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Analysis of the genetic basis for multiple myeloma (MM) has informed many of our current concepts of the biology that underlies disease initiation and progression. Studying these events in further detail is predicted to deliver important insights into its pathogenesis, prognosis and treatment. Information from whole genome sequencing of structural variation is revealing the role of these events as drivers of MM. In particular, we discuss how the insights we have gained from studying chromothripsis suggest that it can be used to provide information on disease initiation and that, as a consequence, it can be used for the clinical classification of myeloma precursor diseases allowing for early intervention and prognostic determination. For newly diagnosed MM, the integration of information on the presence of chromothripsis has the potential to significantly enhance current risk prediction strategies and to better characterize patients with high-risk disease biology. In this article we summarize the genetic basis for MM and the role played by chromothripsis as a critical pathogenic factor active at early disease phases.

Supplementary Data from Myeloma Genome Project Panel is a Comprehensive Targeted Genomics Panel for Molecular Profiling of Patients with Multiple Myeloma

Purpose:We designed a comprehensive multiple myeloma targeted sequencing panel to identify common genomic abnormalities in a single assay and validated it against known standards.Experimental Design:The panel comprised 228 genes/exons for mutations, 6 regions for translocations, and 56 regions for copy number abnormalities (CNA). Toward panel validation, targeted sequencing was conducted on 233 patient samples and further validated using clinical FISH (translocations), multiplex ligation probe analysis (MLPA; CNAs), whole-genome sequencing (WGS; CNAs, mutations, translocations), or droplet digital PCR (ddPCR) of known standards (mutations).Results:Canonical immunoglobulin heavy chain translocations were detected in 43.2% of patients by sequencing, and aligned with FISH except for 1 patient. CNAs determined by sequencing and MLPA for 22 regions were comparable in 103 samples and concordance between platforms was R2 = 0.969. Variant allele frequency (VAF) for 74 mutations were compared between sequencing and ddPCR with concordance of R2 = 0.9849.Conclusions:In summary, we have developed a targeted sequencing panel that is as robust or superior to FISH and WGS. This molecular panel is cost-effective, comprehensive, clinically actionable, and can be routinely deployed to assist risk stratification at diagnosis or posttreatment to guide sequencing of therapies.

Supplementary Table from Myeloma Genome Project Panel is a Comprehensive Targeted Genomics Panel for Molecular Profiling of Patients with Multiple Myeloma

Supplementary Figure from Myeloma Genome Project Panel is a Comprehensive Targeted Genomics Panel for Molecular Profiling of Patients with Multiple Myeloma

Whole genome sequencing (WGS) of newly diagnosed multiple myeloma patients (NDMM) has shown recurrent structural variant (SV) involvement in distinct regions of the genome (i.e. hotspots) and causing recurrent copy number alterations. Together with canonical immunoglobulin translocations, these SVs are recognized as “recurrent SVs”. More than half SVs were not involved in recurrent events. The significance of these “rare SVs” has not been previously examined. In this study, we utilize 752 WGS and 591 RNA-seq data from NDMM patients to determine the role of rare SVs in myeloma pathogenesis. 94% of patients harbored at least one rare SV event. Rare SVs showed an SV-class specific enrichment within genes and superenhancers associated with outlier gene expression. Furthermore, known myeloma driver genes recurrently impacted by point mutations were dysregulated by rare SVs. Overall, we demonstrate the association of rare SVs with aberrant gene expression supporting a driver role in myeloma pathogenesis.SIGNIFICANCECharacterization of multiple myeloma genome revealed that more than half structural variants are not involved in recurrent events. Here, we demonstrate that these rare SVs hold potential for myeloma pathogenesis through their gene expression impact. Rare SVs contribute to MM heterogeneity and have implications for development of individualized treatment.

Despite improvements in outcome, 15-25% of newly diagnosed multiple myeloma (MM) patients have treatment resistant high-risk (HR) disease with a poor survival. The lack of a genetic basis for HR has focused attention on the role played by epigenetic changes. Aberrant expression and somatic mutations affecting genes involved in the regulation of tri-methylation of the lysine (K) 27 on histone 3 H3 (H3K27me3) are common in cancer. H3K27me3 is catalyzed by EZH2, the catalytic subunit of the Polycomb Repressive Complex 2 (PRC2). The deregulation of H3K27me3 has been shown to be involved in oncogenic transformation and tumor progression in a variety of hematological malignancies including MM. Recently we have shown that aberrant overexpression of the PRC2 subunit PHD Finger Protein 19 (PHF19) is the most significant overall contributor to HR status further focusing attention on the role played by epigenetic change in MM. By modulating both the PRC2/EZH2 catalytic activity and recruitment, PHF19 regulates the expression of key genes involved in cell growth and differentiation. Here we review the expression, regulation and function of PHF19 both in normal and the pathological contexts of solid cancers and MM. We present evidence that strongly implicates PHF19 in the regulation of genes important in cell cycle and the genetic stability of MM cells making it highly relevant to HR MM behavior. A detailed understanding of the normal and pathological functions of PHF19 will allow us to design therapeutic strategies able to target aggressive subsets of MM. Supplementary Information The online version contains supplementary material available at 10.1186/s13046-021-02185-1.

