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PURPOSE Binary cardiac response assessment using cardiac biomarkers is prognostic in light chain amyloidosis. Previous studies suggested four-level cardiac responses using N-terminal prohormone of brain natiuretic peptide improves prognostic prediction. This study was designed to validate graded cardiac response criteria using N-terminal prohormone of brain natiuretic peptide/brain natiuretic peptide. PATIENTS AND METHODS This retrospective, multicenter study included patients with light chain amyloidosis who achieved at least a hematologic partial response (PR) and were evaluable for cardiac response. Four response criteria were tested on the basis of natriuretic peptide response depth: cardiac complete response (CarCR), cardiac very good partial response (CarVGPR), cardiac PR (CarPR), and cardiac no response (CarNR). Response was classified as best response and at fixed time points (6, 12, and 24 months from therapy initiation). The study primary outcome was overall survival. RESULTS 651 patients were included. Best CarCR, CarVGPR, CarPR, and CarNR were achieved in 16%, 26.4%, 22.9%, and 34.7% of patients, respectively. Patients in cardiac stage II were more likely to achieve CarCR than patients in cardiac stage IIIA and IIIB (22% v 13.5% v 3.2%; P < .001). A deeper cardiac response was associated with a longer survival (5-year overall survival 93%, 79%, 65%, and 33% for CarCR, CarVGPR, CarPR, and CarNR, respectively; P < .001). Fixed time-point analyses and time-varying covariates Cox regression analysis, to minimize survivorship bias, affirmed the independent survival advantage of deeper cardiac responses. Four-level response performed better than two-level response as early as 12 months from therapy initiation. CONCLUSION Graded cardiac response criteria allow better assessment of cardiac improvement compared with the traditional binary response system. The study re-emphasizes the importance of early diagnosis, which increases the likelihood of deep cardiac responses.

In a short time, single-cell platforms have become the norm in many fields of research, including multiple myeloma (MM). In fact, the large amounts of cellular heterogeneity in MM make single-cell platforms particularly attractive, as bulk assessments can miss valuable information about cellular subpopulations and cell-cell interactions. Single-cell platforms' decreasing cost and increasing accessibility, combined with breakthroughs in obtaining multi-omics data for the same cell and innovative computational programs for analyzing data, have allowed single-cell studies to make important insights into MM pathogenesis, yet there is still much to be done. In this review, we'll first focus on the types of single-cell profiling and the considerations for designing a single-cell profiling experiment. Then we'll discuss what we've learned from single-cell profiling about myeloma clonal evolution, transcriptional reprogramming and drug resistance and about the MM microenvironment during precursor and advanced disease.

High-dose melphalan (HDM) improves progression-free survival in multiple myeloma (MM), yet melphalan is a DNA-damaging alkylating agent; therefore, we assessed its mutational effect on surviving myeloma cells by analyzing paired MM samples collected at diagnosis and relapse in the IFM 2009 study. We performed deep whole-genome sequencing on samples from 68 patients, 43 of whom were treated with RVD (lenalidomide, bortezomib, and dexamethasone) and 25 with RVD + HDM. Although the number of mutations was similar at diagnosis in both groups (7137 vs 7230; P = .67), the HDM group had significantly more mutations at relapse (9242 vs 13 383, P = .005). No change in the frequency of copy number alterations or structural variants was observed. The newly acquired mutations were typically associated with DNA damage and double-stranded breaks and were predominantly on the transcribed strand. A machine learning model, using this unique pattern, predicted patients who would receive HDM with high sensitivity, specificity, and positive prediction value. Clonal evolution analysis showed that all patients treated with HDM had clonal selection, whereas a static progression was observed with RVD. A significantly higher percentage of mutations were subclonal in the HDM cohort. Intriguingly, patients treated with HDM who achieved complete remission (CR) had significantly more mutations at relapse yet had similar survival rates as those treated with RVD who achieved CR. This similarity could have been due to HDM relapse samples having significantly more neoantigens. Overall, our study identifies increased genomic changes associated with HDM and provides rationale to further understand clonal complexity.

Immunoglobulin M (IgM) multiple myeloma (MM) is a rare disease subgroup. Its differentiation from other IgM-producing gammopathies such as Waldenström macroglobulinemia (WM) has not been well characterized but is essential for proper risk assessment and treatment. In this study, we investigated genomic and transcriptomic characteristics of IgM-MM samples using whole-genome and transcriptome sequencing to identify differentiating characteristics from non–IgM-MM and WM. Our results suggest that IgM-MM shares most of its defining structural variants and gene-expression profiling with MM, but has some key characteristics, including t(11;14) translocation, chromosome 6 and 13 deletion as well as distinct molecular and transcription-factor signatures. Furthermore, IgM-MM translocations were predominantly characterized by VHDHJH recombination-induced breakpoints, as opposed to the usual class-switching region breakpoints; coupled with its lack of class switching, these data favor a pre–germinal center origin. Finally, we found elevated expression of clinically relevant targets, including CD20 and Bruton tyrosine kinase, as well as high BCL2/BCL2L1 ratio in IgM-MM, providing potential for targeted therapeutics.

Multiple myeloma (MM) is a heterogeneous disease featured by clonal plasma cell proliferation and genomic instability. The advent of next-generation sequencing allowed unraveling the complex genomic landscape of the disease. Several recurrent genomic aberrations including immunoglobulin genes translocations, copy number abnormalities, complex chromosomal events, transcriptomic and epigenomic deregulation, and mutations define various molecular subgroups with distinct outcomes. In this review, we describe the recurrent genomic events identified in MM impacting patients’ outcome and survival. These genomic aberrations constitute new markers that could be incorporated into a prognostication model to eventually guide therapy at every stage of the disease.

Key Points UBE2T is frequently amplified and/or overexpressed and is required for homologous recombination activity in multiple myeloma cells. UBE2T is a potential therapeutic target to increase chemosensitivity in multiple myeloma cells.

Daratumumab as a single agent (sDARA) or in combination with chemotherapies (cDARA) leads to impressive hematologic and organ responses in AL amyloidosis. However, predictive factors associated with outcomes, and optimal duration of therapy remain unclear. We analyzed 107 patients with AL amyloidosis treated with daratumumab between 2017 and 2020. The median overall survival (OS) was not reached while the median major organ deterioration progression free survival (MOD-PFS) was 36 months in the sDARA cohort and not reached in the cDARA cohort, respectively. Hematologic responseVGPR was achieved in 81% of patients receiving sDARA and 86% of patients treated with cDARA. Several predictive factors were identified on a univariate analysis, including NTproBNP8500 pg/mL but only achievement of at least VGPR and presence of 1q21 gain were independently associated with MOD-PFS and OS on a multivariate analysis. Finally, patients receiving 12 cycles had significantly longer MOD-PFS (30 vs.13 months; (p = .0018) and OS (NR vs. 15 months; p .0001). NTproBNP8500 pg/mL, presence of 1q21 gain and shorter duration of therapy (≤ 12 cycles) are strong negative predictive factors for outcomes with daratumumab therapy in AL amyloidosis.

Many studies over the last 20 years have investigated the role of mitochondrial DNA (mtDNA) alterations in carcinogenesis. However, the status of the mtDNACN in MM and its implication in the pathogenesis of the disease remains unclear. We examined changes in plasma cell mtDNACN across different stages of MM by applying RT-PCR and high-throughput sequencing analysis. We observed a significant increase in the average mtDNACN in myeloma cells compared with healthy plasma cells (157 vs. 40 copies, p = 0.02). We also found an increase in mtDNACN in SMM and newly diagnosed MM (NDMM) paired samples and in consecutive relapses in the same patient. Survival analysis revealed the negative impact of a high mtDNACN in progression-free survival in NDMM (p = 0.005). Additionally, we confirmed the higher expression of mitochondrial biogenesis regulator genes in myeloma cells than in healthy plasma cells and we detected single nucleotide variants in several genes involved in mtDNA replication. Finally, we found that there was molecular similarity between “rapidly-progressing SMM” and MM regarding mtDNACN. Our data provide evidence that malignant transformation of myeloma cells involves the activation of mitochondrial biogenesis, resulting in increased mtDNA levels, and highlights vulnerabilities and potential therapeutic targets in the treatment of MM. Accordingly, mtDNACN tracking might guide clinical decision-making and management of complex entities such as high-risk SMM.

Patients with newly diagnosed multiple myeloma (NDMM) with high-risk disease are in need of new treatment strategies to improve the outcomes. Multiple clinical, cytogenetic, or gene expression features have been used to identify high-risk patients, each of which has significant weaknesses. Inclusion of molecular features into risk stratification could resolve the current challenges. In a genome-wide analysis of the largest set of molecular and clinical data established to date from NDMM, as part of the Myeloma Genome Project, we have defined DNA drivers of aggressive clinical behavior. Whole-genome and exome data from 1273 NDMM patients identified genetic factors that contribute significantly to progression free survival (PFS) and overall survival (OS) (cumulative R2 = 18.4% and 25.2%, respectively). Integrating DNA drivers and clinical data into a Cox model using 784 patients with ISS, age, PFS, OS, and genomic data, the model has a cumlative R2 of 34.3% for PFS and 46.5% for OS. A high-risk subgroup was defined by recursive partitioning using either a) bi-allelic TP53 inactivation or b) amplification (≥4 copies) of CKS1B (1q21) on the background of International Staging System III, comprising 6.1% of the population (median PFS = 15.4 months; OS = 20.7 months) that was validated in an independent dataset. Double-Hit patients have a dire prognosis despite modern therapies and should be considered for novel therapeutic approaches.

