ALBERT

Description: Key Points IGH translocations in myeloma can occur through at least 5 mechanisms. t(11;14) and t(14;20) DH-JH rearrangement-mediated translocations occur indicating these appear to occur in a pregerminal center cell.

Description: Introduction A significant proportion of myeloma patients relapse early and show short survival with current therapies. Molecular diagnostic tools are needed to identify these high risk patients at diagnosis to stratify treatment and offer the prospect of improving outcomes. Two validated molecular approaches for risk prediction are widely used: 1) molecular genetic risk profiling [e.g. del(17p), t(4;14)] 2) gene expression (GEP) risk profiling, [e.g. EMC92 (Kuiper et al., Leukemia 2012)]. We profiled patients from a large multicentric UK National trial using both approaches for integrated risk stratification. Methods A representative group of 221 newly diagnosed, transplant eligible patients (median age 64 years) treated on the UK NCRI Myeloma XI trial were molecularly profiled. DNA and RNA were extracted from immunomagnetically CD138-sorted bone marrow plasma cells. Molecular genetic profiles, including t(4;14), t(14;16), Del(17p), Gain(1q) were generated using MLPA (MRC Holland) and a TC-classification based qRT-PCR assay (Boyle EM, et al., Gen Chrom Canc 2015, Kaiser MF, et al., Leukemia 2013). GEP risk status as per EMC92 was profiled on a diagnostic Affymetrix platform using the U133plus2.0-based, CE-marked MMprofiler (SkylineDx) which generates a standardised EMC92 risk score, called 'SKY92'. Progression-free (PFS) and overall survival (OS) were measured from initial randomization and median follow-up for the analysed group was 36 months. Statistical analyses were performed using R 3.3.0 and the 'survival' package. Results were confirmed in an independent dataset, MRC Myeloma IX, for which median follow-up was 82.7 months. Results Of the 221 analysed patients, 116 were found to carry an established genetic high risk lesion [t(4;14), t(14;16), del(17p) or gain(1q)]. We and others have recently demonstrated that adverse lesions have an additive effect and that co-occurrence of ≥2 high risk lesions is specifically associated with adverse outcome (Boyd KD et al, Leukemia 2011). 39/221 patients (17.6%) were identified as genetic high risk with ≥2 risk lesions (termed HR2). By GEP, 53/221 patients (24.0%) were identified as SKY92 high risk. Genetic and GEP high risk co-occurred in 22 patients (10.0%), 31 patients (14.0%) were high risk only by GEP and 17 patients (7.7%) by genetics only. SKY92 high risk status was associated with significantly shorter PFS (median 17.1 vs. 34.3 months; P

Description: Key Points Inherited genetic variation increases risk to developing multiple myeloma through predisposition to MGUS. Loci identified that increase risk of developing MGUS include 2p23.3, 3p22.1, 3q26.2, 6p21.33, 7p15.3, 17p11.2, and 22q13.1.

Description: Abstract 3490 IGH loci translocations in multiple myeloma are primary events in the aetiology of the disease. There are 5 main translocation partner chromosomes which result in the over-expression of key oncogenes. These translocations are t(4;14), t(6;14), t(11;14), t(14;16) and t(14;20) and result in the over-expression of MMSET and FGFR3, CCND3, CCND1, MAF and MAFB, respectively. The translocations have a major impact on response and survival with the t(4;14), t(14;16) and t(14;20) resulting in poor prognosis. It is therefore imperative that these chromosomal abnormalities be identified. Translocations have traditionally been identified by fluorescence in situ hybridisation (FISH). Using targeted capture techniques, similar to exome capture technology, followed by massively parallel sequencing it should be possible to identify the translocations and the specific breakpoints. We have developed a targeted capture using the SureSelect (Agilent) system by tiling RNA baits across the IGH locus. Baits covered the V, D and J segments as well as being tiled across the entire constant region, including the switch regions. DNA from samples (n=120) were assayed using 150 ng of DNA and a modified capture protocol. The translocation partner had previously been identified by FISH in 36 samples which comprised 11 t(4;14), 3 t(6;14), 11 t(11;14), 9 t(14;16), 2 t(14;20). The remaining 84 samples were assayed by RQ-PCR for over-expression of the partner oncogenes to determine the translocation. Several identified