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This phase 1 study sought to characterize the safety, tolerability, and pharmacokinetic behavior of VLX1570, a small molecule inhibitor of the deubiquitinases (DUBs) that remove sterically bulky ubiquitin chains from proteins during processing in the19S regulatory subunit of the proteasome, in patients with relapsed and refractory multiple myeloma (MM). Fourteen patients were treated with escalating doses of VLX1570 ranging from 0.05 to 1.2 mg/kg as a brief intravenous (IV) infusion on Days 1, 2, 8, 9, 15, and 16 of a 28-day cycle. Due to its poor aqueous solubility, VLX1570 was formulated in polyethylene glycol, polyoxyethylated castor oil, and polysorbate 80 and administered as a brief intravenous (IV) infusion via a central venous catheter. Anti-myeloma effects were noted at doses at or above 0.6 mg/kg, however, two patients treated at the 1.2 mg/kg dose level experienced severe, abrupt, and progressive respiratory insufficiency, which was associated with diffuse pulmonary infiltrates on imaging studies, similar to those rarely noted with bortezomib and other inhibitors of the 20S proteasome, culminating in death. Although the contribution of VLX1570s formulation to the pulmonary toxicity could not be ruled out, the severity and precipitous nature of the toxicity and the steep relationship between dose and toxicity, the study was discontinued. Despite the severe pulmonary toxicity noted with VLX1570, efforts directed at identifying DUB inhibitors with greater therapeutic indices appear warranted based on the unique mechanism of action, robustness of preclinical antitumor activity, and activity of the DUB inhibitors in MM resistant to PIs targeting the 20S proteasome subunit. Funding Agencies|Uppsala University; Vivolux, AB

The changing landscape of treatment options for multiple myeloma has led to a higher proportion of patients achieving deep, long-lasting responses to therapy. With the associated improvement in overall survival, the development of subsequent second malignancies has become of increased significance. The risk of second malignancy after multiple myeloma is affected by a combination of patient-, disease- and therapy-related risk factors. This review discusses recent data refining our knowledge of these contributing factors, including current treatment modalities which increase risk (i.e. high-dose melphalan with autologous stem cell transplant and lenalidomide maintenance therapy). We highlight emerging data towards individualized risk- and response-adapted treatment strategies and discuss key areas requiring future research.

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

Background Experimental and clinical studies have implicated certain chemokines and angiogenic cytokines in multiple myeloma (MM) pathogenesis. To investigate whether systemic concentrations of these markers are associated with future MM risk and progression from its precursor, monoclonal gammopathy of undetermined significance (MGUS), we conducted a prospective study within the Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial. Methods We measured concentrations of 45 immunologic and pro-angiogenic markers in sera from 241 MM case patients, 441 participants with nonprogressing MGUS, and 258 MGUS-free control participants using Luminex-based multiplex assays and enzyme-linked immunosorbent assays. Odds ratios (ORs) and 95% confidence intervals (CIs) were estimated using multivariable logistic regression. We also evaluated absolute risk of progression using weighted Kaplan-Meier estimates. All statistical tests were two-sided. Results Prediagnostic levels of six markers were statistically significantly elevated among MM case patients compared with MGUS-free control participants using a false discovery rate of 10% (EGF, HGF, Ang-2, CXCL12, CCL8, and BMP-9). Of these, three angiogenesis markers were associated with future progression from MGUS to MM: EGF (fourth vs first quartile: OR = 3.01, 95% CI = 1.61 to 5.63, Ptrend = .00028), HGF (OR = 2.59, 95% CI = 1.33 to 5.03, Ptrend = .015), and Ang-2 (OR = 2.14, 95% CI = 1.15 to 3.98, Ptrend = .07). A composite angiogenesis biomarker score substantially stratified risk of MGUS progression to MM beyond established risk factors for progression, particularly during the first 5 years of follow-up (areas under the curve of 0.71 and 0.64 with and without the angiogenesis marker score, respectively). Conclusions Our prospective findings provide new insights into mechanisms involved in MM development and suggest that systemic angiogenesis markers could potentially improve risk stratification models for MGUS patients.

IMPORTANCE: All patients who develop multiple myeloma have a preceding asymptomatic expansion of clonal plasma cells, clinically recognized as monoclonal gammopathy of undetermined significance (MGUS) or smoldering multiple myeloma (SMM). During the past decade, significant progresses have been made in the smoldering multiple myeloma (SMM) classification and risk stratification. OBSERVATIONS: Here, we summarize and discuss current clinical challenges and available models for risk stratification in the setting of SMM. Thanks to several novel more effective and less toxic drugs, these aspects are increasingly becoming important to identify patients eligible for early treatment. However, all proposed criteria were built around indirect markers of disease burden, and therefore they are generally able to identify a fraction of SMM patients where both the transformation into multiple myeloma and the genomic subclonal diversification are already happening. In contrast, next generation sequencing approaches have the potential to identify myeloma precursor disease that will progress even before the major clonal expansion and progression, providing a potential base for a more effective and better precision regarding the optimal timing of treatment initiation. CONCLUSIONS AND RELEVANCE: Thanks to modern technologies, in the near coming future, prognostic models derived from genomic signatures independent from the disease burden will allow to better identify the optimal timing to treat a plasma cell clonal disorder at the very early stages, when the chances of eradication are higher.

Multiple myeloma is a rare plasma cell cancer, and incidence rates among patients of African descent are about twice those among patients of European descent. Rates of multiple myeloma vary among different populations, but the reasons for the racial disparities in multiple myeloma are largely unknown. Epidemiology has identified risk factors for multiple myeloma including race, advanced age, gender, family history, and exposure to different genetic toxins including radiation. Race and ancestry play a large role in predicting the risk for multiple myeloma, yet there exists a paucity of literature that explores the molecular contribution of race and ancestry to disease. In this review, we describe the relevant literature that describes the observed racial differences according to distinct tumor immunobiological and ancestral differences in populations.

The incidence of multiple myeloma is characterized by a steep increase with advancing age. Dramatic improvements in survival have been reported in clinical trials; however, elderly patients are generally underrepresented in these. The aims of this study are to review patterns of incidence and survival in multiple myeloma in the general population. We searched PubMed for population-based studies on trends in incidence and survival published between January 1, 2000 and June 30, 2017 and based on regional or national cancer registries and report the following results of the review. The age-adjusted incidence of multiple myeloma has increased during the second half of the twentieth century in some countries but remained stable in areas with high case ascertainment and access to universal medical care. The crude incidence is increasing globally due to an aging population. Survival rates have improved, and 5-year relative survival rates are now around 50% and over 60% in patients 65-70 years or younger. Preliminary data suggest a 3-fold increase in the prevalence of multiple myeloma. We conclude that the number of multiple myeloma patients is increasing in the general population due to (i) aging populations and (ii) more patients living longer due to modern drugs.

CD99(MIC2) is a widely expressed cell surface glycoprotein and functions as a tumor suppressor involved in downregulation of SRC family of tyrosine kinase. CD99 expression is tightly regulated through B cell development. The principal aims of this study were to investigate the clinical utility of CD99 expression (i) in distinguishing normal plasma cells from primary plasma cell neoplasms; (ii) in detection of minimal residual disease in primary plasma cell neoplasms; (iii) in distinguishing plasma cell component of B-cell lymphomas from primary plasma cell neoplasms. We analyzed expression of CD99 by flow cytometry and immunohistochemistry in lymph nodes, peripheral blood and bone marrow samples. CD99 showed stage-specific expression with highest expression seen in precursor B and plasma cells. In contrast to the uniform bright expression on normal plasma cells, CD99 expression on neoplastic plasma cells was lost in 39 out of 56 (69.6%) cases. Furthermore, eight out of 56 samples (14%) showed visibly (more than ten-fold) reduced CD99 expression. Overall, CD99 expression was informative (absent or visibly dimmer than normal) in 84% of primary plasma cell neoplasm. In the context of minimal residual disease detection, CD99 showed superior utility in separating normal and abnormal plasma cells over currently established antigens CD117, CD81 and CD27 by principal component analysis. Preservation of CD99 expression was strongly associated with Cyclin D1 translocation in myeloma (p

Given the frequent and largely incurable occurrence of multiple myeloma, identification of germline genetic mutations that predispose cells to multiple myeloma may provide insight into disease etiology and the developmental mechanisms of its cell of origin, the plasma cell (PC). Here, we identified familial and early-onset multiple myeloma kindreds with truncating mutations in lysine-specific demethylase 1 (LSD1/KDM1A), an epigenetic transcriptional repressor that primarily demethylates histone H3 on lysine 4 and regulates hematopoietic stem cell self-renewal. In addition, we found higher rates of germline truncating and predicted deleterious missense KDM1A mutations in patients with multiple myeloma unselected for family history compared with controls. Both monoclonal gammopathy of undetermined significance (MGUS) and multiple myeloma cells have significantly lower KDM1A transcript levels compared with normal PCs. Transcriptome analysis of multiple myeloma cells from KDM1A mutation carriers shows enrichment of pathways and MYC target genes previously associated with myeloma pathogenesis. In mice, antigen challenge followed by pharmacologic inhibition of KDM1A promoted PC expansion, enhanced secondary immune response, elicited appearance of serum paraprotein, and mediated upregulation of MYC transcriptional targets. These changes are consistent with the development of MGUS. Collectively, our findings show that KDM1A is the first autosomal-dominant multiple myeloma germline predisposition gene providing new insights into its mechanistic roles as a tumor suppressor during post-germinal center B-cell differentiation. Significance: KDM1A is the first germline autosomal dominant predisposition gene identified in multiple myeloma and provides new insights into multiple myeloma etiology and the mechanistic role of KDM1A as a tumor suppressor during post-germinal center B-cell differentiation. Cancer Res; 78(10); 2747–59. ©2018 AACR.

Based on available data, most multiple myeloma patients have residual myeloma cells in their autologous hematopoietic progenitor cells (HPC) grafts during collection for autologous stemcell transplant. However, available literature is based on older induction regimens, e.g., adriamycin and dexamethasone (VAD). In that setting, attempts to clean the grafts of residual myeloma cells were unsuccessful and did not improve clinical outcome but rather worsened the recovery of blood counts post-high dose melphalan (HDM) resulting in increased risk of severe post-transplant infections. Recently, the modern carfilzomib, lenalidomide and dexamethasone (KRd) regimen has been developed. KRd shows rapid, deep and durable responses in newly diagnosed myeloma patients reflected in rates of up to 60% minimal residual disease (MRD) negativity when evaluating the individual patient’s bone marrow aspirate. To date, no information is available regarding the use of KRd and its impact on residual myeloma cells in autologous HPC grafts. To expand our knowledge on this topic, we designed a prospective study to compare the patient’s MRD status in the bone marrow with MRD status in the corresponding HPC graft. Furthermore, we were interested in evaluating the yield of HPC in relation to the number of given cycles of KRd combination therapy. Collection of HPC between 4 and 8 completed cycles of KRd had very similar HPC yield. Using MRD assays with sensitivity of 1 × 10(−5) we found that treatment with KRd results in an unprecedented purity of the stem cell graft; specifically, 29 of 30 (97%) patients had an MRD negative graft.

