Abstract The detection of genetic abnormalities is required during diagnostic workup and for potential individualization of therapy selection in multiple myeloma (MM) and its precursor conditions. At present, this relies on invasive bone marrow (BM) biopsies, severely limiting early detection, frequent longitudinal monitoring, and the precise selection of therapy. The current standard for detecting genetic alterations in MM is fluorescence in situ hybridization (FISH), which cannot detect point mutations and other clinically relevant alterations. Consequently, the IMS-IMWG guidelines were recently updated to require next-generation sequencing for the classification of high-risk MM. To address these needs, we recently launched GenoPredicta, a CLIA-approved LDT that enables routine monitoring, informing diagnosis, and treatment selection by comprehensively characterizing MM genomes with whole genome sequencing (WGS) from as few as 50 circulating tumor cells (CTCs) isolated from peripheral blood (PB) or tumor cells from BM. Briefly, tumor cells are isolated from samples using fluorescence-activated cell sorting and subjected to WGS, from which copy number alterations, structural variants, and short variants (SNVs/indels) are identified using a fully automated pipeline generating physician-ready clinical reports from raw sequencing data in ∼6h. Analytical validation of GenoPredicta showed complete concordance with FISH results. Identifying alterations in therapeutic targets (e.g., BCMA, GPRC5D) for guiding MM immunotherapies is becoming increasingly important. Here, we describe GenoPredicta results from relapsed/refractory MM patients, highlighting resistance-conferring alterations that can only be detected by WGS, such as deletions in the kilo- to megabase scales that are observed in conjunction with SNVs and indels, leading to biallelic loss/inactivation of the gene, including at subclonal levels. In addition to biallelic loss of BCMA and GPRC5D in response to CAR T or T cell engager therapies, we observed similar resistance mechanisms for targets of immunomodulatory drugs, e.g., CRBN. The observation of these resistance mechanisms was consistent with patients’ clinical histories. In summary, we demonstrate that low input WGS-based characterization of MM from BM or CTCs is a viable replacement for FISH for clinical diagnosis and monitoring, with CTC-based measurements enabling comprehensive profiling of the MM genome. Crucially, this includes genetic alterations that confer resistance to therapy, allowing for both early detection of such alterations and more precise selection and guidance of therapy. The dramatically improved variant calling ability from WGS, especially in low-input CTC applications, extends to other malignancies and will gain wider adoption as sequencing costs continue to decrease. Citation Format: Bruno Paiva, Peter Voorhees, Patricia T. Greipp, Danielle Sookiasian, Julian Hess, Marisa DeMeo, Vicki Pounder, Sarah Calkins, Reid Meyer, Linda B. Baughn, Christine-Ivy Liacos, Meletios-Athanasios Dimopoulos, Alexandra Papadimou, Taouxi Konstantina, Efstathios Kastritis, Jesus Berdeja, Daniel Auclair, Valentina Nardi, Thomas Mullen, Francois Aguet, Shaji Kunnathu Kumar. Whole genome sequencing of multiple myeloma genomes with a novel clinical assay enables identification of genetic alterations underlying immunotherapy resistance [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 1067.

Multiple myeloma (MM) is a plasma cell malignancy marked by profound immune dysfunction and substantial infection-related mortality. MM is preceded by the asymptomatic precursor states monoclonal gammopathy of undetermined significance (MGUS) and smoldering multiple myeloma (SMM), yet whether these patients can mount effective immune responses remains unknown. In this prospective observational study of 731 individuals recruited nationwide through Dana-Farber Cancer Institute, we used SARS-CoV-2 vaccination as a standardized immunological challenge alongside childhood vaccine serology. We show that precursor myeloma is associated with broad dysfunction across innate and adaptive immunity, including accelerated antibody waning, erosion of childhood vaccine titers, impaired antigen-specific T cell expansion, and blunted innate activation. Mechanistically, we link these defects to tumor burden-dependent APRIL depletion and downregulation of APRIL-responsive pathways in normal plasma cells, validated in an independent cohort. These findings have direct implications for cancer vaccine development, immunotherapy, and infection management in early-stage hematologic malignancies.