translocations were verified by PCR. In 90% of samples which had FISH performed the correct IGH translocation was detected using the capture technique. The number of paired reads detecting the translocation varied from 2 to 102. Breakpoints could be determined for all of these samples and were mapped for each translocation group. In the t(4;14) group the breakpoints were clustered around exons 1, 4 and 5, corresponding to the MB4-1, MB4-2 and MB4-3 IgH-MMSET hybrid transcripts. Of the 11 t(4;14) with FISH only 2 did not express FGFR3 and had deletion of der(14). In these samples the breakpoint was located between LETM1 and MMSET, confirming that loss of FGFR3 expression is due to deletion of der(14) and not due to the location of the breakpoint. The sample with the breakpoint furthest from MMSET was located 67 kbp upstream of the start of translation within LETM1, in a position similar to that found in the KMS-11 cell line. In the t(11;14) samples the breakpoints varied dramatically on chromosome 11 but were always centromeric to CCND1. Breakpoints varied from 1.1 kbp centromeric to the start of CCND1 transcription to 1.1 Mbp centromeric, within the PPP6R3 gene. However, most breakpoints (70%) were in the intergenic region between MYEOV and CCND1. The distance from the breakpoint to CCND1 did not inversely correlate with CCND1 expression, in fact the sample with the breakpoint furthest from CCND1, within PPP6R3, had the highest expression of CCND1 as determined by gene expression array. No samples had breakpoints within the mantle cell lymphoma major translocation cluster. However, 2 samples had their breakpoint within 100 bp of one another, indicating a possible common breakpoint. Of the t(6;14) samples 2 had breakpoints in the first intron of CCND3, upstream of the start of translation. The remaining sample had its breakpoint 550 kbp upstream of the transcription start site within UBR2. The t(14;16) samples all had their breakpoints within the last intron of WWOX, 0.48–1.03 Mbp centromeric of MAF and in the location of the common fragile site FRA16D. The breakpoints cluster into 2 groups on either side of the fragile site. The t(14;20) breakpoints were located in the 1.5 Mbp intergenic region centromeric of MAF. The breakpoint furthest from MAF was 1.2 Mbp centromeric of the gene. In conclusion, we have developed and validated a targeted capture and sequencing approach for identifying translocations into the IGH locus in myeloma. This approach is important because of its capacity for high throughput low cost testing strategies that can identify these important prognostic events making a myeloma specific diagnostic platform and personalised medicine a reality for patients with myeloma. Importantly sequence analysis of the peri-breakpoint regions gives insight into molecular mechanisms acting early in the process of myelomagenesis. Disclosures: No relevant conflicts of interest to declare.

Description: Introduction Epigenetic dysregulation is a hallmark of cancer and has significant impact on disease biology. The epigenetic structure of myeloma is heterogeneous and we previously demonstrated that gene specific DNA methylation changes are associated with outcome, using low-resolution arrays. We now performed a high-resolution genome wide DNA methylation analysis of a larger group of patients from a UK national phase III study to further define the role of epigenetic modifications in disease behaviour and outcome. Patients and Methods Highly purified (〉95%) CD138+ myeloma bone marrow cells from 465 newly diagnosed patients enrolled in the UK NCRI Myeloma XI study were analysed. The extracted DNA was bisulfite-converted using the EZ DNA methylation kit (Zymo) and hybridized to Infinium HumanMethylation450 BeadChip arrays. Raw data was processed using the R Bioconductor package "minfi". SNP containing probes and probes on the sex chromosomes were removed. 464 samples and 441293 probes were retained following inspection of quality control metrics. Beta values were summarized across functional genomic units or differentially methylated regions (DMRs) that included: gene bodies, promoters, insulators, CpG-islands and enhancers. K-means was applied to each DMR to cluster patients into 2 groups (high or low methylation) per region. Filters were applied to define a clinically meaningful minimum group size and methylation differences between the groups. Overall survival (OS) and progression free survival (PFS) were assessed by a Cox proportional hazards regression model fitted to each DMR with a time-dependent covariate of the trial pathway. Pathway analyses were performed using GREAT (Stanford University) and GSEA (Broad Institute). Results We identified 589 differentially methylated regions that were significantly associated with PFS and OS when using a cut-off of P

