==== 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 1 Owen RG , Treon SP , Al‐Katib A , Fonseca R , Greipp PR , McMaster ML , et al. Clinicopathological definition of Waldenstrom's macroglobulinemia: Consensus Panel Recommendations from the Second International Workshop on Waldenstrom's macroglobulinemia. Semin Oncol. 2003;30 (2 ):110–5.12720118 2 Treon SP , Xu L , Yang G , Zhou Y , Liu X , Cao Y , et al. MYD88 L265P Somatic Mutation in Waldenström's macroglobulinemia. N Engl J Med. 2012;367 (9 ):826–33.22931316 3 Hunter ZR , Xu L , Yang G , Zhou Y , Liu X , Cao Y , et al. The genomic landscape of Waldenström macroglobulinemia is characterized by highly recurring MYD88 and WHIM‐like CXCR4 mutations, and small somatic deletions associated with B‐cell lymphomagenesis. Blood. 2014;123 (11 ):1637–46.24366360 4 Schop RFJ , Van Wier SA , Xu R , Ghobrial I , Ahmann GJ , Greipp PR , et al. 6q deletion discriminates Waldenström macroglobulinemia from IgM monoclonal gammopathy of undetermined significance. Cancer Genet Cytogenet. 2006;169 (2 ):150–3.16938573 5 García‐Sanz R , Dogliotti I , Zaccaria GM , Ocio EM , Rubio A , Murillo I , et al. 6q deletion in Waldenström macroglobulinaemia negatively affects time to transformation and survival. Br J Haematol. 2021;192 (5 ):843–52.32780894 6 Paiva B , Corchete LA , Vidriales MB , García‐Sanz R , Perez JJ , Aires‐Mejia I , et al. The cellular origin and malignant transformation of Waldenström macroglobulinemia. Blood. 2015;125 (15 ):2370–80.25655603 7 Kaushal A , Nooka AK , Carr AR , Pendleton KE , Barwick BG , Manalo J , et al. Aberrant Extrafollicular B Cells, Immune Dysfunction, Myeloid Inflammation, and MyD88‐Mutant Progenitors Precede Waldenstrom macroglobulinemia. Blood Cancer Discov. 2021;2 (6 ):600–15.34778800 8 Rodriguez S , Celay J , Goicoechea I , Jimenez C , Botta C , Garcia‐Barchino MJ , et al. Preneoplastic somatic mutations including MYD88 L265P in lymphoplasmacytic lymphoma. Sci Adv. 2022;8 :eabl4644.35044826 9 Xu L , Hunter ZR , Yang G , Zhou Y , Cao Y , Liu X , et al. MYD88 L265P in Waldenstrom macroglobulinemia, immunoglobulin M monoclonal gammopathy, and other B‐cell lymphoproliferative disorders using conventional and quantitative allele‐specific polymerase chain reaction. Blood. 2013;121 (11 ):2051–8.23321251 10 Jiménez C , Sebastián E , Chillón MC , Giraldo P , Mariano Hernández J , Escalante F , et al. MYD88 L265P is a marker highly characteristic of, but not restricted to, Waldenström's macroglobulinemia Leukemia. Leukemia. 2013;27 (8 ):1722–8.23446312 11 Xu L , Hunter ZR , Tsakmaklis N , Cao Y , Yang G , Chen J , et al. Clonal architecture of CXCR4 WHIM‐like mutations in Waldenström Macroglobulinaemia. Br J Haematol. 2016;172 (5 ):735–44.26659815 12 Jiménez C , Prieto‐Conde MI , García‐Álvarez M , Alcoceba M , Escalante F , Chillón M del C , et al. Unraveling the heterogeneity of IgM monoclonal gammopathies: a gene mutational and gene expression study. Ann Hematol. 2018;97 (3 ):475–84.29353304 13 Varettoni M , Zibellini S , Defrancesco I , Ferretti VV , Rizzo E , Malcovati L , et al. Pattern of somatic mutations in patients with Waldenström macroglobulinemia or IgM monoclonal gammopathy of undetermined significance. Haematologica. 2017;102 (12 ):2077–85.28983055 14 Varettoni M , Zibellini S , Boveri E , Klersy C , Candido C , Rattotti S , et al. A risk‐stratification model based on the initial concentration of the serum monoclonal protein and myd 88 mutation status identifies a subset of patients with IgM monoclonal gammopathy of undetermined significance at high risk of progression to Waldenström macroglobulinaemia or other lymphoproliferative disorders. Br J Haematol. 2019;187 (4 ):441–6.31276195 15 Bustoros M , Sklavenitis‐Pistofidis R , Kapoor P , Liu CJ , Kastritis E , Zanwar S , et al. Progression risk stratification of asymptomatic waldenström macroglobulinemia. JCO. 2019;37 (16 ):1403–11. 16 Zanwar S , Abeykoon JP , Ansell SM , Gertz MA , Colby C , Larson D , et al. Disease outcomes and biomarkers of progression in smouldering Waldenström macroglobulinaemia. Br J Haematol. 2021;195 (2 ):210–6.34340248 17 Moreno DF , Pereira A , Tovar N , Cibeira MT , Magnano L , Rozman M , et al. Defining an ultra‐low risk group in asymptomatic IgM monoclonal gammopathy. Cancers. 2021;13 (9 ):2055.33922804 18 Correa JG , Cibeira MT , Tovar N , Isola I , Pedrosa F , Díaz T , et al. Prevalence and prognosis implication of MYD88 L265P mutation in IgM monoclonal gammopathy of undetermined significance and smouldering Waldenström macroglobulinaemia. Br J Haematol. 2017;179 (5 ):849–51.27605200 19 Xu L , Hunter ZR , Yang G , Cao Y , Liu X , Manning R , et al. Detection of MYD88 L265P in peripheral blood of patients with Waldenström's macroglobulinemia and IgM monoclonal gammopathy of undetermined significance. Leukemia. 2014;28 (8 ):1698–704.24509637 20 Hindson BJ , Ness KD , Masquelier DA , Belgrader P , Heredia NJ , Makarewicz AJ , et al. High‐throughput droplet digital PCR system for absolute quantitation of DNA copy number. Anal Chem. 2011;83 (22 ):8604–10.22035192 21 Drandi D , Genuardi E , Dogliotti I , Ferrante M , Jiménez C , Guerrini F , et al. Highly sensitive MYD88 L265P mutation detection by droplet digital polymerase chain reaction in Waldenström macroglobulinemia. Haematologica. 2018;103 (6 ):1029–37.29567768 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 35 Austin PC , Fine JP . Practical recommendations for reporting Fine‐Gray model analyses for competing risk data. Stat Med. 2017;36 (27 ):4391–400.28913837 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