Background Renal light chain (AL) amyloidosis manifests as proteinuria with or without renal failure and is associated with a risk of progression to renal replacement therapy (RRT). A significant reduction in circulating amyloidogenic light chain is needed to achieve a renal response. Current renal response criteria are binary defining a renal response as >30% reduction in 24-h proteinuria without worsening estimated glomerular filtration rate (eGFR). Several studies suggest that greater reduction in proteinuria following successful therapy improves renal and overall survival. Methods AL amyloidosis patients diagnosed between 2010 to 2015, achieving at least hematological partial response to therapy and with renal involvement were included. Four renal response categories were formulated based on reduction level in pretreatment 24-h proteinuria in the absence of renal progression: renal complete response (renCR, 24-h proteinuria ≤200 mg/24-h); renal very good partial response (renVGPR, >60% reduction in 24-h proteinuria); renal partial response (renPR, 31-60% reduction in 24-proteinuria); and renal no response (renNR, 30% or less reduction). Renal response was assessed at landmark (6-, 12-, and 24 months from treatment initiation) and as best renal response. Graded renal responses were assessed as predictors for time from diagnosis to RRT and overall survival. Results Seven hundred and thirty-seven patients were included. The median age was 63. Renal stage I, II and III were assigned to 34%, 52% and 14% of patients, respectively. Reduction in 24-h proteinuria from baseline improved over time with a median reduction of 34%, 50% and 71%, at 6-month, 12-month and 24-months, respectively. At best response, renCR, renVGPR, renPR and renNR were achieved in 27%, 34%, 15% and 24% of patients, respectively. A renal response as early as 6 months after therapy initiation was able to predict time to RRT with an increase in RRT risk with lower level of renal response at that time point (5-year RRT 0%, 3%, 9% and 16% for renCR, renVGPR, renPR and renNR, respectively, P Conclusions We validated new graded renal response criteria based on reduction in 24-h proteinuria. These 4-level renal response criteria highlight the importance of achieving a deep renal response to improve renal and overall survival. These findings will allow clinicians to make decision on therapy changes or augmentation based on response depth as early as 6-month before irreversible renal failure has developed.

Background Binary cardiac response assessment using NT-proBNP is prognostic in light chain (AL) amyloidosis. Previous studies suggested that refining the criteria to multi-level cardiac responses improves prognostic prediction. We aimed to validate a graded cardiac response assessment tool in AL amyloidosis using NT-proBNP or BNP. Methods In this retrospective, multicenter study AL amyloidosis patients who were diagnosed between 2010 and 2015, achieved at least a hematological partial response (PR) within 12 months of diagnosis and were evaluable for cardiac response (defined as baseline NT-proBNP >650 pg/mL or BNP >150 pg/mL) were included. The following response criteria were tested: cardiac complete response (carCR, nadir NT-proBNP≤350 pg/mL or BNP≤80 pg/mL); cardiac very good partial response (carVGPR, >60% reduction in NT-proBNP/BNP); Cardiac PR (carPR 31-60% reduction); and cardiac non response (carNR, ≤30% reduction). Response was assessed at fixed time points (6, 12 and 24 months from therapy initiation) and at best response. The primary outcome was overall survival based on depth of cardiac response. Results Six hundred and fifty-one patients were included. Mayo 2004 cardiac stage II, IIIA and IIIB was present in 47.5%, 38% and 14.5% of patients, respectively (by definition, patients with Mayo stage 1 do not have cardiac amyloidosis evaluable for response). Hematological CR, hematological VGPR and hematological PR was achieved in 38%, 39% and 23% of patients, respectively. Cardiac response improved over time with a median percentage reduction in NT-proBNP/BNP compared to baseline of 15%, 37% and 54%, at 6, 12 and 24 months respectively. At best cardiac response, carCR, carVGPR, carPR and carNR were achieved in 16%, 26%, 23% and 35% of patients, respectively. Patients achieving a carCR were more likely to be in cardiac stage II compared to carVGPR or carPR patients (65% vs 47% each, P30% rise in NT-proBNP/BNP) in multivariate analysis that included age, type of first line therapy, light chain burden, cardiac stage and hematological response. Conclusions We validated the prognostic value of graded cardiac response. These response criteria allow better discrimination of patient populations and assessment of treatment effectiveness in an era of improved therapies for AL amyloidosis. The study emphasizes the importance of early diagnosis which increases the likelihood of deep and durable cardiac responses.

Multiple myeloma (MM) is a heterogeneous disease that has an increased incidence in African Americans (AAs). We previously observed that, with equal access to health care, younger AA patients (age < 65 years) have superior overall survival (OS) compared with younger White patients. Because MM prognosis is influenced by 17p deletion (del17p), we investigated racial differences in its occurrence and impact in a large cohort of MM patients from the Veterans Affairs (VA) system. Among 2243 VA patients with MM for whom del17p data were available, del17p was present in 8.83% of all patients, with a significantly lower prevalence in AAs (5.56%) compared with Whites (10.52%; P < .001). The difference was even more pronounced among younger AAs (

Background Emerging data suggest variability in susceptibility and outcome to coronavirus disease 2019 (COVID-19) infection. Identifying risk factors associated with infection and outcomes in cancer patients is necessary to develop healthcare recommendations. Methods We analyzed electronic health records of the US Veterans Affairs Healthcare System and assessed the prevalence of COVID-19 infection in cancer patients. We evaluated the proportion of cancer patients tested for COVID-19 who were positive, as well as outcome attributable to COVID-19, and stratified by clinical characteristics including demographics, comorbidities, cancer treatment, and cancer type. All statistical tests are 2-sided. Results Of 22 914 cancer patients tested for COVID-19, 1794 (7.8%) were positive. The prevalence of COVID-19 was similar across age. Higher prevalence was observed in African American (15.0%) compared with White (5.5%; P < .001) and in patients with hematologic malignancy compared with those with solid tumors (10.9% vs 7.8%; P < .001). Conversely, prevalence was lower in current smokers and patients who recently received cancer therapy ( Conclusion Preexistence of cancer affects both susceptibility to COVID-19 infection and eventual outcome. The overall COVID-19–attributable mortality in cancer patients is affected by age, comorbidity, and specific cancer types; however, race or recent treatment including immunotherapy do not impact outcome.

BackgroundEmerging data suggest variability in susceptibility and outcome to Covid-19 infection. Identifying the risk-factors associated with infection and outcomes in cancer patients is necessary to develop healthcare recommendations.MethodsWe analyzed electronic health records of the US National Veterans Administration healthcare system and assessed the prevalence of Covid-19 infection in cancer patients. We evaluated the proportion of cancer patients tested for Covid-19 and their confirmed positivity, with clinical characteristics, and outcome, and stratified by demographics, comorbidities, cancer treatment and cancer type.ResultsOf 22914 cancer patients tested for Covid-19, 1794 (7.8%) were positive. The prevalence of Covid-19 was similar across all ages. Higher prevalence was observed in African-American (AA) (15%) compared to white (5.5%; PConclusionPre-existence of cancer affects both susceptibility to Covid-19 infection and eventual outcome. The overall Covid-19 attributable mortality in cancer patients is affected by age, co-morbidity and specific cancer types, however, race or recent treatment including immunotherapy does not impact outcome.FundingsVA Office of Research and Development and National Institutes of Health.

Genomic instability (GIN), an increased tendency to acquire genomic alterations, is a cancer hallmark. However, its frequency, underlying causes, and disease relevance vary across different cancers. Multiple myeloma (MM), a plasma cell malignancy, evolves through premalignant phases characterized by genomic abnormalities. Next-generation sequencing (NGS) methods are deconstructing the genomic landscape of MM across the continuum of its development, inextricably linking malignant transformation and disease progression with increasing acquisition of genomic alterations, and illuminating the mechanisms that generate these alterations. Although GIN drives disease evolution, it also creates vulnerabilities such as dependencies on 'superfluous' repair mechanisms and the induction of tumor-specific antigens that can be targeted. We review the mechanisms of GIN in MM, the associated vulnerabilities, and therapeutic targeting strategies.

PURPOSE Multiple myeloma (MM) is accompanied by heterogeneous somatic alterations. The overall goal of this study was to describe the genomic landscape of myeloma using deep whole-genome sequencing (WGS) and develop a model that identifies patients with long survival. METHODS We analyzed deep WGS data from 183 newly diagnosed patients with MM treated with lenalidomide, bortezomib, and dexamethasone (RVD) alone or RVD + autologous stem cell transplant (ASCT) in the IFM/DFCI 2009 study (ClinicalTrials.gov identifier: NCT01191060 ). We integrated genomic markers with clinical data. RESULTS We report significant variability in mutational load and processes within MM subgroups. The timeline of observed activation of mutational processes provides the basis for 2 distinct models of acquisition of mutational changes detected at the time of diagnosis of myeloma. Virtually all MM subgroups have activated DNA repair–associated signature as a prominent late mutational process, whereas APOBEC signature targeting C>G is activated in the intermediate phase of disease progression in high-risk MM. Importantly, we identify a genomically defined MM subgroup (17% of newly diagnosed patients) with low DNA damage (low genomic scar score with chromosome 9 gain) and a superior outcome (100% overall survival at 69 months), which was validated in a large independent cohort. This subgroup allowed us to distinguish patients with low- and high-risk hyperdiploid MM and identify patients with prolongation of progression-free survival. Genomic characteristics of this subgroup included lower mutational load with significant contribution from age-related mutations as well as frequent NRAS mutation. Surprisingly, their overall survival was independent of International Staging System and minimal residual disease status. CONCLUSION This is a comprehensive study identifying genomic markers of a good-risk group with prolonged survival. Identification of this patient subgroup will affect future therapeutic algorithms and research planning.

Castleman disease (CD) is a rare and heterogenous group of disorders sharing in common an abnormal lymph node pathology. CD comprises distinct subtypes with different prognoses. Unicentric CD and multicentric CD are featured by specific systemic manifestations and may be associated with Kaposi sarcoma, non-Hodgkin and Hodgkin lymphoma, and POEMS syndrome. Multicentric CD is classically associated with systemic symptoms and poorer prognosis. In this article, the authors review how to diagnose the disease, keeping in context the clinical findings, biochemical changes and complications associated with CD.