Description: Introduction Hyperdiploidy (HRD) comprises the largest pathogenetic subgroup of myeloma. However, its clinical and molecular characterisation is incomplete. Here, we investigate HRD using a novel high-throughput molecular analysis method (MyMaP - Myeloma MLPA and translocation PCR; Kaiser MF et al., Leukemia 2013; Boyle EM et al., Gen Chrom Canc 2015) in a large cohort of 1,036 patients from the UK NCRI Myeloma XI trial. Materials, Methods and Patients Copy number changes, including gain of chromosomes 5, 9 and 15, as well as translocation status were assayed for 1,036 patients enrolled in the UK NCRI Myeloma XI (NCT01554852) trial using CD138+ selected bone marrow myeloma cells taken at diagnosis. HRD was defined by triploidy of at least 2 of analysed chromosomes 5, 9 or 15. Analysis was performed on standard laboratory equipment with MyMaP, a combination of TC-classification based multiplex qRT-PCR and multiplex ligation-dependent probe amplification (MLPA; MRC Holland). The parallel assessment of multiple loci with copy number alteration (CNA) by MLPA allowed unbiased association studies using a Bayesian approach. Semi-quantitative gene expression data for CCND1 and CCND2 was generated as part of the multiplexed qRT-PCR analysis. Median follow up for the analysis was 24 months. Results Of the 1,036 analysed patients, 475 (46%) were HRD. Of these, 325 (68%) had gain(11q25), 141 (29.7%) gain(1q), 43 (9.1%) del(1p32) and 36 (7.5%) del(17p). Gain(11q25) was significantly associated with HRD (Bayes Factor BF01

Description: Introduction Identifying molecular high risk myeloma remains a diagnostic challenge. We previously reported co-segregation of 〉1 adverse lesion [t(4;14), t(14;16), t(14;20), gain(1q), del(17p)] by iFISH to specifically characterise a group of high risk patients (Boyd et al., Leukemia 2012). However, implementation of this approach is difficult using FISH because of its technical limitations. We recently developed and validated a novel high-throughput all-molecular testing strategy against FISH (MyMaP- Myeloma MLPA and translocation PCR; Kaiser MF et al., Leukemia 2013; Boyle EM et al., Gen Chrom Canc 2015). Here, we molecularly characterised 1,036 patients from the NCRI Myeloma XI trial using MyMaP and validated the co-segregation approach. Materials, Methods and Patients Recurrent translocations and copy number changes were assayed for 1,036 patients enrolled in the NCRI Myeloma XI (NCT01554852) trial using CD138+ selected bone marrow myeloma cells taken at diagnosis. The trial included an intensive therapy arm for younger and fitter and a non-intense treatment arm for elderly and frail patients. Analysis was performed using MyMaP, which comprises TC-classification based multiplex qRT-PCR and multiplex ligation-dependent probe amplification (MLPA; MRC Holland). Median follow up for the analysis was 24 months. Results Adverse translocations [t(4;14), t(14;16), t(14;20)] were present in 18.2% of cases, del(17p) in 9.3%, gain(1q) in 34.5% and del(1p32) in 9.4% of cases. All adverse lesions were associated with significantly shorter PFS and OS by univariate analysis (P 1 adverse lesion, 33.9% had one isolated adverse lesion and 52.6% had no adverse lesion. Presence of 〉1, 1 or no adverse lesion was associated with a median PFS of 17.0, 23.9 and 30.6 months (P =3.0x10-9) and OS at 24 months of 67.9%, 75.0% and 86.0% (P =1.8x10-7), respectively. Del(1p) was associated with shorter PFS and OS for the intensive, but not for the non-intensive therapy arm and was independent of the co-segregation model by multivariate analysis regarding OS (P =0.006). We thus included del(1p) as an additional adverse lesion in the model for younger patients. The groups with 〉1 (19.4% of cases), 1 (31.1%) and no adverse lesions (49.5%) were characterised by median PFS of 19.4, 29.4 and 39.1 months (P =1.2x10-10) and median 24-months survival of 73.8%, 86.4% and 91.5% (P =1.4x10-6), respectively. Hazard Ratio for 〉1 adverse lesion was 3.0 (95% CI 2.1-4.1) for PFS and 3.8 (95% CI 2.2-6.5) for OS. By multivariate analysis, co-segregation of adverse lesions was independent of ISS for PFS/OS in the entire group of 1,036 cases and in the intensive treatment arm. We integrated adverse lesions and ISS into a combined model defining High Risk (〉1 adv les + ISS 2 or 3; 1 adv les + ISS 3) and Low Risk (no adv les + ISS 1 or 2; 1 adv les + ISS 1) and the remainder as Intermediate Risk. The High Risk, Intermediate Risk and Low Risk groups of the total cohort included 11.2%, 41.2% and 41.6% of cases with median PFS of 15.8, 19.8 and 35.2 months (P