Guidelines recommend vaccination starting 12 months after autologous hematopoietic stem cell transplant (aHCT), but there is varying practice for patients on maintenance therapy, with some centers not immunizing at all. Because of decreased vaccine rates among the general population causing loss of herd immunity, we aimed to establish the safety and efficacy of revaccinating multiple myeloma patients on lenalidomide maintenance (LM). Of the 122 patients who were vaccinated after aHCT between 2010 and 2014 at Memorial Sloan Kettering Cancer Center, 91 (75%) were on LM. Vaccine responses were defined by increases between pre- and postvaccination titers. Reponses varied by vaccine type with 76% responding to pertussis, 70% diphtheria, 60% tetanus, 71% Haemophilus influenzae, and 58% pneumococcal. All patients retained minimal levels of polio immunity, but 27% responded with increased titers. Fewer patients received hepatitis A and B, but of those who did, 30% responded to hepatitis A and 40% to hepatitis B. No differences were seen in rates of response for those on LM at time of vaccination compared with those who were not. There were no vaccine-related adverse effects. Reimmunization with inactivated vaccines in patients on LM is therefore both safe and effective, offering this population immunity to vaccine-preventable diseases.

Recent advances in the treatment of multiple myeloma have led to significant improvements in overall survival. One contributor to this progress was the 2012 FDA approval of carfilzomib for the trea...

The past decade, several highly efficacious drugs have been approved for the treatment of multiple myeloma. Many of these newer drugs are less toxic than older chemotherapy drugs. Using modern combination therapy in newly diagnosed multiple myeloma patients, high proportions of newly diagnosed multiple myeloma patients obtain minimal residual disease (MRD) negativity and MRD testing has rapidly become an integral part of clinical trials focusing on patients in this setting. Only recently, MRD negativity was reported in clinical trials focusing on older newly diagnosed multiple myeloma patients (ie, nontransplant candidates), as well as studies focusing on patients with relapsed or refractory multiple myeloma. In the past, deeper responses were rarely seen in these patient categories due to inferior therapies and lack of MRD assays. The reason for the rapidly increased interest in MRD testing in all types of clinical trials is the fact that MRD negativity is closely correlated with longer progression-free survival which has been documented in recent meta-analyses. Consequently, MRD negativity has the potential to soon become a regulatory surrogate end-point for drug approval. This review dissects and discusses current data on MRD in multiple myeloma, it outlines new hypotheses, which can be tested in future clinical studies, and it discusses opportunities and future avenues for translational research. The goal of this article is to stimulate critical analysis of our current treatment landscape and development of future translational research involving MRD testing.

Objectives To characterize the effect of three humanized IgG κ monoclonal antibodies (daratumumab, isatuximab, and elotuzumab) on the interpretation of results generated by protein electrophoresis, immunofixation, free light chain, and heavy/light chain assays performed on human serum. Methods Healthy volunteer serum and serum from multiple myeloma patients were supplemented with clinically relevant concentrations of each of the three monoclonal antibodies. These specimens then underwent analysis via serum protein electrophoresis, immunofixation, serum free light chain quantification, heavy/light chain quantification, total IgG, and total protein. In addition, serum specimens from patients who had undergone treatment with elotuzumab for multiple myeloma underwent similar analysis. Results Addition of the study drugs to serum from both the healthy donor as well as multiple myeloma patients resulted in a visible and quantifiable M-protein on SPEP and a visible IgGκ band by IFE. Increases were also noted in total IgG, IgGκ, and IgGκ/IgGλ-ratios. Analysis of serum from multiple myeloma patients receiving study drug showed similar findings with an additional IgGκ band and quantifiable M-protein with similar migration patterns in specimens drawn after administration. Conclusion The treatment of multiple myeloma patients with monoclonal antibodies results in a visible and quantifiable M-protein that has the potential to falsely indicate poor response to therapy.

In recent years, several new drugs have been approved for the treatment of multiple myeloma. Many of these newer drugs are highly efficacious and less toxic than older chemotherapy drugs. In 2014, the diagnostic criteria for multiple myeloma were revised. The intent with the new criteria was to identify patients who require therapy at an earlier stage than at manifestation of organ complications. A subset of patients who were previously defined as having high-risk smoldering multiple myeloma was redefined as having multiple myeloma. In this context, it is logical to raise questions regarding the optimal clinical management of patients who are diagnosed with smoldering multiple myeloma in the current era. When is the optimal time to start therapy? Do the clinical trajectories for patients suggest there are distinct sub-entities hidden in the current category of smoldering multiple myeloma? How can we move the field forward from here? This paper reviews and dissects data and models on the topics of clinical features, underlying biology, and early treatment trials in smoldering multiple myeloma. The text highlights assumptions, facts, and gaps in the literature. As indicated in the title of the paper, the recurrent theme of the text is this: shall we treat smoldering multiple myeloma in the near future?

In this issue of Blood, Slager et al report novel results from a large familial screening study including 1045 relatives from 310 families that include a patient with chronic lymphocytic leukemia (CLL).

Background: The progression of multiple myeloma (MM) during ongoing therapy is driven by a complex interplay between tumor cells and their surrounding immune microenvironment. We were motivated to conduct a comprehensive and orthogonal investigation of the cellular and humoral immunity of patients with MM treated with lenalidomide maintenance therapy. We compared patients who achieved sustained minimal residual disease (MRD) negativity during the first year of maintenance therapy to those who lost or were unable to attain an MRD negative state. Methods: As part of our prospective phase II clinical trial designed to investigate the MRD dynamics and the efficacy of continuous lenalidomide maintenance in MM (NCT02538198, Lancet Haematology 2021; 8:e422-32), we conducted a pre-planned correlative investigation to elucidate the roles of cellular and humoral immunity. We leveraged single-cell RNA sequencing (scRNAseq) coupled with V(D)J sequencing of peripheral blood mononuclear cells and CyTOF mass cytometry of bone marrow samples collected before and approximately one year after starting maintenance therapy (median 342 days). Proteomic analysis of 92 immuno-oncology related proteins within bone marrow plasma was performed using Olink. Reference-based mapping was used to perform automated cell classification of 31 immune cell subtypes using scRNAseq. A custom 38-marker panel enabled the identification of 21 immune cell subtypes by CyTOF. A total of 40 peripheral blood samples from 20 patients were analyzed by scRNAseq, 28 bone marrow aspirates from 14 patients were analyzed by CyTOF, and 34 plasma samples from 16 patients were analyzed by Olink. Results: Prior to maintenance therapy, 11 (46%) patients completed planned induction therapy and high-dose melphalan (HDM) followed by autologous stem cell transplantation (ASCT); 13 (54%) received planned induction therapy without HDM-ASCT. Through the integration of scRNAseq and CyTOF by bioinformatic analyses, we were able to characterize the cellular composition and phenotypic states of the immune microenvironment before maintenance therapy and after exposure to lenalidomide. Profound and sustained immunosuppression was observed among patients exposed to HDM-ASCT, which associated with accelerated seeding of MM recurrence (Nature Communications 2020;11:3617). Independent of prior exposure to HDM-ASCT, we found differential abundance of immune cell composition to be associated with sustained versus unsustained MRD negativity. Additionally, patients who achieved and sustained MRD negativity (compared to patients with unsustained MRD negativity) had increased frequency of circulating naïve CD8+ T cells in their baseline sample, as well as elevated levels of circulating naïve CD4+ T cells during therapy. When investigating the dynamics of the immune microenvironment longitudinally, circulating regulatory T cells were found to increase during maintenance therapy among patients who had early progression. In the bone marrow, NK cells were more abundant in patients who achieved but could not sustain MRD negativity while vascular endothelial growth factor (VEGF) levels were increased in patients with sustained MRD negativity. We compared our results to a separate report describing the immune microenvironment in patients with MM and healthy donors (Zavidij et al. Nature Cancer 2020;1:493-506) and found the immune landscape of MM patients with sustained MRD negativity to gradually normalize. In contrast, immunosuppression remained present at baseline and during follow-up in MM patients with unsustained MRD negativity. Conclusions: Our findings represent the first detailed characterization of the longitudinal dynamics of the immune microenvironment in relation to low-burden disease in patients with MM treated with lenalidomide maintenance therapy. As expected, HDM-ASCT exposure translated into long-term cellular and humoral immunosuppression, which correlated with dynamics of MM recurrence. Independent of the profound impact of HDM-ASCT on host immunity, the composition of the immune microenvironment varied according to the depth of response. Patients with unsustained MRD negativity had hallmarks of immune dysregulation at baseline and during lenalidomide maintenance, while those who achieved and sustained MRD negativity showed gradual normalization of the immune microenvironment. Disclosures Maura: Medscape: Consultancy, Honoraria; OncLive: Honoraria. Smith: BMS: Consultancy, Membership on an entity's Board of Directors or advisory committees, Patents & Royalties: CAR T cells for MM; Sanofi: Patents & Royalties: GPRC5D antibody based therapies; Novarits: Consultancy; Chimeric Therapeutics: Consultancy; Fate Therapeutics: Research Funding; Eureka Therapeutics: Consultancy. Lesokhin: bristol myers squibb: Research Funding; Genetech: Research Funding; Trillium Therapeutics: Consultancy; pfizer: Consultancy, Research Funding; Janssen: Honoraria, Research Funding; Serametrix, Inc: Patents & Royalties; Behringer Ingelheim: Honoraria; Iteos: Consultancy. Kazandjian: Arcellx: Honoraria, Membership on an entity's Board of Directors or advisory committees; BMS: Honoraria, Membership on an entity's Board of Directors or advisory committees. Green: Bristol Myers Squibb: Membership on an entity's Board of Directors or advisory committees, Patents & Royalties, Research Funding; Cellectar Biosciences: Research Funding; GSK: Membership on an entity's Board of Directors or advisory committees; JANSSEN Biotech: Membership on an entity's Board of Directors or advisory committees, Research Funding; Juno Therapeutics: Patents & Royalties, Research Funding; Legend Biotech: Consultancy; Neoleukin Therapeutics: Membership on an entity's Board of Directors or advisory committees; Seattle Genetics: Membership on an entity's Board of Directors or advisory committees, Research Funding; SpringWorks Therapeutics: Research Funding. Landgren: Celgene: Research Funding; Janssen: Other: IDMC; Janssen: Honoraria; Janssen: Research Funding; Amgen: Honoraria; Amgen: Research Funding; Takeda: Other: IDMC; GSK: Honoraria.