Introduction: Renal light chain (AL) amyloidosis typically manifests as proteinuria with or without renal failure and is associated with a risk of progression to renal replacement therapy (RRT). A significant reduction in circulating amyloidogenic light chain is needed to achieve a renal response. Current renal response criteria are binary defining a renal response as >30% reduction in 24-h proteinuria without worsening estimated glomerular filtration rate (eGFR). Several studies suggest that greater reduction in proteinuria following successful therapy improves renal and overall survival. Methods: AL amyloidosis patients diagnosed between 2010 to 2015, achieving at least hematological partial response (hemPR) to therapy and with renal involvement (defined as 24-h non-selective proteinuria >0.5 g/24-h) were included. Four predefined renal response categories were formulated based on reduction level in pretreatment 24-h proteinuria in the absence of renal progression (≥25% decrease in eGFR): renal complete response (renCR, 24-h proteinuria ≤200 mg/24-h); renal very good partial response (renVGPR, >60% reduction in 24-h proteinuria); renal partial response (renPR, 31-60% reduction in 24-proteinuria); and renal no response (renNR, 30% or less reduction). Renal response was assessed at landmark (6-, 12-, and 24 months from treatment initiation) and as best renal response. Graded renal responses were assessed as predictors for time from diagnosis to RRT and overall survival. Results: Seven hundred and thirty-seven patients were included. The median age was 63. The breakdown of renal stage was: I, 34%; II, 52%; and III, 14%. Eighty percent of patients received 1 line of therapy within 12 months of their diagnosis. Bortezomib-based therapy was given to 60% of the patients; 28% received autologous stem cell transplantation (ASCT) as their first line therapy. Hematological CR was achieved in 44% of patients, followed by hematological very good partial response (38%) and hemPR (18%). RRT was required during follow-up in 15% of patients (n=108) with a median time from diagnosis to RRT of 18 (IQR 6-43) months. Twenty-eight percent of the patients died. The median follow-up of the surviving patients was 69 months (IQR 56-86). Reduction in 24-h proteinuria from baseline improved over time with a median reduction of 34%, 50% and 71% at 6-month, 12-month, and 24-months, respectively. At best response, renCR, renVGPR, renPR and renNR were achieved in 27% (n=199), 34% (n=247), 15% (n=112) and 24% (n=179) of patients, respectively. The median time to best renal response among renal responders was 17 (IQR 8-31) months, longer for renCR (23 months, IQR 10-40) compared to renVGPR (16 months, IQR 9-27) or renPR (11 months, IQR 6-19). A renal response as early as 6 months after therapy initiation was able to predict time to RRT with an increase in RRT risk with lower level of renal response at that time point (5-year RRT risk 0%, 3%, 9% and 16% for renCR, renVGPR, renPR and renNR, respectively, P Conclusions: We validated new graded renal response criteria based on reduction in 24-h proteinuria. These 4-level renal response criteria highlight the importance of achieving a deep renal response to improve renal and overall survival. These findings will allow clinicians to make decisions on therapy changes or augmentation based on response depth as early as 6-months, before irreversible renal failure develops. Figure 1 Figure 1. Disclosures Palladini: Janssen Global Services: Honoraria, Other: advisory board fees; Siemens: Honoraria; Pfizer: Honoraria. Milani: Celgene: Other: Travel support; Janssen-Cilag: Honoraria. Schönland: Sanofi: Research Funding; Prothena: Honoraria, Other: Travel grants; Janssen: Honoraria, Other: Travel grants, Research Funding; Takeda: Honoraria, Other: Travel grants; Pfizer: Honoraria. Hegenbart: Akcea: Honoraria; Alnylam: Honoraria; Janssen: Consultancy, Research Funding; Prothena: Research Funding; Pfizer: Consultancy, Honoraria. Dispenzieri: Oncopeptides: Consultancy; Alnylam: Research Funding; Sorrento Therapeutics: Consultancy; Pfizer: Research Funding; Takeda: Research Funding; Janssen: Consultancy, Research Funding. Kumar: Janssen: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Astra-Zeneca: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Merck: Research Funding; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; Oncopeptides: Consultancy; Carsgen: Research Funding; Beigene: Consultancy; Novartis: Research Funding; Bluebird Bio: Consultancy; Amgen: Consultancy, Research Funding; Roche-Genentech: Consultancy, Research Funding; Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; KITE: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Tenebio: Research Funding; Antengene: Consultancy, Honoraria; BMS: Consultancy, Research Funding; Abbvie: Consultancy, 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, Research Funding; Sanofi: Research Funding. Kastritis: Amgen: Consultancy, Honoraria, Research Funding; Genesis Pharma: Honoraria; Janssen: Consultancy, Honoraria, Research Funding; Takeda: Honoraria; Pfizer: Consultancy, Honoraria, Research Funding. Dimopoulos: Takeda: Honoraria; Janssen: Honoraria; Beigene: Honoraria; BMS: Honoraria; Amgen: Honoraria. Liedtke: Takeda: Membership on an entity's Board of Directors or advisory committees; Janssen Pharmaceuticals: Membership on an entity's Board of Directors or advisory committees; Bristol Myers Squibb: Membership on an entity's Board of Directors or advisory committees; Oncopeptides: Membership on an entity's Board of Directors or advisory committees; Kite: Membership on an entity's Board of Directors or advisory committees; Kura Oncology: Membership on an entity's Board of Directors or advisory committees; Sanofi: Membership on an entity's Board of Directors or advisory committees; Caelum: Membership on an entity's Board of Directors or advisory committees, Other: Clinical Trial Funding; Celgene: Membership on an entity's Board of Directors or advisory committees; GlaxoSmithKline: Membership on an entity's Board of Directors or advisory committees; Pfizer: Honoraria; Alnylam: Membership on an entity's Board of Directors or advisory committees; Karyopharm: Membership on an entity's Board of Directors or advisory committees. Witteles: Pfizer: Honoraria, Research Funding; Alnylam: Honoraria, Research Funding; Eidos: Research Funding. Sanchorawala: Pfizer: Honoraria; Takeda: Research Funding; Celgene: Research Funding; Prothena: Membership on an entity's Board of Directors or advisory committees, Research Funding; Abbvie: Membership on an entity's Board of Directors or advisory committees; Regeneron: Membership on an entity's Board of Directors or advisory committees; Proclara: Membership on an entity's Board of Directors or advisory committees; Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Oncopeptide: Research Funding; Karyopharm: Research Funding; Sorrento: Research Funding; Caelum: Membership on an entity's Board of Directors or advisory committees, 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. Cibeira: Celgene: Honoraria; Amgen: Honoraria, Membership on an entity's Board of Directors or advisory committees; Akcea: 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. Wechalekar: Caelum Biosciences: Other: Clinical Trial Funding; Amgen: Research Funding; Janssen: Consultancy; Celgene: Honoraria; Takeda: Honoraria; Alexion, AstraZeneca Rare Disease: Consultancy. Gertz: Akcea Therapeutics, Alnylam Pharmaceuticals Inc, Prothena: Consultancy; Aurora Biopharma: Other: Stock option; Ionis Pharmaceuticals: Other: Advisory Board; AbbVie Inc, Celgene Corporation: Other: Data Safetly & Monitoring; Akcea Therapeutics, Ambry Genetics, Amgen Inc, Celgene Corporation, Janssen Biotech Inc, Karyopharm Therapeutics, Pfizer Inc (to Institution), Sanofi Genzyme: Honoraria.

Background Focal amplifications and rearrangements drive tumor growth and evolution in cancer. Focally amplified regions often involve the juxtaposition of rearranged segments of DNA from distinct chromosomal loci into a single amplified region and nearly half of these regions can be explained by circular, extrachromosomal DNA (ecDNA) formation. Cancer-associated ecDNA shows a unique circular placing ecDNA at the interface of cancer genomics and epigenetics. As formation of ecDNA represents a manifestation of genomic instability, we have investigated presence and prognostic impact of ecDNA in multiple myeloma (MM). Methods Whole genome (WGS) and transcriptome (RNAseq) sequencing data from CD138 purified MM cells from 191 uniformly-treated newly diagnosed MM patients were used for this analysis. Copy number variants (CNV), single nucleotide variants (SNV) and structural variants (SV) were identified on all WGS samples using Facets, Mutect2 and Manta. Seed data from these CNV results was passed to the AmpliconArchitect tool to determine presence of focally amplified and rearranged segments of DNA. Seed CNV thresholds were set for a minimum CNV size of 100kb and a copy number of equal or greater to 5. Extrachromosomal calls were then annotated using the Amplicon Classifier to determine the presence of ecDNA. Multivariate survival analysis was performed after segregating samples into the conventional myeloma risk classifications including translocations, copy number alterations, ISS, age and mutations associated with risk. Differential expression analysis was performed on transcriptomic data using DEseq2. Results We identified 6.8% of the newly diagnosed patients with ecDNA, 12.5% with complex non-cyclic DNA amplifications and 10.1% with linear amplifications. ecDNA and complex events were targeting MM dependent genes, including MYC/PVT1, IRF4 as well as known driver genes such as CDYL and TRAF2. We further evaluated association between ecDNA, complex rearrangements, linear amplification and patients with none of these amplification types and found that patients with ecDNA had significantly poor PFS (median PFS 22 months vs. 41 months) and OS (median OS 41 months vs. 105 months). Patients having ecDNA in their MM cells did not show any significant enrichment for known translocations, double hit or TP53 mutations. In a multivariate model including ecDNA and all other known MM risk features, ecDNA was found to be an independent predictor of progression free survival.(HR 2.6, CI: 1.26 -5.6, p=0.0082) and overall survival (HR 7.94 CI:3.5-17.9 p < 0.0001). Patients with ecDNA have higher mutational load probability(8798 vs 6982, effect size = 0.64 , probability is 91.1). However, this was not reflected in heterogeneity by using MATH score. We found that patients with ecDNA are likely to have BRAF mutations (OR= 25.07 [2.57 - 330 95% CI], p value = 0.002), however overall RAS/RAF pathway mutations were similar to other patients. Patients with ecDNA showed fragile DNA with more breaks (median segments 197 vs. 125.5, p value = 0.001). Although ecDNA is defined as copy number gain with fragments having 5 or more copies, overall genomic gain between ecDNA and other patients were similar. However, overall genomic loss in patients with ecDNA were higher than others (7% vs. 4.2%, p = 0.06). By differential gene expression analysis we noted 98 differentially expressed genes in MM cells with ecDNA. The downregulated geneset involved pathways responsible for cell death as well as the RAS pathway. Interestingly, CD38 was upregulated in the ecDNA dataset suggesting greater potential for CD38 targeting therapies in these patients. Conclusions ecDNA, as an unique marker of perturbed genomic integrity, is observed in a subset of patients and is an independent prognostic marker in newly diagnosed MM patients. As patients with ecDNA are not fully captured by other risk features its incorporation in an expanded definition of a high risk group of multiple myeloma should be investigated. Future studies will endeavor to explore the biological mechanism through which ecDNA are formed and influences outcomes in myeloma. Figure 1 Figure 1. Disclosures Richardson: Sanofi: Consultancy; GlaxoSmithKline: Consultancy; Karyopharm: Consultancy, Research Funding; AstraZeneca: Consultancy; AbbVie: Consultancy; Oncopeptides: Consultancy, Research Funding; Takeda: Consultancy, Research Funding; Janssen: Consultancy; Protocol Intelligence: Consultancy; Celgene/BMS: Consultancy, Research Funding; Secura Bio: Consultancy; Regeneron: Consultancy; Jazz Pharmaceuticals: Consultancy, Research Funding. Perrot: Abbvie: Honoraria; Amgen: Honoraria, Membership on an entity's Board of Directors or advisory committees; Celgene/BMS: Honoraria, Membership on an entity's Board of Directors or advisory committees; GSK: 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; Sanofi: Honoraria; Takeda: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding. Moreau: Abbvie: Honoraria; Amgen: Honoraria; Janssen: Honoraria; Sanofi: Honoraria; Celgene BMS: Honoraria; Oncopeptides: Honoraria. Thakurta: Oxford University: Other: Visiting Professor; BMS: Current Employment, Current equity holder in publicly-traded company. Anderson: Gilead: Membership on an entity's Board of Directors or advisory committees; Millenium-Takeda: Membership on an entity's Board of Directors or advisory committees; Pfizer: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees; Sanofi-Aventis: Membership on an entity's Board of Directors or advisory committees; Janssen: Membership on an entity's Board of Directors or advisory committees; Bristol Myers Squibb: Membership on an entity's Board of Directors or advisory committees; AstraZeneca: Membership on an entity's Board of Directors or advisory committees; Scientific Founder of Oncopep and C4 Therapeutics: Current equity holder in publicly-traded company, Current holder of individual stocks in a privately-held company; Mana Therapeutics: Membership on an entity's Board of Directors or advisory committees. Munshi: Legend: Consultancy; Karyopharm: Consultancy; Takeda: Consultancy; Janssen: Consultancy; Novartis: Consultancy; Bristol-Myers Squibb: Consultancy; Amgen: Consultancy; Abbvie: Consultancy; Adaptive Biotechnology: Consultancy; Oncopep: Consultancy, Current equity holder in publicly-traded company, Other: scientific founder, Patents & Royalties; Celgene: Consultancy; Pfizer: Consultancy.