Description: Introduction Multiple myeloma (MM) is characterised by the malignant expansion of clonal plasma cells in the bone marrow (BM). We and others have used massive parallel sequencing to describe the somatic aberrations acquired in different subclones in newly diagnosed MM (NDMM). These studies have showed that chemotherapy has an impact on intra-clonal heterogeneity, but more analyses are required in paired presentation/relapse samples and samples from multiple sites at the same and different time points. Materials and methods We have studied 49 paired presentation/relapse patients from a series of 463 NDMM patients entered into the Myeloma XI trial (NCT01554852). To understand the impact of spatial separation within the MM clone and the consideration that MM is a metastatic disease, we examined BM aspirates and compared them to targeted biopsies from extramedullary disease sites in 9 MM patients. These cases were 1 patient with samples bilaterally collected from the hip during the course of the disease, 4 MM cases with plasma cell leukemia (PCL), 3 MM cases with plasmacytomas, 1 MM patient with ascites, and 1 MM case with pleural effusion. DNA from both BM and peripheral blood samples were used for whole exome sequencing plus a pull down of the MYC, IGH, IGL and IGK loci following the SureSelect Target Enrichment System for Illumina Paired-End Sequencing Library v1.5. Exome reads were used to call single nucleotide variants, indels, translocations, and copy number aberrations. Mean sequencing depth was 59.3x. The proportion of mutant tumor cells carrying a mutation was inferred. The presence and proportion of subclones will be defined using bioinformatics tools. Results For the 463 NDMM samples, the following 15 significantly mutated genes are seen KRAS (n=103 mutations), NRAS (n=88), LTB (n=53), DIS3 (n=49), BRAF (n=37), EGR1 (n=22), FAM46C (n=20), IRF4 (n=19), TRAF3 (n=17), HIST1H1E (n=16), TP53 and FGFR3 (n=14), CYLD (n=13), MAX (n=12), and RB1 (n=5). These mutations were seen within all clonal cells and at subclonal levels, consistent with the mutations being acquired at different time points and being associated with different subclonal fitness. We show that NDMM have a mean number of exonic mutations of 61.1±13.0, in contrast to samples taken at the time of relapse, which show an average of 80.6±25.4, Figure 1A. We report diverse patterns of subclonal evolution: no change, subclonal tiding, and subclonal tiding with new subclones arising. We are currently examining samples taken during clinical remission to track subclones at the time of response. For patient with multiple samples taken at different timepoints, 77 mutations were shared across all samples but, of note, specific mutations were seen at the same timepoint in different sites (13/1662 R2R vs 13/1662 R2L), which illustrates the impact of sampling differences in reporting mutation calls and differential response to therapy, Figure 1B. This is also observed in a plasmacytoma case with both a BM aspirate sample containing 11 mutations (including NRAS c.183A〉T and BRAF c.1783T〉C), and a femur plasmacytoma with 18 mutations, of which only 2 are shared with the BM sample, Figure 3. One of these shared lesions is BRAF c.1783T〉C, the cancer clonal fraction of which increases ten-fold, suggesting that the sub-clone with this mutation disseminated from the BM and founded the plasmacytoma. Conclusion Our preliminary data demonstrate that MM subclones not only respond differently to clinical treatment, but also have different biological properties leading to cause extramedullary disease. To our knowledge, this is the first comprehensive genetic analysis of the spatio-temporal heterogeneity in myeloma and reveals genetic differences due to sampling bias. Figure 1. (A) Number of mutations in MM patients at clinical presentation and relapse. Each patient sample is represented by a dot. Lines and error bars correspond to the average