Background: Structural variants (SV) are known to play a critical role in the pathogenesis of multiple cancer types. Using whole genome sequencing (WGS), we recently characterized the SV landscape of 752 newly diagnosed multiple myeloma (MM) patients, identifying 68 SV hotspots and 152 recurrent copy number aberrations (CNA; Rustad et al. Blood Cancer Discovery 2020). Despite comprehensive annotation, more than half of SVs were not linked to any known MM genomic driver. The biological impact of these SV events, here defined as rare SV, occurring in 93% (702/752) of patients, is unknown. Methods: To study the biological impact of rare SVs, we interrogated WGS (n=752) and RNAseq (n=591) in the CoMMpass trial. Recurrent SVs identified by involvement in canonical Ig translocations, recurrent CNAs, or SV hotspots were excluded. All SVs within an event must not involve a recurrent region to be defined rare. To determine SV class-specific gene relationships, breakpoint enrichment was compared against a permuted background model for each SV class and gene expression direction, up to 1 Mb. Genes were considered affected if expression was above a gene specific outlier Z-score of +/- 2. Lastly, we modeled breakpoint density to the nearest MM superenhancer up to 10 MB, and compared to permuted background rates. Results: Of the total 8,942 SVs, 4,959 (55%) were identified as rare. 201 (34%) patients had at least 1 enriched rare SV event associated with gene expression outliers. Amongst over-expressed gene outliers, rare templated insertions and duplications were enriched within the gene body and up to 1 MB away. Rare inversions were enriched in genes 100kb and 1MB away, and rare translocations were associated with outliers 1 MB away. Amongst under-expressed gene outliers, rare complex SVs were enriched within the gene body, while deletion SVs were enriched in the gene body and up to 1 MB away. Rare duplications, translocations and templated insertions were enriched up to 1 MB of superenhancers. Rare templated insertions were significantly enriched against the background model (p < 0.001, Fisher Exact). Overall, 82% (104/126) of gene outliers affected by rare templated insertions were associated with superenhancers, (95 over-expressed, e.g. IRF6 and 9 under-expressed), 54% (130/237) by rare translocations, (105 overexpressed, e.g. FAM46A and 25 under-expressed), and 55% (96/172) by rare duplication events, (93 overexpressed, e.g. MAPK13, and 7 under-expressed). In addition, among the 853 outlier genes affected by enriched rare SVs, at least 15 are involved with B-cell development, suggesting a potential driver role in myeloma pathogenesis. Conclusion: In summary, leveraging WGS and RNA-seq of clinical samples, we demonstrate that rare SVs are frequently associated with aberrant gene expression, expanding our understanding of their potential role in heterogenous clinical response in patients diagnosed with MM. Citation Format: Monika Chojnacka, Benjamin Diamond, Bachisio Ziccheddu, Even Rustad, Kylee Maclachlan, Marios Papadimitriou, Eileen Boyle, Patrick Blaney, Saad Usmani, Gareth Morgan, Ola Landgren, Francesco Maura. Characterizing the landscape of rare structural variant events in newly diagnosed multiple myeloma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 6067.

Purpose: We designed a comprehensive multiple myeloma targeted sequencing panel to identify common genomic abnormalities in a single assay and validated it against known standards. Experimental Design: The panel comprised 228 genes/exons for mutations, 6 regions for translocations, and 56 regions for copy number abnormalities (CNA). Toward panel validation, targeted sequencing was conducted on 233 patient samples and further validated using clinical FISH (translocations), multiplex ligation probe analysis (MLPA; CNAs), whole-genome sequencing (WGS; CNAs, mutations, translocations), or droplet digital PCR (ddPCR) of known standards (mutations). Results: Canonical immunoglobulin heavy chain translocations were detected in 43.2% of patients by sequencing, and aligned with FISH except for 1 patient. CNAs determined by sequencing and MLPA for 22 regions were comparable in 103 samples and concordance between platforms was R2 = 0.969. Variant allele frequency (VAF) for 74 mutations were compared between sequencing and ddPCR with concordance of R2 = 0.9849. Conclusions: In summary, we have developed a targeted sequencing panel that is as robust or superior to FISH and WGS. This molecular panel is cost-effective, comprehensive, clinically actionable, and can be routinely deployed to assist risk stratification at diagnosis or posttreatment to guide sequencing of therapies.

Multiple myeloma (MM) progression is characterized by the seeding of cancer cells in different anatomic sites. To characterize this evolutionary process, we interrogated, by whole genome sequencing, 25 samples collected at autopsy from 4 patients with relapsed MM and an additional set of 125 whole exomes collected from 51 patients. Mutational signatures analysis showed how cytotoxic agents introduce hundreds of unique mutations in each surviving cancer cell, detectable by bulk sequencing only in cases of clonal expansion of a single cancer cell bearing the mutational signature. Thus, a unique, single-cell genomic barcode can link chemotherapy exposure to a discrete time window in a patient's life. We leveraged this concept to show that MM systemic seeding is accelerated at relapse and appears to be driven by the survival and subsequent expansion of a single myeloma cell following treatment with high-dose melphalan therapy and autologous stem cell transplant.

Chromothripsis is detectable in 20-30% of newly diagnosed multiple myeloma (NDMM) patients and is emerging as a new independent adverse prognostic factor. In this study, we interrogate 752 NDMM patients using whole genome sequencing (WGS) to study the relationship of copy number (CN) signatures to chromothripsis and show they are highly associated. CN signatures are highly predictive of the presence of chromothripsis (AUC=0.90) and can be used to identify its adverse prognostic impact. The ability of CN signatures to predict the presence of chromothripsis was confirmed in a validation series of WGS comprised of 235 hematological cancers (AUC=0.97) and an independent series of 34 NDMM (AUC=0.87). We show that CN signatures can also be derived from whole exome data (WES) and using 677 cases from the same series of NDMM, we were able to predict both the presence of chromothripsis (AUC=0.82) and its adverse prognostic impact. CN signatures constitute a flexible tool to identify the presence of chromothripsis and is applicable to WES and WGS data.

The landscape of structural variants (SV) in multiple myeloma remains poorly understood. Here, we performed comprehensive analysis of SVs in a large cohort of 752 patients with multiple myeloma by low-coverage long-insert whole-genome sequencing. We identified 68 SV hotspots involving 17 new candidate driver genes, including the therapeutic targets BCMA (TNFRSF17), SLAM7, and MCL1. Catastrophic complex rearrangements termed chromothripsis were present in 24% of patients and independently associated with poor clinical outcomes. Templated insertions were the second most frequent complex event (19%), mostly involved in super-enhancer hijacking and activation of oncogenes such as CCND1 and MYC. Importantly, in 31% of patients, two or more seemingly independent putative driver events were caused by a single structural event, demonstrating that the complex genomic landscape of multiple myeloma can be acquired through few key events during tumor evolutionary history. Overall, this study reveals the critical role of SVs in multiple myeloma pathogenesis. Significance: Previous genomic studies in multiple myeloma have largely focused on single-nucleotide variants, recurrent copy-number alterations, and recurrent translocations. Here, we demonstrate the crucial role of SVs and complex events in the development of multiple myeloma and highlight the importance of whole-genome sequencing to decipher its genomic complexity. See related commentary by Bergsagel and Kuehl, p. 221. This article is highlighted in the In This Issue feature, p. 215