The genomic landscape of Waldenström Macroglobulinemia (WM) is characterized by recurrent somatic mutations in MYD88, with a lower incidence of mutations affecting CXCR4, ARID1A, CD79B and the NFKB signaling pathway (Hunter et. al. Blood 2014). We aimed to characterize the relationship between single base substitutions (SBS), mutational signatures, copy number aberrations (CNA) and structural variants (SV) in WM. We performed whole genome sequencing (WGS) on 14 primary samples from WM patients at various clinical stages, including IgM monoclonal gammopathy (n=1), smoldering (n=5), newly diagnosed (n=7) and relapsed WM (n=1). We identified a median of 2806 clonal SBS per sample (IQR 1870-3079), and 12/14 (85%) samples harbored MYD88 mutations. To investigate which mutational processes are involved in shaping the genomic landscape of WM we performed a mutational signature analysis. Four previously reported SBS signatures were detected: SBS1 and SBS5 (aging), SBS9 (germinal center; GC) and SBS8, with the contribution of age-related signatures SBS1/SBS5 being directly correlated with age at presentation (R 2=0.44, p=0.014). The GC signature SBS9 demonstrated sustained GC activity, as evidenced by the same proportion of mutations attributable to SBS9 at both the clonal and subclonal level (24%). At the immunoglobulin loci, we observed evidence of clustered SBS84 (AID), reflecting somatic hypermutation, with SBS84 accounting for 30% of signature contribution from subclonal mutations. Overall, these data suggest that, similarly to MM and other hematological malignancies, the interaction between WM and the GC is sustained over time. We have previously demonstrated that SV and complex events are critical in the pathogenesis and clinical outcomes of multiple myeloma. In contrast, in this WM WGS cohort, we found a low prevalence of complex SV, with no chromothripsis detected, and a single chromoplexy event found in 3 patients (21%), all of whom had progressed to symptomatic WM. To explore WM CNA features in a larger cohort, we examined the WGS data together with 38 MYD88-mutated WM samples for which targeted sequencing was available (MSK-IMPACT-Heme 400 gene panel). In this combined dataset (n=52), GISTIC analysis identified significantly deleted regions at 6q16.1, 7q34, 17p13.1 (TP53) and 21q22.2, along with significant amplification at 6p22.1 (HLA-A). To better characterize the HLA loci using the loss of heterozygosity in human leukocyte antigen (LOHHLA) tool (McGranahan et. al. Cell 2017) we found the presence of HLA-specific loss of heterozygosity in 1 sample, while 4 samples had HLA CN >2.5 (all from patients who progressed to symptomatic WM). CNA analysis demonstrated that while some samples harbored typical CNA features, others had minimal changes, with MGUS / smoldering WM samples having less CNA compared with those who progressed to symptomatic WM. The 2 MYD88 wild type WGS contained a clonal gain affecting chromosome 12, which is typically an early event in chronic lymphocytic leukemia. Molecular time analysis (the corrected ratio between duplicated and non-duplicated clonal mutations within large chromosomal gains [Maura et al. Nat Comm 2019]) demonstrated that these 2 chromosomal gain events occurred early in cancer development (relative timing 0.5) and tended to be subclonal. This data suggests that, while MYD88-mutations are central to WM clone establishment and can be observed in precursor disease, CNA may contribute to later phases and disease progression. In summary, WGS in WM allows the demonstration that germinal center activity is sustained over time. CNA in WM are not random in distribution, with specific loci being significantly amplified or deleted, and a potential role for HLA CNA. In contrast to MYD88 mutations, which are carried by stable precursor patients, the subclonal status and late molecular time of most CNA changes suggest a late role in cancer progression. Disclosures Kastritis: Pfizer: Consultancy, Honoraria, Research Funding; Takeda: Honoraria; Janssen: Consultancy, Honoraria, Research Funding; Genesis Pharma: Honoraria; Amgen: Consultancy, Honoraria, Research Funding. Diamond: Sanofi: Honoraria; Medscape: Honoraria. Kazandjian: Arcellx: Honoraria, Membership on an entity's Board of Directors or advisory committees; BMS: Honoraria, Membership on an entity's Board of Directors or advisory committees. Papaemmanuil: Isabl Technologies: Divested equity in a private or publicly-traded company in the past 24 months; Kyowa Hakko Kirin Pharma: Consultancy. Dogan: Roche: Consultancy, Research Funding; Seattle Genetics: Consultancy; EUSA Pharma: Consultancy; Peer View: Honoraria; Takeda: Consultancy, Research Funding; Physicians' Education Resource: Honoraria. Lesokhin: Trillium Therapeutics: Consultancy; Serametrix, Inc: Patents & Royalties; Genetech: Research Funding; Iteos: Consultancy; bristol myers squibb: Research Funding; Behringer Ingelheim: Honoraria; pfizer: Consultancy, Research Funding; Janssen: Honoraria, Research Funding. Landgren: Janssen: Other: IDMC; Janssen: Honoraria; Celgene: Research Funding; Amgen: Honoraria; Janssen: Research Funding; Amgen: Research Funding; Takeda: Other: IDMC; GSK: Honoraria. Palomba: Rheos: Honoraria; Pluto: Honoraria; Lygenesis: Honoraria; Ceramedix: Honoraria; Seres: Honoraria, Other: Stock, Patents & Royalties, Research Funding; Nektar: Honoraria; PCYC: Consultancy; Wolters Kluwer: Patents & Royalties; Notch: Honoraria, Other: Stock; Priothera: Honoraria; Kite: Consultancy; Novartis: Consultancy; Magenta: Honoraria; WindMIL: Honoraria; BeiGene: Consultancy; Juno: Patents & Royalties. Maura: OncLive: Honoraria; Medscape: Consultancy, Honoraria. Dimopoulos: Amgen: Honoraria; BMS: Honoraria; Janssen: Honoraria; Takeda: Honoraria; BeiGene: Honoraria.

Background and Scientific Rationale Lenalidomide maintenance after autologous stem cell transplantation (ASCT) in newly diagnosed multiple myeloma (MM) patients has demonstrated an improved progression free survival (PFS) and overall survival (OS) when compared to placebo or observation in a meta-analysis (McCarthy et al. JCO 2017). Despite these improvements, patients on maintenance had higher rates of second primary malignancy and discontinuation due to treatment-emergent adverse events which may lead to decreased quality of life (QoL). Most MM patients are treated with lenalidomide maintenance in the absence of great alternatives. Thus, there is an unmet clinical need to develop alternative maintenance strategies. Daratumumab is well tolerated and effective in MM, with many studies continuing daratumumab as maintenance until disease progression. The proposed study seeks to further investigate and directly compare patient reported QoL between maintenance lenalidomide and daratumumab. Study Design and Methods This is an open-label single-center randomized pilot study of daratumumab versus lenalidomide maintenance in 100 newly diagnosed MM patients (Figure). Clinical trial registry number : NCT04497961, actively recruiting. Study population A total of 100 patients will be enrolled, randomized 1:1 to each arm (50 patients per arm). Randomization for enrollment will be stratified by patient age ( Inclusion criteria Newly diagnosed MM treated with combination therapy with or without ASCT Documentation of a very good partial response or better.Enrollment within 6 months of completing initial combination therapy.Enrollment following minimum 100-day washout per standard guidelines in ASCT recipients.ECOG performance status ≤ 2. Exclusion criteria Progressive or refractory MM.History of disease refractory to lenalidomide or daratumumab.History of prior anti-myeloma therapy for smoldering MM.Currently receiving other investigational agents to treat MM. Study treatment The length of therapy on both arms is 36 cycles of 28 days (~3 years), or until disease progression or unacceptable toxicity. Subjects under the lenalidomide arm will be treated with a maintenance dose of lenalidomide 10mg per day on days 1 to 21 of each cycle. Subjects under the daratumumab arm will be treated with a standard subcutaneous dose of 1800mg weekly in cycles 1 & 2, followed by every 2 weeks in cycles 3-6 and every 4 weeks cycles 7-36. Study assessments Subjects will complete three validated questionnaires - EORTC QLQ-C30 (general QOL in cancer patients), EORTC QLQ-MY20 (QOL in MM patients) and PRO-CTCAE (adverse events). These will be collected at baseline, day 1 of cycle 2, every cycle day 1 thereafter, at study/therapy discontinuation, and at 1-month post therapy follow-up. The GHS score will be calculated based on questions from the EORTC QLQ-C30. Endpoints Primary To compare differences in GHS scores between patients receiving lenalidomide versus daratumumab maintenance Secondary To explore differences in EORTC QLQ-C30, EORTC QLQ-MY20 and PRO-CTCAE scoresTo compare CTCAE adverse eventsTo evaluate differences in PFS and OSTo estimate differences in minimal residual disease statusTo explore the association between minimal residual disease status and clinical outcomes Exploratory To compare minimal residual disease techniques of multi-parametric flow with next-generation sequencing and mass spectrometryTo assess differences in T cell, NKT, NK cell subtypesTo explore associations between disease biology and clinical outcomes using genomic sequencing.To assess changes in the stool microbiome Statistical methods This protocol is a randomized pilot study to estimate the difference in the global health status for patients receiving lenalidomide maintenance to patients receiving daratumumab maintenance. The primary endpoint is the GHS from EORTC QLQ-C30. The primary analysis will be performed using a linear mixed effects model. The randomization stratification factors will be included as covariates in the regression model. The primary endpoint evaluation will use all GHS available up until the final evaluation at 36 cycles. Figure 1 Figure 1. Disclosures Shah: Celgene/BMS: Research Funding; Janssen: Research Funding. Mailankody: Bristol Myers Squibb/Juno: Research Funding; Fate Therapeutics: Research Funding; Physician Education Resource: Honoraria; Evicore: Consultancy; Legend Biotech: Consultancy; Plexus Communications: Honoraria; Jansen Oncology: Research Funding; Allogene Therapeutics: Research Funding; Takeda Oncology: Research Funding. Korde: Medimmune: Membership on an entity's Board of Directors or advisory committees; Amgen: Research Funding. Hultcrantz: Intellisphere LLC: Consultancy; Daiichi Sankyo: Research Funding; Amgen: Research Funding; GlaxoSmithKline: Membership on an entity's Board of Directors or advisory committees, Research Funding; Curio Science LLC: Consultancy. Hassoun: Celgene, Takeda, Janssen: Research Funding. Scordo: Angiocrine Bioscience: Consultancy, Research Funding; Omeros Corporation: Consultancy; Kite - A Gilead Company: Membership on an entity's Board of Directors or advisory committees; i3 Health: Other: Speaker; McKinsey & Company: Consultancy. Dahi: Kite / Gilead: Membership on an entity's Board of Directors or advisory committees. Lahoud: MorphoSys: Membership on an entity's Board of Directors or advisory committees. Landau: Takeda, Janssen, Caelum Biosciences, Celgene, Pfizer, Genzyme: Membership on an entity's Board of Directors or advisory committees; Takeda: Research Funding; Genzyme: Honoraria. Shah: Janssen: Research Funding; Amgen: Research Funding. Giralt: Actinnum: Membership on an entity's Board of Directors or advisory committees; SANOFI: Membership on an entity's Board of Directors or advisory committees; AMGEN: Membership on an entity's Board of Directors or advisory committees; CELGENE: Membership on an entity's Board of Directors or advisory committees; PFIZER: Membership on an entity's Board of Directors or advisory committees; BMS: Membership on an entity's Board of Directors or advisory committees; GSK: Membership on an entity's Board of Directors or advisory committees; JENSENN: Membership on an entity's Board of Directors or advisory committees; JAZZ: Membership on an entity's Board of Directors or advisory committees. Lesokhin: Behringer Ingelheim: Honoraria; bristol myers squibb: Research Funding; pfizer: Consultancy, Research Funding; Serametrix, Inc: Patents & Royalties; Trillium Therapeutics: Consultancy; Genetech: Research Funding; Iteos: Consultancy; Janssen: Honoraria, Research Funding. Landgren: Amgen: Honoraria; Janssen: Other: IDMC; Amgen: Research Funding; Janssen: Research Funding; Janssen: Honoraria; Celgene: Research Funding; Takeda: Other: IDMC; GSK: Honoraria.