Supplementary data contains additional Materials and Methods used to generate results presented only in the supplementary figures and tables, as well as more detailed bioinformatics methods. Figure S1 shows correlation between clinical measures of disease pathology, survival, and circulating tumor cells enumeration. Figure S2 shows characteristics of isolated circulating tumor cells from peripheral blood of precursor disease patients. Figure S3 shows cohort-level genomic characterization of tumor in MM precursor stages with CTCs. Figure S4 shows longitudinal and tissue-matched genomic characterization of driver mutations. Figure S5 shows comparison of mutational processes between BMPCs and CTCs assigned to most likely PCAWG composite reference signature. Table S1 shows clinical characteristics and sampling of participants in this study. Table S2 shows whole-genome sequencing coverage and library metrics. Table S3 shows clinical BM FISH results and cells recovered for cohort with matched samples. Table S4 shows comparison of BCR sequence between BMPCs and CTCs. Table S5 shows clinical BM FISH results of peripheral blood only cohort and CTCs recovered. Table S6 shows enumeration of single nucleotide variants and short insertions and deletions discovered from WGS of CTCs. Table S7 shows enumeration of structural variants reconstructed from WGS of CTCs.

Mantle cell lymphoma (MCL) is a rare, aggressive B-cell non-Hodgkin lymphoma with a poor patient overall survival rate. While Bruton tyrosine kinase inhibitors (BTKis) have shown benefit in relapsed/refractory MCL (Wang M, et al., 2018; Song Y, et al.,2020; Wang ML et al., 2013) a portion of patients eventually progress. Here, we explored which biomarkers might be associated with resistance to acalabrutinib, a second generation BTKi, by analyzing samples from MCL patients who were on treatment or progressed on acalabrutinib from the ACE-LY-004 study (NCT02213926), as well as using a pre-clinical MCL cell line model. Receptor tyrosine kinase-like orphan receptor 1 (ROR1) is an important oncofetal protein and a target of clinical relevance in MCL. It was previously attributed to a bypass resistance mechanism to first generation BTKi ibrutinib (Zhang et al., 2019). We hence analyzed ROR1 expression levels using available exploratory biomarker flow cytometry data from ACE-LY-004, which revealed that MCL patients who progressed or pre-progressed on acalabrutinib showed a higher percentage of circulating CD5+ROR1+ tumor cells (78.3%; n=7 and 63.6%; n=4 respectively) than patients who continued responding to treatment (23.8%; n=6). Interestingly, although progression was associated with higher frequency of CD5+ROR1+ tumor cells, the per-cell surface ROR1 expression on these cells from patients who pre-progressed/ progressed on acalabrutinib treatment (longitudinal analysis; n=6) was lower. While acute 5-day BTKi treatment in the Jeko-1 MCL cell line led to transiently higher surface expression of ROR1, chronic 4-month exposure of sensitive cells led to ROR1 surface downregulation in acalabrutinib or reversible BTKi acquired resistant (R) Jeko-1 MCL cells. Total ROR1 expression (western blot), was increased in Jeko-1R versus sensitive, suggesting that BTKi chronically treated MCL cells may upregulate intracellular ROR1 and/or partially internalize surface ROR1. To assess potential therapeutic implications of decreased surface expression of ROR1 in MCL post BTKi treatment, we assessed activity of a ROR1-targeted ADC (Zilovertamab vedotin; Wang et al., 2021) in BTKi-R cells. In line with downregulated surface ROR1 levels, the response to ZV was lower in BTKi-R compared to sensitive cells. However, Wnt5a (ROR1 ligand) expression was increased in these Jeko-1R cells, indicating a potential increase in ligand-dependent ROR1 activity. These preliminary results highlight the possible association between ROR1 expression on MCL tumor cells and BTKi response. To further dissect the interplay between ROR1 and second generation/reversible BTKi treatment in MCL, studies utilizing a multi-omics integration approach, BTKi-R MCL cell lines, and ex vivo propagated MCL patient cell models are ongoing. Anuvrat Sircar, Sara Parsa, Laura B. Prickett, Veerendra Munugalavadla, Catherine Rhee, Richard Tourdot, Anders Nelson, Michael Ophir, Wenyan Zhong, Matthew Sung, Daniel Auclair, Christine D. Palmer. Receptor tyrosine kinase-like orphan receptor 1 (ROR1) in BTKi-treated mantle cell lymphoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 5559.

