;10 (5 ):322-26.25006221
+2. Kline RM, Bazell C, Smith E, Schumacher H, Rajkumar R, Conway PH. Centers for Medicare and Medicaid Services: using an episode-based payment model to improve oncology care. J Oncol Pract. 2015;11 (2 ):114-16.25690596
+3. Center for Medicare & Medicaid Innovation. OCM performance-based payment methodology (version 2.1). Prepared by RTI International and Actuarial Research Corporation. In: Oncology care model performance periods 1 and 2 payment methodology [zip file]. December 27, 2017. Available at: https://innovation.cms.gov/initiatives/oncology-care/. May 8, 2018.
+4. Clough JD, Kamal AH. Oncology care model: short-and long-term considerations in the context of broader payment reform. J Oncol Pract. 2015;11 (4 ):319-21.26060221
+5. Skinner J. Causes and consequences of regional variations in health care. In: Pauly MV, McGuire TG, Barros PP, eds. Handbook of Health Economics, Vol 2. Waltham, MA: North Holland; 2011:45-93.
+6. Congressional Budget Office. Geographic variation in health care spending. February 2008. Available at: https://www.cbo.gov/sites/default/files/110th-congress-2007-2008/reports/02-15-geoghealth_0.pdf. Accessed April 27, 2018.
+7. IOM Committee on Geographic Variation in Health Care Spending and Promotion of High-value Care. Geographic variation in spending, utilization and quality: Medicare and Medicaid beneficiaries. Prepared by Acumen. May 2013. Available at: http://www.nationalacademies.org/hmd/~/media/Files/Report%20Files/2013/Geographic-Variation/Sub-Contractor/Acumen-Medicare-Medicaid.pdf. Accessed April 27, 2018.
+8. The Dartmouth Atlas of Health Care. Data by region. About our regions: hospital referral regions. Retrieved June 26, 2017. Available at: http://www.dartmouthatlas.org/data/region/. Accessed April 27, 2018.
+9. Centers for Medicare & Medicaid Services. Medicare data for the geographic variation public use file: a methodological overview. March 2017. Available at: https://www.cms.gov/Research-Statistics-Data-and-Systems/Statistics-Trends-and-Reports/Medicare-Geographic-Variation/Downloads/Geo_Var_PUF_Methods_Paper.pdf. Accessed April 27, 2018.
+10. Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Series B Stat Methodol. 1995;57 (1 ):289-300.
+11. Brooks GA, Li L, Sharma DB, et al . Regional variation in spending and survival for older adults with advanced cancer. J Natl Cancer Inst. 2013;105 (9 ):634-42.23482657
+12. Keating NL, Landrum MB, Lamont EB, Bozeman SR, McNeil BJ. Area-level variations in cancer care and outcomes. Med Care. 2012;50 (5 ):366-73.22437623
+13. Hassett MJ, Neville BA, Weeks JC. The relationship between quality, spending and outcomes among women with breast cancer. J Natl Cancer Inst. 2014;106 (10 ). pii: dju242. Available at: https://academic.oup.com/jnci/article/106/10/dju242/928111. Accessed May 2, 2018.
+14. Brooks GA, Li L, Uno H, Hassett MJ, Landon BE, Schrag D. Acute hospital care is the chief driver of regional spending variation in Medicare patients with advanced cancer. Health Aff (Millwood). 2014;33 (10 ):1793-800.25288424
+15. Landrum MB, Meara ER, Chandra A, Guadagnoli E, Keating NL. Is spending more always wasteful? The appropriateness of care and outcomes among colorectal cancer patients. Health Aff (Millwood). 2008;27 (1 ):159-68.18180491
+16. Skolarus TA, Ye Z, Zhang S, Hollenbeck BK. Regional differences in early stage bladder cancer care and outcomes. Urology. 2010;76 (2 ):391-96.20394976
+17. Fisher ES, Wennberg DE, Stukel TA, Gottlieb DJ, Lucas FL, Pinder EL. The implications of regional variations in Medicare spending. Part 2: health outcomes and satisfaction with care. Ann Intern Med. 2003;138 (4 ):288-98.12585826
+18. Doyle S, Lloyd A, Walker M. Health state utility scores in advanced non-small cell lung cancer. Lung Cancer. 2008;62 (3 ):374-80.18467000
+19. Nafees B, Stafford M, Gavriel S, Bhalla S, Watkins J. Health state utilities for non small cell lung cancer. Health Qual Life Outcomes. 2008;6 (1 ):84.18939982
+20. Trippoli S, Vaiani M, Lucioni C, Messori A. Quality of life and utility in patients with non-small cell lung cancer. Pharmacoeconomics. 2001;19 (8 ):855-63.11596837
+21. Kharroubi SA, Edlin R, Meads D, Browne C, Brown J, McCabe C. Use of Bayesian Markov Chain Monte Carlo methods to estimate EQ-5D utility scores from EORTC QLQ data in myeloma for use in cost-effectiveness analysis. Med Decis Making. 2015;35 (3 ):351-60.25784746
+22. Neumann PJ, Cohen JT, Weinstein MC. Updating cost-effectiveness—the curious resilience of the $50,000-per-QALY threshold. N Engl J Med. 2014;371 (9 ):796-97.25162885
+23. Neumann PJ, Sanders GD, Russell LB, Siegel JE, Ganiats TG, eds. Cost-Effectiveness in Health and Medicine. 2d ed. Oxford: Oxford University Press; 2016.
+24. Schousboe JT, Paudel ML, Taylor BC, et al . Estimation of standardized hospital costs from Medicare claims that reflect resource requirements for care: impact for cohort studies linked to Medicare claims. Health Serv Res. 2014;49 (3 ):929-49.24461126
+25. Centers for Medicare & Medicaid Services. Medicare geographic variation. Public use file. 2017. Retrieved May 5, 2017. Available at: https://www.cms.gov/research-statistics-data-and-systems/statistics-trends-and-reports/medicare-geographic-variation/gv_puf.html. Accessed April 27, 2018.
+26. Hanh J. Medicare physician payment updates and the sustainable growth rate (SGR) system. Congressional Research Service R40907. June 12, 2014. Available at: https://greenbook-waysandmeans.house.gov/sites/greenbook.waysandmeans.house.gov/files/R40907_gb.pdf. Accessed May 8, 2018.
+

PMC10397929.txt

@@ -0,0 +1,65 @@
+
+==== Front
+J Manag Care Spec Pharm
+J Manag Care Spec Pharm
+jmcsp
+Journal of Managed Care & Specialty Pharmacy
+2376-0540
+2376-1032
+Academy of Managed Care Pharmacy
+
+10.18553/jmcp.2018.24.7.712
+Letters
+The Authors Respond
+Carlson Josh J. MPH, PhD 1 *
+Guzauskas Gregory F. MSPH, PhD 1
+Chapman Richard H. PhD, MS 2
+Synnott Patricia G. MS, MALD 2
+Liu Shanshan MS 2
+Russo Elizabeth T. MD 2
+Pearson Steven D. MD, MSc 2
+Brouwer Elizabeth D. MPH 1
+Ollendorf Daniel A. PhD 2
+1 The Comparative Health Outcomes, Policy, and Economics (CHOICE) Institute, University of Washington, Seattle
+2 Institute for Clinical and Economic Review, Boston, Massachusetts
+* Carlsojj@u.washington.edu
+Funding for the Carlson et al. study was provided in part by the Institute for Clinical and Economic Review. Ollendorf, Synnott, Chapman, and Pearson disclosed grants from Blue Shield of California Foundation, California Health Care Foundation, Laura and John Arnold Foundation, Aetna, AHIP, Anthem, Blue Shield of California, CVS Caremark, Express Scripts, Harvard Pilgrim Health Care, OmedaRx, United Healthcare, Kaiser Permanente, Premera, AstraZeneca, Genentech, GlaxoSmithKline, Johnson & Johnson, Merck, National Pharmaceutical Council, Takeda, Pfizer, Novartis, Lilly, Spark Therapeutics, Sanofi, Prime Therapeutics, and Health Care Service Corporation. Carlson disclosed grants from the Institute for Clinical and Economic Review and personal fees from Seattle Genetics, Genentech, and Pfizer. Russo, Guzauskas, Liu, and Brouwer have nothing to disclose.
+
+7 2018
+24 7 10.18553/jmcp.2018.24.7.712Copyright © 2018, Academy of Managed Care Pharmacy. All rights reserved.
+2018
+https://creativecommons.org/licenses/by/4.0/ This article is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use and redistribution provided that the original author and source are credited.
+==== Body
+pmcWe appreciate the opportunity to respond to the letter by Ailawadhi et al. regarding our article “Cost-effectiveness of Drugs to Treat Relapsed/Refractory Multiple Myeloma in the United States” and to provide clarifications where necessary to the points raised in their letter.
+
+We do acknowledge 4 minor errors in our publication and have submitted appropriate corrections to JMCP. We will comment on 2 of those errors here, since the third and fourth were merely typos. The first relates to the progression-free survival (PFS) hazard ratio estimate for the panobinostat/bortezomib/dexamethasone combination (PAN + BOR + DEX) versus lenalidomide (LEN) + DEX in the third-line treatment setting. Our study reported this estimate to be 0.54 (credible range = 0.50-0.83) whereas the correct numbers should be 0.59 (credible range = 0.31-1.10).1 This change also results in a slight revision to the comparative PAN + BOR + DEX versus LEN + DEX estimates, with the new estimates for total incremental costs and quality-adjusted life-years being -$76,328 and 1.19, respectively. Thus, PAN + BOR + DEX is still found to be dominant versus LEN + DEX in this comparison.
+
+The second correction is to add the word “treatment” before the word “cost” in the “Discussion” section paragraph in which we discuss the Jakubowiak et al. study.2 The new sentence should read as follows:
+
+“Finally, we note that 1 of the findings of the Jakubowiak et al. analysis appears to be counterintuitive, in that CFZ + LEN + DEX patients spend approximately 4 years in the postprogression state in the model versus approximately 3 years for LEN + DEX; however, the postprogression treatment costs for LEN+DEX are reported to be higher.”
+
+The overall postprogression costs are slightly higher for carfilzomib (CFZ) + LEN + DEX by $856; however, the key point of the sentence remains valid, especially given the high cost of treating relapsed/refractory multiple myeloma; that is, patients receiving CFZ + LEN + DEX spent about 10 months more in the progression health state but had lower treatment costs and only slightly higher overall health state costs. This increase appears to be due to the use of different distributions of subsequent treatments by treatment arm and the assumption that subsequent treatments would stop after 17 cycles. Jakubowiak et al. suggest that they ran a scenario analysis using the same distribution, but the results of this scenario analysis are not provided.
+
+We take this opportunity to point out an additional issue with the Jakubowiak et al. analysis. Their base case analysis uses 2 different health state utility values for the postprogression health state, even though patients in this health state should be relatively homogenous with respect to their health-related quality of life while in the same health state—that is, the patients will have already progressed on CFZ + LEN + DEX and LEN + DEX and would now be assumed to be treated similarly. Notably, the values applied to the CFZ + LEN + DEX intervention group after progression were higher than those for LEN + DEX (0.664 vs. 0.643). We note this additional bias in response to Ailawadhi et al.’s suggestion that “the cost-effectiveness of CFZ + DEX versus BOR + DEX has been unequivocally established,” with reference to the Jakubowiak et al. study.2
+
+Ailawadhi et al. also suggested additional limitations to the network meta-analysis (NMA) used in our study in terms of comparability. We stated in our article that “trial populations were similar with respect to age, ECOG performance status, ISS stage, receipt of previous stem cell transplant, and number and distribution of previous regimens.” We acknowledged that definitions of disease risk varied. Other studies have used standard and comparable methods to assess PFS. Because PFS was unavailable for MM-009 and MM-010, time-to-progression was used; this approach has been used in other indirect comparisons of the agents of interest, including in the NICE panobinostat review cited by Ailawakhi et al. in their letter. Other recently published NMAs of relapsed/refractory multiple myeloma have taken a similar approach and have included a similar study set in their networks.3-6
+
+In response to the comment that our results lack face validity because “LEN + DEX is estimated to be less effective than BOR + DEX even though all listed trials show that LEN + DEX achieved double the PFS than BOR + DEX,” we note that LEN + DEX is not estimated to be less effective than BOR + DEX. The NMA did not show the 2 regimens to be statistically different. Importantly, trials of LEN + DEX have had follow-up durations that were 2-3 times longer than the BOR+DEX trials and were treat-to-progression, whereas BOR+DEX was given for a fixed number of cycles. Furthermore, and as we have already mentioned, we have corrected the PFS hazard ratio for PAN + BOR + DEX versus LEN + DEX, which now shows that the credible range includes 1. We also note that our study has substantial cautionary language about this result, as shown in this example from the article:
+
+“Results for PAN+BOR+DEX in the third-line setting should be interpreted with great caution because of censoring issues and high rates of toxicity-related discontinuation in the overall and third-line subgroup population of the PANORAMA-1 study. PAN + BOR + DEX is also only 1 of 2 regimens without direct comparative evidence versus LEN + DEX; therefore, greater reliance on the study network and its assumptions regarding minimal heterogeneity across study populations and constant hazards over time was required. While censoring was factored into our analytic approach, the relative treatment effect of PAN + BOR + DEX versus LEN + DEX had much greater uncertainty than the other comparisons.”1
+
+Regarding the comment that “carfilzomib + DEX twice weekly was not even considered for the analysis,” we received clinical expert input that a triplet is preferable to a doublet when a patient can tolerate it, so CFZ + LEN + DEX was therefore much more likely to be used in the settings of interest than CFZ + DEX. Hence, we focused on comparing new triplet therapies to the 2 standard of care approaches—LEN + DEX and BOR + DEX.
+
+Finally, we note that the treatment costs for CFZ were capped at 18 months, in line with the suggested dosing.
+
+Overall, we feel that our analysis and the conclusions we have drawn are robust and well founded.
+==== Refs
+REFERENCES
+
+1. Carlson JJ, Guzauskas GF, Chapman RH, et al. Cost-effectiveness of drugs to treat relapsed/refractory multiple myeloma in the United States. J Manag Care Spec Pharm. 2018;24 (1 ):29-38. Available at: https://www.jmcp.org/doi/10.18553/jmcp.2018.24.1.29.29290170
+2. Jakubowiak AJ, Houisse I, Majer I, et al. Cost-effectiveness of carfilzomib plus dexamethasone compared with bortezomib plus dexamethasone for patients with relapsed or refractory multiple myeloma in the United States. Expert Rev Hematol. 2017;10 (12 ):1107-19.29027825
+3. Botta C, Ciliberto D, Rossi M, et al. Network meta-analysis of randomized trials in multiple myeloma: efficacy and safety in relapsed/refractory patients. Blood Adv. 2017;1 (7 ):455-66.29296961
+4. Dimopoulos MA, Kaufman JL, White D, et al. A comparison of the efficacy of immunomodulatory-containing regimens in relapsed/refractory multiple myeloma: a network meta-analysis. Clin Lymphoma Myeloma Leuk. 2018;18 (3 ):163-73.e166.29456035
+5. van Beurden-Tan CHY, Franken MG, Blommestein HM, Uyl-de Groot CA, Sonneveld P. Systematic literature review and network meta-analysis of treatment outcomes in relapsed and/or refractory multiple myeloma. J Clin Oncol. 2017;35 (12 ):1312-19.28240968
+6. Ruggeri K, Maguire A, Schmitz S, et al. Estimating the relative effectiveness of treatments in relapsed/refractory multiple myeloma through a systematic review and network meta-analysis. Blood. 2015;126 (23 ):2103 [abstract]. Available at: http://www.bloodjournal.org/content/126/23/2103?sso-checked=true. Accessed June 8, 2018.