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

Translocation t(11;14) multiple myeloma (MM) is sensitive to the apoptosis-inducing drug venetoclax, yet the drug lacks FDA approval in MM. Selinexor is an inhibitor of nuclear export that is approved in relapsed/refractory MM. Here, we report that in patients with t(11;14) MM, the combined administration of venetoclax and selinexor was safe and resulted in clinically meaningful responses. This prompted preclinical studies to investigate synergism and molecular mechanisms of action. The combination was synergistic in t(11;14) MM cell lines and caused decreased levels of Cyclin D1 when given in combination as compared to single agents. A 58-year-old African American man and an 81-year-old Caucasian woman with relapsed, refractory t(11;14) MM with CCND1-IGH fusion confirmed by FISH and progression of disease after multiple lines of therapy were treated with venetoclax based on previous data showing efficacy of venetoclax in t(11;14) MM. Both patients responded initially to venetoclax, however, developed resistance and progressive disease. The addition of selinexor recaptured responses (VGPR and MR, respectively) suggesting a beneficial effect of the combination over single agent venetoclax. The treatment course of the 58-year-old man is shown in Figure A and free kappa light chain response in Figure B. Based on these observations, we hypothesized that selinexor with venetoclax was synergistic in patients bearing the t(11;14) translocation. We therefore studied the combination in MM cell lines with (U266-B1, KMS-12-BM, SK-MM2), and without (RPMI-8226, LP-1, OPM-2) t(11;14) translocations. We performed cell viability assays in increasing concentrations of selinexor, venetoclax, and a combination of the two drugs at 72 hours. Synergy was analyzed via the Bliss independence model using Synergy Finder software as well as via the Chou-Talalay method by using CompuSyn software. Average Bliss model synergy scores were -0.5 in non-t(11;14) and 10.2 in t(11;14) MM cells (>10 indicates synergistic effects and 1 are antagonistic. Cell lines that possessed t(11;14) were more sensitive to the drug combination and showed enhanced synergy in those cell lines bearing the CCND1-IGH translocation (Figure C). To better understand molecular mechanisms underlying the observed synergistic effect, we performed western blot analysis in these six cell lines, treating with selinexor (200nM), venetoclax (1μM), the combination, or DMSO control for 24 hours. We measured protein expression with antibodies against Cyclin D1, which is overexpressed in t(11;14) and a cargo of XPO1. Additionally, we measured levels of XPO1, p53, MCL-1, and p65, which we have previously shown to be altered by selinexor treatment (Figure D). We confirmed Cyclin D1 overexpression in t(11;14) cells lines but not in non-t(11;14) cells. Cyclin D1 levels decreased with selinexor, and the reduction was enhanced by adding venetoclax. Similarly, XPO1 levels decreased to a further degree in t(11;14) cell lines with the combination when compared to either drug alone. There was no difference in XPO1 reduction with the treatment combination in non-t(11;14) cell lines. P53 levels increased as a result of selinexor and combination treatment, and the combination also caused decreased levels of p65 in cell lines with and without t(11;14). Venetoclax upregulated MCL-1, but this was mitigated with the addition of selinexor. These effects were statistically more significant in t(11;14) cell lines (Figure E). The combination of selinexor and venetoclax has shown preclinical synergy in other cancer types and is in Phase 1b clinical trials for relapsed, refractory non-Hodgkin lymphoma or acute myeloid leukemia (NCT03955783; NCT04607772). To our knowledge, this is the first report of patients with MM treated with the combination of selinexor and venetoclax. The mechanism behind the preferential synergy in t(11;14) MM is still under investigation; however, the result of our studies suggests a role for Cyclin D1, which is a cargo protein of XPO1. Additionally, while the effect of venetoclax on Cyclin D1 is not well defined, prior studies suggest the interplay between Cyclin D1, BCL2, and other anti- and pro-apoptotic proteins as having a role in oncogenesis. Based on our results, further clinical evaluation of this combination in MM is planned. Figure 1 Figure 1. Disclosures Bradley: AbbVie: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Novartis: Consultancy, Membership on an entity's Board of Directors or advisory committees. Maura: OncLive: Honoraria; Medscape: Consultancy, Honoraria. Kazandjian: Arcellx: Honoraria, Membership on an entity's Board of Directors or advisory committees; BMS: Honoraria, Membership on an entity's Board of Directors or advisory committees. Landgren: Janssen: Other: IDMC; Takeda: Other: IDMC; Celgene: Research Funding; Amgen: Honoraria; Janssen: Honoraria; Janssen: Research Funding; Amgen: Research Funding; GSK: Honoraria. OffLabel Disclosure: Venetoclax for myeloma is not yet FDA approved, but is used at clinician's discretion in patients who possess t(11;14) based upon the previous sub-group analysis of trials with venetoclax.

Background: Patients treated with cytotoxic chemotherapies and/or autologous stem-cell transplantation (ASCT) are at risk for therapy-related myeloid neoplasms (tMN). As these agents yield increased mutation burden in relapsed malignancies and leave evidence of exposure via mutational signatures, we studied the genomic and temporal relationship between chemo exposure and progression of clonal hematopoiesis (CH) to tMN. Methods: We analyzed 32 tMN whole genomes (WG) from 31 patients [27 acute myeloid leukemias (AML), 4 myelodysplastic syndromes]. For 7 patients with tMN post-high-dose melphalan/ASCT, we investigated the presence of antecedent CH using targeted sequencing (MSK-IMPACT; Bolton et al. Nat Gen 2020) on pre-melphalan blood mononuclear cells, granulocytes, or CD34+ apheresis samples. Results: TMN was diagnosed a median of 4.2 years (IQR, 2.6-6.6) following primary treatment. When compared to data from 200 de novo AML from TCGA (NEJM, 2013), tMNs had fewer mutations in FLT3 (9.7% v 28.0%; p = 0.028) and NPM1 (3.2% v 27.0%; p = 0.003). TP53 loss was enriched in tMNs (25.8% v 10.5%; p = 0.035 ). Mutational signature analysis revealed 5 known single base substitution (SBS) signatures in tMN: the hematopoietic stem-cell (SBS-HSC), aging (SBS1), melphalan (SBS-MM1), and platinum signatures (E-SBS1, E-SBS20) (Rustad et al. Nat Comm 2020, Pich et al. Nat Gen 2019). Complex structural variants (SV), defined as ≥3 breakpoint pairs involved in simultaneous copy number changes (Rustad et al. Blood Can Disc 2020), were observed in 7 tMNs; including chromothripsis in 6 tumors (19.4%), chromoplexy in 2 (6.5%), templated insertion in 1 (3.2%), and unspecified complex SV in 2 (6.5%). Chromothripsis has not been previously reported in de novo AML and, in 4 cases, involved chromosome 19 with hyper-amplification of the SMARCA4 locus (≥5 copies). CH variants that became clonal in tumor were seen in 5/7 pre-melphalan/ASCT samples and included mutations in TP53, RUNX1, NCOR1, NF1, CREBBP, DNMT3A, and PPM1D. Chemotherapy introduces hundreds of mutations, leaving each exposed cell with a unique catalogue (i.e., barcode). In fact, TMNs with evidence of chemo signatures had a higher mutation burden (median 1574 single nucleotide variants) than those without (median 938; p = 0.004). Detection of chemo signatures in bulk genome sequencing relies on one cell, with its catalogue of mutations, to expand to clonal dominance (Fig 1a, Landau et al. Nat Comm 2020). Given the long latency between exposure and tMN diagnosis, this single-cell expansion model was expected for all samples exposed to melphalan or platinum-based regimens (i.e., agents with a measurable signature). Strikingly, all patients with pre-tMN platinum exposure (n=7) had evidence of platinum SBS signatures whereas only 2 of 7 patients with prior melphalan/ASCT had a melphalan signature (SBS-MM1). As all platinum-exposed tMN had mutational evidence of exposure, a CH clone must have existed prior to exposure, supporting a single-cell expansion model. Absence of a chemo signature for 5/7 post-melphalan/ASCT tumors despite exposure implies tumor progression driven either by multiple clones in parallel (Fig 1b) or by an unexposed clone. As latency largely excludes the former, this suggests pre-tMN CH clones were re-infused during SCT, thus avoiding chemo exposure (Fig 1c). This is supported by two lines of evidence: 1) tMNs from 2 patients exposed to sequential platinum and melphalan/ASCT had platinum but not melphalan signatures confirming single-cell expansion of the pre-tMN CH clone post-platinum but with escape from exposure to melphalan in the ASCT (Fig 1d); 2) targeted sequencing of pre-tMN samples from melphalan/ASCT patients identified tMN genomic mutations at the CH level in 5/7 cases, including in all 3 tested apheresis samples - one of which (TP53) expanded to dominance without a melphalan signature. Conclusion: WG sequencing identified novel features of tMN revealing the key driver role of complex SV. Mutational signature analyses and targeted sequencing of pre-tMN samples can increase our understanding of tMN pathogenesis and demonstrate that tMNs arising post-ASCT are often driven by CH clones that re-engraft after escaping melphalan exposure. This mode of expansion suggests that a permissive, immunosuppressed, post-transplant environment might play a more important role than chemotherapy-induced mutagenesis in tMN pathogenesis. Figure 1 Figure 1. Disclosures Diamond: Sanofi: Honoraria; Medscape: Honoraria. Watts: Rafael Pharmaceuticals: Consultancy; Genentech: Consultancy; Bristol Myers Squibb: Consultancy; Takeda: Consultancy, Research Funding; Jazz Pharmaceuticals: Consultancy; Aptevo Therapeutices: Research Funding. Kazandjian: Arcellx: Honoraria, Membership on an entity's Board of Directors or advisory committees; BMS: Honoraria, Membership on an entity's Board of Directors or advisory committees. Bradley: AbbVie: Consultancy, Speakers Bureau; Novartis: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau. Bolli: Amgen: Honoraria; Takeda: Honoraria; Janssen: Consultancy, Honoraria; Celgene/BMS: Consultancy, Honoraria. Papaemmanuil: Isabl Technologies: Divested equity in a private or publicly-traded company in the past 24 months; Kyowa Hakko Kirin Pharma: Consultancy. Scordo: Kite - A Gilead Company: Membership on an entity's Board of Directors or advisory committees; i3 Health: Other: Speaker; Omeros Corporation: Consultancy; Angiocrine Bioscience: Consultancy, Research Funding; McKinsey & Company: Consultancy. Lahoud: MorphoSys: Membership on an entity's Board of Directors or advisory committees. Stein: Jazz Pharmaceuticals: Consultancy; Foghorn Therapeutics: Consultancy; Blueprint Medicines: Consultancy; Gilead Sciences, Inc.: Consultancy; Abbvie: Consultancy; Janssen Pharmaceuticals: Consultancy; Genentech: Consultancy; Celgene: Consultancy; Bristol Myers Squibb: Consultancy; Agios Pharmaceuticals, Inc: Consultancy; Novartis: Consultancy; Astellas: Consultancy; Syndax Pharmaceuticals: Consultancy; PinotBio: Consultancy; Daiichi Sankyo: Consultancy; Syros Pharmaceuticals, Inc.: Consultancy. Sauter: Precision Biosciences: Consultancy; Kite/Gilead: Consultancy; Bristol-Myers Squibb: Research Funding; GSK: Consultancy; Gamida Cell: Consultancy; Celgene: Consultancy, Research Funding; Genmab: Consultancy; Novartis: Consultancy; Spectrum Pharmaceuticals: Consultancy; Juno Therapeutics: Consultancy, Research Funding; Sanofi-Genzyme: Consultancy, Research Funding. Hassoun: Celgene, Takeda, Janssen: Research Funding. Mailankody: Bristol Myers Squibb/Juno: Research Funding; Physician Education Resource: Honoraria; Plexus Communications: Honoraria; Takeda Oncology: Research Funding; Jansen Oncology: Research Funding; Fate Therapeutics: Research Funding; Allogene Therapeutics: Research Funding; Legend Biotech: Consultancy; Evicore: Consultancy. Korde: Medimmune: Membership on an entity's Board of Directors or advisory committees; Amgen: Research Funding. Hultcrantz: Daiichi Sankyo: Research Funding; Intellisphere LLC: Consultancy; Curio Science LLC: Consultancy; GlaxoSmithKline: Membership on an entity's Board of Directors or advisory committees, Research Funding; Amgen: Research Funding. Shah: Bristol Myers Squibb: Research Funding; Janssen: Research Funding. Shah: Janssen Pharmaceutica: Research Funding; Amgen: Research Funding. Park: Servier: Consultancy; Affyimmune: Consultancy; Autolus: Consultancy; Minerva: Consultancy; PrecisionBio: Consultancy; BMS: Consultancy; Novartis: Consultancy; Kura Oncology: Consultancy; Curocel: Consultancy; Artiva: Consultancy; Innate Pharma: Consultancy; Intellia: Consultancy; Amgen: Consultancy; Kite Pharma: Consultancy. Landau: Genzyme: Honoraria; Takeda, Janssen, Caelum Biosciences, Celgene, Pfizer, Genzyme: Membership on an entity's Board of Directors or advisory committees; Takeda: Research Funding. Sekeres: BMS: Membership on an entity's Board of Directors or advisory committees; Takeda/Millenium: Membership on an entity's Board of Directors or advisory committees; Novartis: Membership on an entity's Board of Directors or advisory committees. Ho: Blueprint Medicine: Membership on an entity's Board of Directors or advisory committees. Roshal: Celgene: Other: Provision of services; Auron Therapeutics: Other: Ownership / Equity interests; Provision of services; Physicians' Education Resource: Other: Provision of services. Lesokhin: pfizer: Consultancy, Research Funding; Janssen: Honoraria, Research Funding; Iteos: Consultancy; Serametrix, Inc: Patents & Royalties; Genetech: Research Funding; Trillium Therapeutics: Consultancy; bristol myers squibb: Research Funding; Behringer Ingelheim: Honoraria. Morgan: BMS: Membership on an entity's Board of Directors or advisory committees; Jansen: Membership on an entity's Board of Directors or advisory committees; Karyopharm: Membership on an entity's Board of Directors or advisory committees; Oncopeptides: Membership on an entity's Board of Directors or advisory committees; GSK: Membership on an entity's Board of Directors or advisory committees. Landgren: Janssen: Other: IDMC; Janssen: Research Funding; Amgen: Honoraria; Celgene: Research Funding; Janssen: Honoraria; Amgen: Research Funding; Takeda: Other: IDMC; GSK: Honoraria. Maura: OncLive: Honoraria; Medscape: Consultancy, Honoraria.