Background Accurate reconstruction of electronic health record (EHR) treatment sequences is essential for leveraging real-world data (RWD) in clinical research and trial-eligibility platforms in multiple myeloma (MM). However, inconsistencies in RWD treatment documentation and the complexity of line-of-treatment (LOT) assignments in MM hinder reproducibility and limit comparisons against published trials. To address these limitations, we developed HEAL-MM, an automated rule-based algorithm that systematically parses structured medication data to calculate LOTs and segment treatment into standardized, clinically meaningful phases. The algorithm is designed to be interpretable, adaptable to evolving therapeutic frameworks, and compatible with widely adopted EHR data standards. Here, we report its real-world validation against expert-adjudicated treatment sequences to assess concordance, identify edge-case failure points, and support RWD integration. Methods A stratified cohort of 100 MM patients (9,136 structured treatment events) was curated from 114 unique healthcare systems compiled from the multi-institutional HealthTree Foundation harmonized registry. Cohort selection was randomized to ensure coverage across diagnostic eras (pre-2010, 2015–2020, 2022–2025) and treatment exposures, including CAR T-cell therapies, bispecific antibodies, and anti-CD38-based regimens. HEAL-MM sequenced MM regimens chronologically and applied predefined logic rules to identify LOT transitions, the initiation of a new therapeutic course based on drug changes, treatment gaps, and procedural anchors such as stem cell transplant or CAR T. Within each LOT, the algorithm segments and labels therapy into phases, clinically distinct intervals (e.g., induction and maintenance) using rule-based clinical logic. Outputs were benchmarked against expert-labeled adjudications. Concordance was evaluated using concordance index (C-index), stratified by patient, LOT depth and diagnostic era. Results: HEAL-MM demonstrated high concordance with expert-labeled treatment sequences: 0.94 for LOT identification, 0.98 for treatment phase segmentation, and 0.96 for phase label assignment. Achieving complete agreement with all expert annotations in 90% of patients for LOT assignment, 94% for the number of treatment phases identified, 77% for the specific labels applied to each phase, and 75% across all three dimensions simultaneously. Among discordant patients, the absolute difference in final LOT count was 0.5 ± 0.9. Performance was highest in early lines (LOTs 1–3; C-index: 0.97), with seven patients showing discrepancies at this stage. Concordance declined in later lines (LOT ≥4; C-index: 0.87), where 50% of the discordant cases (n=4) were attributable to misclassifications in earlier LOTs, such as medication restarts following >3-month gaps, demonstrating the compounding effect of early errors on longitudinal accuracy. Temporal stratification revealed improved accuracy in post-2020 cases (C-index: 0.94) compared to 2015–2020 cohorts (0.91), reflecting the increasing adoption of structured treatment standards and EHR data quality improvements. To assess HEAL-MM performance at clinically significant junctures, we analyzed treatment transitions that initiated a new LOT or marked a change in phase, achieving C-indices of 0.97 for LOT transitions, 0.97 for treatment phase shifts, and 0.94 for phase label assignment. Conclusions: HEAL-MM provides a validated, scalable solution for standardizing MM treatment structures in RWD environments, as it accurately detects the exact treatment entry of a new LOT 97% of the time. This rule-based framework enables reproducible LOT and phase assignment across heterogeneous EHR systems, supporting real-time cohort identification across institutions and EHR platforms. The algorithm's modular design allows for continuous integration of novel therapeutic categories and evolving treatment logic. While the algorithm maintained high fidelity overall, early-stage misclassifications consistent with increased heterogeneity and documentation ambiguity occasionally propagated downstream, underscoring the compounding effect of upstream errors in longitudinal reconstruction. With its generalizable structure and compatibility with industry-standard data models, HEAL-MM lays the foundation for structured treatment reconstruction in MM across RWD infrastructure and serves as a transferable blueprint for other hematologic malignancies.