Multiple myeloma (MM) progression is characterized by the seeding of cancer cells in different anatomic sites. To characterize this evolutionary process, we interrogated, by whole genome sequencing, 25 samples collected at autopsy from 4 patients with relapsed MM and an additional set of 125 whole exomes collected from 51 patients. Mutational signatures analysis showed how cytotoxic agents introduce hundreds of unique mutations in each surviving cancer cell, detectable by bulk sequencing only in cases of clonal expansion of a single cancer cell bearing the mutational signature. Thus, a unique, single-cell genomic barcode can link chemotherapy exposure to a discrete time window in a patient′s life. We leveraged this concept to show that MM systemic seeding is accelerated at relapse and appears to be driven by the survival and subsequent expansion of a single myeloma cell following treatment with high-dose melphalan therapy and autologous stem cell transplant., In multiple myeloma, disease progresses via seeding to different anatomic sites and clonal expansion. Here, utilising autopsy material, the authors show that systemic seeding accelerates at relapse following treatment.

Introduction: There is considerable heterogeneity in the clinical outcome of newly diagnosed multiple-myeloma (NDMM) with some patients having a good prognosis while others fail to respond or relapse quickly after therapy progressing rapidly to death. Using risk scores based on clinical, biochemical and genetic features it is possible to predict some of this variation giving an ability to segment the disease into risk strata. Clinical studies have suggested that patients with standard-risk disease have benefited more from the recent advances in therapy compared to those with high-risk disease. The development of clinical trials specifically recruiting patients with high-risk disease features offers the potential to improve the outcome of a subgroup of patients with a very poor clinical outcome. To perform such studies is it important to have a unifying definition of high-risk including standard parameters, group size and outcome of individual risk strata so that clinical trial rigor can be achieved (e.g., common entry criteria, statistical power). In order to understand the size and feasibility of such studies we analyzed the Myeloma Genome Project (MGP) dataset to assess multiple risk factors and scores to determine and compare how they perform as risk stratifiers with each other. Methods: The MGP dataset is a large set of molecular and clinical data from 1273 patient with NDMM. Data were available on clinical variables (Albumin (Alb), B2-microglobulin (B2M), LDH, age), cytogenetic variables [t(4;14), t(14;16), t(14;20), 17p-, TP53 mutations, 1q+ and 1p-] and gene expression analysis (GEP70). A literature search was used to identify risk models used in clinical studies. Survival analysis was performed in R. The median follow-up at the time of analysis was 54.5 (53.2-56.5) months. Results: The median patient age was 66 years, with 641 (50.4%) patients over age 65. The sex ratio (M:F) was 1:0.66. African American, White, and Asian constituted 17%, 76%, and 2%, of cases respectively. 26.7% received a stem cell transplant. We determined the size of the strata and actual risk (measure by the hazard ratios, HR) compared to standard risk cases for both PFS and OS of the various clinical models available, data are summarized in Figure 1. When looking at individual risk scores, the HR for progression for t(4;14), TP53 inactivation (deletion and mutations), gain(1q), and del(1p) were 1.4, 1.1, 1.3, and 1.1 respectively. When considering overall survival these HR were 1.4, 1.7, 1.5, and 1.4 respectively. We went on to analyze the impact of these events in combination and show that combined, there is increased specificity, especially for OS (HR 2.3-5.1) but they identify small subsets making up When we looked specifically at the younger patients (=< 65), similar trends were seen with GEP70 by RNA-seq offering one of the most interesting means of identifying HR cases. Conclusion: In this large NDMM dataset, we demonstrate the clear variation in risk groups that occur dependent upon the approach used resulting in heterogeneous levels of risk, strata size, and performance. With the exception of GEP70, none of the single features are sensitive or specific enough to identify all cases. Risk models based on a combination of markers improve the ability to detect true high-risk disease but there remains variability. At a molecular level the inclusion of TP53 inactivation, and 1q+ improve the performance of the ISS. This analysis provides insights into standardizing the definition of high-risk and the generation of consensus definitions for clinical trial entry. Figure 1 Figure 1 Figure 1. Disclosures Braunstein: Jansen: Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; Adaptive: Membership on an entity's Board of Directors or advisory committees; AstraZeneca: Membership on an entity's Board of Directors or advisory committees; Epizyme: Membership on an entity's Board of Directors or advisory committees; Karyopharm: Membership on an entity's Board of Directors or advisory committees; Pfizer: Membership on an entity's Board of Directors or advisory committees; Takeda: Membership on an entity's Board of Directors or advisory committees. Pawlyn: Celgene / BMS: Honoraria, Membership on an entity's Board of Directors or advisory committees; Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees; Amgen: Honoraria; Sanofi: Honoraria, Membership on an entity's Board of Directors or advisory committees. Cairns: Amgen: Research Funding; Merck Sharpe and Dohme: Research Funding; Takeda: Research Funding; Celgene / BMS: Other: travel support, Research Funding. Jackson: GSK: Consultancy, Honoraria, Speakers Bureau; takeda: Consultancy, Honoraria, Research Funding, Speakers Bureau; amgen: Consultancy, Honoraria, Speakers Bureau; celgene BMS: Consultancy, Honoraria, Research Funding, Speakers Bureau; J and J: Consultancy, Honoraria, Speakers Bureau; oncopeptides: Consultancy; Sanofi: Honoraria, Speakers Bureau. Morgan: BMS: Membership on an entity's Board of Directors or advisory committees; Jansen: Membership on an entity's Board of Directors or advisory committees; Karyopharm: Membership on an entity's Board of Directors or advisory committees; Oncopeptides: Membership on an entity's Board of Directors or advisory committees; GSK: Membership on an entity's Board of Directors or advisory committees. Davies: Takeda: Membership on an entity's Board of Directors or advisory committees; Sanofi: Membership on an entity's Board of Directors or advisory committees; Oncopeptides: Membership on an entity's Board of Directors or advisory committees; Constellation: Membership on an entity's Board of Directors or advisory committees; Janssen: Membership on an entity's Board of Directors or advisory committees; Celgene/BMS: Consultancy, Membership on an entity's Board of Directors or advisory committees.