Introduction: Stromal cells in the bone marrow microenvironment maintain a complex bidirectional relationship with the malignant plasma cells and have been implicated in the growth and survival of multiple myeloma (MM) cells along with the development of drug resistance and disease progression. We hypothesized that the perpetual induction of gene expression changes similar to those induced by stromal interactions may lead to an aggressive disease phenotype and possible stromal independence. We investigated the chromatin remodeling and associated gene expression changes in malignant plasma cells induced by stromal cells and applied the findings to large sets of patient data. Methods: We interrogated the chromatin accessibility (ATAC-seq) and gene expression profiles (RNA-seq) of malignant plasma cells (INA6, MM1S, RPMI) after co-culture with stromal cells (HS5). We identified de novo accessible enhancers and associated them with their putative target genes within conserved topologically associating domains based on their proximity in the linear genome. The in vitro data were then applied to data from patients with newly diagnosed MM (n=559). Using proportional hazards regression and automated feature selection algorithms we selected 10 of the 68 overexpressed target genes based on their association with progression-free survival. We devised a dichotomous classifier based on the expression of these 10 genes and validated its independent prognostic significance and ability to predict therapeutic resistance in two additional patient cohorts (n=214 and n=635, respectively). We applied gene set enrichment analysis to interrogate whether the gene expression profile induced by these adverse stromal interactions recapitulates the transcriptional program of extramedullary malignant plasma cells using single-cell RNA-seq. Results: Co-culture of malignant plasma cells with stromal cells induced widespread chromatin remodeling including de novo accessibility of known cis-regulatory elements and the overexpression of their putative target genes (68 overexpressed genes with associated accessible enhancers, overlap p=1.05x10 -13). The expression of 10 of these 68 genes was independently associated with progression-free survival in the UAMS TT2/3 data set (n=559). A summary measure of the expression of these 10 genes was dichotomized to classify approximately 30% of patients as having adverse stromal interactions (ASI+). The 10-gene ASI+ classifier was associated with overall survival in three independent patient cohorts with newly diagnosed MM: UAMS TT2/3 (29% ASI+, HR 1.93, 95% CI 1.43-2.62, p Conclusions: We identified permissive chromatin states and associated transcriptional programs induced by the interactions between MM cells and bone marrow stromal cells. The constitutive induction of genes in the malignant plasma cells of patients with newly diagnosed MM supported transcriptional programs associated with therapeutic resistance, accelerated disease dissemination, and impaired long-term survival. These changes may allow MM cells to acquire the capacity to sustain extramedullary growth. The development of novel therapeutic approaches to overcome these adverse stromal interactions remains an unmet need in patients with MM. Disclosures Munshi: Adaptive Biotechnology: Consultancy; Karyopharm: Consultancy; Abbvie: Consultancy; Bristol-Myers Squibb: Consultancy; Celgene: Consultancy; Janssen: Consultancy; Takeda: Consultancy; Amgen: Consultancy; Oncopep: Consultancy, Current equity holder in publicly-traded company, Other: scientific founder, Patents & Royalties; Novartis: Consultancy; Legend: Consultancy; Pfizer: Consultancy.

Background: Two prospective phase II studies evaluated the efficacy of the anti-CD38 monoclonal antibody Daratumumab as a single agent (sDARA) in patients with relapsed AL amyloidosis and reported impressive hematologic and organ response rates. However, both studies included small number of patients and in one study sDARA was administered for 6 cycles (n=40) while in the other study, it was administered until hematologic progression, toxicity or for up to 24 months (n=22). Furthermore, predictive factors associated with hematologic and organ responses, and optimal duration of therapy remain unclear. Methods: We retrospectively studied the clinical and biological characteristics of patients with AL amyloidosis treated with sDARA, between April 2017 and December 2020, to identify factors associated with hematologic and organ response rates and impact on progression-free survival and overall survival (OS). Criteria for hematologic and organ response were defined according to the consensus criteria of ISA. Major organ deterioration progression-free survival (MOD-PFS) was used as a composite of endpoints from the time between sDARA initiation and whichever of the following occurred first: death, development of end stage cardiac or renal failure, or hematologic progression. OS was defined as the length of time between initiation of sDARA and the date of death or last follow-up. To account for immortal time bias, a landmark analysis for MOD-PFS and OS starting at the 12-month mark was performed to evaluate the impact of treatment duration on outcomes. Results: Eighty six patients with AL amyloidosis received sDARA. The median age was 67 years (range, 39-86) and 65% were male, 75% with lambda AL amyloidosis. Of the 86 patients, 38% had t(11;14) and 11% had 1q gain. At time of sDARA initiation, 35% of patients had >2 organs involved, including 22% with BU stage III (14% IIIa and 8% IIIb) and 13% with renal stage III disease. Majority (44%) of the patients received sDara as 2 nd line therapy, but 2% received as frontline, 21% as 3 rd line and 33% as 4 th line or more. Half of the patients (45%) were previously treated with HDM/SCT and 87% with proteasome inhibitor based regimen. Patients received a median of 12 cycles of sDARA (range 1-36) with a median follow-up time of 21.6 months (range 1.2-48.3). Hematologic CR and VGPR were achieved by 44% and 37% of patients, respectively and significantly associated with prolonged MOD-PFS and OS (Figure A and B). The median time to cardiac and renal responses were 6.8 months (range 0.9-12) and 6.7 months (range 1.8-42), respectively. At 12 months, cardiac and renal responses were observed in 47% and 61%, respectively and correlated with depth of hematologic response. The median OS and MOD-PFS were not reached (95% CI 40-NR) and 36 months (95% CI 28-NR), respectively. Achievement of cardiac response was associated with improved MOD-PFS (42 vs. 25 months; HR 0.25, 95% CI 0.08-0.78; p=0.01) and OS (NR vs 26 months; HR 0.20, 95% CI 0.04-0.89; p=0.02) and achievement of renal response was associated with improved MOD-PFS (NR vs. 13 months; HR 0.06, 95% CI 0.02-0.20; p180 mg/L, bone marrow infiltration >10%, 24h proteinuria >3.5 g and NTproBNP >8500 pg/mL were significantly associated with lower MOD-PFS and OS, but only achievement of VGPR or CR and presence of 1q Gain were independently associated with MOD-PFS and OS (Figure C and D) on a multivariate analysis. Finally, on a landmark analysis, patients who received >12 cycles vs Conclusion: sDARA confers very high rates of hematologic responses (81% of patients achieving >VGPR) in patients with relapsed AL amyloidosis and leads to prolonged OS and MOD-PFS, which is independent of the number of previous lines of treatment. Our data confirmed that achievement of hematologic response is a major predictor of OS and MOD-PFS in AL amyloidosis, and revealed that presence of 1q Gain is associated with lower response rate to sDARA. Longer duration of therapy (>12 cycles) with sDARA was associated with prolonged MOD-PFS and OS. Figure 1 Figure 1. Disclosures Sloan: Stemline: Honoraria; Abbvie: Honoraria; Astra Zeneca: Membership on an entity's Board of Directors or advisory committees; Pharmacosmos: Membership on an entity's Board of Directors or advisory committees. Sanchorawala: Karyopharm: Research Funding; Pfizer: Honoraria; Regeneron: Membership on an entity's Board of Directors or advisory committees; Takeda: Membership on an entity's Board of Directors or advisory committees, Research Funding; Prothena: Membership on an entity's Board of Directors or advisory committees, Research Funding; Proclara: Membership on an entity's Board of Directors or advisory committees; Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene: Research Funding; Caleum: Membership on an entity's Board of Directors or advisory committees, Research Funding; Sorrento: Research Funding.