Although most patients with multiple myeloma respond to treatment initially, therapy resistance develops almost invariably, and only a subset of patients show durable responses to immunomodulatory therapies. Although the immune microenvironment has been extensively studied in patients with myeloma, its composition is currently not used as prognostic markers in clinical routine. We hypothesized that the outcome of immune signaling pathway engagement can be highly variable, depending on which 2 cellular populations participate in this interaction. This would have important prognostic and therapeutic implications, suggesting that it is crucial for immune pathways to be targeted in a speci fic cellular context. To test this hypothesis, we investigated a cohort of 25 patients with newly diagnosed multiple myeloma. We examined the complex regulatory networks within the immune compartment and their impact on disease progression. Analysis of immune cell composition and expression pro files revealed signi ficant differences in the Bcell compartment associated with treatment response. Transcriptional states in patients with short time to progression demonstrated an enrichment of pathways promoting B -cell differentiation and in flammatory responses, which may indicate immune dysfunction. Importantly, the analysis of molecular interactions within the immune microenvironment highlights the dual role of signaling pathways, which can either be associated with good or poor prognosis depending on the cell types involved. Our findings therefore argue that therapeutic strategies targeting ligand-receptor interactions should take into consideration the composition of the microenvironment and the speci fic cell types involved in molecular interactions.

Classic Hodgkin Lymphoma (cHL) is characterized by the presence of abnormal large cells known as Hodgkin Reed-Sternberg (HRS) cells. Curative intent chemotherapy has a 90% response rate, but for refractory patients, current immunotherapies remain insufficient. Ongoing research aims to enhance therapy and patient outcomes by focusing on the tumor microenvironment (TME), a complex environment that includes diverse non-malignant cells, including immune cells, which constitute a critical component of the TME. Utilizing multiplex analysis to comprehend these intricate interactions can pave the way for identifying novel strategies to combat cancer more effectively. CD30-targeted therapies and PD1/PDL1 inhibitors are under investigation clinically and could potentially benefit from adding checkpoint markers such as TIM-3 to enhance patient outcomes by addressing T-cell exhaustion. Such combination strategies will require assays and analysis strategies that can describe both spatial and multi-marker data. Multiplex immunofluorescence (mIF) is a valuable tool for analyzing spatial biology, patient selection, mechanism of action, pharmacodynamics and biomarker assays predictive of response. Describing the relationship between T cell exhaustion and response in cHL may guide the development of targeted treatments aimed at restoring the immune system's ability to recognize and eliminate cancer cells. Twenty-five to thirty percent of cHL patients with advanced stages do not respond to standard therapies. To understand the mechanisms of resistance, we developed a mIF panel (CD4, CD8, CD30, PD-1, PD-L1, and TIM3) for formalin-fixed and paraffin-embedded cHL tissues on the OPAL Tyramide Signal Amplification system and the Leica Bond RX. A set of commercially sourced cHL whole tissue samples (n=5) were used to develop and validate the panel, with qualitative evaluations by a Hematopathologist affirming that the mIF and 3, 3'-diaminobenzidine (DAB) staining yielded comparable results. Subsequently, a cohort of cHL samples (n=34) were stained and underwent exploratory image (Halo) and data (Microsoft Excel and R Studio) analyses. The image analysis strategy includes cell segmentation and phenotyping for all markers in the panel, identifying individual cell populations and multi-marker co-expressing cell populations. Additionally, spatial analysis describes the relationships between key phenotypes in the TME. The quantity of exhausted T cells, their average distance to HRS cells and the average count of exhausted T cells within different proximities of HRS cells will be presented. The development of this mIF assay for cHL demonstrates its consistent application at a mid-scale level across a diverse patient population with varying medical backgrounds and treatment histories. This approach provides a deeper understanding of immune response diversity and cellular composition within cHL. Future deployment of the panel against clinical trial samples with outcomes and responses will yield further insights.

