Clinical, genomic, and transcriptomic differences between myelodysplastic syndrome/myeloproliferative neoplasm with ring sideroblasts and thrombocytosis (<scp>MDS/MPN‐RS‐T</scp>) and myelodysplastic syndrome with ring sideroblasts (<scp>MDS‐RS</scp>)
Authors: Guillermo Montalban‐Bravo, Rashmi Kanagal‐Shamanna, Faezeh Darbaniyan, Maria Tariq Siddiqui, Koji Sasaki, Yue Wei, Hui Yang, Kelly S. Chien, Kiran Naqvi, Elias Jabbour, Tapan M. Kadia, Naval Daver, Courtney DiNardo, Farhad Ravandi, Naveen Pemmaraju, Prithviraj Bose, Srdan Verstovsek, Sherry Pierce, Carlos Bueso‐Ramos, Keyur Patel, Kim‐Anh Do, Hagop Kantarjian, Guillermo Garcia‐Manero
Journal: American Journal of Hematology (2021)

Myelodysplastic syndrome/myeloproliferative neoplasm with ring sideroblasts and thrombocytosis (MDS/MPN-RS-T), initially recognized as the provisional entity refractory anemia with ring sideroblasts and thrombocytosis (RARS-T),1 is a disease entity characterized by anemia, bone marrow dysplasia with ring sideroblasts, and persistent thrombocytosis ≥450 × 109/L with proliferation of large and morphologically atypical megakaryocytes.2 Next-generation sequencing (NGS) has identified recurrent somatic mutations in SF3B1, JAK2, and other genes providing further evidence of the clonal nature of this disease.3, 4 Despite clinical, histological, and molecular similarities with MDS with ring sideroblasts (MDS-RS),5 the clinical outcomes of these entities are different. Although the presence of SF3B1 mutations in the context of >15% bone marrow ring sideroblasts supports the diagnosis of MDS/MPN-RS-T, the degree of thrombocytosis is variable, and the current diagnostic cutoff for RS percentage and platelet count leads to diagnostic challenges and might not be reflective of underlying disease biology.6 To date, there is no data evaluating specific functional pathways that might explain phenotypic and clinical differences beyond diverse frequencies of JAK2 mutations. In order to evaluate the clinical and genomic features of MDS/MPN-RS-T, we evaluated all patients with MDS/MPN-RS-T treated at the University of Texas MD Anderson Cancer Center (MDACC) from 2000–2020. Diagnosis was based on WHO 2017 criteria and confirmed by two independent hematopathologists. In addition, and in order to compare the clinical and genomic features of MDS/MPN-RS-T with those of MDS-RS, we evaluated a cohort of 88 patients with MDS-RS treated at MDACC during the same time period. Whole bone marrow DNA was subject to 81-gene targeted NGS analysis in 24 patients (46%) with MDS/MPN-RS-T and 27 (31%) patients with MDS-RS (Table S1). To evaluate the transcriptomic differences of MDS/MPN-RS-T and MDS-RS, CD34+ cells from bone marrow samples of four patients with MDS/MPN-RS-T, seven patients with MDS-RS, and 17 healthy individuals obtained from AllCells (Emeryville, CA) were isolated using the CD34 MicroBead Kit and RNA was isolated using the PicoPure RNA isolation kit, following manufacturer's instructions. Fastq files were mapped to the human genome (build GRCh38) in TopHat2 using the default options. Differential gene expression analysis was conducted using DESeq2 in R version 3.6.2. Pathway enrichment analysis was performed using gene set enrichment analysis, with the fgsea library in R. A total of 52 patients met diagnostic criteria of MDS/MPN-RS-T. The median age was 66 years (range 51–80), median platelets, hemoglobin, erythropoietin and LDH levels were 575 × 109/L, 9.2 g/dL, 41.6 IU/L, 504 IU/L, respectively. A total of 24 (38%) patients received therapy including hydroxyurea (n = 17), erythroid stimulating agents (n = 17), anagrelide (angrelide hydrochloride) (n = 8), lenalidomide (n = 8) or hypomethylating agents (HMA) (n = 16). Patients receiving HMAs had received a median of two prior therapies (range 0–5). Nine patients (75%) received azacitidine (of which one was in conjunction with ruxolitinib), two patients (17%) received decitabine, and one patient (8%) received decitabine and then azacitidine (in combination with an investigational agent). When compared to the MDS-RS cohort, patients with MDS/MPN-RS-T were younger (median age 66 vs. 70, p = .006), and had a higher median WBC count (7.3 × 109/L vs. 5.0 × 109/L, p = .006), platelet count (575 × 109/L vs. 221 × 109/L, p < .001), and absolute neutrophil count (4.85 × 109/L vs. 2.80 × 109/L, p < .001). No differences in hemoglobin, bone marrow ring sideroblast percentage (50% vs. 50%, p = .698), LDH, bone marrow blast percentage, ferritin, and erythropoietin levels were identified. Frequencies of identified mutations and their variant allelic frequencies (VAFs) compared to MDS-RS are shown in Figure 1A, B. The median number of mutations was higher in MDS/MPN-RS-T than in MDS-RS (three vs. two, p < .001). The SF3B1 mutations were the most frequent in both entities (MDS/MPN-RS-T: 92%, MDS-RS: 82%), had similar median VAFs (34% vs. 32%, p = .619), and involved the hot spot codon K700E in 64% and 43% of MDS-RS and MDS/MPN-RS-T (p = .227), respectively. As expected, 58% of patients with MDS/MPN-RS-T had JAK2 V617F mutations but were also more likely to have mutations in kinase signaling genes (NF1, SETBP1, CBL, CBLB, FLT3 TKD, and MPL) compared to MDS-RS (29% vs. 4%, p = .019). Four (40%) of JAK2 negative MDS/MPN-RS-T patients had mutations in kinase signaling genes. Detailed clonal compositions of the patients with MDS/MPN-RS-T are shown in Figure 1C. There were no differences in the frequencies of TET2 mutations between either entities. However, there was a trend for the median VAF of TET2 mutations in MDS/MPN-RS-T to be lower than in MDS-RS (1.5% vs. 21.1%, p = .177), suggesting a likely subclonal nature of these mutations compared to MDS-RS in which they appeared as dominant events. With a median follow up of 55.0 months (95% CI 42.9–67.8), among patients with MDS/MPN-RS-T who received HMA, the median number of cycles was eight (0–53) and three (25%) patients achieved response, assessed by 2006 IWG response criteria, including hematological improvement in two patients and complete response (CR) in one patient who, at the time of HMA therapy after four prior therapies (hydroxyurea, lenalidomide, ruxolitinib and an investigational agent), had 6% bone marrow blasts. Disease progression was seen in a total of five patients (42%, two patients from the responder group), with increased transfusion dependence in one patient (8%), worsening cytogenetics in one patient (8%), increased bone marrow blast count in one patient (8%), development of myelofibrosis after 8 months in one patient (8%) after introduction of HMA therapy and transformation to AML in one patient (8%) 46 months after initiating HMAs. The NGS data at the time of failure to HMA was available in two patients (Figure S1). In one patient, progression after HMA therapy was associated with expansion of a WT1 R380fs, NRAS G12A and RUNX1 R162K mutation and emergence of previously not detected KRAS G12D mutation as well as loss of pre-treatment ASXL1 G646fs and JAK2 V617F mutation. In a second patient, there were no significant changes in genomic landscape and VAF of identified mutations. A total of one (1.9%) and five (5.6%) patients with MDS/MPN-RS-T and MDS-RS suffered transformation to acute myeloid leukemia (AML), respectively (p = .415). Time to transformation was 83.6 months in the patient with MDS/MPN-RS-T, and the median time to transformation for patients with MDS-RS was 63.7 months (range 32.4–73.9). There were no significant differences in survival between MDS/MPN-RS-T and MDS-RS (median OS of 69 months vs. NR, p = .213, Figure 1D). However, diverging survival was observed in patients surviving beyond 4 years with patients with MDS/MPN-RS-T having significantly worse outcomes (HR 2.9, 95% CI 1.2–7.5, p = .020). Among patients with MDS/MPN-RS-T, female sex (HR 4.7, 95% CI 1.5–14.7, p = .008), absolute monocyte count (HR 0.03, 95% CI 0.01–0.70, p = .030), and LDH (HR 1.0, 95% CI 1.0–1.0, p = .002) influenced survival. RNA-sequencing of CD34+ bone marrow cells identified distinct transcriptomic profiles of MDS/MPN-RS-T when compared to both healthy controls and MDS-RS. Compared to healthy controls, two pathways were significantly upregulated and 58 were downregulated in MDS/MPN-RS-T while five pathways were upregulated and 69 were downregulated in MDS-RS. Compared to MDS-RS, 29 pathways were significantly upregulated and 26 were downregulated in MDS/MPN-RS-T. The most significantly upregulated pathways in MDS/MPN-RS-T included genes involved in platelet activation and aggregation, cytokine signaling, and signaling through G protein-coupled (GPC) receptors (Figure 1E). Compared to both healthy controls and MDS-RS, MDS/MPN-RS-T