Introduction Belantamab mafodotin is an antibody drug conjugate targeting B-cell maturation antigen (BCMA) on plasma cells and was the first BCMA-targeted drug approved by the FDA. Single agent effect was around 31-34% in a recent phase II study (Lonial et al, Lancet Oncology 2020). Side effects, including keratopathy and reduced visual acuity, necessitate dose reduction and dose delays that may limit efficacy. Patients enrolled on clinical trials, however, often do not reflect a real-world population due to eligibility criteria that limit comorbidities and include wash-out periods not typical of clinical practice. The aim of this study was to assess response rates, dose modifications, and frequency of ocular adverse events in patients treated with belantamab mafodotin in a real-world setting. Methods All patients treated with commercial drug belantamab mafodotin at Memorial Sloan Kettering Cancer Center since October 2020 were included in the study. Descriptive statistics were used to assess patient characteristics, response rates, and rate of adverse events. Results Forty-two relapsed/refractory multiple myeloma patients were treated with belantamab mafodotin between October 2020 and the data cut off July 2021, including 55% (N=23) women; median age was 67 years. Twelve patients had been included in the Expanded Access Program with belantamab mafodotin prior to transitioning to commercial drug. Thirty patients (71%) had high risk cytogenetics, including gain 1q, t(4;14), t(14;16), t(14;20), complex karyotype, and MYC-translocations. Patients had been treated with a median of 7 (range 4-14) prior lines of therapy. All patients had received an immunomodulatory agent, a proteasome inhibitor, and an anti-CD38 antibody. Eleven patients had received a BCMA-targeted agent in clinical trials, including bi-specific antibodies, chimeric antigen receptor (CAR) T cells, and one patient had received prior trial therapy with belantamab mafodotin. Patients received a median of 3 cycles (range 1-17) of belantamab mafodotin. The majority, 95% (N=40) as single agent, while two patients were treated with belantamab mafodotin in combination with other standard myeloma treatments. The overall response rate (ORR) in all patients was 43%; 18/42 patients. Of these, 7 achieved a partial response (PR); 5 patients achieved a very good partial response (VGPR), 4 patients achieved a complete response (CR) and 1 patient achieved a stringent CR (Table 1). Median duration of response for patients in the ORR cohort was 11 months (95% confidence interval: 9.1-not reached). Three patients achieved a minimal response (MR), 6 patients had stable disease (SD), and 1 patient was not evaluable for response assessment. After a median follow up of 7.5 months, 14 patients had progressive disease and 10 patients had died. Sixteen patients continued on belantamab mafodotin therapy at the time of data cutoff, July 31 st 2021. In a separate analysis of patients not included in the Expanded Access Program, the ORR was 33%; 5 achieved a PR, 4 patients VGPR, 1 patient CR. Two patients achieved a MR, 3 patients had SD, and 14 patients had progressive disease. Twenty-seven patients (64%) had any grade of ocular toxicity on ophthalmology exam. Using the Keratopathy and Visual Acuity (KVA) scale, 12 patients had grade 1 keratopathy, 7 had grade 2, and 8 had grade 3 keratopathy. Fourteen patients had presence of corneal microcysts. Eighteen patients experienced a decline in best corrected visual acuity (BCVA); 10 patients had grade 1, 5 had grade 2, and 3 patients had grade 3 decline in BCVA. The dose of belantamab mafodotin was reduced from the starting dose of 2.5 mg/kg to 1.92 mg/kg in 17 patients: 16 patients due to ocular toxicity and 1 patient due to baseline cytopenia. Additionally, one or more doses were delayed in 9 patients due to keratopathy; the majority of patients (N=8) were able to continue therapy with maintained response on a lower dose after the delay. Conclusion In this heavily pre-treated multiple myeloma population, the ORR was 33-43% which is similar or better than reported in a recent phase II study. The rate of ocular toxicity (64% keratopathy) was also comparable to previous reports. These data demonstrate encouraging results for belantamab mafodotin treatment in the real-world setting with responses and toxicities comparable to clinical trial subjects. Additional patients and updated follow up will be reported at the meeting. Figure 1 Figure 1. Disclosures Hultcrantz: Intellisphere LLC: Consultancy; Curio Science LLC: Consultancy; Daiichi Sankyo: Research Funding; Amgen: Research Funding; GlaxoSmithKline: Membership on an entity's Board of Directors or advisory committees, Research Funding. Peterson: GlaxoSmithKline: Consultancy. Hassoun: Celgene, Takeda, Janssen: Research Funding. Korde: Medimmune: Membership on an entity's Board of Directors or advisory committees; Amgen: Research Funding. Mailankody: Bristol Myers Squibb/Juno: Research Funding; Jansen Oncology: Research Funding; Physician Education Resource: Honoraria; Plexus Communications: Honoraria; Fate Therapeutics: Research Funding; Takeda Oncology: Research Funding; Allogene Therapeutics: Research Funding; Evicore: Consultancy; Legend Biotech: Consultancy. Landau: Takeda, Janssen, Caelum Biosciences, Celgene, Pfizer, Genzyme: Membership on an entity's Board of Directors or advisory committees; Takeda: Research Funding; Genzyme: Honoraria. Shah: Amgen: Research Funding; Janssen Pharmaceutica: Research Funding. Lahoud: MorphoSys: Membership on an entity's Board of Directors or advisory committees. Scordo: McKinsey & Company: Consultancy; Angiocrine Bioscience: Consultancy, Research Funding; Omeros Corporation: Consultancy; Kite - A Gilead Company: Membership on an entity's Board of Directors or advisory committees; i3 Health: Other: Speaker. Landgren: Celgene: Research Funding; Janssen: Other: IDMC; Janssen: Research Funding; Janssen: Honoraria; Amgen: Honoraria; Amgen: Research Funding; Takeda: Other: IDMC; GSK: Honoraria. Giralt: CELGENE: Membership on an entity's Board of Directors or advisory committees; BMS: Membership on an entity's Board of Directors or advisory committees; AMGEN: Membership on an entity's Board of Directors or advisory committees; SANOFI: Membership on an entity's Board of Directors or advisory committees; Actinnum: Membership on an entity's Board of Directors or advisory committees; PFIZER: Membership on an entity's Board of Directors or advisory committees; JANSENN: Membership on an entity's Board of Directors or advisory committees; GSK: Membership on an entity's Board of Directors or advisory committees; JAZZ: Membership on an entity's Board of Directors or advisory committees. Lesokhin: Trillium Therapeutics: Consultancy; Behringer Ingelheim: Honoraria; bristol myers squibb: Research Funding; Genetech: Research Funding; Iteos: Consultancy; Janssen: Honoraria, Research Funding; pfizer: Consultancy, Research Funding; Serametrix, Inc: Patents & Royalties.