and the standard error of the mean values, respectively. Difference was not statistically significant (p 〉0.05, t-test). (B) MM patient analysed at presentation and following two relapses (top). The number of mutations increases through disease (bottom, left panel). Venn plot shows the number of shared and specific mutations for each time point (bottom, right panel). (C) Case with a MM sample (green) and a femur plasmacytoma (blue). Venn plot shows shared and specific mutations to the bone marrow or the plasmacytoma site. Figure 1. (A) Number of mutations in MM patients at clinical presentation and relapse. Each patient sample is represented by a dot. Lines and error bars correspond to the average and the standard error of the mean values, respectively. Difference was not statistically significant (p 〉0.05, t-test). (B) MM patient analysed at presentation and following two relapses (top). The number of mutations increases through disease (bottom, left panel). Venn plot shows the number of shared and specific mutations for each time point (bottom, right panel). (C) Case with a MM sample (green) and a femur plasmacytoma (blue). Venn plot shows shared and specific mutations to the bone marrow or the plasmacytoma site. Disclosures Jones: Celgene: Other: Travel support, Research Funding. Peterson:University of Arkansas for Medical Sciences: Employment. Brioli:Celgene: Honoraria; Janssen: Honoraria. Pawlyn:Celgene: Honoraria, Other: Travel support; The Institute of Cancer Research: Employment. Gregory:Janssen: Honoraria; Celgene: Honoraria. Davies:Onyx-Amgen: Membership on an entity's Board of Directors or advisory committees; Array-Biopharma: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees; Takeda-Millennium: Membership on an entity's Board of Directors or advisory committees; University of Arkansas for Medical Sciences: Employment. Morgan:CancerNet: Honoraria; University of Arkansas for Medical Sciences: Employment; MMRF: Honoraria; Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Weisman Institute: Honoraria; Bristol Myers Squibb: Honoraria, Membership on an entity's Board of Directors or advisory committees; Takeda-Millennium: Honoraria, Membership on an entity's Board of Directors or advisory committees.

Description: Introduction With a multifactorial mechanism of action and excellent PFS associated with prolonged exposure, lenalidomide (len) is an attractive candidate for maintenance therapy. Len exerts its action by interaction with cereblon (CRBN) which forms a ubiquitin ligase complex with cullin-4A (CUL4), damaged DNA binding protein 1 (DDB1) and regulator of cullins 1 (ROC1). Downstream effects are mediated via Ikaros, Aiolos, MYC, IRF4, basigin (BSG) and solute carrier family 16 member 1 (SLC16A1). The impact of selective pressure on MM clonal architecture and mutational load has not been assessed. Although not a DNA damaging agent there is an apparent effect of maintenance len increasing the risk of second cancers and a suggestion that it could select for aggressive clones in high risk disease. We addressed the hypothesis that len may increase the rate of mutation at relapse by performing whole exome sequencing (WES) on 70 paired presentation/relapse samples from patients enrolled to the Myeloma XI trial (MXI), 35 of whom received maintenance Len and 35 not. Methods WES was performed to a median depth of 125x on 70 presentation/relapse pairs from patients enrolled to the MXI trial. MXI is a phase III study comparing thalidomide, len and bortezomib induction combinations and len vs observation maintenance treatment in both transplant eligible (TE) and transplant non-eligible (TNE) NDMM patients. We selected patients who had completed induction +/- ASCT and been randomised to receive maintenance therapy with len or observation. All patients had disease progression determined by IMWG criteria at the time of the relapse sample. Of the 70 patients, 30 were enrolled in the TE pathway and 40 in the TNE pathway. The median time to relapse following maintenance randomisation was 323 days (296 len vs 325 observation). 