+

PMC10397991.txt

@@ -0,0 +1,305 @@
+
+==== Front
+J Manag Care Spec Pharm
+J Manag Care Spec Pharm
+jmcsp
+Journal of Managed Care & Specialty Pharmacy
+2376-0540
+2376-1032
+Academy of Managed Care Pharmacy
+
+30247101
+10.18553/jmcp.2018.24.10.1019
+Research
+Costs Associated with Productivity Loss Among U.S. Patients Newly Diagnosed with Multiple Myeloma Receiving Oral Versus Injectable Chemotherapy
+Merola David PharmD 1
+Yong Candice PhD 2
+Noga Stephen J. MD, PhD 2
+Shermock Kenneth M. PharmD, PhD 3 *
+1 Bernard J. Dunn School of Pharmacy, Shenandoah University, Winchester, Virginia.
+2 Millennium Pharmaceuticals, Cambridge, Massachusetts.
+3 Department of Pharmacy, The Johns Hopkins Hospital, Baltimore, Maryland.
+* AUTHOR CORRESPONDENCE: Kenneth M. Shermock, PharmD, PhD, Center for Medication Quality and Outcomes, The Johns Hopkins Hospital, 600 N. Wolfe St., Carnegie 180, Baltimore, MD 21287. Tel.: 410.502.7674; E-mail: ken@jhmi.edu.
+This study was funded by Millennium Pharmaceuticals, a wholly owned subsidiary of Takeda Pharmaceutical Company. Yong and Noga are employees of Millennium Pharmaceuticals. Merola reports personal fees from Millennium Pharmaceuticals during the time of this study.
+
+10 2018
+24 10 10.18553/jmcp.2018.24.10.1019Copyright © 2018, Academy of Managed Care Pharmacy. All rights reserved.
+2018
+https://creativecommons.org/licenses/by/4.0/ This article is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use and redistribution provided that the original author and source are credited.
+
+BACKGROUND:
+
+The use of novel drug agents in the treatment of multiple myeloma (MM) has been associated with improved therapeutic outcomes and survival; however, MM continues to pose a significant economic burden on patients and health care systems. Evaluating economic implications of therapies can provide key points of distinctions between available treatment options. Patients with MM may experience productivity loss, including lost days from work or inability to work due to MM symptoms or to undergoing treatment. Although direct costs of illness have been well described in the literature, indirect costs associated with MM are understudied.
+
+OBJECTIVE:
+
+To compare the extent of disability benefit use and resultant workplace productivity loss among U.S. adult patients with newly diagnosed MM who received oral versus injectable MM therapy.
+
+METHODS:
+
+A retrospective cohort study was conducted using the Truven Health Analytics MarketScan Commercial Claims and Encounters, Medicare Supplemental Coordination of Benefits, and Health and Productivity Management databases (2008-2015). Workplace absenteeism, as measured by disability benefit use, was evaluated 1 year before and 1 year after first MM diagnosis. Patients receiving only oral chemotherapy were compared with those who received injectable therapy. Absenteeism days and associated costs were compared among study groups using multivariable zero-inflated Poisson regression.
+
+RESULTS:
+
+The final study cohort included 299 patients with newly diagnosed MM, of whom 73 received oral therapy only and 226 received injectable therapy. Treatment type was a significant predictor of disability benefit use. Patients who received injectable therapy missed an average of 110 work days in the 1 year after diagnosis, compared with 87 for patients receiving only oral therapy (difference of 23 days, 95% CI = 19-26, P < 0.001). Treatment type was also a significant predictor of costs associated with lost productivity. Patients who received injectable therapy experienced productivity loss valued at $18,315, compared with patients who only received oral drug therapy ($14,429). The difference between these estimates was statistically significant ($3,886, 95% CI = $3,540-$4,231, P < 0.001).
+
+CONCLUSIONS:
+
+Patients newly diagnosed with MM face significant losses in productivity. Patients receiving injectable MM therapy use significantly more disability benefits and incur higher productivity costs, compared with those receiving oral MM therapy. Further studies elucidating the nature of the differences between injectable and noninjectable chemotherapy users are needed.
+==== Body
+pmc What is already known about this subject
+
+Cancer as a whole may be one of the costliest health conditions for employers and patients.
+
+Multiple myeloma poses a significant economic burden on patients and the health care system.
+
+Direct costs related to treatment and medical care of myeloma have been well described in the literature; however, indirect costs are understudied.
+
+What this study adds
+
+This study compared the extent of workplace productivity loss in patients newly diagnosed with multiple myeloma receiving oral versus injectable chemotherapy.
+
+In the year following initial diagnosis with multiple myeloma, patients experience significant productivity loss, particularly those receiving injectable chemotherapy.
+
+Multiple myeloma (MM) is a hematologic malignancy that arises from plasma cells and is the 14th most common type of cancer in the United States.1 Treatment of MM has markedly improved in recent years with the introduction of novel agents, which possess immunomodulatory, antiangiogenesis, and antitumor properties.2 The use of these agents has been associated with improved therapeutic outcomes and survival rates, which are likely to continue improving as optimal therapeutic regimens become established and novel agents continue to emerge in the marketplace.3-5
+
+Despite advancements in treatment, MM poses a significant economic burden on patients and health care systems.6 Among several domains of patients’ livelihood affected by the illness, employment and disability have been described to be particularly affected in recent literature.7 Cancer as a whole is among the costliest health conditions, with a projected cost of $173 billion in the year 2020.8 Annual productivity costs associated with malignancies have been estimated to be approximately $1,601 per patient in those at high risk for health conditions.9
+
+Understanding the scope of financial burden due to disease is important to multiple health care stakeholders. Direct costs related to treatment and medical care of MM have been well described.10-14 However, indirect costs associated with this particular cancer are understudied.15 The extent of productivity loss may vary across treatment modalities: a study by Petrucci et al. (2013) showed that the highest and lowest work-related, annual productivity costs per patient were reported by those who underwent autologous stem cell transplant (ASCT; €9,538 ± €17,612, equivalent to $7,963 ± $14,700 in 2015 U.S. dollars) and asymptomatic patients (€22 ± €95, equivalent to $18.36 ± $79.29 in 2015 U.S. dollars), equivalent to 16.1% and 2.3% of the total cost of illness, respectively.16 As well, patients receiving intravenous or subcutaneous therapies may experience additional treatment burden and productivity loss due to the frequent trips to the clinic for treatment administration.17,18
+
+Losses in productivity due to complications and complexity of parenteral chemotherapy administration are relevant to patient preference and treatment satisfaction, which are important considerations in determining the value of particular treatment modalities. Recent literature suggests that the mode of drug administration is an important determinant of patient preference. A study by Simchowitz et al. (2010) concluded that an all-oral drug regimen was preferred among patients with relapsed/remitting MM and that drug administration was the most important factor in determining preference.19 In a small cross-sectional study of newly diagnosed MM patients, decreased economic burden and activity impairment was observed among users of oral versus injectable MM therapy.20 Workplace productivity loss is becoming an increasingly important outcome, as older adults are choosing to stay in the workforce longer.21 Furthermore, treatment options that enable patients to maintain greater workplace productivity can help defray direct costs of illness.
+
+The purpose of this study was to examine the workplace productivity loss (measured by work absenteeism, short-term disability, and long-term disability days) among U.S. adult patients with an initial diagnosis of MM (IDMM) and estimate the resultant indirect costs due to productivity loss in these patients. These outcomes are compared among subjects who received oral versus injectable anticancer therapy in a retrospective cohort analysis. We hypothesized a greater indirect cost of illness posed by injectable versus oral chemotherapy agents.
+
+Methods
+
+Data Source
+
+Analyses were conducted using the Truven Health Analytics MarketScan Commercial Claims and Encounters (CCAE) with Medicare Supplemental Coordination of Benefits and Health and Productivity Management (HPM) databases. These longitudinal datasets contain commercial claims from more than 100 million individual patients, including active employees, retirees, dependents of enrollees, and Medicare beneficiaries.22 Data elements include detailed information on prescription drug and medical claims, as well as absenteeism and disability benefit utilization. Collectively, these data elements enable estimation of indirect costs among patients receiving various treatments. All observations were deidentified and validated by Truven Health Analytics to ensure completeness, accuracy, and compliance with the Health Insurance Portability and Accountability Act (HIPAA) of 1996. Consequently, approval of this research by an institutional review board was not mandated.
+
+Sample Selection
+
+Subjects aged at least 18 years with an IDMM between January 1, 2008, and December 31, 2014, were included in our study. MM diagnosis was identified using International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes 203.00, 203.01, 203.02. The date of the first claim that contained 1 of these diagnosis codes was considered the index date.23
+
+To enhance the specificity of selecting patients with MM, only subjects with at least 1 MM diagnosis code appearing in any inpatient claim or at least 2 MM diagnosis codes that appeared in outpatient claims and were 60-365 days apart were included. Also, subjects must have been continuously enrolled in a plan that included medical and disability benefits for 12 months before and after the index date to be included. Any evidence of another primary cancer (ICD-9-CM codes 140.xx-195.xx, 199.xx-202.xx, and 204.0x-209.xx) or metastatic disease (ICD-9-CM codes 196.xx-198.xx) before the index date warranted exclusion.
+
+Once ascertained using these criteria, the MM study cohort was stratified according to 2 treatment types—recipients of oral chemotherapy and recipients of injectable chemotherapy only. The injectable chemotherapy stratum was selected on the basis of having at least 1 outpatient drug claim indicating a National Drug Code (NDC) number, Healthcare Common Procedure Coding System (HCPCS) code, or Current Procedural Terminology, 4th Edition (CPT-4), code corresponding to an injectable chemotherapy agent used in the treatment of MM during the study period. Consequently, this stratum contained subjects who used injectable chemotherapy, with or without oral chemotherapy agents.
+
+The oral chemotherapy stratum was selected similarly, using NDC numbers and HCPCS codes corresponding to oral agents, but had no claims indicating injectable chemotherapy. Oral and/or injectable dosage forms of the following MM-specific chemotherapy drugs were included in our analysis: cyclophosphamide, melphalan, vincristine sulfate, doxorubicin hydrochloride, interferon alfa-2b, lenalidomide, pomalidomide, thalidomide, bortezomib, carfilzomib, ixazomib citrate, daratumumab, elotuzumab, panobinostat, bendamustine, vorinostat, and dexamethasone. HCPCS/CPT-4 codes and NDC numbers used to identify treatment type are shown in Appendix A and Appendix B, respectively (available in online article).
+
+Variables
+
+Baseline descriptive characteristics were described for subjects in each stratum using the following variables: age; gender; plan type (health maintenance organization [HMO], point-of-service [POS], preferred provider organization [PPO], and other); employee status (i.e., full time, part time, and other); industry; and geographic region. Additionally, comorbidity burden before each subject’s index date was measured using the Charlson Comorbidity Index (CCI) using ICD-9-CM diagnosis codes contained in outpatient and inpatient claims.24,25
+
+Productivity loss was evaluated using several key variables found within workplace absenteeism (WAB), short-term disability (STD), and long-term disability (LTD) claim files. A claim qualified for (a) STD, if it was for an extended period of absence, excluding weekend days, due to a particular diagnosis and was typically capped by employers at 6 months; (b) LTD, if it was for an extended period of absence, excluding weekend days, due to a particular diagnosis and typically occurred after STD benefits had ended; and (c) WAB, if it was for any days absent from work (e.g., sick, leave, recreational, other, or Family Medical Leave Act), except for those due to disability. Two variables—corresponding to the first day of missed work and first day of return to work for each patient claim—were used to obtain the number of absenteeism days for each patient. Workplace productivity loss (WPL), an aggregate of absenteeism days, was calculated using claims for WAB, STD, and LTD benefits.
+
+Absenteeism days were captured at the month-level for each benefit type. Consequently, if patients used 2 benefits in a given month, it was not possible to discern exactly when each benefit was used within that month. When calculating WPL in these instances, absenteeism days linked to the most frequently used benefit were included in the measure; absenteeism days associated with any other benefit in that month were disregarded. For example, if a patient used 20 days of STD and 5 days of WAB in the same month, 20 days were counted for that entire month. This was done to yield a conservative measure of missed workdays (by avoiding the potential to double-count absenteeism days).
+
+From the disease outcomes perspective, there is no evidence indicating injectable therapies (vs. oral therapies) have better outcomes beyond a 1-year period. Consequently, productivity loss is most likely better reflected in the short term following initial diagnosis. For this reason, outcomes were analyzed at the patient-month level throughout a 12-month baseline and 12-month follow-up period.
+
+Estimated Costs of Productivity Loss
+
+The financial burden of productivity loss was calculated in 2015 U.S. dollars for each patient-month by multiplying the number of absenteeism days (as indicated in the aggregate WPL measure) by the average daily wage for all occupations, as reported by the U.S. Bureau of Labor Statistics.26 In addition, multipliers corresponding to each benefit type, derived from a study conducted by Goetzel et al. (1999), were applied to account for extra costs incurred to maintain productivity during employee absenteeism (Table 1).27 Data indicating the number of absenteeism days for each benefit were captured at the month-level. As explained above, when 2 benefits were used in a given month, the most frequently used benefit (and corresponding multiplier) was employed in our calculation, and any other benefit and its corresponding multiplier were disregarded. Monthly costs were calculated in the manner described here for each patient and subsequently summated for the 12 months following an IDMM diagnosis. The resultant figures were used in our statistical analyses to estimate differences in costs associated with productivity loss between study groups.
+
+Table 1 Multipliers Used to Calculate Cost Burden of Workplace Productivity Loss
+
+WAB	(Absenteeism days from aggregate WPL measure) × (1.3a) × (23.23b) × (8c)	
+STD and LTD	(Absenteeism days from aggregate WPL measure) × (1.3a) × (23.23b) × (8c) × (0.6d)	
+aAn adjustment to account for extra costs incurred by overstaffing—a measure frequently commissioned by companies to maintain company-wide productivity when employees become unavailable due to unforeseen events (e.g., illness or vacation).21
+
+bMean hourly wage across all occupations in the United States reported by the U.S. Bureau of Labor Statistics in May 2015.20
+
+cNumber of hours in a typical work day.
+
+dSTD and LTD benefits generally cover 60% of the estimated value of a work day.
+
+LTD = long-term disability; STD = short-term disability; WAB = workplace absenteeism.
+
+Statistical Analyses
+
+The study cohort’s baseline characteristics, categorical variables (e.g., gender) were described using proportions, and continuous variables (e.g., age) were described using the mean, median, and standard deviation. Student’s t-tests and chi-square tests were used to evaluate differences in continuous and categorical variables between different study cohort groups, respectively. The rank sum test was used to evaluate differences in the median age across study groups.
+
+Days of lost productivity and their associated economic value were compared using multivariable zero-inflated Poisson regression.28 This model offered 2 benefits that were very important to our analysis. First, it explicitly accounted for the presence of large numbers of “zeros.” Many patients actually did not use absenteeism days. The model accounted for this from a statistical perspective. Second, it allowed us to adjust for potential confounding variables, as one would do in a multivariable least squares regression. The following variables were used as covariates in the models: age, index year, gender, CCI, and plan type.