Introduction: The t(4;14) translocation causes overexpression of NSD2 in ~12% of patients with multiple myeloma (MM) and is associated with poor clinical outcomes. NSD2 catalyzes dimethylation of histone H3 lysine 36 (H3K36me2), which is dramatically increased in t(4;14) MM. The molecular mechanisms by which altered H3K36me2 potentiates an oncogenic gene expression program are poorly understood. One function of H3K36me2 is the recruitment of DNA methyltransferase enzymes such as DNMT3B, leading to DNA methylation (DNAm). DNAm plays important roles in both plasma cell differentiation and oncogenesis, yet how DNAm is dysregulated in t(4;14) MM is largely unknown. Methods: We analyzed whole genome bisulfite sequencing (WGBS) DNAm data from 415 primary MM samples from the MMRF CoMMpass trial (NCT01454297) which had been integrated with genetic, transcriptional, and clinical information. Differential DNAm analysis between t(4;14) and non-t(4;14) MM samples was performed using the DSS Bioconductor package. KMS11 isogenic cell lines which express either high (NTKO) or low (TKO) levels of NSD2 were profiled for chromatin marks using cleavage under targets and tagmentation (CUT&Tag) and DNAm using WGBS. Three t(4;14) MM cell lines (KMS11, KMS18, H929) were treated with KTX-1031, a catalytic inhibitor of NSD2. Currently, KTX-1001 is a first-in-class catalytic NSD2 inhibitor in a phase 1 clinical trial (NCT05651932). Following 8 days of treatment, chromatin modifications, DNAm, and gene expression were assayed using CUT&Tag, WGBS, and RNAseq. DNMT3B protein levels were assayed by western blot. Results: Analysis of CoMMpass samples showed higher levels of global DNAm in the t(4;14) subtype compared to other subtypes (p = 2.9e-8) and >2M differentially methylated loci (DML) (FDR < 0.01), with 92% of DML having higher DNAm in t(4;14) MM samples. A correlation analysis between DNAm and gene expression determined that ~232K DML correlated with RNA levels of the nearest gene (FDR < 0.01). These RNA-correlated DML were enriched near genes identified by Zhan et al. (Blood, 2006) as defining the t(4;14) gene expression subtype (OR = 55.9, p < 1e-16), suggesting that changes in DNAm at these regions may facilitate the t(4;14) MM gene expression program. Epigenetic profiling of KMS11 NTKO (NSD2-high) and TKO (NSD2-low) cells showed higher global DNAm in NTKO cells (p < 1e-9). Furthermore, regions of elevated H3K36me2 in NTKO cells were enriched for increases in DNAm (OR = 1.85, p < 1e-15). Treating t(4;14) MM cell lines with the NSD2 inhibitor KTX-1031 reversed the chromatin changes observed in t(4;14) patients, resulting in reduced H3K36me2 and increased levels of the EZH2 modification H3K27me3. These epigenetic changes corresponded with downregulation of 539 genes and upregulation of 133 genes (FDR < 0.05) in KMS 11 t(4;14) cells. While the NSD2 inhibitor globally depleted H3K36me2 and elevated H3K27me3, these changes were most pronounced proximal to and in the gene bodies of downregulated genes. NSD2 inhibition for 8 days significantly reduced levels of DNMT3B protein, but only slightly reduced global DNAm (~2%). Importantly, the NSD2 inhibitor reversed much of the t(4;14) transcriptional program described by Zhan et al. (FDR = 5.5e-4). Furthermore, gene set enrichment analysis demonstrated that the NSD2 inhibitor resulted in reduced expression of genes involving NFKB (FDR < 0.001) and KRAS (FDR < 0.05) signaling. Conclusions: Through analyses of patient and cell line data, we provide evidence that NSD2-mediated increases in H3K36me2 directly dysregulate DNAm in t(4;14) MM, leading to DNAm hypermethylation as compared to non-t(4;14) MM. Many DNAm changes are correlated with transcriptional changes and occur primarily near disease-relevant genes. Treatment with catalytic NSD2 inhibitor for 8 days reverses much of the chromatin and gene expression changes associated with t(4;14) MM. NSD2 inhibition also reduces DNMT3B protein levels after 8 days and may disrupt the t(4;14) DNAm hypermethylation program after more prolonged treatment, resulting in further gene expression changes. We will present these results alongside data from extended NSD2 inhibitor treatments. We posit that elevated NSD2 in t(4;14) MM causes excessive H3K36me2 leading to relative DNA hypermethylation and transcriptional dysregulation which can be reversed through extended pharmacologic inhibition of NSD2.