Introduction: Treatment combinations involving CD38 targeted monoclonal antibodies have significantly prolonged the median duration and depth of response in myeloma (MM), reflected in minimal residual disease-negativity (MRD-) rates of over 70% in newly diagnosed patients (Landgren et al. JAMA Onc 2021). Key to improving our understanding of treatment failures is the combined use of single cell analysis of the microenvironment with genome wide assessment of tumor genetics to decipher the mechanisms of disease resistance. Methods: We isolated malignant plasma cells from bone marrow (BM) samples using CD138+magnetic or flow (CD38, CD138, and CD45) sorting from 60 newly diagnosed MM patients treated with KRd with daratumumab (D-KRd n=46; NCT03290950) and without daratumumab combination therapy (KRd, n=14; NCT01402284). Fifty-five baseline samples underwent whole genome sequencing (WGS), median coverage of 80x using somatic DNA as a normal comparator. The BM cellular content of 22 patients (44 samples-5 failed) treated with D-KRd (10 MRD+ and 12 MRD-) underwent 5'single cell RNA-sequencing with an additional capture of the TCR and surface protein markers (CITEseq) to interrogate the single cell composition of the immune microenvironment at baseline (T1, n=20) and at the end of induction (T2, n=19). Paired (T1/T2) single-cell data were obtained in 17 patients and paired WGS and single cell data (T1) were available in 15 patients. MRD-, sustained MRD- (defined as two MRD- results, the first at the end of the induction (T2) and the subsequent at the first year of follow-up (T3)) and progression/loss of MRD- were used as clinical endpoints for this study. Results: After a median follow up of 29 months, 36 (54%) patients achieved MRD-; 34 (51%) had sustained MRD- >1 year after completion of combination therapy. Overall, 10 (15%) patients had clinical progression and two conversions from MRD- to MRD+. A comprehensive catalogue of MM-genomic events associated with these three clinical endpoints was defined. Deletion (del) 13, biallelic loss CYLD, del XBP1, del 20q13.12 (CD40), and 8q gains were associated with MRD+ and failure to achieve sustained MRD-. Presence of del RPL5 and multiple chromothripsis events significantly correlated with early progression and loss of MRD-. Interestingly, structural variants (SV) involving IKFZ3 were seen in all three negative clinical endpoints (p We interrogated the BM microenvironment at baseline and correlated its composition with the tumor genomic architecture. Across 15 evaluable patients, del XBP1 were associated with fewer memory B-cells (p=0.03), naïve B-cell (p=0.01) and dendritic cells (p=0.03) compared to the wild type. Also, low dendritic cell at baseline cases were observed in patients with del 20q13.12 (CD40) (p=0.03). Interestingly, low level of plasmacytoid dendritic cells at baseline was associated with failure to achieve MRD- and sustained MRD-. Patients with 6p24 amplification showed a reduced number of CD8 effectors 1 and 2 (p When comparing baseline (T1) and end of induction (T2), significant differences were seen between sustained MRD+ and MRD-. We identified significantly depleted NK, and naïve and memory B-cell after D-KRd MRD- patients had significantly more CD14+ monocytes both at T1 and T2 than their MRD+ counterparts (Fig. 1). Differential expression suggests that inflammatory response genes including IL1B are upregulated in the absence of sustained MRD- whereas genes implicated in IL2, IL6, and IFNα response as well as adipocyte differentiation are associated with sustained MRD response. Conclusion: We show, for the first time, evidence of complex interplay between MM tumor genetics and the microenvironment in the context of D-KRd treated patients. Our results highlight the importance of genomic-based mechanisms in the persistence of disease (IKZF3, XBP1) as well as heterogeneity in the composition of the BM microenvironment, with the monocytes pointing towards the importance of inflammation. Figure 1 Figure 1. Disclosures Maura: OncLive: Honoraria; Medscape: Consultancy, Honoraria. Hassoun: Celgene, Takeda, Janssen: Research Funding. Mailankody: Jansen Oncology: Research Funding; Allogene Therapeutics: Research Funding; Bristol Myers Squibb/Juno: Research Funding; Legend Biotech: Consultancy; Takeda Oncology: Research Funding; Fate Therapeutics: Research Funding; Physician Education Resource: Honoraria; Plexus Communications: Honoraria; Evicore: Consultancy. Hultcrantz: Intellisphere LLC: Consultancy; GlaxoSmithKline: Membership on an entity's Board of Directors or advisory committees, Research Funding; Curio Science LLC: Consultancy; Amgen: Research Funding; Daiichi Sankyo: Research Funding. Scordo: Omeros Corporation: Consultancy; i3 Health: Other: Speaker; Kite - A Gilead Company: Membership on an entity's Board of Directors or advisory committees; Angiocrine Bioscience: Consultancy, Research Funding; McKinsey & Company: Consultancy. Kazandjian: Arcellx: Honoraria, Membership on an entity's Board of Directors or advisory committees; BMS: Honoraria, Membership on an entity's Board of Directors or advisory committees. Shah: Janssen Pharmaceutica: Research Funding; Amgen: Research Funding. Landau: Takeda, Janssen, Caelum Biosciences, Celgene, Pfizer, Genzyme: Membership on an entity's Board of Directors or advisory committees; Takeda: Research Funding; Genzyme: Honoraria. Giralt: SANOFI: Membership on an entity's Board of Directors or advisory committees; PFIZER: Membership on an entity's Board of Directors or advisory committees; GSK: Membership on an entity's Board of Directors or advisory committees; JAZZ: Membership on an entity's Board of Directors or advisory committees; JANSENN: Membership on an entity's Board of Directors or advisory committees; AMGEN: Membership on an entity's Board of Directors or advisory committees; Actinnum: Membership on an entity's Board of Directors or advisory committees; CELGENE: Membership on an entity's Board of Directors or advisory committees; BMS: Membership on an entity's Board of Directors or advisory committees. Dogan: Physicians' Education Resource: Honoraria; Seattle Genetics: Consultancy; EUSA Pharma: Consultancy; Roche: Consultancy, Research Funding; Takeda: Consultancy, Research Funding; Peer View: Honoraria. Lesokhin: Behringer Ingelheim: Honoraria; pfizer: Consultancy, Research Funding; Janssen: Honoraria, Research Funding; Iteos: Consultancy; Genetech: Research Funding; bristol myers squibb: Research Funding; Trillium Therapeutics: Consultancy; Serametrix, Inc: Patents & Royalties. Davies: Janssen: Consultancy, Honoraria; BMS: Consultancy, Honoraria; Amgen: Consultancy, Honoraria; Abbvie: Consultancy, Honoraria; Takeda: Consultancy, Honoraria; Roche: Consultancy, Honoraria. Korde: Amgen: Research Funding; Medimmune: Membership on an entity's Board of Directors or advisory committees. Morgan: BMS: Membership on an entity's Board of Directors or advisory committees; Jansen: Membership on an entity's Board of Directors or advisory committees; Karyopharm: Membership on an entity's Board of Directors or advisory committees; Oncopeptides: Membership on an entity's Board of Directors or advisory committees. Landgren: Janssen: Other: IDMC; Celgene: Research Funding; Amgen: Honoraria; Janssen: Research Funding; Janssen: Honoraria; Amgen: Research Funding; Takeda: Other: IDMC; GSK: Honoraria.