+
+All data management and analyses were carried out using StataMP, version 14.1 (StataCorp, College Station, TX).
+
+Results
+
+The attrition to the study cohort as each exclusion criterion was applied can be seen in Figure 1. A total of 90,238 individuals were aged at least 18 years and received an IDMM from 2008 to 2014 in our dataset. Of these, 299 patients met all selection criteria and were included in the final analytic cohort. In our cohort, patients who used oral chemotherapy only (n = 73) had a mean age of 52 years and were mostly male (66%). Similarly, patients who used injectable chemotherapy (n = 226) had a mean age of 51 years and were also mostly male (71%). Baseline characteristics of each study group are depicted in Table 2.
+
+FIGURE 1 Application of Exclusion Criteria to Assemble Final Study Cohort
+
+Table 2 Characteristics of Multiple Myeloma Treatment Groups
+
+	Oral Chemotherapy Users	Injectable Chemotherapy Users	P Valuea	
+Sample size (n)	73	226		
+Age at baseline, years	
+  Mean	52.0	51.4	0.579	
+  Median	52.0	53.0	0.929	
+  SD	5.4	7.2		
+Age group, years, n (%)			0.153	
+  18-44	7 (9.6)	40 (17.7)		
+  45-54	38 (52.1)	94 (41.6)		
+  55-64	28 (38.4)	92 (40.7)		
+Sex, n (%)			0.374	
+  Male	48 (65.8)	161 (71.4)		
+  Female	25 (34.3)	65 (28.6)		
+Index year, n (%)			0.005	
+  2009	13 (17.8)	14 (6.2)		
+  2010	11 (15.1)	28 (12.4)		
+  2011	15 (20.6)	31 (13.7)		
+  2012	13 (17.8)	46 (20.4)		
+  2013	15 (20.6)	57 (25.2)		
+  2014	6 (8.2)	50 (22.1)		
+Region, n (%)			0.641	
+  Northeast	14 (19.2)	51 (22.6)		
+  North Central	18 (24.7)	47 (20.8)		
+  South	23 (31.5)	83 (36.7)		
+  West	18 (24.7)	45 (19.9)		
+Plan type, n (%)			0.092	
+  HMO	12 (16.4)	26 (11.5)		
+  POS	2 (2.7)	24 (10.6)		
+  PPO	48 (65.8)	130 (57.5)		
+  Other	11 (15.1)	46 (20.4)		
+CCI score, n (%)			0.171	
+  0	32 (43.8)	90 (39.8)		
+  1	16 (21.9)	53 (23.5)		
+  2	7 (9.6)	44 (19.5)		
+  > 3	18 (24.7)	39 (17.3)		
+aT-test was used to evaluate mean age and rank sum for median age; chi-square test was used for remaining categorical variables.
+
+CCI = Charlson Comorbidity Index; HMO = health maintenance organization; POS = point-of-service; PPO = preferred provider organization.
+
+Of the overall cohort, 32% (n = 96) used some form of disability benefit before an IDMM, compared with 63% (n = 188) after diagnosis. In the prediagnosis period, the most frequently used benefit was WAB (n = 70), followed by STD (n = 30) and LTD (n = 1). Alternatively, in the postdiagnosis period, STD was the most frequently used benefit (n = 143), followed by WAB (n = 63) and LTD (n = 24). Benefit use observed in each study group is summarized in Table 3. The average number of disability days used over the entire cohort was 25 days (median = 0) in the year before diagnosis and 105 days (median = 64) in the year after initial diagnosis (P < 0.001 by t-test and rank sum test). Among patients who used any disability benefits, the median disability days used was 51 (mean = 78) in the year before diagnosis and 154 (mean = 167) in the year after initial diagnosis (P < 0.001, rank sum test).
+
+Table 3 Productivity Loss and Associated Costs of Multiple Myeloma Patients Receiving Treatment
+
+	Oral Chemotherapy Users (n = 73)	Injectable Chemotherapy Users (n = 226)	P Valuea	
+Benefit users before diagnosis, n (%)			0.707	
+  WAB	18 (24.7)	52 (23.0)		
+  STD	6 (8.2)	24 (10.6)		
+  LTD	0 (0)	1 (0.4)		
+Benefit users after diagnosis, n (%)			0.830	
+  WAB	17 (23.3)	46 (20.4)		
+  STD	35 (48.0)	108 (47.8)		
+  LTD	5 (6.9)	19 (8.4)		
+Predicted number of missed work days in year after MM diagnosis,b days (95% CI)	87.2 (79.2-95.3)	109.7 (95.3-120.0)	< 0.001	
+Predicted costs of missed work in year after MM diagnosis,b USD (95% CI)	$14,605 ($13,312-$15,900)	$18,500 ($16,862-$20,139)	< 0.001	
+aChi-square test was used to evaluate categorical variables.
+
+bParameter estimates from multivariable zero-inflated Poisson regression.
+
+CI = confidence interval; LTD = long-term disability; MM = multiple myeloma STD = short-term disability; USD = U. S. dollars; WAB = workplace absenteeism.
+
+In the adjusted comparison of the number of missed work days, treatment type was a significant predictor of the number of missed work days per patient in the year following an IDMM diagnosis. Patients who received injectable therapy missed an average of 110 work days. In contrast, patients receiving oral therapy missed an average of 87 work days. This difference was statistically significant (22.5 days, 95% CI = 19-26, P < 0.001). In the same multivariable model, older age was associated with fewer missed work days. Patients aged 45 years or older experienced an average of 26 fewer missed work days compared with individuals who were younger than 45 years (95% CI = –31-21 days, P < 0.001). Additionally, patients with a CCI score greater than zero experienced on average 6.8 additional missed work days compared with patients whose CCI score was zero (95% CI = 4.3-9.4, P < 0.001).
+
+Treatment type was also a significant predictor of costs associated with lost productivity. Patients who received injectable therapy experienced productivity loss valued at $18,500, compared with patients who only received oral drug therapy ($14,605). This difference was statistically significant ($3,894, 95% CI = $3,547-$4,241, P < 0.001). Patients aged between 45 and 54 years experienced $2,302 less costs associated with productivity loss, on average (95% CI = –$2,511-$2,094, P < 0.001) compared with patients aged less than 45 years. Patients aged 55 years and older experienced $2,580 less costs associated with productivity loss (95% CI = –$2,811-$2,347, P < 0.001) compared with patients aged less than 45 years. Additionally, patients with a CCI score greater than zero experienced an average of $1,515 greater costs associated with productivity loss compared with patients whose CCI score was zero (95% CI = $1,377-$1,653, P < 0.001).
+
+Discussion
+
+Loss of productivity is a significant contributor of financial hardship in patients with cancer. It has been estimated that up to 62% of cancer survivors report accruing debt due to their oncologic therapies, with average losses of productivity in the range of $380 to $8,236.28 There is tremendous variability in the measurement of productivity loss in the published literature, including missed work days, limitations in housework or school, lost income, and time spent in bed.29-31 Here, we present the first study to valuate productivity loss as measured by disability benefit use among MM patients receiving injectable versus oral chemotherapy. Although overall survival in patients with MM has significantly increased in the past decade due to improved treatments, this study demonstrates an increasing financial burden posed by MM therapies. Our findings highlight the importance of considering indirect costs alongside efficacy, safety, and direct costs during the treatment decision-making process.
+
+The overall study cohort exhibited a use of 87-110 absenteeism days, on average, in the year following initial MM diagnosis, with higher use of absenteeism days for patients on injectable versus oral therapy. Average costs associated with these days ranged from approximately $14,500 to $18,000, depending on treatment type, with higher costs associated with patients on injectable therapy versus oral therapy. These results compare with a similar study conducted on breast cancer patients (60 absenteeism days valuated at $4,900) and patients with non-Hodgkin lymphoma (65 days valuated at $12,741).33,34 Productivity loss has also been evaluated with similar figures in patients with colorectal malignancies.35
+
+Comparisons among these studies should be interpreted with caution, however, since there is significant variability in the methodologies used to evaluate productivity loss. For example, our monetary figures were adjusted to account for overstaffing, while analogous estimations by Yu et al. (2016) were not.34 Regardless, our findings demonstrate that an initial diagnosis with MM is related to a significant increase in absenteeism days used in the subsequent 12 months.
+
+Consideration of the other sources of economic burden imposed by MM in the context of our findings is instructive. Several studies that evaluated the direct expenses of various MM treatments found an average monthly cost ranging from $13,876 to $47,417.36,37 These investigations demonstrate that the direct costs of treatment from the payer perspective are similar in magnitude to our estimates of indirect costs from the patient perspective.
+
+The indirect costs associated with workplace absenteeism are significant and contribute to the overall economic burden of MM. Furthermore, our results showed that patients who received injectable therapy experienced a greater increase in lost productivity compared with patients who received oral therapy.
+
+Limitations
+
+There are several limitations associated with our analyses. Importantly, there are very limited clinical data in the Truven CCAE database, which reduced our capacity to establish clinical causes and conditions that led to the observed choices of therapy and subsequent differences in productivity loss. For example, patients may have had to take time off work due to various reasons including disease-related symptoms, treatment-related toxicities, travel time to and from the clinic, and for treatment administration. Because we did not have access to this information, our analysis is silent on these and related phenomena. As demonstrated in a previous study, complications and side effects of chemotherapy, such as neutropenia, have been shown to explain differences in short-term disability use.38 Other potential explanatory factors that are not represented in the Truven database include the type of occupation, complexity of chemotherapy administration, and treatment response. Socioeconomic status was also unavailable in our dataset and may have influenced the measured outcomes. For instance, subjects with high socioeconomic status may have exhibited more confidence in taking time off work. To address this, our multivariable model was adjusted for health-plan type, which could have captured some aspect of this potential confounder.
+
+Our small sample size was an additional limitation, especially with respect to the number of patients receiving oral chemotherapy (n = 73). Although our estimations reached statistical significance, larger sample sizes would likely allot more representative figures, as well as enable an exploration of the relative differences in outcomes of each chemotherapy agent. Furthermore, our study population was among working adults in the United States with disability benefits. These results, therefore, may not be generalizable to other populations.
+
+The scope of our study was limited to the consideration of productivity-related losses. Although these losses are important to patients and employers, the scope is somewhat limited and falls short of a full cost-effectiveness analysis. The requisite data sources to accomplish such an analysis were unavailable to us. Nevertheless, we feel this current work is a valuable contribution.
+
+Conclusions
+
+Future studies are needed to elucidate the conditions that lead to greater productivity loss among injectable chemotherapy users. Data that permit a larger sample size, more clinical details, and greater follow-up times would be helpful in tracking treatment outcomes of MM therapies. A more comprehensive picture, in this regard, may empower patients and clinicians with strategies to reduce the overall economic burden imposed by MM and, in turn, promote greater quality of care. This study examined the productivity loss and associated costs in the year following a new diagnosis of MM. The greater productivity loss observed among patients treated with injectable therapy may continue to persist over a longer time horizon (e.g., 2 or 3 years after diagnosis) as patients increasingly receive long-term treatment.
+
+An initial diagnosis with MM is associated with an increased use of disability benefits in the year following initial diagnosis. Injectable chemotherapy use appears related to a greater use of disability benefits than oral chemotherapy use in the setting of this particular cancer.
+
+APPENDIX A HCPCS/CPT-4 Codes of MM-Specific Anticancer Agents Analyzed
+
+Medication	HCPCS/Procedure Codes (Route of Administration)	
+Cyclophosphamide	J8530 (Oral), J9070 (Injectable), J9080 (Injectable), J9090-J9097 (Injectable)	
+Melphalan	J8600 (Oral), J9245 (Injectable)	
+Vincristine sulfate	J9370 (Injectable), J9375 (Injectable), J9380 (Injectable)	
+Doxorubicin hydrochloride	J9000 (Injectable), J9001 (Injectable)	
+Liposomal doxorubicin	J9002 (Injectable)	
+Interferon alfa-2b	J9214 (Injectable), S0146 (Injectable), S0148 (Injectable)	
+Bortezomib	J9041 (Injectable)	
+Carfilzomib	J9047 (Injectable)	
+Bendamustine	J9033 (Injectable)	
+Dexamethasone	J1094 (Injectable), J1095 (Injectable), J1100 (Injectable), J8540 (Oral), S0173 (Oral)	
+Prednisone	J7506 (Oral)	
+CPT-4 = Current Procedural Terminology, 4th Edition; HCPCS = Healthcare Common Procedure Coding System; MM = multiple myeloma.