Multiple myeloma is a treatable, but currently incurable, hematological malignancy of plasma cells characterized by diverse and complex tumor genetics for which precision medicine approaches to treatment are lacking. The Multiple Myeloma Research Foundation’s Relating Clinical Outcomes in Multiple Myeloma to Personal Assessment of Genetic Profile study ( NCT01454297 ) is a longitudinal, observational clinical study of newly diagnosed patients with multiple myeloma (n = 1,143) where tumor samples are characterized using whole-genome sequencing, whole-exome sequencing and RNA sequencing at diagnosis and progression, and clinical data are collected every 3 months. Analyses of the baseline cohort identified genes that are the target of recurrent gain-of-function and loss-of-function events. Consensus clustering identified 8 and 12 unique copy number and expression subtypes of myeloma, respectively, identifying high-risk genetic subtypes and elucidating many of the molecular underpinnings of these unique biological groups. Analysis of serial samples showed that 25.5% of patients transition to a high-risk expression subtype at progression. We observed robust expression of immunotherapy targets in this subtype, suggesting a potential therapeutic option. Longitudinal genomic and transcriptomic profiling of 1,143 patients with multiple myeloma by the Relating Clinical Outcomes in Multiple Myeloma to Personal Assessment of Genetic Profile study yields an improved copy number and gene expression subtype scheme, most notably a high-risk proliferative subtype associated with complete loss of RB1 or MAX.

Background. Identifying the biologyof high-risk multiple myeloma (MM) is critical to improving outcomes. Current genetic alterations do not faithfully identify high-risk proliferative disease and there are limited data that integrate genetic, epigenetic, and transcriptional information with outcomes. We generated DNA methylation (DNAm) data on 415 MMRF CoMMpass samples and identified distinct epigenetic programs of high-risk disease. Methods. Institutional IRB approval was obtained and DNA from CD138+ CoMMpass samples were analyzed by whole genome bisulfite sequencing on Illumina NovoSeq instruments. Reads were aligned to the GRCh38 genome with Bismark and DNAm values were obtained at 16,374,655 CpGs at an average coverage of 24x. WGBS from the BluePrint project were used for comparison to normal plasma cells. CoMMpass whole genome sequencing and RNA-seq were obtained from the Genomic Data Commons and outcome data (IA21) were provided by the MMRF. Results. Newly diagnosed MM samples (N=370) exhibited DNA hypomethylation (median 45%, range 23-77%) as compared to normal plasma cells (median 69%), which occurred in late replicating regions of the genome (P=2.8e-135). Dimensionality reduction (t-SNE) of DNAm data clustered samples by subtype, most notably t(4;14) and t(14;16) samples. DNAm also better defined the high-risk Proliferation (PR) subtype as compared to unsupervised RNA analysis. Integration of DNAm and RNA data identified 1,494,744 loci predictive of gene expression (FDR≤0.01). The DNAm loci most tightly correlated with expression included genes highly relevant to MM pathology including cell cycle (e.g. CDKN2C, CCND2), proteasome function (e.g. PSMA8), immune signaling (e.g. CD28, CD27, IL5RA), and immune targets (e.g. GPRC5D). Differential DNAm between MM subtypes indicated the high-risk t(4;14) subtype had the most distinct DNAm program with 2,075,489 Differentially Methylated Loci (DML; FDR≤0.01). The majority (92%) of t(4;14) DML had higher levels of DNAm as compared to other subtypes and this was specific to the late replicating regions of the genome (P=3.5e-38). Correlating t(4;14)-specific gene expression and DNAm identified several co-regulated genes including cell adhesion (ITGA7, JAM3) and frizzled (FZD8, FZD2) pathway components. The PR subtype had the second most unique DNAm program with 418,474 DML (FDR ≤0.01). In contrast to the t(4;14) subtype, 99.95% of PR DML had less DNAm than other subtypes. PR hypomethylated loci were enriched for motifs of E2F transcription factors, which contain a CpG in their binding motif. Consistent with this, expression of E2F1 and E2F2 were significantly higher in the PR subtype. Analysis of DNAm and E2F1 binding in MM.1S cells showed that E2F1 exclusively bound unmethylated regions of the genome, corroborating the motif analysis of CoMMpass PR subtype DML. Conclusion. MM exhibits a dramatically remodeled DNAm program with extensive DNA hypomethylation in late replicating regions of the genome, suggesting this hypomethylation reflects the proliferative history of MM. This phenomenon is consistent with normal plasma cell differentiation and other B cell malignancies. However, the t(4;14) subtype had a distinct DNAm program, which is likely connected to aberrant levels of H3K36me2 catalyzed by NSD2. H3K36me2 is recognized by the PWWP domains of DNA methyltransferases, potentially explaining the higher levels of DNAm in t(4;14). This suggests novel NSD2 inhibitors require time to not only deplete H3K36me2, but also reprogram t(4;14)-specific DNAm. Finally, consistent with DNA hypomethylation reflecting the proliferative history of MM, the PR subtype had the most pronounced DNA hypomethylation. Notably, the PR subtype had reduced DNAm at E2F1 motifs and E2F1 exclusively bound unmethylated regions of the genome, suggesting the DNAm restricts the proliferative program of high-risk MM.