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

Background and Scientific Rationale: Multiple myeloma (MM) is often preceded by the premalignant conditions monoclonal gammopathy of undetermined significance (MGUS) and smoldering multiple myeloma (SMM). Obesity, low adiponectin levels, and diets high in inflammatory, insulinemic foods or lacking plant-based foods are known risk factors for the development of MGUS/SMM, as well as for progression to MM. Therefore, there is an opportunity to study a dietary intervention in cancer progression among patients with MGUS/SMM, for which the standard of care is observation even though some patients will eventually progress to MM. This is a pilot nutrition-based intervention study of a whole food, plant-based diet (WFPBD) in overweight and obese MGUS and SMM patients to enable weight loss, assess associated changes in disease biomarkers, epigenetics, and the gut microbiome. We expect that the findings will enable larger lifestyle-based studies of prevention and survivorship in plasma cell disorders. Study Design and Methods: This is a single-arm, single-center pilot study of a WFPBD for 12 weeks and nutrition counseling for 24 weeks which will enroll 20 patients (Figure). Clinical trial registry number: NCT04920084, actively recruiting. Study Population and Inclusion Criteria i) SMM or MGUS ii) Body mass index ≥25 iii) Monoclonal protein spike ≥0.2 g/dL or abnormal free light chain ratio with increased level of the appropriate involved light chain iv) ECOG performance status 0-3 v) Willingness to comply with study-related procedures Statistical Methods: The average weight loss from baseline at 12 weeks will be reported as sample mean along with 95% confidence interval. Adherence will be estimated by sample proportion, with confidence intervals based on exact binomial distribution. Patients who have completed evaluation at 12 weeks will be evaluable for the weight loss outcome. All patients who have received at least one WFPBD intervention will be evaluable for adherence assessment. We will consider this intervention promising if 1) we detect weight loss at 12 weeks and 2) estimated adherence to the intervention is ≥70%. Study Treatment: For 12 weeks, patients will receive two premade meals per day, for lunch and dinner for 6 days per week, prepared and shipped by Plantable. The meals will have a low glycemic index and contain vegetables, whole grains, and plant-based fats that have undergone minimal processing. Detailed recommendations for snacks and breakfasts meeting the standard of a WFPBD will also be given to supplement their daily calorie needs with access to an online portal or phone application from Plantable which contains education materials and access to a coach daily for 24 weeks. Patients will receive dietary education and counseling from a research dietitian every 2 weeks for the 12-week intervention period. Endpoints: Primary: - To determine the feasibility of a WFPBD, as measured by weight loss and adherence at 12 weeks. Secondary: - To determine the feasibility of a WFPBD, as measured by safety, and quality of life. - To assess weight loss at 24, and 52 weeks. - To assess alterations in metabolic, and myeloma markers. Exploratory: - To assess alterations in T cell and plasma cell epigenetic markers - To assess alterations in the fecal microbiome - To assess changes in body composition as determined by PET imaging and correlate with weight change as well as disease markers. Figure 1 Figure 1. Disclosures Shah: Janssen: Research Funding; Celgene/BMS: Research Funding. Adintori: Vidafuel Inc.: Current holder of stock options in a privately-held company. Mailankody: Jansen Oncology: Research Funding; Physician Education Resource: Honoraria; Bristol Myers Squibb/Juno: Research Funding; Plexus Communications: Honoraria; Fate Therapeutics: Research Funding; Takeda Oncology: Research Funding; Allogene Therapeutics: Research Funding; Legend Biotech: Consultancy; Evicore: Consultancy. Korde: Medimmune: Membership on an entity's Board of Directors or advisory committees; Amgen: Research Funding. Hultcrantz: Intellisphere LLC: Consultancy; Amgen: Research Funding; GlaxoSmithKline: Membership on an entity's Board of Directors or advisory committees, Research Funding; Daiichi Sankyo: Research Funding; Curio Science LLC: Consultancy. Hassoun: Celgene, Takeda, Janssen: Research Funding. D'Souza: Sanofi, Takeda, Teneobio, CAELUM, Prothena: Research Funding; Imbrium, Pfizer, BMS: Membership on an entity's Board of Directors or advisory committees; Janssen, Prothena: Consultancy. Giralt: AMGEN: Membership on an entity's Board of Directors or advisory committees; PFIZER: Membership on an entity's Board of Directors or advisory committees; JAZZ: Membership on an entity's Board of Directors or advisory committees; GSK: Membership on an entity's Board of Directors or advisory committees; SANOFI: Membership on an entity's Board of Directors or advisory committees; Actinnum: Membership on an entity's Board of Directors or advisory committees; JENSENN: Membership on an entity's Board of Directors or advisory committees; BMS: Membership on an entity's Board of Directors or advisory committees; CELGENE: Membership on an entity's Board of Directors or advisory committees. Iyengar: Novartis: Consultancy; Seattle Genetics: Consultancy; Novartis: Research Funding. Landgren: Janssen: Other: IDMC; Janssen: Honoraria; Amgen: Honoraria; Celgene: Research Funding; Janssen: Research Funding; Amgen: Research Funding; Takeda: Other: IDMC; GSK: Honoraria. van den Brink: Pluto Therapeutics: Current holder of stock options in a privately-held company, Other: has consulted, received honorarium from or participated in advisory boards ; Novartis (Spouse): Other: has consulted, received honorarium from or participated in advisory boards; Forty-Seven, Inc.: Honoraria; MagentaTherapeutics: Honoraria; Da Volterra: Other: has consulted, received honorarium from or participated in advisory boards; Wolters Kluwer: Patents & Royalties; Ceramedix: Other: has consulted, received honorarium from or participated in advisory boards ; Notch Therapeutics: Honoraria; DKMS (nonprofit): Other; Pharmacyclics: Other; Kite Pharmaceuticals: Other; GlaskoSmithKline: Other: has consulted, received honorarium from or participated in advisory boards; Lygenesis: Other: has consulted, received honorarium from or participated in advisory boards ; Nektar Therapeutics: Honoraria; Rheos: Honoraria; Seres: Other: Honorarium, Intellectual Property Rights, Research Fundingand Stock Options; Amgen: Honoraria; Therakos: Honoraria; WindMILTherapeutics: Honoraria; Juno Therapeutics: Other; Merck & Co, Inc: Honoraria; Frazier Healthcare Partners: Honoraria; Priothera: Research Funding; Synthekine (Spouse): Other: has consulted, received honorarium from or participated in advisory boards; Jazz Pharmaceuticals: Honoraria. Lesokhin: pfizer: Consultancy, Research Funding; Genetech: Research Funding; Trillium Therapeutics: Consultancy; Behringer Ingelheim: Honoraria; Serametrix, Inc: Patents & Royalties; bristol myers squibb: Research Funding; Janssen: Honoraria, Research Funding; Iteos: Consultancy.

Background: BCMA targeted CAR T cell therapy has shown promising results in patients with relapsed/refractory multiple myeloma (RRMM), but relapses are common. Additional treatment options with novel therapeutic targets or mechanisms of action are needed. Here we report on the safety and efficacy of MCARH109, the first-in-class G Protein Coupled Receptor Class C Group 5 Member D (GPRC5D) targeted CAR T cell therapy (Smith EL et al. Sci. Trans Med 2019) in RRMM including patients who relapsed post BCMA targeted CAR T cell therapy. Methods: This is a phase I first-in-human, dose escalation trial of MCARH109; patients received lymphodepleting chemotherapy with fludarabine 30 mg/m 2 daily and cyclophosphamide 300 mg/m 2 daily for 3 days followed by a single infusion of MCARH109. The trial followed a standard 3+3 design with the following dose cohorts to date: 25X10 6, 50X10 6, 150X10 6, 450X10 6 viable CAR + T cells. The primary objective is to assess safety of MCARH109; secondary objectives include anti-myeloma efficacy, expansion and persistence of MCARH109 using quantitative polymerase chain reaction (qPCR) on peripheral blood and bone marrow samples. Results: 18 patients with RRMM were enrolled and underwent apheresis between September 15, 2020 and July 14, 2021. 12 patients have completed MCARH109 infusion to date, with 6 patients currently undergoing manufacturing and pending treatment. Of the 12 patients treated, median age was 59 (37-76) years and patients received a median of 8 (4-14) lines of therapy. 11 (92%) were penta-exposed, all patients were triple refractory, and 7 (58%) had prior treatment with BCMA targeted therapy including 6 (50%) who received prior BCMA CAR T therapy. 3 (25%) patients had non-secretory myeloma and 6(50%) patients had extramedullary plasmacytoma at baseline. 11 (92%) were refractory to last line of therapy and 11 (92%) patients received bridging therapy after apheresis prior to MCARH109 infusion; all patients were refractory to bridging therapy. There were no dose limiting toxicities. Cytokine release syndrome (CRS) grade 1-3 occurred in 11 (92%) patients with only one patient with grade 3 event; 4 (25%) patients received tocilizumab and 1 (8%) received dexamethasone for the treatment of CRS (Table). There were no neurologic toxicities reported to date; 3 (25%) patients had grade 1 nail changes possibly related to MCARH109 (Table). As of July 28, 2021, all treated patients have been followed for at least 2 weeks (median: 13.0 weeks; range: 2.0-39.1 weeks) and 10 (83%) had at least a minimal response or better (2 responses unconfirmed): 2 minimal response, 3 partial response, 3 very good partial response, 2 stringent complete response (sCR). 5 (56%) of the first 9 patients were minimal residual disease (MRD) negative in the bone marrow by multicolor flow cytometry (sensitivity: 10 -5). 6 (100%) patients with prior BCMA CAR T therapy had a response with 2 patients achieving sCR. We also noted robust MCARH109 expansion in the peripheral blood using qPCR across the first 3 dose levels with available data (peak expansion vector copy number/mL, median: 404,467; range: 44,670- 3,560,000; Table). With a median follow-up of 13 weeks, 9 (75%) patients are progression free and followed without additional therapy. Conclusions: MCARH109 is the first-in-class GPRC5D targeted CAR T cell therapy for MM and has a very manageable safety profile with no serious or unexpected toxicities; this dose escalation study is ongoing with additional patients planned for treatment at higher doses. Efficacy is promising in heavily pre-treated RRMM, reflected in high rates of clinical response as well as MRD-negativity, including at doses as low as 25x10 6 CAR T cells. Clinically important, all 6 patients who relapsed after BCMA CAR T therapy responded to GPRC5D targeted CAR T therapy, including 2 patients who achieved sCR. Figure 1 Figure 1. Disclosures Mailankody: Allogene Therapeutics: Research Funding; Physician Education Resource: Honoraria; Bristol Myers Squibb/Juno: Research Funding; Takeda Oncology: Research Funding; Fate Therapeutics: Research Funding; Jansen Oncology: Research Funding; Evicore: Consultancy; Legend Biotech: Consultancy; Plexus Communications: Honoraria. Shah: Janssen: Research Funding; Celgene/BMS: Research Funding. Lesokhin: pfizer: Consultancy, Research Funding; Iteos: Consultancy; Trillium Therapeutics: Consultancy; Genetech: Research Funding; Serametrix, Inc: Patents & Royalties; bristol myers squibb: Research Funding; Janssen: Honoraria, Research Funding; Behringer Ingelheim: Honoraria. Korde: Medimmune: Membership on an entity's Board of Directors or advisory committees; Amgen: Research Funding. Hassoun: Celgene, Takeda, Janssen: Research Funding. Hultcrantz: GlaxoSmithKline: Membership on an entity's Board of Directors or advisory committees, Research Funding; Amgen: Research Funding; Daiichi Sankyo: Research Funding; Intellisphere LLC: Consultancy; Curio Science LLC: Consultancy. Shah: Amgen: Research Funding; Janssen: 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. Scordo: Angiocrine Bioscience: Consultancy, Research Funding; Omeros Corporation: Consultancy; Kite - A Gilead Company: Membership on an entity's Board of Directors or advisory committees; i3 Health: Other: Speaker; McKinsey & Company: Consultancy. Roshal: Auron Therapeutics: Other: Ownership / Equity interests; Provision of services; Celgene: Other: Provision of services; Physicians' Education Resource: Other: Provision of services. Landgren: Janssen: Other: IDMC; Janssen: Honoraria; Janssen: Research Funding; Amgen: Honoraria; Celgene: Research Funding; Amgen: Research Funding; Takeda: Other: IDMC; GSK: Honoraria. Dogan: Physicians' Education Resource: Honoraria; Seattle Genetics: Consultancy; Peer View: Honoraria; Takeda: Consultancy, Research Funding; Roche: Consultancy, Research Funding; EUSA Pharma: Consultancy. Giralt: Actinnum: Membership on an entity's Board of Directors or advisory committees; JENSENN: Membership on an entity's Board of Directors or advisory committees; GSK: Membership on an entity's Board of Directors or advisory committees; AMGEN: Membership on an entity's Board of Directors or advisory committees; CELGENE: Membership on an entity's Board of Directors or advisory committees; PFIZER: Membership on an entity's Board of Directors or advisory committees; JAZZ: Membership on an entity's Board of Directors or advisory committees; SANOFI: Membership on an entity's Board of Directors or advisory committees; BMS: Membership on an entity's Board of Directors or advisory committees. Park: Autolus: Consultancy; Kite Pharma: Consultancy; PrecisionBio: Consultancy; Minerva: Consultancy; Curocel: Consultancy; Intellia: Consultancy; Amgen: Consultancy; Affyimmune: Consultancy; Innate Pharma: Consultancy; Novartis: Consultancy; Servier: Consultancy; Kura Oncology: Consultancy; Artiva: Consultancy; BMS: Consultancy. Rivière: FloDesign Sonics: Other: Provision of Services; Juno Therapeutics: Patents & Royalties; The Georgia Tech Research Corporation (GTRC): Other: Provision of Services (uncompensated); Centre for Commercialization of Cancer Immunotherapy: Other: Provision of Services; Fate Therapeutics: Other: Provision of Services, Patents & Royalties. Brentjens: BMS: Consultancy, Patents & Royalties, Research Funding; Gracell Biotechnologies, Inc: Consultancy, Ended employment in the past 24 months; sanofi: Patents & Royalties; Caribou: Patents & Royalties. Smith: Eureka Therapeutics: Consultancy; Fate Therapeutics: Research Funding; Chimeric Therapeutics: Consultancy; Novarits: Consultancy; Sanofi: Patents & Royalties: GPRC5D antibody based therapies; BMS: Consultancy, Membership on an entity's Board of Directors or advisory committees, Patents & Royalties: CAR T cells for MM. OffLabel Disclosure: MCARH109 is an experimental GPRC5D targeted CART therapy