+
+APPENDIX B NDC Numbers for MM-Specific Anticancer Agents Analyzed
+
+Medication	Route of Administration	NDC Number	
+Bendamustine	Injectable	63459039120, 63459039008, 63459034804, 63459039602, 63459039502	
+Cyclophosphamide	Injectable	10019094510, 10019095701, 00015050541, 00781324494, 10019093601, 10019093650, 10019093901, 10019094401, 10019094450, 10019095601, 10019095616, 00015050641, 00781325594, 10019093701, 10019093710, 10019094201, 10019094501, 10019094510, 10019095701, 10019095711, 00015050241, 00781323394, 10019093501, 10019093525, 10019093801, 10019094301, 10019094325, 10019095501, 10019095550, 00013563601, 00013563670, 00015054812, 00015054841, 00013560601, 00013560693, 00015053941, 00013564601, 00013564670, 00015054912, 00015054941, 00013561601, 00013561693, 00015054641, 00013562601, 00013562693, 00015054712, 00015054741	
+Oral	00015050401, 00054412925, 00054808925, 54569571200, 54868521800, 54868521801, 54868521802, 00015050301, 00015050302, 00054413025, 00054813025, 54569571300, 54868500500, 54868500501, 00054038225, 00054038325	
+Melphalan	Oral	00173004535, 52609000105, 54868433900, 54868433901, 54868433902, 54868433903, 54868433904, 59572030250	
+Injectable	00173013093, 52609300100, 59572030101, 67457019501, 67457021501, 67457057901, 68152010900	
+Daratumumab	Injectable	57894050205, 57894050220	
+Elotuzumab	Injectable	00003229111, 00003452211	
+Pomalidomide	Oral	59572050100, 59572050121, 59572050200, 59572050221, 59572050300, 59572050321, 59572050400, 59572050421	
+Doxorubicin hydrochloride	Injectable	00013108691, 00069017001, 10019092001, 53905081010, 55390023110. 55390024110, 67457047810, 00015335322, 00013111601, 00013111683, 00013109601, 00013109691, 53905081110, 55390023210, 55390024210. 00013110601, 00013110679, 00015335222, 00069017101, 10019092102, 55390023301, 55390024301, 67457043650, 00013113601, 00013114601, 00013116601, 00013117601, 00013117687, 00013123691, 00013124691, 00013125679, 00013126683, 00013128683, 00069303020, 00069303120, 00069303220, 00069303320, 00069303420, 00069400405, 00069401510, 00069402625, 00069403001,00069403101, 00069403201, 00069403301, 00069403401, 00069403701, 00703504001, 00703504301, 00703504303, 00703504601, 25021020705, 25021020725, 25021020751, 45963073355, 45963073357, 45963073360, 45963073368, 53150031410, 53150031501, 53150031701, 53150032010, 53905081310, 53905081410, 53905081501, 53905081601, 55390023510, 55390023610, 55390023701, 55390023801, 55390024510, 55390024610, 55390024701, 55390024801, 62756082640, 62756082740, 63323010161, 63323088305, 63323088310, 63323088330, 67457039300, 67457039354, 67457039400, 67457039410, 67457039525, 67457039610, 17314960001, 17314960002, 47335004940, 47335005040, 47335008250, 47335008350, 59676096001, 59676096002	
+Bortezomib	Injectable	63020004901	
+Carfilzomib	Injectable	76075010101	
+Ixazomib citrate	Oral	63020007801, 63020007802, 63020007901, 63020007902, 63020008001, 63020008002	
+Panobinostat lactate	Oral	00078065006, 00078065106, 00078065206	
+Vorinostat	Oral	00006056840	
+Interferon alfa-2b	Injectable	00085057102, 00085435001, 00085111001, 00085435101, 00085028502, 00085064705, 54868308501, 00085012002, 00085053901, 00085435201, 54868334100, 00085113301, 00085117901, 00085116801, 00085118401, 00085125401, 00085117902, 00085124201, 00085118402, 00085119102, 00085123501	
+Vincristine sulfate	Injectable	00013745601, 00013745686, 00013746686, 00703440211, 00703441211, 61703030906, 61703030916, 61703030925, 61703030926, 20536032201	
+Lenalidomide	Oral	59572041000, 59572041028, 59572041030, 59572041500, 59572041521, 59572042000, 59572042021, 59572042500, 59572042521, 59572042525, 59572040500, 59572040528, 59572040530, 59572040200, 59572040228	
+Thalidomide	Oral	59572021015, 59572021095, 59572021513, 59572021593, 59572022016, 59572022096, 59572010512, 59572010513, 59572010592, 59572010593, 59572020514, 59572020517, 59572020594, 59572020597	
+Dexamethasone	Oral	00054317644, 00472097208, 00472097233, 00603114556, 00603114756, 00677060142, 00904097209, 54569103400, 54879000308, 55045142801, 55289041004, 60346097731, 60432046600, 60432046608, 64679081008, 64980050924, 68850000108, 00054317757, 00054317763, 00054817716, 63629269601, 49884008301, 49884008310, 00006004168, 00054417925, 00054817925, 49884008401, 49884008410, 51655021277, 54868092700, 55045266502, 58016029000, 58016029002, 58016029003, 58016029012, 58016029015, 58016029020, 58016029030, 58016029073, 58016029089, 00006006368, 00054418025, 00054818025, 00247062407, 00247062412, 00247062420, 00247062421, 00247101004, 00247101012, 21695029030, 23490540401, 33261055830, 33261055860, 33261055890, 49884008501, 49884008510, 52959039201, 52959039212, 52959039221, 52959039228, 52959039230, 54569032200, 54569032203, 54569032205, 54868091600, 55045130804, 55045130805, 55289090310, 55289090312, 55289090320, 58016029300, 58016029312, 58016029315, 58016029320, 58016029330, 60346055012, 60346055015, 60346055030, 60713002810, 60713002815, 63629412901, 63874044401, 63874044412, 63874044415, 63874044420, 63874044421, 63874044430, 66267006612, 66267006630, 66336055012, 66336055021, 68115009612, 68115009614, 68387017221, 00006006312, 00254266706, 00603319111, 52959150421, 54569311000, 60904008527, 00054418125, 00054817425, 21695072812, 35356035930, 00054418225, 00054818125, 00603319221, 00904024560, 49884008601, 49884008610, 54868174400, 55700026321, 55700026355, 61919026921, 61919026955, 63629412701, 68115043510, 00095008635, 00095008651, 44183050735, 44183050851, 44183050921, 00054418325, 00054817625, 21695074510, 21695074512, 43063026607, 54569033601, 54569033603, 54868315700, 54868315701, 62682500002, 63874047104, 63874047105, 63874047110, 00006009750, 00054418425, 00054817525, 00677084901, 10544021206, 16590026906, 16590026910, 16590026960, 16590026972, 21695038204, 21695038206, 21695038208, 21695038220, 21695038260, 23490540701, 23490540702, 33261062502, 33261062530, 33261062560, 33261062590, 47463020230, 49884008701, 49884008703, 49884008710, 49999005906, 49999005930, 52959054704, 52959054710, 52959054711, 52959054712, 52959054716, 52959054720, 52959054730, 52959054750, 54569032402, 54569032404, 54569032406, 54569032407, 54569032409, 54569572900, 54868021800, 54868021801, 54868021802, 54868021803, 54868021804, 54868021805, 54868021806, 54868021807, 54868021808, 54868021809, 55045197001, 55045197002, 55045197004, 55045197005, 55045197008, 55048020230, 55289058204, 55289058206, 55289058210, 55289058228, 55887037730, 57866358101, 58016078100, 58016078110, 58016078112, 58016078114, 58016078115, 58016078120, 58016078121, 58016078124, 58016078128, 58016078130, 58016078140, 58016078150, 60346047906, 60346047908, 60346047915, 60346047919, 60346047950, 63629374201, 63629374202, 63629374203, 63874079501, 63874079510, 63874079512, 63874079514, 63874079515, 63874079520, 63874079521, 63874079524, 63874079528, 63874079530, 63874079540, 63874079550, 66267006704, 66267006708, 66267006710, 66267006712, 66267006720, 66267006721, 66336047906, 66336047915, 66336047921, 66336047940, 66336047960, 68115009720, 68115009730, 00054418625, 00054818325, 49884012901, 49884012903, 54868590300, 00095008921, 00095008735, 00095008851, 54868533400	
+Injectable	54569395300, 62295305807, 00314089775, 00551011005, 54569142300, 54868397700, 62295305907, 62295305706, 45861013101, 63187034801, 76420076901, 63323050601, 63323050616, 00551017410, 00641036721, 00641036725, 00703352403, 23490541401, 68258896202, 00069017701, 00069017702, 00069454101, 00069454102, 00703352401, 54868609900, 63323051610, 67457042010, 00069019201, 00069019202, 00069454501, 00069454502, 00517493025, 21695084830, 23490541300, 23490541301, 54569304000, 54868087106, 55045321203, 55150023930, 63323016530, 67457042100, 67457042130, 00069017801, 00069017802, 00069454301, 00069454302, 00517490525, 54569302700, 54569302701, 54569464800, 54569464801, 54569464802, 54868087100, 55150023805, 63323016505, 63323016526, 67457042254, 68258897201, 68258897202, 68258897205, 00006762803, 00006762825, 00069017901, 00069017902, 00069454701, 00069454702, 00314089605, 00314089610, 00314089630, 00314089670, 00314089675, 00517490125, 00551003705, 00551003730, 00703350104, 00703351304, 49999043425, 54569157500, 54569472800, 55150023701, 63323016501, 63323016516, 66758050122, 67457042300, 67457042312, 76045010610, 00006764603, 45861013301, 76420076601, 45861013401, 76420081001, 45861013201, 76420076701	
+MM = multiple myeloma; NDC = National Drug Code.
+==== Refs
+References
+
+1. National Institute of Health. Cancer stat facts: myeloma. November 2016. Available at: https://seer.cancer.gov/statfacts/html/mulmy.html. Accessed July 17, 2018.
+2. Nathwani N, Larsen JT, Kapoor P. Consolidation and maintenance therapies for newly diagnosed multiple myeloma in the era of novel agents. Curr Hematol Malig Rep. 2016;11 (2 ):127-36.26893062
+3. Yong K, Delforge M, Driessen C, et al. Multiple myeloma: patient outcomes in real-world practice. Br J Haematol. 2016;175 (2 ):252-64.27411022
+4. Durie BG, Hoering A, Abidi MH, et al. Bortezomib with lenalidomide and dexamethasone versus lenalidomide and dexamethasone alone in patients with newly diagnosed myeloma without intent for immediate autologous stem-cell transplant (SWOG S0777): a randomised, open-label, phase 3 trial. Lancet. 2017;389 (10068 ):519-27.28017406
+5. Kumar SK, Dispenzieri A, Lacy MQ, et al. Continued improvement in survival in multiple myeloma: changes in early mortality and outcomes in older patients. Leukemia. 2014;28 (5 ):1122-28.24157580
+6. de Souza JA, Muffly L. The overlooked COST of multiple myeloma. Lancet Haematol. 2015;2 (10 ):e394-95.26686034
+7. Goodwin JA, Coleman EA, Sullivan E, et al. Personal financial effects of multiple myeloma and its treatment. Cancer Nursing. 2014;36 (4 ):301-08.
+8. Mariotto AB, Yabroff KR, Shao Y, et al. Projections of the cost of cancer in the United States: 2010-2020. J Natl Cancer Inst. 2011;103 (8 ):699.
+9. Mitchell RJ, Bates P. Measuring health-related productivity loss. Popul Health Manag. 2011;14 (2 ):93-98.21091370
+10. Zhou X, Xia J, Mao J, et al. Real-world outcome and healthcare costs of relapsed or refractory multiple myeloma: A retrospective analysis from the Chinese experience. Hematology. 2016;21 (5 ):280-86.26900623
+11. Aguiar PM, Lima TM, Storpirtis S. Systematic review of the economic evaluations of novel therapeutic agents in multiple myeloma: what is the reporting quality? J Clin Pharm Ther. 2016;41 (2 ):189-97.27009796
+12. Borg S, Nahi H, Hansson M, et al. Cost effectiveness of pomalidomide in patients with relapsed and refractory multiple myeloma in Sweden. Acta Oncol. 2016;55 (5 ):554-60.27123742
+13. Chen W, Yang Y, Chen Y, et al. Cost-effectiveness of bortezomib for multiple myeloma: a systematic review. Clinicoecon Outcomes Res. 2016;8 :137-51.27217786
+14. Jakubowiak AJ, Campioni M, Benedict Á, et al. Cost-effectiveness of adding carfilzomib to lenalidomide and dexamethasone in relapsed multiple myeloma from a U.S. perspective. J Med Econ. 2016;19 (11 ):1061-74.27224006
+15. Kamal KM, Covvey JR, Dashputre A, et al. A systematic review of the effect of cancer treatment on work productivity of patients and caregivers. J Manag Care Spec Pharm. 2017;23 (2 ):136-62. Available at: https://www.jmcp. org/doi/10.18553/jmcp.2017.23.2.136.28125370
+16. Petrucci MT, Calabrese E, Levi A, et al. Cost of illness in patients with multiple myeloma in Italy: the CoMiM study. Tumori. 2013;99 (4 ):e193-202.24326862
+17. Kumar SK, Vij R, Noga SJ, et al. Treating multiple myeloma patients with oral therapies. Clin Lymphoma Myeloma Leuk. 2017;17 (7 ):391-462.28601492
+18. Baz R, Lin HM, Hui A-M, et al. Development of a conceptual model to illustrate the impact of multiple myeloma and its treatment on health-related quality of life. Support Cancer Care. 2015;23 (9 ):2789-2797
+19. Simchowitz B, Shiman L, Spencer J, et al. Perceptions and experiences of patients receiving oral chemotherapy. Clin J Oncol Nurs. 2010;14 :447-53.20682500
+20. Romanus D, DasMahapatra P, Hoole M, et al. Treatment satisfaction and burden of illness with oral vs injectable multiple myeloma therapy in patients with newly diagnosed disease (NDMM). Value Health. 2017;20 (9 ):A454 [abstract].
+21. Pew Research Center. More older Americans are working, and working more, than they used to. June 20, 2016. Available from: http://www.pewre-search.org/fact-tank/2016/06/20/more-older-americans-are-working-and-working-more-than-they-used-to/. Accessed July 17, 2018.
+22. Truven Health Analytics. Health research data for the real world: the MarketScan databases. White paper. July 2011. Available from: http://truvenhealth.com/portals/0/assets/PH_11238_0612_TEMP_MarketScan_WP_ FINAL.pdf. Accessed July 17, 2018.
+23. Princic N, Gregory C, Wilson T, et al. Development of an algorithm to identify patients with Multiple Myeloma using administrative claims data. Blood. 2015;126 :4521.
+24. Quan H, Sundararajan V, Halfon P, et al. Coding algorithms for defining comorbidities in ICD-9-CM and ICD-10 administrative data. Med Care. 2005;43 (11 ):1073-77.16224299
+25. Charlson ME, Pompei P, Ales KL, McKenzie CR. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chron Dis. 1987;40 (5 ):373-83.3558716
+26. U.S. Bureau of Labor Statistics. May 2015 national occupational employment and wage estimates. March 2016. Available at: https://www.bls.gov/oes/2015/may/oes_nat.htm#00-0000. Accessed July 17, 2018.
+27. Goetzel RZ, Hawkins, K, Ozminkowski RJ, et al. The health and productivity cost burden of the “top 10” physical and mental health conditions affecting six large US employers in 1999. J Occup Environ Med. 2003;45 (1 ):5-14.12553174
+28. Long JS, Freese J. Models for count outcomes. In: Regression Models for Categorical Dependent Variables Using Stata. 3rd ed. College Station, TX: Stata Press; 2014:481-560.
+29. Altice CK, Banegas MP, Tucker-Seeley RD, Yabroff KR. Financial hardships experienced by cancer survivors. J Natl Cancer Inst. 2017;109 (2 ):djw205.27754926
+30. Dowling EC, Chawla N, Forsythe LP, et al. Lost productivity and burden of illness in cancer survivors with and without other chronic conditions. Cancer. 2013;119 (18 ):3393-401.23794146
+31. Chirikos TN, Russell-Jacobs A, Cantor AB. Indirect economic effects of longterm breast cancer survival. Cancer Pract. 2002;10 (5 ):248-55.12236838
+32. Guy GP Jr, Ekwueme DU, Yabroff KR, et al. Economic burden of cancer survivorship among adults in the United States. J Clin Oncol. 2013;31 (30 ):3749-57.24043731
+33. Meadows ES, Johnston SS, Cao Z, et al. Illness-associated productivity costs among women with employer-sponsored insurance and newly diagnosed breast cancer. J Occup Environ Med. 2010;52 (4 ):415-20.20357681
+34. Yu JS, Hansen RN, Valderrama A, et al. Indirect costs and workplace productivity loss associated with non-Hodgkin lymphoma. Leuk Lymphoma. 2016;57 (11 ):2636-43.27077242
+35. Yabroff KR, Warren JL, Knopf K, et al. Estimating patient time costs associated with colorectal cancer care. Med Care. 2005;43 (7 ):640-48.15970778
+36. MacEwan JP, Batt K, Yin W, et al. Economic burden of multiple myeloma among patients in successive lines of therapy in the United States. Leuk Lymphoma. 2017;13 :1-9.
+37. Arikian SR, Milentijevic D, Binder G, et al. Patterns of total cost and economic consequences of progression for patients with newly diagnosed multiple myeloma. Curr Med Res Opin. 2015;31 (6 ):1105-15.25785551
+38. Song X, Fowler R, Ruiz K, et al. Impact of neutropenic complications on short-term disability in patients with cancer receiving chemotherapy . J Med Econ. 2009;12 (2 ):154-63.19594323
+

PMC10398024.txt

@@ -0,0 +1,43 @@
+
+==== Front
+J Manag Care Spec Pharm
+J Manag Care Spec Pharm
+jmcsp
+Journal of Managed Care & Specialty Pharmacy
+2376-0540
+2376-1032
+Academy of Managed Care Pharmacy
+
+10.18553/jmcp.2018.24.5.487
+Letters
+Letter--Incorporating Real-World Evidence and Patient Value Criteria into Value-Based Frameworks for Relapsed/Refractory Multiple Myeloma
+Orlowski Robert Z. MD, PhD 1 *
+1 Department of Lymphoma/Myeloma, Division of Cancer Medicine The University of Texas MD Anderson Cancer Center Houston, TX.