Mutational properties. A, The maximum VAF attained by each of the 99 patients with CH. B, Distribution of VAF among different variants. C, Distribution of the types of single-nucleotide bp changes seen in all detected mutations.

Supplementary data contains additional Materials and Methods used to generate results presented only in the supplementary figures and tables, as well as more detailed bioinformatics methods. Figure S1 shows correlation between clinical measures of disease pathology, survival, and circulating tumor cells enumeration. Figure S2 shows characteristics of isolated circulating tumor cells from peripheral blood of precursor disease patients. Figure S3 shows cohort-level genomic characterization of tumor in MM precursor stages with CTCs. Figure S4 shows longitudinal and tissue-matched genomic characterization of driver mutations. Figure S5 shows comparison of mutational processes between BMPCs and CTCs assigned to most likely PCAWG composite reference signature. Table S1 shows clinical characteristics and sampling of participants in this study. Table S2 shows whole-genome sequencing coverage and library metrics. Table S3 shows clinical BM FISH results and cells recovered for cohort with matched samples. Table S4 shows comparison of BCR sequence between BMPCs and CTCs. Table S5 shows clinical BM FISH results of peripheral blood only cohort and CTCs recovered. Table S6 shows enumeration of single nucleotide variants and short insertions and deletions discovered from WGS of CTCs. Table S7 shows enumeration of structural variants reconstructed from WGS of CTCs.

Correlation of expression of canonical cell type markers across different modalities

The mutational spectrum of CH in 986 patients with multiple myeloma. A, The total number of patients harboring one or more mutations in each gene. B, Number of patients harboring mutations in one or two different genes. C, Commutation plot showing CH, tumor and germline mutations present in 129 patients: each column represents a single patient. The top row denotes the maximum VAF in each patient, with darker shades of red indicating higher VAF. The bar graph on the right designates the percentage of the different mutation subtypes for each gene out of all detected mutations.

This file contains supplementary methods, supplementary references, supplementary figures S1-S6, supplementary tables S1, S3-S6, S8-S15, and S17, and legends for all supplementary figures and tables. Supplementary Figure S1. Summary of Ewing sarcoma tumor and cell line cohorts. Supplementary Figure S2. RNASeq validation of mutations identified by WES and WGS. Supplementary Figure S3. Power calculations for the detection of significantly mutated genes. Supplementary Figure S4. Copy number variants in Ewing sarcoma tumors. Supplementary Figure S5. ETS1 deletions associated with EWS/FLI rearrangements. Supplementary Figure S6. Copy number variants in diagnostic vs. treated Ewing sarcoma tumors. Supplementary Figure S7. SCNAs in paired Ewing sarcoma tumor samples. Supplementary Table S1. Tissue samples. Supplementary Table S3. Multiply mutated genes. Supplementary Table S4. Rearrangements and fusions detected by WGS and RNASeq. Supplementary Table S5. Mutational significance for tumor/normal pairs. Supplementary Table S6. Rate of coding mutations in Ewing sarcoma tumors. Supplementary Table S8. Mutational significance for all tumors. Supplementary Table S9. Gene set enrichment analyses of mutated genes. Supplementary Table S10. Significance of arm-level SCNAs. Supplementary Table S11. Multiply mutated genes of interest in all sample cohorts. Supplementary Table S12. Immunohistochemical staining of STAG2 in Ewing sarcoma tumors. Supplementary Table S13. Gene set enrichment analyses of STAG2 expressing vs. STAG2 loss. Supplementary Table S14. Gene and gene-fragment RPKM Values for EWS and ETS genes. Supplementary Table S15. ETS1 copy-number changes. Supplementary Table S17. Gene set enrichment analyses of tumors vs. cell lines.

PDF file 258K, Genome wide copy number profile.

Mutations identified from WES and WGS of 26 tumors with paired normals.

PDF file 1573K, Loss of heterozygosity across RMS tumor samples.