Introduction Large clinical data sets suggest that the natural history and prognosis of newly diagnosed multiple myeloma (NDMM) differs between patients of European and African ancestry, with the latter group exhibiting an earlier age at onset and poorer overall prognosis in some studies. The use of next generation sequencing (NGS) to characterize the genomic landscape of multiple myeloma (MM) suggests that the observed phenotypic differences between these groups of patients may reflect distinct underlying genomic profiles and mutational processes. Thus far, characterizations of this type have focused principally on patients of African ancestry (AA). Here, we characterize the genomic features and outcomes of a large series of patients of Hispanic or Latin American ancestry (HL) as compared to their Non-Hispanic white (NHW) counterparts. Methods Subjects were selected from the MMRF CoMMpass SM trial, a study that includes 1,154 patients with updated outcome data as of March, 2020. Within this data set, 760 patients had information on race and ethnicity. Among these, 55 HL patients and 478 NHW patients possessed complete clinical and genomic information. We analyzed baseline whole exome sequencing (WES) and long insert whole genome sequencing (WGS) as previously described (Walker, et al. Blood 2019). Our analysis focused on 63 known driver mutations in multiple myeloma and 39 sites of common copy number variation across the study population. Complex structural variants and tumor telomere length were called using previously described bioinformatic tools (Boyle et al. Leukemia 2021). Survival analysis was undertaken using the Kaplan-Meier method with hazard ratios determined by the Cox proportional hazards model. Results In a comparison of clinical features between the Hispanic and NHW population, we did not identify any differences in age of onset, gender, presenting cytogenetics, International Staging System Score (ISS), and IMWG Risk Category. The proportion of patients undergoing autologous stem cell transplantation was similar between groups. We identified no statistically significant differences in the presence of characteristic translocations involving IgH locus or in hyperdiploidy status. No statistically significant differences in tumor mutational burden or loss-of-heterozygosity percentage emerged between HL and NHW patients. We examined non-synonymous variations (NSV) and copy number variations at the loci of known MM driver genes and encountered no statistically significant differences in NSV, copy number, or biallelic status. We further categorized genes into pathways relevant to the pathogenesis of MM and discovered no difference in the proportions of patients harboring mutations in genes related to the MEK/ERK and NF-κB pathways, cell cycle regulation, and epigenetic modification. We were unable to the distinguish either population based on the presence of chromothripsis or in the overall preponderance of an APOBEC mutational signature. Tumor telomere length was not significantly different between the populations. An analysis of overall and progression free survival (PFS) with a median duration of follow up of 44 months revealed a trend toward poorer outcomes among the HL population that did not reach statistical significance. Median PFS was 24 months in HL patients and 35 months in the NHW population (p = 0.19). Median OS was not reached in either ethnic subgroup. In terms of overall survival, age, ISS score, overall number of driver mutations, and the presence of chromothripsis emerged with a negative impact on outcome (Figures 1a, 1b). These variables with the exception of chromothripsis retained their significant impact on progression free survival (Figure 2a, 2b). Conclusion The correlation between Hispanic or Latin American ancestry and underlying disease biology in MM has yet to be fully elucidated. In our analysis, which was based on self-declared ancestry as opposed to admixture, no obvious differences in significant measures of genomic variation known to impact prognosis in MM emerged between HL and NHW patients. These results may help to inform the future large-scale studies to ascertain the impact of genomics, disease biology and socioeconomic factors on outcomes in this heterogeneous patient population. Figure 1 Figure 1. Disclosures Walker: Bristol Myers Squibb: Research Funding; Sanofi: Speakers Bureau. Morgan: BMS: Membership on an entity's Board of Directors or advisory committees; Jansen: Membership on an entity's Board of Directors or advisory committees; Karyopharm: Membership on an entity's Board of Directors or advisory committees; Oncopeptides: Membership on an entity's Board of Directors or advisory committees.