35 patients (50%) achieved a CR as their best response, 26 (37%) a VGPR and 9 (13%) a PR. The median age was 66 and 69 for those receiving len and those being observed respectively. High risk disease status was confirmed in 33 (47%) patients at presentation (≥ 1of t(4;14), t(14;16), t(14;20), +1q, -17p, -1p). Results The median number of non-silent mutations (NSM) found at presentation and relapse was 37 and 41 respectively (p=0.25). In patients receiving len maintenance the median number of NSM at presentation was 37 vs 34 at relapse (p=0.69). In those being observed the median number of NSM at presentation was 42 vs 52 at relapse (p=0.21). Mutations in genes important in myeloma pathogenesis seen in more than one patient at presentation included KRAS (16), NRAS (14), DIS3 (6), HIST1H1E (2), RB1 (2), EGR1 (2), TP53 (2) and FAM46C (2). These were seen in a total of 37 (53%) patients. One patient had both an NRAS and KRAS mutation. At relapse 7 patients lost mutations (NRAS (3), KRAS (3), DIS3 (1)) and 6 patients gained mutations (KRAS (2), NRAS (2), TP53 (1), FAM46C(1)). Paired presentation/relapse copy number (CN) data (MLPA) was available for 38 patients (54%). At relapse there was evidence of a change in CN status with 5 (13%) patients gaining CN changes associated with high risk (gain 1q (4), del 17p (1). Six patients (9%) were found to have mutations in genes associated with len action; CRBN (1), IRF4 (1), DDB1 (2), SLC16A1 (2). No mutations were found in Ikaros, Aiolos, ROC1, CUL4 or BSG. The CRBN mutation was found at relapse only, in a patient who had achieved a CR and undergone 232 days of len maintenance. The IRF4 mutation was seen at presentation and relapse in a patient who achieved CR and received 754 days of len prior to relapse. Both patients with DDB1 mutations received len induction, ASCT, achieved CR and were randomised to observation. In one patient the mutation was seen at presentation and relapse whilst in the other only at relapse. Both patients with mutations in SLC16A1 were treated with len induction and ASCT to CR. In one patient, randomised to observation the mutation was seen at both time points and they relapsed after 156 days. The other, with the mutation present at presentation only was randomised to len maintenance and relapsed after 256 days. Conclusions This is the largest study comparing the genetics of presentation/relapse myeloma in a len treated population. Overall, the number of mutations at presentation vs relapse remained stable. We show that len does not affect the mutational load at relapse but may select for mutations conferring len resistance although at present further analysis is required to confirm this. Disclosures Jones: Celgene: Honoraria, Research Funding. Pawlyn:Celgene: Consultancy, Honoraria, Other: Travel Support; Takeda Oncology: Consultancy. Cook:Amgen: Consultancy, Honoraria, Research Funding, Speakers Bureau; Glycomimetics: Consultancy, Honoraria; Bristol-Myers Squibb: Consultancy, Honoraria; Sanofi: Consultancy, Honoraria, Speakers Bureau; Takeda: Consultancy, Honoraria, Research Funding, Speakers Bureau; Celgene: Consultancy, Honoraria, Research Funding, Speakers Bureau; Janssen: Consultancy, Honoraria, Research Funding, Speakers Bureau. Jenner:Amgen: Consultancy, Honoraria, Other: Travel support; Janssen: Consultancy, Honoraria, Other: Travel support, Research Funding; Novartis: Consultancy, Honoraria; Celgene: Consultancy, Honoraria, Research Funding; Takeda: Consultancy, Honoraria, Other: Travel support. Drayson:Abingdon Health: Equity Ownership, Membership on an entity's Board of Directors or advisory committees. Davies:Janssen: Consultancy, Honoraria; Celgene: Consultancy, Honoraria; Takeda: Consultancy, Honoraria. Kaiser:BMS: Consultancy, Other: Travel Support; Amgen: Consultancy, Honoraria; Takeda: Consultancy, Other: Travel Support; Celgene: Consultancy, Honoraria, Research Funding; Janssen: Consultancy, Honoraria; Chugai: Consultancy. Jackson:Takeda: Consultancy, Honoraria, Other: Travel support, Research Funding, Speakers Bureau; MSD: Consultancy, Honoraria, Speakers Bureau; Celgene: Consultancy, Honoraria, Other: Travel support, Research Funding, Speakers Bureau; Roche: Consultancy, Honoraria, Speakers Bureau; Amgen: Consultancy, Honoraria, Speakers Bureau; Janssen: Consultancy, Honoraria, Speakers Bureau. Morgan:Celgene: Consultancy, Honoraria, Research Funding; Takeda: Consultancy, Honoraria; Bristol Meyers: Consultancy, Honoraria; Janssen: Research Funding; Univ of AR for Medical Sciences: Employment.

Description: Key Points Coexistent hyperdiploidy or t(11;14) does not abrogate the poor prognosis associated with adverse cytogenetics in myeloma patients. Single-cell analysis reveals that hyperdiploidy may precede IGH translocation in the clonal history of a proportion of patients with both.