Introduction: Classical Hodgkin lymphoma (cHL) is characterized by a small fraction of Hodgkin and Reed-Sternberg (HRS) tumor cells (~1%) which are surrounded by an extensive immune infiltrate. The rare nature of HRS cells limits the ability to study the genomics of cHL using standard platforms. To circumvent this, our group has optimized fluorescence-activated cell sorting to isolate HRS cells and intratumor B- and T- cells and to perform whole exome sequencing (WES; Reichel, Blood 2015). To date, however, there have been no reports on whole genome sequencing (WGS) of cHL. Methods: We performed flow-sorting of HRS cells and WGS to define the genomic landscape of cHL including: i) mutational processes involved in pathogenesis, ii) large and focal copy number variants, iii) structural variants including complex events, iv) the sequence and evolution of molecular events in cHL. We interrogated WGS from 25 cases of cHL: 10 pediatric patients (age40). Intra-tumoral T-cells were used as germline control. An additional 36 cHL cases were evaluated by WES. Results: The average depth of coverage among the 25 WGS cases was 27.5x. After having identified and removed amplification-based palindromic sequencing artifacts, we observed a median of 5006 single base substitutions (SBS; range 1763-18436). Pediatric and AYA patients had a higher SBS burden compared to older adults (median 5279 vs. 2945, p=0.009). Five main SBS signatures were identified: SBS1 and SBS5 (aging), SBS2 and SBS13 (APOBEC), and SBS25 (chemotherapy, in a relapsed case). A dNdScv driver discovery analysis performed on the combined WES and WGS cases identified 24 driver genes including BCL7A and CISH which had not been previously reported as drivers in cHL. An investigation of copy number alterations (CNAs) confirmed high ploidy in cHL (median 2.95, range 1.66-5.33). Whole genome duplication was identified in 64% cases. We also observed clear evidence of complex events such as chromothripsis (n=4), double minutes (dm, n=2), breakage-fusion-bridge (bfb; n=4). Some of these events were responsible for the acquisition of distinct drivers. For example, we observed one dm and one bfb responsible for CD274 and REL gains, respectively (>10 copies). Leveraging the high prevalence of large chromosomal gains, we performed an investigation of the relative timing of acquisition of driver mutations. Clonal mutations within chromosomal gains can be defined as duplicated (VAF~66%; acquired before the gain) or non-duplicated (VAF~33%; acquired before or after the gain). Sixty-one percent (152/249) of driver genes were duplicated suggesting that they were acquired prior to large chromosomal gains. Next, we used the corrected ratio between duplicated and non-duplicated mutations within large chromosomal gains to estimate the molecular time of each duplicated segment (Rustad, Nat Comm 2020). In 11/22 genomes the final CNA profile was acquired through at least two temporally distinct events. To convert these relative estimations into absolute timing (i.e., the age at which events occurred), we leveraged the clock-like mutation signatures (SBS1, SBS5). We first confirmed that the SBS1 and SBS5 mutation rate were constant over time (R 2=0.84; p Conclusion: Here we report the first WGS in cHL. We identify novel drivers and genomic mechanisms involved in cHL pathogenesis. We found that mutations in driver genes are often acquired earlier then chromosomal gains, potentially preceding the cHL diagnosis by several years. In addition, we observed key differences in biology of cHL across age groups including accelerated mutagenesis and increased mutational burden among younger patients. Disclosures Maura: OncLive: Honoraria; Medscape: Consultancy, Honoraria. Oberley: Caris LIfe Science: Current Employment. Lim: EUSA Pharma: Honoraria. Landgren: Janssen: Other: IDMC; Celgene: Research Funding; Janssen: Honoraria; Amgen: Honoraria; Janssen: Research Funding; Amgen: Research Funding; Takeda: Other: IDMC; GSK: Honoraria. Moskowitz: Merck & Co., Inc.: Research Funding. Roshal: Celgene: Other: Provision of services; Auron Therapeutics: Other: Ownership / Equity interests; Provision of services; Physicians' Education Resource: Other: Provision of services. Elemento: Owkin: Consultancy, Other: Current equity holder; AstraZeneca: Research Funding; Champions Oncology: Consultancy; Volastra Therapeutics: Consultancy, Other: Current equity holder, Research Funding; One Three Biotech: Consultancy, Other: Current equity holder; Eli Lilly: Research Funding; Johnson and Johnson: Research Funding; Freenome: Consultancy, Other: Current equity holder in a privately-held company; Janssen: Research Funding. Roth: Janssen: Consultancy; Merck: Consultancy.

Background: Cancer screening is performed worldwide for several malignancies. Monoclonal gammopathy of undetermined significance (MGUS) precedes multiple myeloma (MM) and related lymphoproliferative disorders (LP). However, less than 5% of all MM patients are diagnosed during their precursor state and individuals who develop MM while being monitored for MGUS have better overall survival and fewer complications, compared to MM patients diagnosed without knowledge of MGUS. Thus, population-based screening for MGUS could identify candidates for early treatment of MM/LPs. To evaluate whether systematic screening is beneficial, we performed the Iceland Screens, Treats, or Prevents Multiple Myeloma (iStopMM) study, the first population-based screening study for MGUS that includes a randomized clinical trial (RCT) of follow-up and treatment strategies. Methods: All living residents of Iceland on September 9th, 2016 who were born before 1976 (N=148,708) were invited to participate. Of those, 80,759 (54.3%) provided informed consent for screening. Serum samples were collected from participants alongside clinical blood sampling in the Icelandic health service between September 2016 and the end of 2020. All samples were shipped to the Binding Site in Birmingham, UK, for screening. Samples were tested for M-proteins by capillary zone electrophoresis and immunofixation electrophoresis performed to confirm and characterize suspected M-proteins. Free light chains (FLCs) were measured using the FreeLite® assay. Individuals with a previous diagnosis of MM/LPs/MGUS (N=237) were excluded. Per protocol and informed consent, participants with MGUS were randomized to one of the three study arms: Arm 1 where participants are not contacted; Arm 2 where individuals are followed based on current guidelines; and Arm 3 where individuals are followed with a more intensive diagnostic and monitoring strategy. Participants who progress are offered early treatment. All participants repeatedly answered questionnaires on quality of life and mental health. Results: A total of 75,422 participants (93.4%) provided a serum sample for screening. Of those, 3,725 (4.9%) had MGUS. The prevalence of MGUS was dependent on age with 2.3%, 6.2%, and 12.9% diagnosed in age groups 40-59, 60-79, and 80-103 years, respectively. The prevalence of MGUS was higher in males, 5.9% vs 4.1% (p The RCT includes 3,487 newly diagnosed MGUS individuals with 1164, 1159, and 1164 individuals in arms 1, 2 and 3, respectively (Table). The median age at diagnosis was 69 years in arms 1 and 2, and 70 years in arm 3. Females constituted 45.9% and the isotypes were IgG (50%), IgA (10%), IgM (18%) and biclonal (8%). The median M-protein concentration was 0.34 g/dL. A total of 428 light-chain MGUS cases were randomized. The demographic distribution was well balanced between the three arms. After a median follow-up of 3 years, 194 patients in the RCT have been diagnosed with any LP: 9 in arm 1, 92 in arm 2, and 133 in arm 3 (p Conclusion: In this large prospective population-based screening study including >75,000 screened persons, we have identified 3,725 individuals with monoclonal gammopathy. In the RCT, after 3 years of follow-up, we show that active screening identifies significantly higher number of individuals with full-blown malignancy and smoldering disease, illustrating the fact that early detection and intervention is achievable. Although our findings are encouraging, until final results of the iStopMM study become available, including data on survival and quality of life, we advise against systematic MGUS screening in healthy individuals. Figure 1 Figure 1. Disclosures Kristinsson: Amgen: Research Funding; Celgene: Research Funding. Kampanis: The Binding Site: Current Employment. Hultcrantz: Curio Science LLC: Consultancy; Daiichi Sankyo: Research Funding; GlaxoSmithKline: Membership on an entity's Board of Directors or advisory committees, Research Funding; Amgen: Research Funding; Intellisphere LLC: Consultancy. Durie: Amgen: Other: fees from non-CME/CE services ; Amgen, Celgene/Bristol-Myers Squibb, Janssen, and Takeda: Consultancy. Harding: The Binding Site: Current Employment, Membership on an entity's Board of Directors or advisory committees, Patents & Royalties. Landgren: Janssen: Research Funding; Celgene: Research Funding; Janssen: Honoraria; Janssen: Other: IDMC; Amgen: Honoraria; Takeda: Other: IDMC; Amgen: Research Funding; GSK: Honoraria.

The multiple myeloma field is moving forward quickly. In the past 12 months, many new insights have been generated through preclinical and clinical studies. Several ongoing efforts are designed to challenge old conventions which were established decades ago.