was characterized by downregulation of genes involved in DNA damage response, regulation of apoptosis, telomere maintenance, and RNA synthesis (Figure 1F). MDS-RS was characterized by downregulation of genes involved in signaling by GPC receptors, MAPK signaling, mRNA splicing, cytokine signaling, and signaling through interleukins compared to both control and MDS/MPN-RS-T (Figure 1E). Current diagnostic criteria distinguish MDS/MPN-RS-T from MDS-RS mainly due to the presence of thrombocytosis and atypical megakaryocytes. Similar to a recent study,7 we observed that MDS/MPN-RS-T was characterized by frequent co-dominance of SF3B1 and JAK2 mutations and presence of minor kinase signaling mutations not observed in MDS-RS. Prior studies have suggested that patients with MDS/MPN-RS-T had improved outcomes compared to MDS-RS.5 Our data suggests that outcomes of these entities are similar with disease outcomes diverging beyond 4 years from the time of diagnosis. In addition, we observed that used of HMA in patients with heavily pretreated MDS/MPN-RS-T could lead to hematological improvement and responses in 25% of patients. Upregulation of cytokine and interleukin signaling mediated through GPC receptors, and downregulation of genes involved in apoptosis and DNA damage are unique transcriptomic features of MDS/MPN-RS-T likely driven by genotype. These unique genomic and transcriptomic characteristics of MDS/MPN-RS-T support the classification of MDS/MPN-RS-T based on genomic features beyond the presence of SF3B1 mutation, and might represent potential therapeutic avenues for this rare disease. This work was supported in part by the University of Texas MD Anderson Cancer Center Support Grant CA016672 and the University of Texas MD Anderson MDS/AML Moon Shot. Kim-Anh Do (KAD) was partially supported by a Cancer Center Support Grant NCI Grant P30 CA016672, NIH grants UL1TR003167, 5R01GM122775, the prostate cancer SPORE P50 CA140388, CPRIT grant RP160693, and the Moon Shots funding at MD Anderson Cancer Center. Elias Jabbour: This author declares research support and an advisory role with Adaptive, AbbVie, Amgen, Pfizer, Cyclacel LTD, Takeda, and Bristol Myers Squibb. Tapan Kadia: This author declares research support and an advisory role with Amgen, Bioline RX, Pfizer, Jazz, Bristol Myers Squibb, Celgene, Genentech, Pharmacyclics, Takeda, and AbbVie. Courtney DiNardo: This author declares consultancy fee for Abbvie, Agios, Celgene and honoraria from Medimmune, Daiichi Sankyo, Abbvie, Agios, Jazz, Celgene, and Syros. Farhad Ravandi: This author declares research support and an advisory role with Macrogeni, Selvita, Cyclacel LTD, Menarini Ricerche, and Xencor. Hagop Kantarjian: This author declares research support and an advisory role with Actinium, and research support from AbbVie, Agio, Amgen, Ariad, Astex, Bristol Myers Squibb, Cyclacel, Daiichi-Sankyo, Immunogen, Jazz Pharma, Novartis, and Pfizer. Guillermo Garcia-Manero: This author declares research support and an advisory role with Bristol Myers Squibb, Astex, and Helsinn, and research support from Amphivena, Novartis, AbbVie, H3 Biomedicine, Onconova, and Merck. The remaining authors declare no conflict of interest. Guillermo Montalban-Bravo and Guillermo Garcia-Manero: Concept and design; administrative support; provision of study materials and patients; data collection, analysis, interpretation; and manuscript writing and final approval. Sherry Pierce: Provision of study materials; data collection, and final approval. Rashmi Kanagal-Shamanna, Faezeh Darbaniyan, Maria Tariq Siddiqui, Koji Sasaki, Yue Wei, Hui Yang, Kelly S. Chien, Kiran Naqvi, Elias Jabbour, Tapan M. Kadia, Naval Daver, Courtney DiNardo, Farhad Ravandi, Naveen Pemmaraju, Prithviraj Bose, Srdan Verstovsek, Carlos Bueso-Ramos, Keyur Patel, Kim-Anh Do, and Hagop Kantarjian: Collection and assembly of data; data analysis and interpretation; manuscript writing; and final approval of manuscript. The datasets generated during and/or analyzed during the current study are not publicly available due to patient privacy concerns but are available from the corresponding author on reasonable request. Table S1. PCR-based next generation sequencing (NGS) 81-Gene panel Figure S1. Clonal changes associated with failure to hypomethylating agents. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.