+* ROrlowski@mdanderson.org
+Orlowski has received research funding from Amgen, BioTheryX, Bristol-Myers Squibb, Celgene Corporation, and Takeda Pharmaceuticals; honoraria from Amgen, Bristol-Myers Squibb, Celgene Corporation, Janssen, Millennium Pharmaceuticals, and Onyx Pharmaceuticals; and is a member of advisory boards for Amgen, Bristol-Myers Squibb, Celgene Corporation, Incyte, Kite, Legend Biotech, Sanofi-Aventis, and Takeda Pharmaceuticals.
+
+5 2018
+24 5 10.18553/jmcp.2018.24.5.487Copyright © 2018, Academy of Managed Care Pharmacy. All rights reserved.
+2018
+https://creativecommons.org/licenses/by/4.0/ This article is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use and redistribution provided that the original author and source are credited.
+==== Body
+pmcIn the article “Evaluating Oncology Value-Based Frameworks in the U.S. Marketplace and Challenges in Real-World Application: A Multiple Myeloma Test Case,” published in the JMCP January 2018 issue, Djatche et al. report on their evaluation of 3 U.S. value-based frameworks for comparing carfilzomib, elotuzumab, and ixazomib plus lenalidomide-dexamethasone as triplet regimens for relapsed/refractory multiple myeloma. This timely analysis highlights a number of important shortcomings of currently available value-based frameworks, and the authors provide valuable constructive recommendations for future modifications to these tools. Notably, they highlight the limitations of the value-based frameworks in terms of consistency of treatment evaluations and with regards to adaptability to address multiple targeted stakeholder perspectives.
+
+This latter point is of particular relevance in relapsed/refractory multiple myeloma. As the authors highlight, the value-based frameworks mainly rely on evidence from randomized clinical trials and do not necessarily capture other aspects and value criteria of importance to the patient perspective in the real-world setting, such as convenience and preference. In this context, an additional important recommendation may be the inclusion of real-world evidence within value-based framework evaluations, particularly in clinical settings in which there is divergence between the efficacy reported from phase 3 trials and the effectiveness reported in real-world patient populations. Such a scenario has been reported recently in multiple myeloma, highlighting notable discrepancies between real-world effectiveness and clinical trial efficacy for a number of approved regimens, particularly those incorporating agents administered via intravenous or subcutaneous injection.1,2
+
+This gap between effectiveness and efficacy may be related to the convenience and patient preference associated with oral therapy, as reported recently in multiple myeloma.3,4 The importance of these aspects is magnified in myeloma because they contribute to the feasibility of long-term treatment, and evidence is emerging of a correlation between real-world treatment duration and overall survival.5 In order to achieve this real-world surrogate endpoint for survival, namely long-term treatment duration, regimens are likely to need to fulfill the value criteria of importance to patients, supporting the inclusion of real-world evidence and patient perspectives within revised value-based framework methodologies.
+
+Acknowledging the limitations of the present value-based frameworks, Djatche et al. draw conclusions from their framework evaluations that rank carfilzomib-based therapy as most valued, followed by elotuzumab-based and ixazomib-based therapy. However, recent real-world data in relapsed/refractory myeloma patients indicate that those receiving ixazomib-based therapy have a longer duration of therapy and prolonged time to next therapy compared with those receiving carfilzomib- or bortezomib-based therapy. In addition, the discrepancy between these real-world findings and respective clinical trials appears most striking for carfilzomibbased therapy.2 Considering these findings and the recent reports on patient preference for oral versus injectable therapies, 3,4 it may be hypothesized that the ranking of the regimens would be altered if evaluated using a value-based framework incorporating real-world evidence and value criteria related to patient perspective. Because of its oral route of administration and its similar effectiveness and efficacy,1,2 ixazomib-based therapy could possibly be valued more highly in such a comprehensive value-based framework.
+
+Acknowledgments
+
+Writing assistance was provided by Steve Hill of FireKite (an Ashfield Company, part of UDG Healthcare) and was funded by Millennium Pharmaceuticals, a wholly owned subsidiary of Takeda Pharmaceutical Company, in compliance with Good Publication Practice-3 (GPP3) guidelines (Battisti WP, et al. Ann Intern Med. 2015;163:461-64).
+==== Refs
+REFERENCES
+
+1. Richardson PG, San Miguel JF, Moreau P, et al . Real-world and clinical trial data in relapsed/refractory multiple myeloma (RRMM): evaluating treatment duration and comparing effectiveness and efficacy. Blood. 2017;130 (Suppl 1 ):3149 [Abstract].
+2. Chari A, Romanus D, Luptakova K, et al . Duration of therapy (DOT) and time to next therapy (TTNT) of bortezomib, carfilzomib and ixazomib combinations with lenalidomide/dexamethasone (VRd, KRd, IRd) in patients (pts) with relapsed/refractory multiple myeloma (RRMM): clinical practice in the United States vs clinical trial experience. Blood. 2017;130 (Suppl 1 ):1818 [Abstract].
+3. Romanus D, DasMahapatra P, Hoole M, et al . Treatment satisfaction and burden of illness with oral vs injectable multiple myeloma therapy in patients with newly diagnosed disease (NDMM). Value Health. 2017;20 (9 ):A454 [Abstract].
+4. Bauer S, Mueller S, Ratsch B, et al . Patient preferences regarding treatment options for relapsed refractory multiple myeloma (RRMM). Value Health. 2017;20 (9 ):A451 [Abstract].
+5. Hari PN, Romanus D, Palumbo A, et al . Prolonged duration of therapy is associated with improved survival in patients treated for relapsed/refractory multiple myeloma in routine clinical care in the United States. Clin Lymphoma Myeloma Leuk. 2018;18 (2 ):152-60.29395837
+

PMC10398323.txt

@@ -0,0 +1,510 @@
+
+==== Front
+J Manag Care Spec Pharm
+J Manag Care Spec Pharm
+jmcsp
+Journal of Managed Care & Specialty Pharmacy
+2376-0540
+2376-1032
+Academy of Managed Care Pharmacy
+
+29290170
+10.18553/jmcp.2018.24.1.29
+Research
+Cost-effectiveness of Drugs to Treat Relapsed/Refractory Multiple Myeloma in the United States
+Carlson Josh J. MPH, PhD 1 *
+Guzauskas Gregory F. MSPH, PhD 1
+Chapman Richard H. PhD, MS 2
+Synnott Patricia G. MS, MALD 2
+Liu Shanshan MS 2
+Russo Elizabeth T. MD 2
+Pearson Steven D. MD, MSc 2
+Brouwer Elizabeth D. MPH 1
+Ollendorf Daniel A. PhD 2
+1 Pharmaceutical Outcomes Research and Policy Program, University of Washington, Seattle.
+2 Institute for Clinical and Economic Review, Boston, Massachusetts.
+* AUTHOR CORRESPONDENCE: Josh J. Carlson, MPH, PhD, Associate Professor, Pharmaceutical Outcomes Research and Policy Program, University of Washington, 1959 N.E. Pacific St., H-375I Box 357630, Seattle, WA 98195-7630. Tel.: 206.543.9649; E-mail: Carlsojj@u.washington.edu.
+Funding for this work was provided in part by the Institute for Clinical and Economic Review, which collaborated on the design, conduct, and reporting of this evaluation. During the conduct of this study, Ollendorf, Synnott, Chapman, and Pearson report grants from Blue Shield of California Foundation, California Health Care Foundation, and Laura and John Arnold Foundation and also report other grants from Aetna, AHIP, Anthem, Blue Shield of California, CVS Caremark, Express Scripts, Harvard Pilgrim Health Care, OmedaRx, United Healthcare, Kaiser Permanente, Premera, AstraZeneca, Genentech, GlaxoSmithKline, Johnson & Johnson, Merck, National Pharmaceutical Council, Takeda, Pfizer, Novartis, Lilly, Spark Therapeutics, Sanofi, Prime Therapeutics, and Health Care Service Corporation outside the submitted work.
+
+Carlson reports grants from the Institute for Clinical and Economic Review during the conduct of the study and personal fees from Seattle Genetics, Genentech, and Pfizer outside the submitted work. Russo, Guzauskas, Liu, and Brouwer have nothing to disclose.
+
+Study concept and design were contributed by Carlson, Guzauskas, and Ollendorf. Guzauskas, Chapman, Synnott, and Liu collected the data, and Carlson, Guzauskas, Chapman, and Ollendorf contributed to data analysis, along with Synnott and Liu. The manuscript was written by Carlson, Guzauskas, and Brouwer, along with Chapman, Synnott, and Ollendorf, and revised by Carlson, Brouwer, and Guzauskas, along with Chapman, Synnott, and Ollendorf.
+
+1 2018
+24 1 10.18553/jmcp.2018.24.1.29Copyright © 2018, Academy of Managed Care Pharmacy. All rights reserved.
+2018
+https://creativecommons.org/licenses/by/4.0/ This article is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use and redistribution provided that the original author and source are credited.
+
+This article has been corrected. See J Manag Care Spec Pharmacy. 2018;24(7):714.
+
+BACKGROUND:
+
+New 3-drug regimens have been developed and approved to treat multiple myeloma (MM). The absence of direct comparative data and the high cost of treatment support the need to assess the relative clinical and economic outcomes across all approved regimens.
+
+OBJECTIVE:
+
+To evaluate the cost-effectiveness of treatments for relapsed and/or refractory MM from a U.S. health system perspective.
+
+METHODS:
+
+We developed a partition survival model with 3 health states (progression-free, progression, and death) to evaluate the following regimens: carfilzomib (CFZ), elotuzumab (ELO), ixazomib (IX), daratumumab (DAR), and panobinostat (PAN) in combination with lenalidomide (LEN) or bortezomib (BOR) plus dexamethasone (DEX) in the second and/or third line of therapy. To estimate relative treatment effects, we developed a network meta-analysis and applied progression-free survival hazard ratios to baseline parametric progression-free survival functions derived from pooled data on LEN+DEX. We estimated overall survival using data on the relationship between progression-free survival and overall survival from a large meta-analysis of MM patients. Modeled costs included those related to drug treatment, administration, monitoring, adverse events, and progression. Utilities were from publicly available data and manufacturer data, if published sources were unavailable.
+
+RESULTS:
+
+Model results showed that regimens containing DAR yielded the highest expected life years (DAR range: 6.71-7.38 vs. non-DAR range: 3.25-5.27) and quality-adjusted life-years (QALY; DAR range: 4.38-5.44 vs. non-DAR range: 2.04-3.46), with DAR+BOR+DEX (second line) and PAN+BOR+DEX (third line) as the most cost-effective options (incremental cost-effectiveness ratio: $50,700 and cost saving, respectively). The applicability of the PAN+BOR+DEX result may be challenging, however, because of ongoing toxicity concerns. In the probabilistic sensitivity analysis, second-line DAR+BOR+DEX and third-line PAN+BOR+DEX had an 89% and 87% probability of being cost-effective at the $150,000 per QALY threshold, respectively.
+
+CONCLUSIONS:
+
+The introduction of newer drugs and regimens to treat second- and third-line relapsed/refractory MM appears to provide clinical benefits by lengthening progression-free and overall survival and improving quality of life. However, only the addition of DAR or PAN may be considered cost-effective options according to commonly cited thresholds, and PAN+BOR+DEX results require cautious interpretation. Achieving levels of value more closely aligned with patient benefit would require substantial discounts from the remaining agents evaluated.
+==== Body
+pmc What is already known about this subject
+
+Multiple myeloma (MM) treatment has historically been anchored by 2 drugs, bortezomib and lenalidomide, each in combination with dexamethasone.
+
+Over the past decade, 6 more drugs entered the market and have demonstrated improved outcomes compared with standard care.
+
+What this study adds
+
+This study models comparative clinical and economic outcomes of combination drug therapies for MM treatment.
+
+There is considerable uncertainty about comparative long-term outcomes, such as overall survival and the comparative trade-offs between effectiveness, toxicity, and costs for these therapies and their various combinations.
+
+This study provides a comprehensive analysis of the value of these drug combinations and can inform health care decision making for a variety of stakeholders.
+
+Multiple myeloma (MM) is a hematological cancer that occurs when bone marrow produces malignant plasma cells that enter the bloodstream. MM is the second most common hematological malignancy, with 25,000 new cases diagnosed every year in the United States.1 The disease disproportionately affects older people, with the median age of onset at 66 years.1 Despite recent advances, prognosis remains relatively poor, with a 5-year survival rate of 48.5%.1 MM progression can be relatively slow in many individuals, often involving multiple rounds of remission after treatment followed by subsequent relapse. About 100,000 individuals are currently living with the disease in the United States.1
+
+Over the past decade, treatment of MM in the United States has been anchored by 2 drugs: bortezomib (Velcade; BOR) and lenalidomide (Revlimid; LEN), often given in combination with dexamethasone (DEX). Other medications have more recently become available specifically for the treatment of relapsed or refractory disease, including pomalidomide (Pomalyst; POM), carfilzomib (Kyprolis; CFZ), ixazomib (Ninlaro; IX), daratumumab (Darzalex; DAR), elotuzumab (Empliciti; ELO), and panobinostat (Farydak; PAN).
+
+These new agents have demonstrated improved outcomes compared with standard care approaches and are recommended as treatment options by national clinical guidelines.2 Collectively, they represent important clinical advances in a disease setting that has historically lacked a variety of treatment options. However, there remains considerable uncertainty regarding the comparative long-term outcomes (i.e., overall survival) and the comparative trade-offs between effectiveness and toxicity. In addition, there is considerable concern and uncertainty about the cost and value of these therapies, since the cost of a single course of drug therapy is estimated to range from $75,000 to $250,000 for U.S. patients with relapsed and/or refractory disease.3 These estimates may actually be conservative, given the increasing use of triple therapy and “treat to progression” labeling for the newest agents.3
+
+The increasing cost of U.S. health care, and specifically for cancer, has generated renewed discussion about the value of medical technologies. The availability of effective treatment options for MM patients is of paramount importance. However, in an era of continuing increases in health care spending and drug prices, it is also important to understand the relationship between costs and outcomes achieved.
+
+The objective of this study was to assess the comparative clinical and economic outcomes for drugs used to treat patients with relapsed and/or refractory MM in the second or third line of therapy from a U.S. health system perspective. This analysis can be used to inform health care decision making for a wide range of MM stakeholders.