On an average, 1% of monoclonal gammopathy of undermined significance (MGUS) and 10% of smoldering Multiple Myeloma (SMM) progress to symptomatic MM every year within the first five years of diagnosis. The probability of progression significantly decreases for SMM patients after first 5 years. However, a distinct subset of SMM patients progress within 2 years and are re-classified as high-risk patients based on risk markers such as 20/2/20 or certain genomic features. Although recent studies have evaluated the high-risk genomic features for SMM but genomic background of SMM patients who do not progress to MM after long-term follow-up (>= 5 years) has not been described. Here, we evaluated transcriptomic and genomic changes enriched in non-progressor (NP) (no progression after 5 years of follow-up) precursor conditions (N=31) with those progressed within short period of time (N=71) and compared them with changes observed in newly diagnosed MM (N=192). Additionally, using transcriptome, epigenome and whole genome profiling we also studied additional unique samples from 18 patients at their precursor stage as well as when progressed to MM. Overall, we have observed significantly lower mutational load for NP SMM from progressor SMM (median SNV 4900 vs. 7881 p < 3e-04) with high sensitivity (0.83) and specificity (0.65) to separate NP from progressors. We have further developed a deep learning model by using more than 4500 genome wide features using ten-fold cross validation. This model indicated that not only the load but also the patterns of mutations (type, location, frequency) are different between two conditions. We also found that NP samples have significantly lower heterogeneity (p < 0.05). However, progressed samples showed similar mutational load and heterogeneity at precursor stage and MM. Among CNA differences, absence of gain or deletion of chr8 (not involving MYC region) were strong predictor of NP (OR=7.2 95% CI 2.2-24). Focal genomic loss was also significantly lower for NP (p=0.004) which was also reflected by low genome scar score (GSS) (p=0.07). Structural variant and copy number signature analysis also showed that NPs were showing significantly low exposure to non-clustered variable size genomic deletions. We observed similar frequency of primary translocations [t(11;14), t(4;14), and t(14;16)] in both progressor and NP samples as well as newly diagnosed MM. MYC translocation with any partner was not observed in NP samples, whereas 37% of progressor samples had a MYC translocations (OR=12.8). Adding all these differences including chr8 CNAs, MYC translocations, mutation burden, GSS, focal deletions, all driver mutations as well as primary translocations into recursive partitioning model to predict non-progressor SMM, we have identified a simple genomic model only involving chr8 CN changes and overall mutational burden to achieve a high sensitivity (0.82) and specificity (74%). Our transcriptomic analysis measured the distance between progressor and NP SMM as well as MM and found that NP SMM has greater difference with MM which is closer to progressor SMM. We quantified transcriptomic heterogeneity by using molecular degree of perturbation. This analysis showed that consistent with DNA changes, DNA repair pathway and MYC target genes are expressed similarly in NP SMM as in normal plasma cells compared to progressor SMM. Epigenomic analysis yielded 75 SEs regions differentially utilized between precursor and symptomatic MM stage using paired samples. The targeted genes included BMP6, PRDM1, STAT1, SERTAD2 and RAB21 and possibly regulating genes related to oncogenic KRAS activities. In conclusion, we define genomic characterization of non-progressor SMM and our results now provide the basis to develop molecular definition of SMM as well as risk driving features. Disclosures Munshi: Janssen: Consultancy; Pfizer: Consultancy; Legend: Consultancy; Novartis: Consultancy; Adaptive Biotechnology: Consultancy; Oncopep: Consultancy, Current equity holder in publicly-traded company, Other: scientific founder, Patents & Royalties; Takeda: Consultancy; Abbvie: Consultancy; Karyopharm: Consultancy; Amgen: Consultancy; Celgene: Consultancy; Bristol-Myers Squibb: Consultancy.

Introduction: Binary cardiac response assessment using NT-proBNP is prognostic in light chain (AL) amyloidosis. Previous studies suggested that refining the criteria to multi-level cardiac responses improves prognostic prediction. We validate a graded cardiac response assessment tool in AL amyloidosis using NT-proBNP or BNP. Methods: In this retrospective, multicenter study AL amyloidosis patients who were diagnosed between 2010 and 2015, achieving at least a hematological partial response (PR) within 12 months of diagnosis and were evaluable for cardiac response (defined as baseline NT-proBNP >650 pg/mL or BNP >150 pg/mL) were included. The following response criteria were tested: cardiac complete response (carCR, nadir NT-proBNP≤350 pg/mL or BNP≤80 pg/mL); cardiac very good partial response (carVGPR, >60% reduction in NT-proBNP/BNP); Cardiac PR (carPR 31-60% reduction); and cardiac non response (carNR, ≤30% reduction). Response was assessed at fixed time points (6, 12 and 24 months from therapy initiation) and at best response. The primary outcome was overall survival based on depth of cardiac response. Multivariate Cox proportional models were analyzed to determine independent prognostic factors for OS and time to cardiac progression using variables with p-value Results: Six hundred and fifty-one patients were included. The median age was 64 years. Mayo 2004 cardiac stage II, IIIA and IIIB was present in 47.5%, 38% and 14.5% of patients, respectively (by definition, patients with Mayo stage I do not have cardiac amyloidosis evaluable for response). Seventy-six percent of patients received only one line of therapy within the initial 12 months of diagnosis. Bortezomib-based therapy was the most common (70.2% of patients) followed by autologous stem cell transplantation (ASCT) in 15.7% of patients. Hematological CR, hematological VGPR and hematological PR as best response was achieved in 38%, 39% and 23% of patients, respectively. Forty-three percent of the patients have died, with 36% of the patients dying within 5-years of diagnosis. The median follow-up of the surviving patients is 70 months (IQR 56-84). Cardiac response was evaluable using NT-proBNP in 494 patients (75.9%), BNP in 109 patients (16.7%) and both NT-proBNP and BNP in 48 patients (7.4%). The latter two were grouped together for further analysis. The median time to best cardiac response among responders was 12 months (IQR 7-21 months; 18 months for carCR, 11.5 months for carVGPR and 9.5 months for carPR). Cardiac response improved over time with a median percentage reduction in NT-proBNP/BNP compared to baseline of 15%, 37% and 54%, at 6, 12 and 24 months respectively. Cardiac responses at 6-, 12- and 24-months are depicted in Figure 1A. At best cardiac response, carCR, carVGPR, carPR and carNR were achieved in 16%, 26%, 23% and 35% of patients, respectively. Patients who achieved a carCR had lower cardiac stage at diagnosis compared to patients who achieved carVGPR or carPR (stage II 65% vs 47% vs 47%, respectively; P30% rise in NT-proBNP/BNP) in multivariate analysis that included age, type of first line therapy, light chain burden, cardiac stage, and hematological response. Conclusions: We validated the prognostic value of graded cardiac response. These response criteria allow better discrimination of patient populations and assessment of treatment effectiveness in an era of improved therapies for AL amyloidosis. The study emphasizes the importance of early diagnosis which increases the likelihood of deep and durable cardiac responses. Figure 1 Figure 1. Disclosures Dispenzieri: Oncopeptides: Consultancy; Pfizer: Research Funding; Sorrento Therapeutics: Consultancy; Takeda: Research Funding; Alnylam: Research Funding; Janssen: Consultancy, Research Funding. Palladini: Siemens: Honoraria; Pfizer: Honoraria; Janssen Global Services: Honoraria, Other: advisory board fees. Schönland: Janssen: Honoraria, Other: Travel grants, Research Funding; Takeda: Honoraria, Other: Travel grants; Sanofi: Research Funding; Pfizer: Honoraria; Prothena: Honoraria, Other: Travel grants. Hegenbart: Alnylam: Honoraria; Janssen: Consultancy, Research Funding; Prothena: Research Funding; Pfizer: Consultancy, Honoraria; Akcea: Honoraria. Kumar: Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Carsgen: Research Funding; Novartis: Research Funding; Merck: Research Funding; Tenebio: Research Funding; Bluebird Bio: Consultancy; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; Astra-Zeneca: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Roche-Genentech: Consultancy, Research Funding; Antengene: Consultancy, Honoraria; Oncopeptides: Consultancy; Amgen: Consultancy, Research Funding; KITE: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Beigene: Consultancy; Janssen: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Abbvie: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; BMS: Consultancy, Research Funding; Adaptive: Membership on an entity's Board of Directors or advisory committees, Research Funding; Sanofi: Research Funding. Kastritis: Genesis Pharma: Honoraria; Takeda: Honoraria; Janssen: Consultancy, Honoraria, Research Funding; Pfizer: Consultancy, Honoraria, Research Funding; Amgen: Consultancy, Honoraria, Research Funding. Dimopoulos: Beigene: Honoraria; Takeda: Honoraria; BMS: Honoraria; Janssen: Honoraria; Amgen: Honoraria. Liedtke: GlaxoSmithKline: Membership on an entity's Board of Directors or advisory committees; Kite: Membership on an entity's Board of Directors or advisory committees; Karyopharm: Membership on an entity's Board of Directors or advisory committees; Takeda: Membership on an entity's Board of Directors or advisory committees; Janssen Pharmaceuticals: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees; Pfizer: Honoraria; Sanofi: Membership on an entity's Board of Directors or advisory committees; Alnylam: Membership on an entity's Board of Directors or advisory committees; Caelum: Membership on an entity's Board of Directors or advisory committees, Other: Clinical Trial Funding; Bristol Myers Squibb: Membership on an entity's Board of Directors or advisory committees; Oncopeptides: Membership on an entity's Board of Directors or advisory committees; Kura Oncology: Membership on an entity's Board of Directors or advisory committees. Witteles: Eidos: Research Funding; Alnylam: Honoraria, Research Funding; Pfizer: Honoraria, Research Funding. Sanchorawala: Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene: Research Funding; Proclara: Membership on an entity's Board of Directors or advisory committees; Prothena: Membership on an entity's Board of Directors or advisory committees, Research Funding; Abbvie: Membership on an entity's Board of Directors or advisory committees; Caelum: Membership on an entity's Board of Directors or advisory committees, Research Funding; Regeneron: Membership on an entity's Board of Directors or advisory committees; Takeda: Research Funding; Karyopharm: Research Funding; Oncopeptide: Research Funding; Pfizer: Honoraria; Sorrento: Research Funding. Landau: Genzyme: Honoraria; Takeda: Research Funding; Takeda, Janssen, Caelum Biosciences, Celgene, Pfizer, Genzyme: Membership on an entity's Board of Directors or advisory committees. Cibeira: Amgen: Honoraria, Membership on an entity's Board of Directors or advisory committees; Akcea: Honoraria, Membership on an entity's Board of Directors or advisory committees; Celgene: Honoraria; Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees. Gertz: Akcea Therapeutics, Ambry Genetics, Amgen Inc, Celgene Corporation, Janssen Biotech Inc, Karyopharm Therapeutics, Pfizer Inc (to Institution), Sanofi Genzyme: Honoraria; Akcea Therapeutics, Alnylam Pharmaceuticals Inc, Prothena: Consultancy; AbbVie Inc, Celgene Corporation: Other: Data Safetly & Monitoring; Ionis Pharmaceuticals: Other: Advisory Board; Aurora Biopharma: Other: Stock option.