Introduction: Multiple myeloma (MM) is a genetically heterogeneous disease where risk stratification and outcomes are associated with translocations involving the immunoglobulin (Ig) loci and MYC, copy number abnormalities including gain(1q), del(1p), and del(17p), as well as mutations. Additionally, MM tumors may harbor rare mutations in genes that are targetable in other tumors, such as in IDH1 and IDH2. Therefore, we designed a comprehensive MM targeted sequencing panel to interrogate the common genomic abnormalities in MM and validated it against known standards. Methods: The targeted panel was designed to include the exons of 228 genes which are either frequently mutated, associated with prognosis or risk stratification, clinically actionable, or sites of important copy number abnormalities. Additional targets were added across the genome to identify hyperdiploidy. These targeted regions encompass the mutation detection part of the panel and involve approximately 990 kb. The Ig loci and region surrounding MYC were tiled to capture translocations and copy number changes. In total, this translocation part of the panel involves approximately 4.7 Mb. The mutation and translocation panels are manufactured separately and combined during the assay resulting in a 5:1 sequencing ratio, respectively, which prevents over-sequencing of the large translocation panel. 100 ng DNA extracted from CD138+ bone marrow cells (n=223) and from non-tumor tissue (peripheral blood or saliva) was processed using the HyperCap workflow (KAPA Biosystems). Of the 223, 48 samples were processed in a clinical diagnostic laboratory. Adapter ligated DNA was hybridized with a mixture of the mutation and translocation panel and purified, amplified libraries were sequenced using 75 bp paired end reads. Sequences were aligned to hg19 and mutations and translocations identified using Strelka and Manta. Copy number was determined using the ratio of non-tumor to tumor reads in each targeted region. Data were validated using clinical FISH (translocations, n=116), MLPA (copy number, n=101), known standards (mutations), ddPCR (mutations), and whole genome sequencing (WGS; translocations and copy number, n=122). Results: Canonical IgH translocations were detected in 43.2% of patients by the panel, and all agreed with WGS. FISH detected one additional "variant" t(4;14), but did not detect 4 translocations detected by both sequencing methods. In the remainder of the samples no canonical IgH translocation was detected, agreeing with FISH results. Non-canonical translocations were detected in 14.5% of samples, 43% of which were to the MYC locus. MYC translocations were detected in 37.3% of samples with copy number abnormalities occurring surrounding MYC in 32.7% of samples. Overall, MYC abnormalities were detected in 46.4% of samples. Copy number was determined by panel sequencing and MLPA for 22 regions that were directly comparable between the technologies in 101 patient samples and 13 myeloma cell lines. The copy number concordance between the technologies was 96.9% and 99.6% in patient samples and cell lines, respectively. For the important prognostic regions, the concordance was R 2=0.962 (CDKN2C), R 2=0.986 (CKS1B), and R 2=0.973 (TP53). Panel copy number data were also compared to WGS data and showed complete concordance across the three prognostic regions, which the exception of 2 samples. In these 2 samples a homozygous deletion was detected by the panel but not by WGS. The deletions were 6.2 and 8.0 kb in size, one encompassing the coding sequencing of TP53 and the other exons 1-4 of TP53. A larger homozygous deletion of 36.3 kb was detected by both sequencing methods. Mutation detection validation was performed using Horizon Discovery samples with known variant allele frequencies (VAF) for common mutations. We were able to determine the sequencing VAF for 74 mutations across 5 samples which had a concordance of R 2=0.9849 between the expected and observed frequencies. The minimum detected VAF was 1.3% at an average depth of 891x. We also performed ddPCR on 6 patient samples with the common KRAS, NRAS and BRAF mutations which resulted in a VAF concordance of R 2=0.9983. Conclusion: We have developed a targeted sequencing panel for MM patient samples that is as robust or better than both FISH and WGS. A full protocol for sample processing and analysis is available, and has been used in a clinical diagnostic laboratory. Disclosures Ahsan: Bristol Myers Squibb: Current Employment, Current equity holder in publicly-traded company. Abu Zaid: Pieris: Current equity holder in publicly-traded company; Incyte: Research Funding; Pharamcyclic: Research Funding; Syndax: Consultancy, Research Funding. Ramasamy: Amgen: Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Travel, Conference registration, Research Funding; Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Travel, Conference registration, Research Funding; Takeda: Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Travel, Conference registration, Research Funding; Celgene (BMS): Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Travel, Conference registration, Research Funding; GSK: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Oncopeptides: Honoraria, Membership on an entity's Board of Directors or advisory committees; Adaptive biotech: Honoraria, Membership on an entity's Board of Directors or advisory committees; Karyopharm: Honoraria, Membership on an entity's Board of Directors or advisory committees; Pfizer oncology: Honoraria, Membership on an entity's Board of Directors or advisory committees; Sanofi: Honoraria, Membership on an entity's Board of Directors or advisory committees. Yong: GSK: Honoraria; Amgen: Honoraria; BMS: Research Funding; Sanofi: Honoraria, Research Funding; Takeda: Honoraria; Autolus: Research Funding; Janssen: Honoraria, Research Funding. Morgan: Takeda: Honoraria. Abonour: Celgene-BMS: Membership on an entity's Board of Directors or advisory committees, Research Funding; Takeda: Research Funding; Jensen: Honoraria, Research Funding; GSK: Consultancy, Honoraria, Research Funding. Flynt: Bristol Myers Squibb: Current Employment. Ansari-Pour: Bristol Myers Squibb: Consultancy. Gooding: Bristol Myers Squibb: Research Funding. Thakurta: Bristol Myers Squibb: Current Employment, Current equity holder in publicly-traded company. Walker: Bristol Myers Squibb: Research Funding; Sanofi: Speakers Bureau.