+
+Methods
+
+Model Approach
+
+The following pharmaceutical regimens were included in the analysis: LEN+DEX, BOR+DEX, CFZ+LEN+DEX, ELO+LEN+DEX, IX+LEN+DEX, PAN+BOR+DEX, DAR+LEN+DEX, and DAR+BOR+DEX (Table 1). We developed a 3-state partition survival model, which included a progression-free survival (PFS) state, progressed disease with subsequent treatments, and death. Patients in the PFS state could be either on or off treatment to account for patients who stop therapy but remain in the PFS state. We used a cycle length of 1 week to reflect the dosing schedules for included drug regimens. The recommended dosage schedules for the regimens of interest were based on indications for treatment of relapsed and/or refractory disease labeled by the U.S. Food and Drug Adminstration, as well as expert input regarding common treatment approaches for the populations of interest. We used a health sector perspective, a lifetime horizon, a 3% discount rate for costs and outcomes, and a half-cycle correction. The upper bound willingness to pay for cost-effectiveness was $150,000 per quality-adjusted life-years (QALY) gained, which approaches the upper limit of commonly cited thresholds.4
+
+TABLE 1 Key Model Parameters
+
+Survival Hazard Ratios	Base Case	Lower	Upper	PSA Distribution	Source	
+Second-line PFS hazard ratios vs. LEN-DEX	
+CFZ-LEN-DEX	0.69	0.53	0.91	LogNormal	Network meta-analysis	
+ELO-LEN-DEX	0.70	0.56	1.00	LogNormal	Network meta-analysis	
+IX-LEN-DEX	0.74	0.65	1.19	LogNormal	Network meta-analysis	
+DAR-LEN-DEXa	0.37	0.27	0.52	LogNormal	Network meta-analysis	
+DAR-BOR-DEXa	0.39	0.28	0.53	LogNormal	Network meta-analysis	
+Third-line PFS hazard ratios vs. LEN-DEX	
+BOR-DEX	0.93	0.58	2.04	LogNormal	Network meta-analysis	
+CFZ-LEN-DEX	0.69	0.54	0.87	LogNormal	Network meta-analysis	
+ELO-LEN-DEX	0.70	0.49	0.87	LogNormal	Network meta-analysis	
+IX-LEN-DEX	0.74	0.40	0.84	LogNormal	Network meta-analysis	
+PAN-BOR-DEX	0.59	0.31	1.10	LogNormal	Network meta-analysis	
+DAR-LEN-DEXa	0.37	0.27	0.52	LogNormal	Network meta-analysis	
+DAR-BOR-DEXa	0.39	0.28	0.53	LogNormal	Network meta-analysis	
+Hazard ratio for OS vs. PFS	0.41	0.31	0.58	LogNormal	24	
+Quality of Life	Base Case	Lower	Upper	PSA Distribution	Source	
+Second-line health state utilities	
+Progression-free, on treatment	0.82	0.78	0.88	Beta	Data on fileb	
+Progression-free, off treatment	0.84	0.82	0.97	Beta	Data on fileb	
+Progressed disease	0.65	0.62	0.74	Beta	Data on fileb	
+Third-line health state utilities	
+Progression-free, on treatment	0.65	0.52	0.78	Beta	25	
+Progression-free, off treatment	0.72	0.58	0.86	Beta	37	
+Progressed disease	0.61	0.49	0.73	Beta	25	
+Adverse event disutility	0.08	0.07	0.08	Beta	25	
+Costs	Base Case	Lower	Upper	PSA Distribution	Source	
+Drug acquisition and administration costs,c $	
+Bortezomib 3.5 mg vial	1,503.00	1,202.40	1,803.60	Normal	RED BOOK	
+Bortezomib administration	111.42	89.14	133.70	Normal	CPT 96409	
+Carfilzomib 60 mg vial	1,971.50	1,577.20	2,365.80	Normal	RED BOOK	
+Carfilzomib administration	209.24	167.39	251.09	Normal	CPT 96360, 96361, 96413	
+Dexamethasone per mg	0.32	0.26	0.39	Normal	RED BOOK	
+Elotuzumab 300 mg vial	1,776.00	1,420.80	2,131.20	Normal	RED BOOK	
+Elotuzumab 400 mg vial	2,368.00	1,894.40	2,841.60	Normal	RED BOOK	
+Elotuzumab administration	227.87	182.30	273.44	Normal	CPT 96413, 96415, 96417	
+Ixazomib capsule	3,006.00	2,404.80	3,607.20	Normal	RED BOOK	
+Lenalidomide capsule	552.98	442.38	663.58	Normal	RED BOOK	
+Panobinostat capsule	1,222.22	977.78	1,466.67	Normal	RED BOOK	
+Daratumumab 400 mg vial	1,850.40	1,480.32	2,220.48	Normal	RED BOOK	
+Daratumumab 100 mg vial	462.60	370.08	555.12	Normal	RED BOOK	
+Daratumumab administration	399.83	319.86	479.80	Normal	CPT 96413, 96415, 96417	
+Health State Cost per Week	Progression-Free AE Prophylaxis, $	Progressed Disease Treatment, $	
+LEN-DEX	22.00	427.45	
+CFZ-LEN-DEX	104.73	367.30	
+ELO-LEN-DEX	85.37	369.26	
+IX-LEN-DEX	85.26	377.05	
+PAN-BOR-DEX	39.16	337.41	
+DAR-LEN-DEX	49.45	273.54	
+DAR-BOR-DEX	90.71	283.94	
+aDAR hazard ratios were assessed for a general population and were assumed to be equivalent in second- and third-line settings.
+
+bAmgen. Data provided in response to ICER data request. QOL/Utility Data from ASPIRE Cost Effectiveness Model. February 22, 2016.
+
+cCosts assessed March 2016.
+
+BOR = bortezomib; CFZ = carfilzomib; CPT = Current Procedural Terminology; DAR = daratumumab; DEX = dexamethasone; ELO = elotuzumab; IX = ixazomib; LEN = lenalidomide; OS = overall survival; PAN = panobinostat; PFS = progression-free survival; PSA = probabilistic sensitivity analysis.
+
+Network Meta-analysis
+
+A Bayesian network meta-analysis (NMA) was performed to combine evidence on direct and indirect comparisons across the treatment regimens of interest (Appendix A, available in online article).5 The evaluated trials specified similar inclusion criteria. Each trial included adult patients aged ≥18 years with measurable relapsed and/or refractory MM. All patients had previously received therapies and had adequate renal, hepatic, and hematologic function. The trial populations were also similar with respect to age, Eastern Cooperative Oncology Group (ECOG) performance status, International Staging System (ISS) stage, receipt of previous stem cell transplant, and number and distribution of previous regimens.
+
+Quantitative analyses focused on PFS and were conducted using the NetMetaXL tool (http://www.netmetaxl.com/). Adjusted hazard ratios from the randomized trials were log-transformed and entered into the spreadsheet, and 95% confidence intervals (CIs) on log scale were used to specify variance estimates (i.e., standard errors).6-15 A total of 40,000 iterations each were used for “burn-in” (for model convergence) and model (for model results) simulations. Review of the deviance information criterion statistics, as well as comparison of the residual deviance with the number of unconstrained data points, was used to assess the best model fit under multiple alternative assumptions. Although a random effects approach was preferred, the available network was constructed of primarily single-study connections and necessitated a fixed-effects model to preserve statistically significant effects observed in trials.
+
+We also conducted sensitivity analyses based on the shape and scale parameters of digitized parametric survival curves to test and address the potential violation of the proportional hazards assumption.16 We did this for the overall dataset and a subset of data from the carfilzomib, ixazomib, and elotuzumab trials to assess whether inclusion of more contemporary data for LEN+DEX had a material effect on results. In this instance, 30,000 iterations were used for burn-in and model simulations.
+
+Survival Curve Estimation
+
+We fit parametric survival curves to PFS Kaplan-Meier data for the baseline comparator (LEN+DEX) in the second- and third-line settings, using the approach described by Hoyle and Henley (2011).17 LEN+DEX was chosen as the baseline because clinical experts considered LEN+DEX to be the most commonly used comparator and because of the availability of LEN+DEX survival data by line of therapy. To do this, we extracted data points from digitized survival curves, then used the extracted values, the number of surviving patients at each time interval, and maximum likelihood functions to estimate the underlying individual patient data.7,8,18 We assumed that the rate of censoring was the same between the second- and third-line settings, which allowed us to estimate the number at risk at set time points for the second-and third-line curves from the pooled number at risk data.
+
+Base case PFS curves for LEN+DEX were derived from parametric fits to pooled Kaplan-Meier data from the MM-009 and MM-010 trials of LEN+DEX.7,8 For the base case, we selected the Weibull parametric function from the candidate distributions based on (a) face validity (log-normal and log-logistic fits exhibited unrealistically elongated tails) and (b) Akaike Information Criterion, a graphical assessment of each parametric function and a knowledge of the expected extrapolation of the PFS times. We then used PFS hazard ratios acquired from our network meta-analysis results, applied to the baseline comparator curve (LEN+DEX), to derive PFS curves for the other interventions by line of treatment (Table 1).6,13,14,19-23 We assumed that the regimens’ NMA-derived treatment effects were consistent for the second- and third-line settings, since stratified analyses suggested similar effect sizes for most comparisons (data not shown).
+
+The data on overall survival (OS) for these regimens were not uniformly available and were prone to bias because of crossover to the active comparator, as well as the availability of different drugs after progression in trials conducted at different points in time. Therefore, we applied a 2.45-month (95% CI = 1.7-3.2) increase in OS for each additional month of PFS for each regimen-specific OS curve, based on a systematic review of this relationship in studies of nearly 23,000 MM patients.24 This approach has been used previously in support of model submissions to health technology assessment agencies.25 We operationalized this estimate by deriving an OS to PFS hazard ratio (1/2.45 = 0.41), which we then applied to each regimen’s PFS curve to estimate the corresponding OS curve. We varied this parameter in a sensitivity analysis and ran a scenario analysis using a weighted average estimate of the relationship of PFS to OS from the available clinical trials in our assessment (3.27-month increase in OS for each additional month of median PFS).
+
+Model Parameters: Health State Utilities
+
+Health state utilities were derived from publicly available literature and/or data made available to us by manufacturers. We used consistent, sequence-specific health state utility values across treatments evaluated in the model. For the progression-free health state, different utilities were applied, depending on whether the patient was on or off treatment, to represent treatment-related decreased quality of life. We applied a regimen-weighted disutility for experiencing any grade 3/4 adverse event (AE). The total percentage of patients who experienced any grade 3/4 AEs for each regimen was multiplied by the AE disutility and then subtracted from the total QALYs gained during PFS for each regimen. We assumed that the total time that patients experienced any grade 3/4 AE was 1 month.
+
+Model Parameters: Adverse Events
+
+The model included grade 3/4 AEs derived from key clinical trials and/or each drug’s prescribing information. The model included any reported grade 3/4 AEs that occurred in at least 5% of patients for any of the treatment comparators.3,26 The frequency of each event was reported as the proportion of patients experiencing the event, as well as the total number of occurrences based on published sources (Appendix B, available in online article). This proportion was then multiplied by the average cost per event to derive a total AE cost for each regimen. Costs per AE were based on a previously published analysis, supplemented by data from the Centers for Medicare & Medicaid Services (CMS) list of Medicare Severity-Diagnosis Related Groups (MS-DRGs) for the fiscal year 2016.3,26
+
+Model Parameters: Cost Inputs
+
+All patients were assumed to initiate study medications at cycle 0 (beginning of the model). For all regimens, patients received planned doses based on individual patient characteristics. The estimation of drug use was derived from several factors, including the relative dose intensity reported in trials or directly provided by manufacturers and the dosing schedule where the dose may be fixed by weight or by body surface area (BSA), assuming average patient characteristics of 1.7 meters tall and 80 kilogram body mass (BSA = 1.92; formula: 0.007184*[mass ^0.425]*[height^0.725]; unpublished data. Amgen. Data provided in response to ICER data request. QOL/Utility Data from ASPIRE Cost Effectiveness Model. February 22, 2016).
+
+The treatment utilization and costs of treat-to-progression regimens were applied to all patients who remained in the PFS health state over time. In finite-cycle regimens, patients could remain in the PFS state after active treatment concluded. No vial sharing was assumed in the base case. Drug unit costs were applied to the utilization estimates to calculate total estimated treatment costs.
+
+We used the wholesale acquisition cost for each drug and noted each available formulation.25 Based on the regimen-specific dosage previously specified, the model used the lowest cost combination of tablets and/or vials for each regimen. Drug administration costs were determined from the Final 2016 Medicare Coding & Payment for Drug Administration Services under the Physician Fee Schedule.27 All drug and administration costs were varied by ±20% in sensitivity analyses.
+
+Other Direct Costs.
+
+We included per cycle AE prophylaxis and monitoring costs based on management guidelines and the literature.6 The cost of postprogression treatment was calculated using a treatment landscape analysis to estimate the proportion of patients who received different available treatments upon progression. The specific treatment distribution was derived from Farr et al. (2016).28 The model assumed that patients received 1 further line of treatment lasting 124 days followed by best supportive care.28 We then calculated a mean cost per week for each regimen by averaging the cost of additional treatment (weighted by the distribution) and supportive care over the mean time in the progression health state.
+
+Model Outcomes
+
+The model estimated the amount of time, on average, that patients spent progression-free and in progression. Unadjusted and utility-adjusted time spent in each health state were summed to provide estimates of life expectancy and quality-adjusted life expectancy. Model outcomes of interest for each intervention included cost, quality-adjusted life expectancy, life expectancy, and mean time in the progression-free and postprogression health states, as well as the relative cost-effectiveness for each intervention compared with the next best comparator. We also provided the results of pairwise comparisons by line versus a standard comparator, LEN-DEX.
+
+Sensitivity Analyses
+
+One-way sensitivity analyses used 95% CIs from clinical evidence when available and plausible values from the published literature when absent. We also conducted a probabilistic sensitivity analysis by jointly varying all model parameters over 4,000 simulations, then calculating 95% credible range estimates for each model outcome. The following distributions were applied to model variables: hazard ratios (log-normal distribution), utility values (beta distribution), proportions of AEs (beta distribution), costs of drugs (normal distribution), costs of AEs (normal distribution), monthly cost of progression (normal distribution), and administration costs (normal distribution).
+
+We ran 4 scenario analyses: (1) using an unadjusted estimate of the relationship of median PFS to median OS based on a weighted average from the trials in our analysis that report both outcomes (3.27-month increase in OS for each additional month of median PFS); (2) using BOR+DEX as the baseline comparator; (3) adjusting the second- and third-line baseline curves to reflect more recent LEN+DEX regimen curves using the relationship between the ASPIRE trial LEN+DEX data and the MM-009/MM-010 pooled LEN+DEX data6,7; and (4) using different second-line utility estimates for triplet (0.83, 0.85, and 0.66 for PFS on treatment, PFS off treatment, and progression, respectively) versus doublet regimens (0.81, 0.83, 0.4, respectively) derived from the ASPIRE trial data (triplet regimens include 3 drugs [e.g., CFZ+LEN+DEX]; doublet regimens include 2 drugs [e.g., LEN+DEX].7,8,18
+
+Results
+
+Network Meta-analysis
+
+Results of the NMA are included in Table 1. Trial populations were similar with respect to age, ECOG performance status, ISS stage, receipt of previouis stem cell transplant, and number and distribution of previous regimens. (Appendix A). Definitions of disease risk varied, but the percentage of patients with high-risk disease ranged from 13%-32% across studies reporting this element. All of the regimens had favorable hazard ratios compared with LEN+DEX; ELO+LEN+DEX and IX+LEN+DEX had the greatest uncertainty. Results for PAN+BOR+DEX in the third-line setting should be interpreted with great caution because of censoring issues and high rates of toxicity-related discontinuation in the overall and third-line subgroup populations of the PANORAMA-1 study.14 PAN+BOR+DEX is also only 1 of 2 regimens without direct comparative evidence versus LEN+DEX; therefore, greater reliance on the study network and its assumptions regarding minimal heterogeneity across study populations and constant hazards over time was required. While censoring was factored into our analytic approach, the relative treatment effect of PAN+BOR+DEX versus LEN+DEX had much greater uncertainty than the other comparisons.