Multiple myeloma (MM) is a complex and heterogenous disease which is dependent on the surrounding microenvironment for growth and survival. In MM, dysregulation of transcriptional control is a major driver of tumor transformation and progression. To evaluate transcriptional programs activated in MM cells in the context of the bone marrow milieu, we have performed extensive transcriptomic analysis by RNA-seq and ATAC-seq using bone marrow stromal cells (BMSC) derived from MM patients and stromal cell line (HS5) in co-culture with various MM cell lines. We observed that both cell-cell interactions and soluble factors secreted by BMSC or HS5 cells significantly downregulated expression of Inhibitor of DNA Binding 2 (ID2), while footprint analysis of the open chromatin regions in MM cells upon interaction with BMSC revealed enrichment for binding motifs of the TCF family of transcription factors (E proteins). Inhibitors of DNA binding (ID) proteins control crucial transcriptional programs in B cell maturation via their heterodimerization with E proteins which are members of the basic helix-loop-helix (bHLH) class I family of transcription factors, repressing their DNA binding and therefore transcriptional activity. We found that ID2 expression is significantly lower in primary CD138+ MM cells from patients compared to normal plasma cells (NPCs). Moreover, we have previously implicated the B-cell factor TCF3 as a novel MM dependency. Using MM cell lines and primary samples, we observed elevated enhancer activity at TCF3 locus in primary malignant plasma cells compared to NPCs, which resulted in significant upregulation of TCF3 expression in MM patients. We also showed that TCF3 is regulated by a large proximal enhancer that is bound by MYC and is highly sensitive to chemical perturbation of enhancer co-activators such as BRD4. Genetic perturbation of TCF3 confirmed its critical role on MM cell growth and viability especially in IgH MYC translocated MM cell lines. We here further explored the role of ID2-TCF3 axis and the hypothesis that lower expression of ID2 drives higher TCF3 activity in MM cells, which is further enhanced in presence of the bone marrow microenvironment. Genetic modulation of ID2 significantly affected MM cell viability, with MM cells ectopically expressing ID2 displaying a cell growth arrest even in the presence of the supportive BM milieu. To define the mechanism of the observed oncosuppressive role of ID2 in MM, which is in line with preliminary observations in other hematological malignancy but in contrast with the pro-tumoral role described in solid tumors, we first performed immunoprecipitation of ID2 followed by mass spectrometry in 3 MM cell lines, and identified a very consistent and specific interaction with E proteins TCF3 and TCF12. Next, to explore the transcriptional programs dependent on ID2 we performed RNA-seq of 2 MM cell lines after ID2 overexpression. In line with our in vitro data, gene ontology and gene set enrichment analysis showed a significant downregulation of genes involved in E2F pathway, cell cycle progression and regulation of gene transcription. Interestingly, among the known TCF3 targets in B cells, only XBP-1 was significantly downregulated in MM cells after ID2 overexpression, suggesting the existence of a cell-specific TCF3 dependent transcriptional program in MM. Indeed, ATAC-seq experiments revealed ID2 overexpression led to a significant decrease of TCF3 binding motifs in open chromatin regions, confirming the relevance of ID2 in regulating TCF3 transcriptional activity in MM. In conclusion, while both E and ID proteins have been implicated in malignant transformation, their role in supporting MM transcriptional deregulation and tumor growth in the context of the microenvironment is being defined. Here, we have identified ID2 as a major regulator of the TCF3 dependent transcriptional program in MM, whose downregulation is essential to maintain MM proliferation and to mediate the benefits induced by MM-stroma interaction. Disclosures Anderson: Millenium-Takeda: Membership on an entity's Board of Directors or advisory committees; Sanofi-Aventis: Membership on an entity's Board of Directors or advisory committees; Gilead: Membership on an entity's Board of Directors or advisory committees; Janssen: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees; Bristol Myers Squibb: Membership on an entity's Board of Directors or advisory committees; Pfizer: Membership on an entity's Board of Directors or advisory committees; Scientific Founder of Oncopep and C4 Therapeutics: Current equity holder in publicly-traded company, Current holder of individual stocks in a privately-held company; AstraZeneca: Membership on an entity's Board of Directors or advisory committees; Mana Therapeutics: Membership on an entity's Board of Directors or advisory committees. Munshi: Janssen: Consultancy; Amgen: Consultancy; Takeda: Consultancy; Abbvie: Consultancy; Oncopep: Consultancy, Current equity holder in publicly-traded company, Other: scientific founder, Patents & Royalties; Celgene: Consultancy; Karyopharm: Consultancy; Adaptive Biotechnology: Consultancy; Novartis: Consultancy; Legend: Consultancy; Pfizer: Consultancy; Bristol-Myers Squibb: Consultancy.

Background On average 10% of SMM patients progress to symptomatic MM per year with in first 5 years of diagnosis. However, a subset of SMM patients re-classified as high risk patients on the basis of risk markers which identify risk of progression within 2 years. Although recent studies have evaluated the high-risk SMM, genomic background of SMM patients who do not progress to MM after long-term follow-up (>=5 years) has not been described. Methods Here, we evaluated transcriptomic and genomic changes enriched in non-progressor (NP) (no progression after 5 years of follow-up) precursor conditions (N=31) with those progressed within short period time (N=71) and compared them with changes observed in newly diagnosed MM (N=192). Additionally, using transcriptome, epigenome and whole genome profiling we also studied additional unique samples from 18 patients at their precursor stage as well as when progressed to MM. Results Overall, we have observed significantly lower mutational load for NP SMM (median SNV 5460 vs. 7018, p 90% accuracy and >0.80 area under the curve on ROC using ten-fold cross validation. This indicated that not only the load but also the patterns of mutations (type, location, frequency) are different between two conditions. We also found that NP samples have significantly lower heterogeneity (p 80%) with AUC score 0.80. Our transcriptomic analysis measured the distance between progressor and NP SMM as well as MM and found that NP SMM are less similar to MM which is closer to progressor SMM. Epigenomic analysis yield that 75 SEs regions were differentially utilized between precursor and symptomatic MM stage. The targeted genes included BMP6, PRDM1, STAT1 and RAB21 and possibly regulating genes related to oncogenic KRAS activities. Conclusions In conclusion, our results now provide the basis to develop genomic definition of SMM as well as risk driving features.

We recently shown that high-dose melphalan (HDM) followed by autologous stem cell transplant (ASCT) as first line therapy in young ( A significant change in frequency of driver mutations including RAS/RAF, FAM46C, TP53, and DIS3 was not observed at the time of relapse. Clonality level was increased only for KRAS (p=0.054), while all other specific driver genes had similar clonality level at diagnosis and relapse. Interestingly, a significant increase in mutations involving MYO16 and SLC7A8 genes was observed at relapse in both arms, implicating components of the induction regimen (RVD). Investigating the mutational signature utilization in only newly acquired mutations identified 4 signatures: APOBEC, HR Double Strand Repair, clock-like signature, and unknown. k-means clustering analysis of samples based on signature utilization showed four distinct clusters. All patients clustering with high DNA repair signature utilization were in the HDM arm (65% HDM patients), the majority of whom achieved CR or sCR (74%); these patients acquired 8308 (range 3302-19107) new mutations between diagnosis and relapse. None of the RVD only treated patients were in this cluster. The remaining 35% HDM group patients were clustered with RVD samples and showed unknown signature utilization. Furthermore, motif enrichment analysis identified CYWR and ATGAGATV (p < 1e-130) as enriched motifs around the new mutations in HDM compared to RVD cohort. Importantly and as expected, DNA damage repair pathway genes were frequently targeted in the HDM group: 72% HDM samples accumulated DDR gene mutations vs. only 17% in the RVD alone arm (p < 0.001). At the time of relapse, 100% HDM arm patients had at least one DDR gene mutation and 80% had two or more, while only 37% RVD only group had one or more such mutation. Finally, we have reconstructed phylogenetic and evolutionary trajectories based on mutation and copy-number data from samples at diagnosis and relapse. The clonal composition in both arms was similar at diagnosis; however, HDM caused a significant shift to more subclonal mutations at relapse. chromothripsis and chromoplexy events were detected in 30% patients at diagnosis, which remained constant at relapse regardless of treatment. In summary, we describe significant accumulation of mutations following high dose melphalan. This fundamental molecular change in the disease at relapse, suggests the need for reappraisal of the optimal use and sequencing of high dose melphalan in the era of novel agents. Disclosures Fulciniti: NIH: Research Funding. Richardson:Celgene/BMS, Oncopeptides, Takeda, Karyopharm: Research Funding. Thakurta:Oxford University: Other: visiting professor; Bristol Myers Squibb: Current Employment, Current equity holder in publicly-traded company. Perrot:Amgen, BMS/Celgene, Janssen, Sanofi, Takeda: Consultancy, Honoraria, Research Funding. Moreau:Sanofi: Consultancy, Honoraria; Celgene/Bristol-Myers Squibb: Consultancy, Honoraria; Janssen: Consultancy, Honoraria; Takeda: Honoraria; Novartis: Honoraria; Abbvie: Consultancy, Honoraria; Amgen: Consultancy, Honoraria. Anderson:Bristol Myers Squibb: Membership on an entity's Board of Directors or advisory committees; Oncopep and C4 Therapeutics.: Other: Scientific Founder of Oncopep and C4 Therapeutics.; Sanofi-Aventis: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees; Millenium-Takeda: Membership on an entity's Board of Directors or advisory committees; Gilead: Membership on an entity's Board of Directors or advisory committees; Janssen: Membership on an entity's Board of Directors or advisory committees. Parmigiani:Phaeno Biotehnologies: Current equity holder in publicly-traded company; CRA Health: Current equity holder in publicly-traded company; Foundation Medicine Institute: Consultancy; Delphi Diagnostics: Consultancy; BayesMendel Laboratory: Other: Co-lead. Munshi:Amgen: Consultancy; AbbVie: Consultancy; Karyopharm: Consultancy; Takeda: Consultancy; Adaptive: Consultancy; Janssen: Consultancy; C4: Current equity holder in private company; OncoPep: Consultancy, Current equity holder in private company, Membership on an entity's Board of Directors or advisory committees, Patents & Royalties; BMS: Consultancy; Legend: Consultancy.