Introduction Copy number abnormalities (CNA) and structural variants (SV) are crucial to driving cancer progression and in multiple myeloma (MM). Chr1 CNA are seen in up to 40% of cases and associate with poor prognosis. Variants include deletions, gains, translocations and complex SV events such as chromothripsis (CT), chromoplexy (CP) and templated insertions (TI) which result in aberrant transcriptional patterns. Abnormal expression of genes on chr1 lead to the adverse clinical outcome and studies focussed on 1p12, 1p32.3 and 1q12-21 identified potential causal genes including TENT5C, CDKN2C, CKS1B, PDZK1, BCL9, ANP32E, ILF2, ADAR, MDM2 and MCL1 but none fully explain the clinical behavior. To address this deficiency and to relate chromatin structure to gene deregulation we present a multiomic bioinformatic analysis of SV, CNA, mutation and expression changes in relation to the chromatin structure of chr1. Methods We analysed data derived from 1,154 CoMMpass trial patients. We analyzed 972 NDMM patients with whole exome for mutations, and 752 whole genomes for copy number, translocations, complex rearrangements such as CP, CT and TI as previously described. Using GISTIC 2.0, we identified hotspots of CNA. This information was then analyzed in conjunction to the RNA-seq data derived from 643 patients to determine the aberrant transcriptional landscape of chr1. Using HiC data derived from U266 MM cell line, we associated these changes with TAD structures, A/B compartments, and histone marks along chr1, to gene expression changes, and recurrent SV. Using the cell line dependency map for CRISPR knockdown of the gene set on chr1 derived from 20 MM cell lines we related cell viability to chr1 copy number status. Results We identified 7 hotspots of deletion, 9 of gain, 3 of CT and 2 of templated-insertion across chr1. We mapped these regions to epigenetic plots and show that gained regions are hypomethylated compared to the rest of chr1 (Wilcoxon, p=0.0002). Overall 69% of gain(1q) and 45% of the non-gained hotspots were in A compartments (χ 2=11, p=0.0009) and had an overall higher compartment score (p=0.01).The recurrent regions of loss on 1p confirm the clinical relevance of this region. The critical importance of TENT5C, CDKN2C and RPL5 is identified by the impact of deletion, mutation and the rearrangement of superenhancers. Further this convergence of multiple oncogeneic mechanisms to a single locus points to a number of novel candidate drivers including FUB1 and NTRK1.We provide important new information on 1q21.1-1q25.2 encompassing 145-180Mb a transcriptionally dense region containing 6 GISTIC 2.0 hotspots of gain (G2-G7). The hotspots occur within TAD structures that correlate upregulation of known drivers listed above and also identified novel potential upregulated drivers including POU2F1, a transcription factor, CREG1, an adenovirus E1A protein that both activates and represses gene expression promoting proliferation and inhibiting differentiation (G6) and BTG2 a G1/S transition regulator (G8). These data for copy number gain provides strong evidence for the prognostic relevance of of multiple drivers within deregulated TADs rather than single candidate genes. It also highlights the importance of the chromatin structure of Chr1 in the generation of these events.Using dependency map CRISPR data we identified 320 essential genes for at least one cell line (>1). A common set of 31 genes were identified including 3 proteasome subunits (PSMA5, PSMB2, PSMB4), three regulators of ubiquitin-protein transferase activity (RPL5, RPL11, CDC20), splicing (SF3B4, SF3A3, SFPQ, RNPC3, SRNPE, PRPF38A, PRPF38B) and DTL. A common dependency for 1q+ or 1p- was not identified but a number of dependencies were identified in more than one cell line including UQCRH, SLCA1, CLSPN in 1p- cell lines and IPO9, PPIAL4G, and MRPS2 in 1q+. Conclusion We present an elegant anatomic map of chr1 at the genetic and epigenetic levels providing an unprecedented level of resolution for the relationships of structural variants to epigenetic, expression and mutation status. The analysis highlights the importance of active chromatin in gene deregulation by SV and CNA where the importance of multiple gene deregulation within TAD structures is critical to MM pathogenesis. The implications are that we could improve prognostic assignment and identify new targets for therapy by further characterizing these relationships. Figure 1 Figure 1. Disclosures Braunstein: Jansen: Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; Adaptive: Membership on an entity's Board of Directors or advisory committees; AstraZeneca: Membership on an entity's Board of Directors or advisory committees; Epizyme: Membership on an entity's Board of Directors or advisory committees; Karyopharm: Membership on an entity's Board of Directors or advisory committees; Pfizer: Membership on an entity's Board of Directors or advisory committees; Takeda: Membership on an entity's Board of Directors or advisory committees. Davies: Takeda: Membership on an entity's Board of Directors or advisory committees; Sanofi: Membership on an entity's Board of Directors or advisory committees; Oncopeptides: Membership on an entity's Board of Directors or advisory committees; Constellation: Membership on an entity's Board of Directors or advisory committees; Janssen: Membership on an entity's Board of Directors or advisory committees; Celgene/BMS: Consultancy, Membership on an entity's Board of Directors or advisory committees.