+
+Cost-effectiveness
+
+The results for the aggregate discounted clinical and economic outcomes by line and regimen are provided in Tables 2 and 3. In the second line, total QALYs ranged from a low of 2.59 for LEN+DEX to a high of 5.44 for DAR+LEN+DEX. Total life years ranged from 3.53 for LEN+DEX to 7.38 for DAR+LEN+DEX. Total costs ranged from $189,357 for BOR+DEX to $845,527 for DAR+LEN+DEX. In the third line, results followed a similar pattern, with total QALYs ranging from a low of 2.04 for LEN+DEX to a high of 4.38 for DAR+LEN+DEX. Total life years ranged from 3.25 for LEN+DEX to 6.97 for DAR+LEN+DEX. Total costs ranged from $175,315 for BOR+DEX to $789,202 for DAR+LEN+DEX.
+
+TABLE 2 Comparative Outcomes
+
+Regimen	Second Line	Third Line (All Comparators)	Third Line (PAN-BOR-DEX Omitted)	
+	Total Cost, $	QALYs	ICER	Total Cost, $	QALYs	ICER	Total Cost, $	QALYs	ICER	
+LEN-DEX	309,997	2.59	Dominated	281,754	2.04	Dominated	281,754	2.04	Dominated	
+BOR-DEX	189,357	2.74	Dominant	175,315	2.16	Dominant	175,315	2.16	Dominant	
+IX-LEN-DEX	622,378	3.27	Dominated	566,512	2.60	Dominated	566,512	2.60	Dominated	
+ELO-LEN-DEX	665,728	3.41	Dominated	608,651	2.71	Dominated	608,651	2.71	Dominated	
+CFZ-LEN-DEX	492,872	3.45	Dominated	459,868	2.74	Dominated	459,868	2.74	Dominated	
+PAN-BOR-DEX				190,876	3.23	14,598				
+DAR-BOR-DEX	447,182	5.29	50,704	423,119	4.38	248,762	423,119	4.38	60,359	
+DAR-LEN-DEX	845,527	5.44	2,707,547	789,202	4.38	Equal outcomes, higher cost vs. DAR-BOR-DEX	789,202	4.38	Equal outcomes, higher cost vs. DAR-BOR-DEX	
+BOR = bortezomib; CFZ = carfilzomib; DAR = daratumumab; DEX = dexamethasone; ELO = elotuzumab; ICER = incremental cost-effectiveness ratio; IX = ixazomib; LEN = lenalidomide; OS = overall survival; PAN = panobinostat; PFS = progression-free survival; QALY = quality-adjusted life-year.
+
+TABLE 3 Results per Regimen
+
+Second Line	LEN-DEX	BOR-DEX	CFZ-LEN-DEX	ELO-LEN-DEX	IX- LEN-DEX	DAR- LEN-DEX	DAR- BOR-DEX	
+Total costs, $	309,997	189,357	492,872	665,728	622,378	845,527	447,182	
+  Drug acquisition	264,898	133,774	432,799	596,124	571,390	762,407	368,096	
+  Supportive care	528	1,608	1,882	2,607	2,491	4,947	2,515	
+  Administration	−	8,226	8,377	14,698	−	23,981	22,960	
+  Progression	40,221	41,167	45,358	45,143	44,330	50,723	51,003	
+  Adverse event	4,351	4,583	4,457	7,156	4,166	3,469	2,607	
+Total QALYs	2.59	2.74	3.45	3.41	3.27	5.44	5.29	
+  PFS	1.41	1.50	1.91	1.89	1.81	3.13	3.05	
+  Progression	1.17	1.24	1.54	1.52	1.46	2.31	2.24	
+Total life-years	3.53	3.73	4.71	4.66	4.46	7.38	7.11	
+  PFS	1.73	1.83	2.34	2.31	2.21	3.82	3.67	
+  Progression	1.80	1.91	2.37	2.34	2.25	3.55	3.44	
+ICER vs. LEN-DEX	−	-792,583	211,458	430,009	454,684	187,728	50,704	
+Third Line	LEN-DEX	BOR-DEX	CFZ-LEN-DEX	ELO-LEN-DEX	IX- LEN-DEX	PAN-BOR-DEX	DAR-LEN-DEX	DAR-BOR-DEX	
+Total costs, $	281,754	175,315	459,868	608,651	566,512	190,876	789,202	423,119	
+  Drug acquisition	237,670	121,751	401,201	541,632	516,793	131,500	707,051	344,684	
+  Supportive care	473	1,441	1,779	2,364	2,255	411	4,579	2,403	
+  Administration		7,365	8,113	13,394	−	3,095	22,394	21,412	
+  Progression	39,261	40,175	44,318	44,105	43,298	46,744	51,708	52,014	
+  Adverse event	4,351	4,583	4,457	7,156	4,166	9,127	3,469	2,607	
+Total QALYs	2.04	2.16	2.74	2.71	2.60	3.23	4.38	4.38	
+  PFS	1.00	1.07	1.37	1.36	1.30	1.69	2.28	2.35	
+  Progression	1.03	1.09	1.37	1.36	1.30	1.54	2.10	2.03	
+Total life-years (OS)	3.25	3.44	4.37	4.32	4.14	4.93	6.97	6.71	
+  PFS	1.55	1.64	2.12	2.09	2.00	2.41	3.52	3.38	
+  Progression	1.70	1.79	2.25	2.23	2.14	2.52	3.44	3.33	
+ICER vs. LEN-DEX	−	-853,800	252,293	484,168	508,021	Dominant	216,360	60,359	
+BOR = bortezomib; CFZ = carfilzomib; DAR = daratumumab; DEX = dexamethasone; ELO = elotuzumab; ICER = incremental cost-effectiveness ratio; IX = ixazomib; LEN = lenalidomide; OS = overall survival; PAN = panobinostat; PFS = progression-free survival; QALY = quality-adjusted life-year.
+
+Table 2 shows the comparative results using a league table approach, which listed interventions from lowest to highest QALYs and then calculated an incremental cost-effectiveness ratio (ICER) for each intervention compared with the next best option. Interventions that were dominated were removed from the calculations, and a new ICER was computed versus the next best comparator. Table 2 shows that in the second line, BOR+DEX dominates LEN+DEX; DAR+BOR+DEX has an ICER of $50,704 versus BOR+DEX; and DAR+LEN+DEX has an ICER of $2,707,547 versus DAR+BOR+DEX. In the third line, BOR+DEX dominates LEN+DEX; PAN+BOR+DEX has an ICER of $14,124 versus BOR+DEX; DAR+BOR+DEX has an ICER of $248,762; and DAR+BOR+DEX is cost minimizing versus DAR+LEN+DEX. If we remove PAN+BOR+DEX because of the challenges mentioned in the Network Meta-analysis section (including high levels of treatment discontinuation in the PANORAMA-1 trial and lack of direct comparative evidence to LEN+DEX, among others), DAR+BOR+DEX has an ICER of $60,359 versus BOR+DEX.
+
+The results of the pairwise analyses can be found in Table 3. ICERS for new second-line regimens versus LEN+DEX were estimated to be $51,000 per QALY for DAR+BOR+DEX, followed by DAR+LEN+DEX ($188,000) and CFZ+LEN+DEX ($211,000), with greater than $400,000 per QALY for ELO+LEN+DEX and IX+LEN+DEX. In the third line, ICERs for new regimens versus LEN+DEX were estimated to range from dominant for PAN+BOR+DEX to $60,000 per QALY for DAR+BOR+DEX, followed by DAR+LEN+DEX ($216,000), CFZ+LEN+DEX ($253,000), and approximately $500,000 per QALY for ELO+LEN+DEX and IX+LEN+DEX. As noted previously, results for PAN+BOR+DEX should be interpreted with caution because of population censoring and reliance on indirect treatment comparisons in the NMA. In both settings, incremental costs were driven primarily by increased drug costs rather than progression, supportive care, or AE costs.
+
+Sensitivity Analyses
+
+In each one-way analysis (not shown), results were by far most sensitive to the PFS hazard ratios for each intervention versus LEN+DEX, followed by the estimated link between PFS and OS (2.45 months of OS for each month of PFS, per Felix et al., 201324), drug costs, dosage intensity, and health state utilities.6
+
+Probabilistic sensitivity analysis showed variability in model outcomes (Figure 1). In the second-line setting among the new agents, DAR+BOR+DEX had an 87% probability of being cost-effective at $150,000 per QALY, while all other agents had a 0% probability of being cost-effective at that threshold. In the third-line setting, PAN+BOR+DEX had an 87% probability of being cost-effective at $150,000 per QALY; however, this probability declined with increasing willingness to pay for the better survival outcomes of DAR+BOR+DEX. No other new agents were cost-effective at a cost-effectiveness acceptability threshold of $150,000 per QALY.
+
+FIGURE 1 Cost-effectiveness Acceptability Curves
+
+We performed several scenario analyses (data not shown). The resulting ICERs varied but no regimens were found to have ICERs below $150,000 per QALY that were not already estimated to be so in the primary analysis.
+
+Finally, we performed a threshold analysis to estimate the unit price coincident with commonly used cost-effectiveness thresholds, by holding all other parameters constant (including BOR, LEN, and DEX costs) and identifying the threshold price for each novel agent that made the overall regimen cost-effective at different willingness-to-pay thresholds. We also implemented this analysis with the probabilistic sensitivity analysis to calculate 95% credible range estimates for each threshold price. Table 4 demonstrates that most agents, in combination regimens, would require substantial discounts to meet the highest cost-effectiveness threshold of $150,000 per QALY, and some would not even be cost-effective at a price of $0, given the existing cost of the other drugs in the regimen.
+
+TABLE 4 Drug Cost Thresholds
+
+	Second Line, $	Third Line, $	
+WTP Threshold	50,000	100,000	150,000	50,000	100,000	150,000	
+CFZ-LEN-DEX	55	649	1,242	0	445	946	
+(−906-1,063)	(−68-1,733)	(405-2,661)	(−938-622)	(−633-1,518)	(−386-2,417)	
+ELO-LEN-DEX	−69	252	572	−126	162	449	
+(−535-619)	(−141-903)	(138-1,272)	(−644-484)	(−266-692)	(34-1,032)	
+IX-LEN-DEX	−278	127	533	-347	19	385	
+(−903-567)	(−294-830)	(84-1,329)	(−1,046-593)	(−502-769)	(−40-1,180)	
+PAN-BOR-DEX				3,459	4,344	5,229	
+			(2,389-5,552)	(2,668-8,242)	(2,792-10,987)	
+DAR-LEN-DEX	−165	567	1,298	−293	351	995	
+(−779-486)	(−51-1,239)	(614-2,093)	(−902-417)	(−239-1,080)	(338-1,800)	
+DAR-BOR-DEX	1,840	2,582	3,324	1,708	2,397	3,087	
+(1,495-2,278)	(2,139-3,050)	(2,674-3,976)	(1,374-2,114)	(1,948-2,959)	(2,479-3,845)	
+Note: Results reflect threshold prices for the first listed drug in each triplet regimen only (all other parameter values held constant).
+
+BOR = bortezomib; CFZ = carfilzomib; DAR = daratumumab; DEX = dexamethasone; ELO = elotuzumab; IX = ixazomib; LEN = lenalidomide; PAN = panobinostat; WTP = willingness to pay.
+
+Discussion
+
+We evaluated 8 drugs in 8 regimens for 2 relapsed and/or refractory MM populations, second and third lines. All the regimens evaluated were estimated to increase time in the progression-free health state and overall survival versus the standard regimens of LEN+DEX and BOR+DEX, but at substantial additional cost compared with these doublet regimens. As such, the value for most of these regimens, according to commonly used thresholds for cost-effectiveness, would be considered questionable. A few regimens were estimated to be in the cost-effective range, including DAR-BOR-DEX (second line) and PAN-BOR-DEX (third line), although the analysis for PAN+BOR+DEX was reserved to the third line and had data challenges that limited our willingness to make strong conclusions about its value. If we remove PAN+BOR+DEX from the third-line analysis, DAR-BOR-DEX becomes the most cost-effective. The LEN+DEX-based regimens with the best ICERs were DAR+LEN+DEX and CFZ+LEN+DEX, although neither regimen fell below the $150,000 per QALY threshold due, in part, to the high cost of LEN and the longer duration of therapy because of increased PFS compared with doublet regimens.
+
+The analyses reported here reveal that important advances in the treatment of relapsed and/or refractory MM have been made over the past decade, which have expanded treatment options and improved patient outcomes. However, only a few regimens have done so in a cost-effective manner.
+
+Our findings highlight a few key implications for stakeholders facing treatment, recommendation, or coverage and reimbursement decisions related to relapsed and/or refractory MM. First, there are cost-effective options for patients in this setting. DAR, the agent with the highest estimated life expectancy and QALY outcomes, demonstrated good value when used in combination with BOR-DEX in the second- and third-line settings. Depending on the clinical situation and patient and provider preferences, this regimen may be preferred over other regimens. Second, new agents combined with BOR uniformly had better ICERs relative to the same new agents in combination with LEN. This is primarily a function of the drug cost for BOR and LEN, respectively. The cost per month for BOR is about half as much as that for LEN. Couple this with the treat-to-progression dosing schedules, and the BOR-based regimens were uniformly more cost-effective compared with the LEN-based regimens.
+
+Importantly, incremental drug costs included additional costs of the new drug as well as extended use of LEN+DEX or BOR-DEX because of improved PFS for the entire regimen. For example, the total treatment cost of LEN in the preprogression state when given as part of the CFZ+LEN+DEX regimen is $260,392 versus $239,745 when given as part of the LEN+DEX regimen because of the longer time in the progression-free state and therefore longer time on treatment. With treat-to-progression strategies, the additional clinical benefits of extending time in the progression-free health state come with consistent extra costs, whereas regimens that generally include 1 or more agents with a fixed dosing schedule (i.e., CFZ+LEN+DEX and PAN+BOR+DEX) do not incur the same amount of additional cost. In this context, the cost and value in real-world settings will be different if the clinical community deviates from the fixed dosing strategies suggested in the prescribing information.
+
+The drug cost threshold analysis highlights a current challenge to providing cost-effective cancer treatment over and above that related to the cost of individual drugs, that is, the issue of adding new agents to existing regimens and creating expensive combination regimens of 2 or more drugs. This challenge has been discussed previously and remains a challenge globally.29,30 Specifically, manufacturers of new drugs that are used in combination with other, often expensive, drugs have no influence over the cost of the other drugs; therefore, manufacturers have a limited ability to establish prices in line with a given health system’s willingness to pay for health gains. As our threshold analysis revealed, for many of the drugs evaluated, the discounts that would be required to achieve commonly used cost-effectiveness thresholds were unrealistic and at times entered into the negative space. However, these are real extra costs to the health system and must be factored into economic analyses.
+
+There are limited options available to address this issue. Indication-based pricing has gained some traction as a potential option to align the use of drugs with the specific value delivered to patients and patient populations, but the challenges with creating this system for an individual drug therapy would only be exacerbated for combination regimens. CMS has indicated a willingness to explore innovative contracts, especially in oncology, but for indication-based pricing to work with combination regimens, the payer would need to establish a comprehensive indication-based pricing process that applied to all manufacturers, and we are a long way from such a system at present.31 Some companies have indicated that they are willing to entertain decreased costs for drugs used in combination if they make all the drugs.32 Another possibility is for manufacturers to work together to provide a group discount. This option has begun to gain traction in Europe, where manufacturers can work with large public payers, although implementation in the United States would be challenged by its fragmented health system.33
+
+We found limited data on the cost-effectiveness of drugs to treat MM in the United States. A recently published cost-effectiveness analysis by Jakubowiak et al. (2016) examined CFZ+LEN+DEX versus LEN+DEX in relapsed MM from a U.S. perspective.34 Although the total incremental cost was similar to that in our model ($179,400 vs. $183,000, respectively), the estimate of QALYs gained with CFZ+LEN+DEX was notably different (1.67 vs. 0.86).