Multiple myeloma (MM) is a proliferation of terminally differentiated plasma cells (PC) producing monoclonal immunoglobulins (Ig), most commonly IgG and IgA (50% and 25% respectively), and less frequently, light-chain only disease, non-secretory, and IgD. IgM-MM is a rare entity ( We performed deep whole-genome sequencing on five IgM samples as well as 211 MM and 34 WM samples, and transcriptome sequencing on the IgM samples as well as 30 MM, 35 WM, and 3 PC. All IgM-MM samples harbored t(11;14) which combines super enhancers in Ig genes with CCND1. All translocations involved VHDHJH regions (Figure 1A) at the immunoglobulin heavy chain (IGH) locus, compared to IgG/IgA MM samples that had predominantly switch-region translocations (Figure 1B/C). Switch-region translocations are generated through class-switch recombination (CSR) in mature B-cells in germinal centers (GC), and VHDHJH translocations occur during recombination at the early pro-B-cell stage in the bone marrow (BM). While IgG/IgA-MM displayed evidence of CSR with deletions between IGHM switch-region and IGHG/IGHA switch regions, IgM-MM had no such events. IgM-MM therefore appears to undergo malignant transformation prior to late-stage B-cell maturation, after which CSR is unlikely, which is supported by a lack of progression of IgM-monoclonal gammopathy of undetermined significance (MGUS) to non-IgM-MM. IgM-MM also displayed similar copy number variation (CNV) patterns and driver mutations compared to non-IgM-MM suggesting similar progression events. Unsupervised hierarchical clustering using differentially expressed genes between non-IgM-MM and WM separated the IgM-MM samples within non-IgM-MM. This indicates a closer molecular homology to MM compared to WM with a unique signature for this group not accounted for by the t(11;14) translocation. Running the same analysis using only B-cell specific transcription factors (TFs) yielded similar results, with separation of WM and MM and preferential clustering of IgM-MM within the latter while also exhibiting a unique signature (Figure 1D). Some noteworthy examples were the upregulation of PBX3, PAX5, BCL11A, and ATF2, and the downregulation of PRDM1 and BCL3 compared to non-IgM-MM. The loss of PAX5 and upregulation of PRDM1 in B-cells has been implicated in promoting commitment to PC differentiation, while BCL11A was found essential for early B-cell progenitor development through the GC but extinguished in terminally differentiated PC. It appears that IgM-MM has therefore a more immature phenotype compared with non-IgM-MM, which further supports the previously discussed findings of its pre-GC origin and lack of terminal development. Three clinically relevant targets were noted to be upregulated in IgM-MM, Bruton's tyrosine kinase (BTK), CD20 and BCL-2. BTK was significantly higher in IgM-MM compared to non-IgM-MM (log2fold=1.3; FDR0.2). This could elucidate a more prominent role for BTK-inhibition in the IgM-MM subgroup. Furthermore, as documented in t(11;14)-MM, IgM-MM had elevated transcript levels of CD20 with possible targeting using anti-CD20 antibodies. Finally, elevated levels of BCL-2 in both IgM and non-IgM-t(11;14)-MM were observed, an established target for both single-agent and combination therapy. Interestingly, although not significant, IgM-MM had lower transcript levels of BCL-XL and MCL-1 compared to non-IgM-t(11;14)-MM, believed to be a predictor of higher sensitivity to venetoclax, and therefore an important guide for treatment choice. Clinical data however is lacking, and further investigations are needed to fully understand the potential role of these drugs in treating IgM-MM. In summary we describe a unique genomic and transcriptomic profile of IgM-MM, compared to both non-IgM-MM and WM, that describes its cellular origin and provides the rationale for potential therapeutic intervention. Disclosures Fulciniti: NIH: Research Funding. Anderson:Gilead: Membership on an entity's Board of Directors or advisory committees; Janssen: Membership on an entity's Board of Directors or advisory committees; Sanofi-Aventis: Membership on an entity's Board of Directors or advisory committees; Bristol Myers Squibb: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees; Millenium-Takeda: Membership on an entity's Board of Directors or advisory committees; Oncopep and C4 Therapeutics.: Other: Scientific Founder of Oncopep and C4 Therapeutics.. Parmigiani:Phaeno Biotehnologies: Current equity holder in publicly-traded company; CRA Health: Current equity holder in publicly-traded company; Foundation Medicine Institute: Consultancy; Delphi Diagnostics: Consultancy; BayesMendel Laboratory: Other: Co-lead. Treon:Bristol-Meyer-Squibb: Honoraria, Research Funding; Pharmacyclics: Honoraria, Research Funding. Mohty:Novartis: Consultancy, Honoraria, Research Funding, Speakers Bureau; Celgene: Consultancy, Honoraria, Research Funding, Speakers Bureau; GSK: Consultancy, Honoraria, Research Funding, Speakers Bureau; BMS: Consultancy, Honoraria, Research Funding, Speakers Bureau; Stemline: Consultancy, Honoraria, Research Funding, Speakers Bureau; Jazz Pharmaceuticals: Consultancy, Honoraria, Research Funding, Speakers Bureau; Sanofi: Consultancy, Honoraria, Research Funding, Speakers Bureau; Takeda: Consultancy, Honoraria, Research Funding, Speakers Bureau; Amgen: Consultancy, Honoraria, Research Funding, Speakers Bureau; Janssen: Consultancy, Honoraria, Research Funding, Speakers Bureau. Munshi:Takeda: Consultancy; BMS: Consultancy; OncoPep: Consultancy, Current equity holder in private company, Membership on an entity's Board of Directors or advisory committees, Patents & Royalties; C4: Current equity holder in private company; Janssen: Consultancy; Adaptive: Consultancy; Legend: Consultancy; Amgen: Consultancy; Karyopharm: Consultancy; AbbVie: Consultancy.

Homologous recombination deficiency conferred by alterations in BRCA1 or BRCA2 are common in breast tumors and can drive sensitivity to platinum chemotherapy and PARP inhibitors. Alterations in nucleotide excision repair (NER) activity can also impact sensitivity to DNA damaging agents, but NER activity in breast cancer has been poorly characterized. Here, we apply a novel immunofluorescence-based cellular NER assay to screen a large panel of breast epithelial and cancer cell lines. Although the majority of breast cancer models are NER proficient, we identify an example of a breast cancer cell line with profound NER deficiency. We show that NER deficiency in this model is driven by epigenetic silencing of the ERCC4 gene, leading to lack of expression of the NER nuclease XPF, and that ERCC4 methylation is also strongly correlated with ERCC4 mRNA and XPF protein expression in primary breast tumors. Re-expression of XPF in the ERCC4-deficient breast cancer rescues NER deficiency and cisplatin sensitivity, but does not impact PARP inhibitor sensitivity. These findings demonstrate the potential to use functional assays to identify novel mechanisms of DNA repair deficiency and nominate NER deficiency as a platinum sensitivity biomarker in breast cancer.

The therapeutic landscape of multiple myeloma (MM) has dramatically changed in the last 15 years with the advent of immunomodulatory drugs and proteasome inhibitors. However, majority of MM patients relapse, and new therapies are needed. Various agents with diverse mechanisms of action and distinct targets, including cellular therapies, monoclonal antibodies, and small molecules, are currently under investigation. In this review, we report novel drugs recently approved or under advanced investigation that will likely be incorporated in the future as new standard for MM treatment, focusing on their mechanisms of action, cellular targets, and stage of development.

We analyzed whole genomes of unique paired samples from smoldering multiple myeloma (SMM) patients progressing to multiple myeloma (MM). We report that the genomic landscape, including mutational profile and structural rearrangements at the smoldering stage is very similar to MM. Paired sample analysis shows two different patterns of progression: a “static progression model”, where the subclonal architecture is retained as the disease progressed to MM suggesting that progression solely reflects the time needed to accumulate a sufficient disease burden; and a “spontaneous evolution model”, where a change in the subclonal composition is observed. We also observe that activation-induced cytidine deaminase plays a major role in shaping the mutational landscape of early subclinical phases, while progression is driven by APOBEC cytidine deaminases. These results provide a unique insight into myelomagenesis with potential implications for the definition of smoldering disease and timing of treatment initiation., Smoldering MM (SMM) is a premalignant stage of multiple myeloma (MM). Here the authors perform whole genome sequencing of unique paired samples of SMM progressing to MM, and show that the genomic landscape at the SMM stage is very similar to MM, but trajectories of evolution can vary from patient to patient.

Multiple myeloma is a plasma cell malignancy characterized by molecular and clinical heterogeneity. The outcome of the disease has been dramatically improved with the advent of new drugs in the past few years. However, even in this context of increasing therapeutic options, important challenges remain, such as accurately evaluating patients' prognosis and predicting sensitivity to specific treatments and drug combinations. Transcriptomic studies have largely contributed to help decipher multiple myeloma complexity, characterizing multiple myeloma subgroups distinguished by different outcomes. Microarrays and, more recently, RNA sequencing allow evaluation of expression of coding and noncoding genes, alternate splicing events, mutations, and novel transcriptome modifiers, providing new information regarding myeloma biology, prognostication, and therapy. In this review, we discuss the role and impact of gene expression profiling studies in myeloma. Clin Cancer Res; 22(22); 5434–42. ©2016 AACR. See all articles in this CCR Focus section, “Multiple Myeloma: Multiplying Therapies.”

Specific niches within the tumor bone marrow (BM) microenvironment afford a sanctuary for multiple myeloma (MM) clones due to stromal cell-tumor cell interactions, which confer survival advantage and drug resistance. Defining the sequelae of tumor cell interactions within the MM niches on an individualized basis may provide the rationale for personalized therapies. To mimic the MM niche, we here describe a new 3D co-culture ex-vivo model in which primary MM patient BM cells are co-cultured with mesenchymal stem cells (MSC) in a hydrogel 3D system. In the 3D model, MSC with conserved phenotype (CD73+CD90+CD105+) formed compact clusters with active fibrous connections, and retained lineage differentiation capacity. Extracellular matrix molecules, integrins, and niche related molecules including N-cadherin and CXCL12 are expressed in 3D MSC model. Furthermore, activation of osteogenesis (MMP13, SPP1, ADAMTS4, and MGP genes) and osteoblastogenic differentiation was confirmed in 3D MSC model. Co-culture of patient-derived BM mononuclear cells with either autologous or allogeneic MSC in 3D model increased proliferation of MM cells, CXCR4 expression, and SP cells. We carried out immune profiling to show that distribution of immune cell subsets was similar in 3D and 2D MSC model systems. Importantly, resistance to novel agents (IMiDs, bortezomib, carfilzomib) and conventional agents (doxorubicin, dexamethasone, melphalan) was observed in 3D MSC system, reflective of clinical resistance. This 3D MSC model may therefore allow for studies of MM pathogenesis and drug resistance within the BM niche. Importantly, ongoing prospective trials are evaluating its utility to inform personalized targeted and immune therapy in MM.