Introduction While Multiple myeloma (MM) is a disease of the elderly diagnosed at a median age of 69 years with nearly a third being above the age 75, little is known about the impact of aging processes on either disease biology or clinical outcomes. Treatment decisions are complicated, and it is important to take account three interacting variables: tumor genetics, comorbidities and the efficacy and toxicity of the treatment selected. While frailty scores help stratify elderly MM patients by functional status, quantitative measures of aging could provide biological markers to enhance clinical staging systems, standardize decision making, and guide treatment choices in the elderly MM population. In this work, we characterized the genetics of older MM patients compared to younger patients, and determined the associations of age with clonal hematopoiesis and telomere length (TL), both of which have been shown to be impacted by aging. Methods Using the MMRF CoMMpass IA15 data, we analyzed 972 NDMM patients with whole genome long insert sequencing with matching whole exomes. Using paired samples, we determined mutations (Mutect2 and Strelka), copy number (Control-FREEC v. 11.4), translocations (Manta v. 1.4.0), complex rearrangements (ChainFinder and ShatterSeek), as previously described and TL using Telomerecat. Looking at the germline data, we quantified Clonal Hematopoiesis of Indeterminate Potential (CHIP) and quantified TL using the same approach. Results The overall survival of patients aged over sixty-five is significantly worse than patients younger than this age (HR 1.7 (CI 95% 1.3-2.3), p Using a Bayesian approach, we show that, that del(16p) and del(6q) were more frequent in older patients (Corr=0.10, BF=1.1 and Corr=0.13, BF=11). Similarly, mutational signatures did not substantially differ between age groups with the exception of the proportion of APOBEC (SBS2 and 5) which was higher in the group over age > 80 (χ2=11, p=0.02). We determined both simple and complex structural variants and found that the prevalence of chromothripsis increased with age (χ2=10.8, p=0.001). To determine whether this may be related to chromosomal instability occurring as a consequence of aging we examined the extent of telomere attrition. A significant negative correlation was identified between TL and age (F=9.5, p=0.002) but there were no correlations with complex rearrangements. We did, however, find that TL was significantly shorter in the TP53 (χ2=9, p=0.002) and ATM (χ2=7.2, p=0.007) mutated groups suggesting TL shortening may be associated with DNA instability. To further determine the association of short TL in malignant plasmacells with adverse outcomes we ranked patients based on TL quartile and determined the impact on outcome for the shortest TL. We show that 14%, 29%, 24%, 29%, and 21% of the >50, 50-60, 60-70-70-80, and >80 year old patients were within this short TL group. There was a significant correlation with adverse overall-survival both in the younger and older patients, Figure 1A. To understand and quantify the impact of aging of the normal hematopoietic system on outcomes in MM we quantified CHIP and TL on the germline samples. CHIP was seen in 156 patients (16%) and DNMT3A, ASXL1, and TET2 were the more frequent mutations. Patients with CHIP were significantly older (χ2=3.9, p=0.005), as it was seen in 22% of the over 80. The only signatures identified using a fitting approach for these CHIP mutations were the two age related mutational signatures (SBS1 and SBS5). Interestingly, patients with CHIP did not have significantly adverse clinical outcome. To understand the impact of genetics and markers of aging in the older population we performed a multivariate analysis on the subset of patients over age 65 (n=375). Like others, we found that the well described prognostic genetic risk factors (del(17p), TP53 mutations, t(4;14), t(14;16), and amp1q) did not appear to contribute to the independent assessment of risk when taking into account age, ISS, and performance status (ECOG≥2). We show that in this population of older myeloma that short TL was, however, an independent marker for negative outcome, Figure 1B. Conclusion: We highlight the importance of TL, a composite factor that takes into account both DNA instability, copy number losses, and aging as a potential novel biological marker to assess outcome and aid personalized treatment decisions in older patients with MM. Disclosures Bauer: Synthekine: Current Employment. Braunstein:Janssen: Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene: Membership on an entity's Board of Directors or advisory committees; Takeda: Membership on an entity's Board of Directors or advisory committees; Karyopharm: Membership on an entity's Board of Directors or advisory committees; Amgen: Membership on an entity's Board of Directors or advisory committees; TG Therapeutics: Membership on an entity's Board of Directors or advisory committees; AstraZeneca: Membership on an entity's Board of Directors or advisory committees; Verastem: Membership on an entity's Board of Directors or advisory committees; Epizyme: Membership on an entity's Board of Directors or advisory committees; Morphosys: Membership on an entity's Board of Directors or advisory committees. Landgren:Amgen: Consultancy, Honoraria, Research Funding; Takeda: Other: Independent Data Monitoring Committees for clinical trials, Research Funding; Janssen: Consultancy, Honoraria, Other: Independent Data Monitoring Committees for clinical trials, Research Funding; Janssen: Consultancy, Honoraria, Other: Independent Data Monitoring Committees for clinical trials, Research Funding; Seattle Genetics: Research Funding; Celgene: Consultancy, Honoraria, Research Funding; Glenmark: Consultancy, Honoraria, Research Funding; Takeda: Other: Independent Data Monitoring Committees for clinical trials, Research Funding; Binding Site: Consultancy, Honoraria; Cellectis: Consultancy, Honoraria; BMS: Consultancy, Honoraria; Karyopharma: Research Funding; BMS: Consultancy, Honoraria; Binding Site: Consultancy, Honoraria; Karyopharma: Research Funding; Merck: Other; Glenmark: Consultancy, Honoraria, Research Funding; Adaptive: Consultancy, Honoraria; Seattle Genetics: Research Funding; Pfizer: Consultancy, Honoraria; Juno: Consultancy, Honoraria; Juno: Consultancy, Honoraria; Cellectis: Consultancy, Honoraria; Pfizer: Consultancy, Honoraria; Celgene: Consultancy, Honoraria, Research Funding; Merck: Other. Davies:Janssen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Oncopeptides: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Roche: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Takeda: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Adaptive Biotech: Honoraria; Sanofi: Honoraria, Membership on an entity's Board of Directors or advisory committees; Celgene/BMS: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding. Morgan:Karyopharm: Consultancy, Honoraria; GSK: Consultancy, Honoraria; Amgen: Consultancy, Honoraria; Roche: Consultancy, Honoraria; Celgene: Consultancy, Honoraria, Research Funding; Takeda: Consultancy, Honoraria; Bristol-Myers Squibb: Consultancy, Honoraria; Janssen: Research Funding.

SummaryThe landscape of structural variants (SVs) in multiple myeloma remains poorly understood. Here, we performed comprehensive classification and analysis of SVs in multiple myeloma, interrogating a large cohort of 762 patients with whole genome and RNA sequencing. We identified 100 SV hotspots involving 31 new candidate driver genes, including drug targets BCMA (TNFRSF17) and SLAMF7. Complex SVs, including chromothripsis and templated insertions, were present in 61 % of patients and frequently resulted in the simultaneous acquisition of multiple drivers. After accounting for all recurrent events, 63 % of SVs remained unexplained. Intriguingly, these rare SVs were associated with up to 7-fold enrichment for outlier gene expression, indicating that many rare driver SVs remain unrecognized and are likely important in the biology of individual tumors.

New therapeutic targets for advanced colorectal cancer (CRC) are critically needed. Our laboratory recently performed an insertional mutagenesis screen in mice to identify novel CRC driver genes and, thus, potential drug targets. Here, we define Transmembrane 9 Superfamily 2 (TM9SF2) as a novel CRC oncogene. TM9SF2 is an understudied protein, belonging to a well conserved protein family characterized by their nine putative transmembrane domains. Based on our transposon screen we found that TM9SF2 is a candidate progression driver in digestive tract tumors. Analysis of The Cancer Genome Atlas (TCGA) data revealed that approximately 35% of CRC patients have elevated levels of TM9SF2 mRNA, data we validated using an independent set of CRC samples. RNAi silencing of TM9SF2 reduced CRC cell growth in an anchorage-independent manner, a hallmark of cancer. Furthermore, CRISPR/Cas9 knockout of TM9SF2 substantially diminished CRC tumor fitness in vitro and in vivo. Transcriptome analysis of TM9SF2 knockout cells revealed a potential role for TM9SF2 in cell cycle progression, oxidative phosphorylation, and ceramide signaling. Lastly, we report that increased TM9SF2 expression correlates with disease stage and low TM9SF2 expression correlate with a more favorable relapse-free survival. Taken together, this study provides evidence that TM9SF2 is a novel CRC oncogene.