Abstract: 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 & lt;10% of patients. We then analyzed the purely clinical scores (ISS) and combined clinical/genetic scores. We show again, that the more specific risk scores (double hit, Boyd IV, GEP70) identify between 7-13% of cases with HR (2-3.1) for OS. When we looked specifically at the younger patients (= & lt; 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.

Abstract: Chromosome 1 (chr1) copy-number abnormalities (CNA) and structural variants (SV) are frequent in newly diagnosed multiple myeloma (NDMM) and are associated with a heterogeneous impact on outcomes, the drivers of which are largely unknown. Experimental Design: A multiomic approach comprising CRISPR, gene mapping of CNAs and SVs, methylation, expression, and mutational analysis was used to document the extent of chr1 molecular variants and their impact on pathway utilization. Results: We identified two distinct groups of gain(1q): focal gains associated with limited gene-expression changes and a neutral prognosis, and whole-arm gains, which are associated with substantial gene-expression changes, complex genetics, and an adverse prognosis. CRISPR identified a number of dependencies on chr1 but only limited variants associated with acquired CNAs. We identified seven regions of deletion, nine of gain, three of chromothripsis (CT), and two of templated insertion (TI), which contain a number of potential drivers. An additional mechanism involving hypomethylation of genes at 1q may contribute to the aberrant gene expression of a number of genes. Expression changes associated with whole-arm gains were substantial and gene set enrichment analysis identified metabolic processes, apoptotic resistance, signaling via the MAPK pathway, and upregulation of transcription factors as being key drivers of the adverse prognosis associated with these variants. Conclusions: Multiple layers of genetic complexity impact the phenotype associated with CNAs on chr1 to generate its associated clinical phenotype. Whole-arm gains of 1q are the critically important prognostic group that deregulate multiple pathways, which may offer therapeutic vulnerabilities.

Abstract: 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.

Abstract: 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 ( & gt;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.