Multiple myeloma is a heterogeneous disease featured by different molecular subtypes. In the last decade, new therapeutics including second- and third-generation proteasome inhibitors and immunomodulatory agents, monoclonal antibodies, and other novel targeted agents have completely transformed the outcome of the disease. The task ahead is to develop strategies to identify effective combinations and sequences of agents that can exploit the genetic make-up of myeloma cells to improve efficacy. Moreover, a subgroup of high-risk patients who experience early disease relapse and shorter survival also requires early identification and specific intervention. Next-generation sequencing (NGS) technologies now allow us to accomplish some of these goals. As described here, besides improving our understanding of the disease, it is beginning to influence our clinical decisions and therapeutic choices. In this article, we describe the current state-of-the-art role of NGS in myeloma from identifying high-risk disease, to drug selection, and, ultimately, to guide personalized therapy.

Multiple Myeloma is preceded by precursor states of monoclonal gammopathy of undermined significance (MGUS) and smoldering multiple myeloma (SMM). Studies have shown that progression to symptomatic MM five years after diagnosis is 1% for MGUS and 10% for SMM. However, based on the genomic background, this rate is highly variable, especially for SMM patients. Recent studies have evaluated the high-risk genomic features for SMM, but the genomic background of SMM patients who do not progress to MM after long-term follow-up (>= 5 years) has not been described. Here, we evaluated genomic changes enriched in non-progressor (NP) (no progression after 5 years of follow-up) precursor conditions (N=31) with those progressed within a short time (N=71) as well as newly diagnosed Myeloma (N=192). We also studied additional unique samples from 18 patients at their precursor stage as well as when progressed to Myeloma. We report a similar large-scale CN alteration in non-progressor SMM compared to progressor SMMs or MM at diagnosis. However, whole-genome sequencing data showed that the overall mutational load for non-progressor SMM samples was lower than Progressor MGUS/SMM (median SNV 5460 vs. 7018). This difference significantly increased for mutations affecting the coding regions. NP samples at diagnosis had 26% and 53% less coding mutations (missense, nonsense, and frameshift mutations) compared to progressor MGUS/SMM (p=0.008) and newly diagnosed MM (p < 0.001) respectively. We observed very low NRAS (3%, OR=8.86) and BRAF (3%, OR=2.17), mutation frequency in non-progressor SMM samples compared to newly diagnosed MM. We did not observe driver mutations in FAM46C, TTN, CYLD, TP53, KMT2C, IRF4, HIST1H1E that are otherwise frequently mutated in high-risk SMM or symptomatic MM. None of the non-progressor SMM samples had MYC alteration. We observed t(11;14), t(4;14), and t(14;16) translocations at similar frequency compared to newly diagnosed MM samples. We also observed a significant difference in non-recurrent focal deletions. Based on our recent data in newly-diagnosed MM, we quantified genomic scar score, and observed that non-progressor SMM have lower GSS (median=3,IQR=[1-9]) compared to progressor MGUS/SMM (median=11,IQR=[5-15] / median=9,IQR=[9-15], respectively) as well as MM samples at diagnosis (median=9, IQR= [5-16],p=0.002). We further validated this observation in an independent cohort with 69 SMM samples in whom progressor SMM patients had high GSS (median =4, IQR=[2-7.75]), compared to delayed progressor (> four years) or non-progressor SMM (median =1.5, IQR= [0-3.5]; p=0.029). Moreover, non-progressor SMM had significantly low utilization of APOBEC and DNA repair mutational processes. Next, we compared non- progressor SMM with progressor SMM using RNAseq data. We identified 1653 differentially expressed genes (DEG) (762 up-regulated and 891 down-regulated). Genes that were upregulated in non-progressor SMM samples were enriched in IL6/JAK/STAT3 and IL2/STAT5 signaling and the regulation of cytokine secretion. Whereas genes up-regulated in progressor SMM were enriched in MYC targets, DNA repair, and mTOR pathways. Moreover, genes that control the translational initiation, translational elongation, mitochondrial translation, and ATP control were among the top highly expressed genes in progressor SMM. We used our MGUS/SMM to MM paired samples and showed that the E2F target, MYC target, and G2/M checkpoint pathways are more active at MM. We measured the distance between progressor and non-progressor SMM as well as MM and found that non-progressor SMM is less similar to MM compared to progressor SMM. In conclusion, the global CNA and translocations are similar between progressor and non-progressor SMM and symptomatic MM and confirm their role in the development of precursor condition but not adequate for progression to MM, which requires additional hits. On the other hand, lower GSS score reflecting genomic stability along with lower SNVs, low DNA damage and APOBEC mutational processes, down-regulated MYC target genes, and low DNA repair activation define low-risk SMM. These results now provide the basis to develop a genomic definition of SMM. Disclosures Fulciniti: NIH: Research Funding. Parmigiani:Phaeno Biotehnologies: Current equity holder in publicly-traded company; CRA Health: Current equity holder in publicly-traded company; Foundation Medicine Institute: Consultancy; Delphi Diagnostics: Consultancy; BayesMendel Laboratory: Other: Co-lead. Munshi:Janssen: Consultancy; Adaptive: Consultancy; Legend: Consultancy; Amgen: Consultancy; AbbVie: Consultancy; Karyopharm: Consultancy; Takeda: Consultancy; C4: Current equity holder in private company; BMS: Consultancy; OncoPep: Consultancy, Current equity holder in private company, Membership on an entity's Board of Directors or advisory committees, Patents & Royalties.

Multiple myeloma (MM) has a heterogeneous genome, evolving through both pre-clinical and post-diagnosis phases. Here, using sequences from 67 MM genomes serially collected from 30 patients together with public datasets, we establish a hierarchy of driver lesions. Point mutations, structural variants and copy number aberrations define at least 7 genomic subgroups of MM, each with distinct sets of co-operating driver mutations. Complex structural events are major drivers of MM, including chromothripsis, chromoplexy and a replication-based mechanism of templated insertions: these typically occur early. Hyperdiploidy also occurs early, with individual chromosomes often gained in more than one chronological epoch of MM evolution, showing a preferred order of acquisition. Positively selected point mutations frequently occur in later phases of disease development, as do structural variants involving MYC. Thus, initiating driver events of MM, drawn from a limited repertoire of structural and numerical chromosomal changes, shape preferred trajectories of subsequent evolution.

Understanding the profile of oncogene and tumor suppressor gene mutations with their interactions and impact on the prognosis of multiple myeloma (MM) can improve the definition of disease subsets and identify pathways important in disease pathobiology. Using integrated genomics of 1273 newly diagnosed patients with MM, we identified 63 driver genes, some of which are novel, including IDH1, IDH2, HUWE1, KLHL6, and PTPN11. Oncogene mutations are significantly more clonal than tumor suppressor mutations, indicating they may exert a bigger selective pressure. Patients with more driver gene abnormalities are associated with worse outcomes, as are identified mechanisms of genomic instability. Oncogenic dependencies were identified between mutations in driver genes, common regions of copy number change, and primary translocation and hyperdiploidy events. These dependencies included associations with t(4;14) and mutations in FGFR3, DIS3, and PRKD2; t(11;14) with mutations in CCND1 and IRF4; t(14;16) with mutations in MAF, BRAF, DIS3, and ATM; and hyperdiploidy with gain 11q, mutations in FAM46C, and MYC rearrangements. These associations indicate that the genomic landscape of myeloma is predetermined by the primary events upon which further dependencies are built, giving rise to a nonrandom accumulation of genetic hits. Understanding these dependencies may elucidate potential evolutionary patterns and lead to better treatment regimens.

International audience; Although long intergenic non-coding RNAs (lincRNA) role in various cancers is described, their significance in Multiple Myeloma (MM) remains poorly defined. Here we have studied the lincRNA profile and their clinical impact in MM. We performed RNA-seq on MM cells from 308 newly diagnosed and uniformly treated patients, 16 normal plasma cells and utilized RNA-seq data from 532 newly diagnosed patients from CoMMpass study to analyze for lincRNAs. We observed 869 differentially expressed lincRNAs in MM compared to normal plasma cells. We identified 14 lincRNAs associated with PFS and calculated a risk score to stratify patients. The median PFS between high vs low-risk groups was 17 months vs not-reached (NR); and OS 30 months vs NR, respectively (p < 0.0001 for both). In the independent validation dataset between high and low-risk groups, PFS was 27 vs 42 months (HR 2.06 [1.44−2.96]; p < 0.0005); and 4-year OS 62% vs 86% (HR 2.76 [1.51–5.05]; p < 0.0005) confirming significant clinical relevance of lincRNA in MM. Importantly, lincRNA signature was able to further identify patients with significant differential outcomes within each low and high-risk categories identified using standard risk categorization including cytogenetic/FISH, ISS, and MRD negative or positive. Our results suggest that lincRNAs have an independent effect on MM outcome and provide a rationale to evaluate its molecular and biological impact.

Multiple myeloma is a plasma cell malignancy characterized by recurrent IgH translocations and well described genomic heterogeneity. Although transcriptome profiles in multiple myeloma has been described, landscape of expressed fusion genes and their clinical impact remains unknown. To provide a comprehensive and detailed fusion gene cartography and suggest new mechanisms of tumorigenesis in multiple myeloma, we performed RNA sequencing in a cohort of 255 newly diagnosed and homogeneously treated multiple myeloma patients with long follow-up. Here, we report that patients have on average 5.5 expressed fusion genes. Kappa and lambda light chains and IgH genes are main partners in a third of all fusion genes. We also identify recurrent fusion genes that significantly impact both progression-free and overall survival and may act as drivers of the disease. Lastly, we find a correlation between the number of fusions, the age of patients and the clinical outcome, strongly suggesting that genomic instability drives prognosis of the disease., Multiple myeloma is a malignancy of plasma cells in the blood. Here, the authors establish the landscape of fusion genes within this disease, identifying novel recurrent fusion genes that impact survival and may drive disease progression.