+
+This difference appears to be a result of 2 key differences between models. First, the independently modeled PFS and OS curves in the Jakubowiak et al. analysis yielded much more favorable estimates of treatment effect for CFZ+LEN+DEX than those reported in the ASPIRE trial versus LEN+DEX (PFS odds ratio = 0.51 [model] vs. 0.69 [published hazard ratio]; OS hazard ratio = 0.70 [model] vs. 0.79 [published hazard ratio]). These differences appear to explain a modeled increase in PFS that was over 5 months longer than the median PFS observed in the ASPIRE trial (unpublished data. Amgen. Data provided in response to ICER data request. QOL/Utility Data from ASPIRE Cost Effectiveness Model. February 22, 2016) Second, Jakubowiak et al. used a log-logistic parametric function for the PFS curve, which is prone to long tails in the distribution, whereas we used a Weibull function. Finally, we note that 1 of the findings of the Jakubowiak et al. analysis appears to be counterintuitive, in that CFZ+LEN+DEX patients spend approximately 4 years in the postprogression state in the model versus approximately 3 years for LEN+DEX; however, the postprogression treatment costs for LEN+DEX are reported to be higher.
+
+Limitations
+
+Our analysis had several limitations that warrant mention. We had limited data for all agents on OS. Therefore, we used a method supported by other methodologists, and used in previous analyses, to estimate OS using PFS.25,35 Although directly observed data on OS would be preferred, we felt this method allowed for an unbiased and uniformly applicable approach to estimated OS using PFS, the primary endpoint from all the pivotal trials. This approach would be expected to limit potential bias across drugs given that the agents and regimens evaluated were all approved at different times, so the standard of care, especially after progression, would be expected to vary and potentially affect OS. The observed relationship in any individual study may have been different than that applied uniformly in our model. We therefore tested the estimate in 1-way sensitivity and scenario analyses and found that, while the assumed relationship of PFS to OS was a sensitive parameter, its effect was much less than that of varying PFS hazard ratios and did not substantially affect the primary findings.
+
+We did not have sufficient data for each regimen in the second and third lines. Therefore, we used line-specific data from LEN+DEX, the baseline comparator, and applied the same treatment effect for the new regimens to the separate baseline population estimates, under the assumption that the treatment effect (i.e., the hazard ratio) was consistent across the second and third lines. This assumption had validity in that we found no consistent evidence of a differential treatment effect by line of therapy, and the trials were powered to detect differences in the overall effect in the full intent-to-treat population.
+
+Finally, we note that PFS results in the tables will not match those seen in clinical trials because of our anchoring of hazard ratios to the baseline survival curves for LEN+DEX rather than use of observed survival curves in each trial. However, because of the fixed-effects nature of the NMA, relative effects from each trial are essentially preserved. Our drug cost estimates also had good face validity when compared against an analysis performed by Potluri et al. (2015) using the MarketScan claims database (total LEN+DEX cost in the model: $280,000 vs. Potluri et al.: approximately $310,000).36
+
+Conclusions
+
+The introduction of newer drugs and regimens to treat second- and third-line relapsed and/or refractory MM appears to provide clinical benefits by lengthening PFS and OS and improving quality of life. However, only the addition of DAR or PAN may be considered cost-effective options according to commonly cited thresholds, and PAN+BOR+DEX results require cautious interpretation. Achieving levels of value more closely aligned with patient benefit would require substantial discounts for the remaining agents evaluated.
+
+APPENDIX A Key Trials Included in the Network Meta-analysis
+
+Key Trials	Patient Characteristics	Treatment	Comparator	Harms (Treatment Arm)	
+ASPIRE
+Open-label RCT
+Phase 3
+Carfilzomib (CFZ)	• Median age: 64
+• ECOG = 2: 9.5%
+• ISS Stage III: 20%
+• Previous SCT: 57%
+• High risk: 12.6%
+• Prior regimens (median): 2
+• Prior BOR: 65.8%
+• Prior LEN: 19.8%	CFZ+LEN+DEX
+(n = 396)	LEN+DEX
+(n = 396)	• Discontinued d/t AEs: 15%
+• SAEs: 60%
+• Tx-related deaths: 2%	
+• Median f/u: 32.3 m	• Median f/u: 31.5 m	
+• OS HR: 0.79 (95% CI: 0.63-0.99; P = 0.04)
+• PFS HR: 0.69 (95% CI: 0.57-0.83)	
+• Median PFS: 26.3 m
+• ORR: 87.1%	• Median PFS: 17.6 m
+• ORR: 66.7%, P < 0.001	
+CASTOR
+Open-label RCT
+Phase 3
+Daratumumab (DAR)	• Median age: 64
+• ECOG = 2: NR
+• ISS Stage III: 22%
+• Previous SCT: 61%
+• del(17p): 10%
+• Prior regimens (median): 2
+• Prior BOR: 66%
+• Prior LEN: 76%	DAR+BOR+DEX
+(n = 251)	BOR+DEX
+(n = 247)	• Discontinued d/t AEs: 7%
+• SAEs: 76%
+• Tx-related deaths: 5%	
+• Median f/u: 7.4 m	
+• Deaths: 11.6%	• Deaths: 14.6%	
+• PFS HR: 0.39 (95% CI: 0.28-0.53; P < 0.001)	
+• Median PFS: NR
+• ORR: 82.9%	• Median PFS: 7.2 m
+• ORR: 63.2%	
+POLLUX
+Open-label RCT
+Phase 3
+Daratumumab (DAR)	• Median age: 65
+• ECOG = 2: 5%
+• ISS Stage III: 20%
+• Previous SCT: 63%
+• del(17p): 8%
+• Prior regimens (median): 1
+• Prior BOR+LEN: 15%	DAR+LEN+DEX
+(n = 286)	LEN+DEX
+(n = 283)	• Discontinued d/t AEs: 8%
+• SAEs: 49%
+• Tx-related deaths: 4%	
+• Median f/u: 13.5 m	
+• Deaths: 10.5%	• Deaths: 15.9%	
+• PFS HR: 0.37 (95% CI: 0.27-0.52; P < 0.001)	
+• Median PFS: NR
+• ORR: 92.9%	• Median PFS: 18.4 m
+• ORR: 76.4%	
+ELOQUENT-2
+Open-label RCT
+Phase 3
+Elotuzumab (ELO)	• Median age: 66
+• ECOG = 2: 9%
+• ISS Stage III: 21%
+• Previous SCT: 54%
+• del(17p): 32%
+• Prior regimens (median): 2
+• Prior BOR: 70%
+• Prior LEN: 6%	ELO+LEN+DEX
+(n = 321)	LEN+DEX
+(n = 325)	• Discontinued d/t AEs: 13%
+• SAEs: 65%
+• Tx-related deaths: 2%	
+• Median f/u: 24.5 m
+• OS HR: 0.71 (95% CI: 0.54-0.93)
+• PFS HR: 0.70 (95% CI: 0.57-0.85; P < 0.001)	
+• Median PFS: 19.4 m
+• ORR: 79%	• Median PFS: 14.9 m
+• ORR: 66%, P < 0.001	
+TOURMALINE-MM1
+Double-blind RCT
+Phase 3 (unpublished)
+Ixazomib (IX)	• Median age: 66
+• ECOG = 2: 6%
+• ISS Stage III: 13%
+• Previous SCT: 57%
+• High risk: 19%
+• Prior regimens (median): 2
+• Prior BOR: 69%
+• Prior LEN: 12%	IX+LEN+DEX
+(n = 360)	Placebo+LEN+DEX
+(n = 362)	• Discontinued d/t AEs: 13%
+• SAEs: 40%
+• Tx-related deaths: NR	
+• Median f/u (PFS): 23 m	
+• Deaths: 22.5%	• Deaths: 24.8%	
+• PFS HR: 0.74 (95% CI: 0.59-0.94; P = 0.012)	
+• Median PFS: 20.6 m
+• ORR: 78%	• Median PFS: 14.7 m
+• ORR: 72%, P < 0.001	
+PANORAMA-1
+Double-blind RCT
+Phase 3
+Panobinostat (PAN)	• Median age: 63
+• ECOG = 2: 5%
+• ISS Stage III: 22%
+• Previous SCT: 58%
+• 1 prior regimen: 51%
+• Prior BOR+DEX: 38%
+• Prior LEN: 21%	PAN+BOR+DEX
+(n = 387)	Placebo+BOR+DEX
+(n = 381)	• Discontinued d/t AEs: 36%
+• SAEs: 60%
+• Tx-related deaths: 3%	
+• Median f/u: 6.4 m	• Median f/u: 5.9 m	
+• OS HR: 0.87 (95% CI: 0.69-1.10; P = 0.26)
+• PFS HR: 0.63 (95% CI: 0.52-0.76; P < 0.0001)	
+• Median PFS: 11.99 m
+• ORR: 60.7%	• Median PFS: 8.08 m
+• ORR: 54.6%, P = 0.09	
+AE = adverse event; BOR = bortezomib; CI = confidence interval; del(17p) = deletion in 17 p region of tumor protein 53 gene; DEX = dexamethasone; d/t = due to; ECOG=Eastern Cooperative Oncology Group; f/u = follow-up; HR = hazard ratio; ISS = International Staging System; LEN = lenalidomide; NR = not reported; ORR = objective response rate; OS = survival; PFS = progression-free survival; RCT = randomized controlled trial; SAE = serious adverse event; SCT = stem cell transplant; Tx = treatment.
+
+APPENDIX B Grade 3/4 Adverse Event Rates3,26
+
+	LEN-DEX %	BOR-DEX %	CFZ-LEN-DEX %	ELO-LEN-DEX %	IX-LEN-DEX %	PAN-BOR-DEX %	DAR-LEN-DEX %	DAR-BOR-DEX %	Per AE Cost $	
+n = 353	n = 64	n = 392	n = 318	n = 360	n = 381	n = 360	n = 381		
+Anemia	17.50	19.50	14.80	18.90	11.70	17.80	12.40	14.40	971	
+Arrythmias	3.00	2.00	NR	NR	5.50	3.00	NR	NR	6,998	
+Back pain	3.00	3.00	NR	5.00	0.80	0.80	1.40	NR	10,728	
+Cataract	2.80	NR	NR	6.30	NR	NR	NR	NR	3,700	
+Deep vein thrombosis	3.40	NR	4.10	5.70	3.00	NR	1.80	NR	31,645	
+Diarrhea	3.50	8.00	3.80	5.00	6.40	25.50	5.30	3.70	9,738	
+Fatigue	5.10	11.90	7.70	12.60	4.00	23.90	6.40	4.50	8,437	
+Hyperglycemia	6.30	NR	4.60	17.00	2.20	NR	NR	NR	166	
+Hypertension	1.50	NR	4.30	NR	3.00	NR	NR	6.60	5,478	
+Hypocalcemia	3.60	2.00	2.60	11.30	4.40	5.00	NR	NR	1,155	
+Hypokalemia	5.20	7.00	10.50	11.30	4.40	18.00	NR	NR	1,707	
+Lymphopenia	27.40	40.20	46.40	76.70	32.50	53.60	5.30	9.50	166	
+Nausea	0.40	0.50	0.20	0.90	1.70	5.50	1.40	NR	11,934	
+Neutropenia	34.70	11.40	38.80	33.60	21.90	34.50	51.90	12.80	166	
+Peripheral/sensory neuropathy	1.50	14.60	1.70	3.80	2.50	17.60	NR	4.50	783	
+Pneumonia	8.10	10.30	8.90	14.20	6.00	12.60	7.80	8.20	14,855	
+Thrombocytopenia	14.30	31.40	25.80	19.20	25.30	67.30	12.70	45.30	166	
+Vomiting	0.70	1.30	NR	0.30	1.10	7.30	1.10	NR	11,934	
+AE = adverse event; BOR = bortezomib; CFZ = carfilzomib; DAR = daratumumab; DEX = dexamethasone; ELO = elotuzumab; IX = ixazomib; LEN = lenalidomide; NR = not reported; PAN = panobinostat.
+==== Refs
+REFERENCES
+
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+

PMC10437421.txt

@@ -0,0 +1,38 @@
+
+==== Front
+J Manag Care Pharm
+J Manag Care Pharm
+jmcp
+Journal of Managed Care Pharmacy : JMCP
+1083-4087
+1944-706X
+Academy of Managed Care Pharmacy
+
+18774881
+10.18553/jmcp.2008.14.S7-A.12
+Cea
+Current and Emerging Treatments for Multiple Myeloma
+Schwartz Rowena N.
+Vozniak Michael
+9 2008
+14 7 Suppl A 10.18553/jmcp.2008.14.S7-A.12Copyright � 2008, Academy of Managed Care Pharmacy. All rights reserved.
+2008
+https://creativecommons.org/licenses/by/4.0/ This article is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use and redistribution provided that the original author and source are credited.
+BACKGROUND:
+
+The prognosis and treatment of multiple myeloma (MM) has evolved greatly over the past decade. The development and incorporation of new agents such as immunomodulators and proteasome inhibitors into therapy has improved outcomes and is helping patients enjoy longer periods of remission.
+
+OBJECTIVES:
+
+To review current treatments for MM, including overview of drug therapy and management of adverse effects of therapy and comorbidities. Additionally, an overview of agents being studied and evaluated for use in MM and myeloma-related conditions, such as metastatic bone disease and venous thromboembolism, will be discussed.
+
+SUMMARY:
+
+Great strides have been made regarding the understanding of disease pathology in MM, leading to therapies that may be targeted to each individual, based on their unique biology of disease. Therapy is currently tailored based on patient issues and stage of disease, but may soon betailored individually based on the cytogenetic profile of a patient. Recent treatment guidelines have been published by the National Comprehensive Cancer Network which were updated with impressive results fromclinical trials involving agents such as immunomodulators and proteasome inhibitors. This guideline also provides information on the management of myeloma and treatment-related morbidities. As with the treatment of any cancer, clinicians must weigh risk versus benefit when determining the most appropriate therapy. Currently, corticosteroids, lenalidomide, thalidomide, and bortezomib are all used in patient swith MM. The use of chemotherapy, including high-dose therapy with stemcell transplant, is an important component of treatment for many patients.The use of high-dose therapy is continually being evaluated, and the issueof risk versus benefit is weighed for individual patients. Depending on the prognosis, it may be of benefit to endure the toxicity of higher doses toachieve a better overall response and achieve longer remission periods. Although stem cell transplantation is often performed in MM to improve survival and remission rates, some patients are unable to undergo transplant for a variety of reasons, including age (older than 65 years), comorbidities, and/or organ dysfunction.Newer drug therapies and combinations of therapy are being evaluated to better manage this population and patients who previously received high-dose chemotherapy and a stem-cell transplant. Additionally, the management of relapsed, or refractory, disease continues to be a challenge in treating the myeloma patient. Despite aggressive and improved treatments, most myeloma patients will eventually have resistance to therapyor relapse. Treatment strategies in these patients are also evolving.
+
+CONCLUSIONS:
+
+Major advancements in the diagnosis, staging, and treatmentof myeloma offer promise in the future for changing MM from a terminal illness into a chronic, manageable condition.
+==== Body
+pmc
+