BACKGROUND: Pesticides have been reported to be associated with malignant and non-malignant kidney disease. Few studies have examined the relationship between individual pesticides and kidney dysfunction. OBJECTIVE: We evaluated the associations of pesticide use with measured kidney function among male pesticide applicators in the Biomarkers of Exposure and Effect in Agriculture (BEEA) study, a subcohort in the Agricultural Health Study. METHODS: Serum creatinine was measured in 1,545 BEEA participants and estimated glomerular filtration rate (eGFR) was calculated with the chronic kidney disease epidemiology collaboration (CKD-EPI) equation. Using reported information on lifetime use of 41 pesticides, multivariable linear and logistic regression was used to examine associations with eGFR modeled continuously and with CKD (eGFR

Background Awareness of second malignancies in patients with multiple myeloma (MM) has been increasing during recent years. We have previously shown that second malignancies are associated with a decreased life expectancy in MM patients. Information regarding prior and second malignancies in MM is limited as these patients are often excluded from clinical trials and previously published results from other groups have been conflicting. In the present study we aimed to evaluate two hypotheses. Firstly we hypothesize that prior malignancy is a proxy for genetic instability that could be a risk factor for subsequent malignancy development in MM patients. There is limited data regarding this association in the literature and in two recent registry studies the results were inconclusive. Secondly, to further assess the clinical implication of prior malignancies in MM patients we assessed survival in these patients compared to MM patients without a history of prior malignancy. Patients and Methods All patients diagnosed with MM from January 1, 1973 to December 31, 2013 were identified from the Swedish Cancer Register. All prior and subsequent malignant diagnoses were identified through cross-linkage within the registry. A Cox regression model was used to calculate hazard ratios (HRs) and 95% confidence intervals (CIs) where prior malignancy was compared in MM patients who developed a subsequent malignancy and MM patients who did not. In another Cox regression model, survival was compared in MM patients with and without a prior malignancy. The same method was used to estimate if there was a dose-dependent relationship, i.e. if an increasing number of prior malignancies was associated with a poorer outcome. Results A total of 22,359 patients were diagnosed with MM during the study period. Of these, 2,620 (12%) patients had one or more prior malignancy diagnosis at the time of MM diagnosis and 1,243 (6%) patients developed subsequent malignancies. Among the MM patients who developed a subsequent malignancy, 148 (12%) had a prior malignancy diagnosis. Hematological malignancies were 7% of prior malignancies and 17% of subsequent malignancies. MM patients with a prior malignancy diagnosis did not have increased risk of developing a subsequent malignancy compared to MM patients without a prior malignancy (HR 1.0, 95% CI 0.9-1.2). MM patients with a prior malignancy diagnosis had a statistically significant 10% increased risk of death (HR=1.1, 95% CI 1.1-1.2, p Summary and Conclusions In our large population-based study we found that prior malignancy negatively impacts survival in MM patients and that more than one prior malignancy decreases survival even further. Interestingly, a prior malignancy did not increase the risk of developing a subsequent malignancy in MM patients. We confirmed prior reports of solid tumors being more common than hematological malignancies, both prior and subsequent to the MM diagnosis. A prior malignancy was associated with a reduced survival in MM patients without being a risk factor for subsequent malignancies. The underlying explanation for this is probably multifactorial, and could include reduced dose intensity of chemotherapy, complications from treatment, or that MM that develops after another malignancy might be biologically different. Given the increase of cancer survivors in general, our findings are of importance both for the individual patients and their families as well as for the treating physician. Figure Survival in MM patients without a prior cancer diagnosis compared to MM patients with one and two or more prior cancer diagnoses Figure. Survival in MM patients without a prior cancer diagnosis compared to MM patients with one and two or more prior cancer diagnoses Disclosures Landgren: Takeda: Membership on an entity's Board of Directors or advisory committees; Merck: Membership on an entity's Board of Directors or advisory committees; Medscape: Employment, Other: Chairman for Medscape Myeloma Program; Amgen: Speakers Bureau; Celgene: Speakers Bureau; BMS: Speakers Bureau.

While recent progress has been made in the management of multiple myeloma, it remains a highly fatal malignancy especially among patients with relapsed-refractory disease. Immunotherapy with adoptive T cells targeting myeloma-associated antigens are at various stages of development and have brought about a new hope for cure. This is a review on the emerging field of adoptively transferred engineered T cell based approaches, with an in-depth focus on chimeric antigen receptors (CAR) targeting multiple myeloma. The recent results from CAR T cells targeting B cell maturation antigen are encouraging but eventual resistance to the CAR T cell therapies remain problematic. With newer approaches in therapies for multiple myeloma, the role of transplantation is evolved to form a platform for T cell therapies.

Objective An increased risk of multiple myeloma (MM) has been observed among individuals with low prediagnostic circulating levels of adiponectin, a metabolic hormone that is typically underexpressed among those with overweight or obesity. To assess whether adiponectin may influence myeloma development or progression to frank MM, circulating adiponectin levels were compared across patients with different stages of MM and its precursor, monoclonal gammopathy of undetermined significance (MGUS). Methods Adiponectin was measured in 213 patients with MGUS, smoldering MM, or fully developed MM. Differences in adiponectin levels across patient groups were assessed using multivariate linear regression. Results Relative to MGUS patients, adiponectin levels were statistically significantly lower among those with smoldering and fully developed MM, both overall (16%-20% decrease; P = 0.048) and among those with IgG/IgA isotypes (26%-28% decrease; P = 0.004). Among MGUS patients, adiponectin levels were significantly lower for those with the higher-risk IgM isotype compared with those who had IgG/IgA isotypes (42% decrease; P = 0.036). Conclusions The findings of this study, the largest to investigate adiponectin levels in patients with different stages of MM and the first to evaluate associations with clinical characteristics, suggest that reduced expression of adiponectin may be associated with progression from MGUS to MM.

// Caleb K. Stein 1 , Charlotte Pawlyn 2 , Shweta Chavan 1 , Leo Rasche 1 , Niels Weinhold 1 , Adam Corken 1 , Amy Buros 1 , Pieter Sonneveld 3 , Graham H. Jackson 4 , Ola Landgren 5 , Tariq Mughal 6, 7 , Jie He 6 , Bart Barlogie 1 , P. Leif Bergsagel 8 , Faith E. Davies 1 , Brian A. Walker 1 , Gareth J. Morgan 1 1 The Myeloma Institute, University of Arkansas for Medical Sciences, Little Rock, Arkansas, USA 2 The Institute of Cancer Research, London, UK 3 Eramus University, Rotterdam, NL 4 Department of Haematology, Newcastle University, Newcastle-upon-Tyne, UK 5 Memorial Sloan Kettering Cancer Center, New York, New York, USA 6 FoundationOne Medicine, Cambridge, Massachusetts, USA 7 Tufts Medical Center, Boston, Massachusetts, USA 8 Mayo Clinic, Scottsdale, Arizona, USA Correspondence to: Caleb K. Stein, email: CKStein@uams.edu Keywords: multiple myeloma, gene expression profiling, mutational analysis, translocation cyclin D (TC) Received: January 13, 2017 Accepted: February 15, 2017 Published: February 24, 2017 ABSTRACT We examined a set of 805 cases that underwent DNA sequencing using the FoundationOne Heme (F1H) targeted sequencing panel and gene expression profiling. Known and likely variant calls from the mutational data were analyzed for significant associations with gene expression defined translocation cyclin D (TC) molecular subgroups. The spectrum of KRAS, NRAS, and BRAF codon mutations varied across subgroups with NRAS mutations at Q61 codon being common in hyperdiploid (HRD) and t(11;14) myeloma while being rare in MMSET and MAF. In addition, the presence of RAS-RAF mutations was inversely associated with NFκB pathway activation in all subgroups excluding MAF. In the MMSET subgroup, cases with low FGFR3 expression frequently had RAS-RAF mutations. Conditional inference tree analysis determined that mutation and homozygous deletion of TP53, CDKN2C, and RB1 were key prognostic factors associated with adverse outcome in a non-relapse clinical setting. In conclusion, this study highlights the heterogeneity in the distribution and clinical outcomes of RAS codon and other mutations in multiple myeloma dependent upon primary molecular subgroup.

We aimed to identify whether the use of autologous hematopoietic cell transplantation (HCT) impacts outcomes for multiple myeloma patients with gains of chromosome 1q (+1q). We retrospectively identified 95 patients, 21% having +1q. For patients with +1q, the overall response rate to induction was 85%, with 40% having ≥ VGPR and 20% achieving a CR, similar to non +1q patients (p = .64). The median PFS from diagnosis with +1q was 2.1 years (95% CI: 1.2-not reached (NR)) vs 4.3 years (95% CI: 3.3 yrs-NR) without +1q (p = .003). Median OS from diagnosis was 4.4 years (95% CI: 2.9-NR) vs not reached, respectively (p = .005). On molecular analysis using the Foundation One Heme assay, the most common mutations seen in +1q patients included TP53 (38%) and KRAS (25%). Overall, gain of 1q portends worse PFS and OS which was not negated by auto HCT. Such patients will likely require additional therapy to improve their survival.

With growing numbers of elderly multiple myeloma patients, reliable tools to assess their vulnerability are required. The objective of the analysis herein was to develop and validate an easy to use myeloma risk score (revised Myeloma Comorbidity Index) that allows for risk prediction of overall survival and progression-free survival differences in a large patient cohort. We conducted a comprehensive comorbidity, frailty and disability evaluation in 801 consecutive myeloma patients, including comorbidity risks obtained at diagnosis. The cohort was examined within a training and validation set. Multivariate analysis determined renal, lung and Karnofsky Performance Status impairment, frailty and age as significant risks for overall survival. These were combined in a weighted revised Myeloma Comorbidity Index, allowing for the identification of fit (revised Myeloma Comorbidity Index ≤3 [n=247, 30.8%]), intermediate-fit (revised Myeloma Comorbidity Index 4–6 [n=446, 55.7%]) and frail patients (revised Myeloma Comorbidity Index >6 [n=108, 13.5%]): these subgroups, confirmed via validation analysis, showed median overall survival rates of 10.1, 4.4 and 1.2 years, respectively. The revised Myeloma Comorbidity Index was compared to other commonly used comorbidity indices (Charlson Comorbidity Index, Hematopoietic Cell Transplantation-Specific Comorbidity Index, Kaplan-Feinstein Index): if each were divided in risk groups based on 25% and 75% quartiles, highest hazard ratios, best prediction and Brier scores were achieved with the revised Myeloma Comorbidity Index. The advantages of the revised Myeloma Comorbidity Index include its accurate assessment of patients’ physical conditions and simple clinical applicability. We propose the revised Myeloma Comorbidity Index to be tested with the “reference” International Myeloma Working Group frailty score in multicenter analyses and future clinical trials. The study was registered at the German Clinical Trials Register (DRKS-00003868).

Chromothripsis is emerging as a strong and independent prognostic factor in multiple myeloma (MM), predicting shorter progression-free (PFS) and overall survival (Rustad BioRxiv 2019). Reliable detection requires whole genome sequencing (WGS), with 24% prevalence in 752 newly diagnosed multiple myeloma (NDMM) from CoMMpass (NCT01454297, Rustad BioRxiv 2019) compared with 1.3% by array-based techniques (Magrangeas Blood 2011). In MM, chromothripsis presents differently to solid cancers. Although the biological impact is similar across malignancies, in MM the structural complexity of chromothriptic events is typically lower. In addition, chromothripsis can occur early in MM development and remain stable over time (Maura Nat Comm 2019). Computational algorithms for chromothripsis detection (e.g. ShatterSeek; Cortes-Ciriano Nat Gen 2018) were developed in solid cancers and are accurate in that setting. Running ShatterSeek on 752 NDMM patients with low coverage WGS from CoMMpass, we observed a high specificity for chromothripsis (98.3%) but poor sensitivity (30.2%). ShatterSeek detected chromothripsis in 64/752 samples (8.5%), with 85% confirmed on manual curation; however, missed 114 cases located by manual curation. This indicates that MM-specific computational methods are required. We hypothesized that a signature analysis approach using copy number variation (CNV) may provide an accurate estimation of chromothripsis. We adapted CNV signature analysis, developed in ovarian cancer (Macintyre Nat Gen 2018), to now detect MM-specific CNV and structural features. The analysis utilizes 6 fundamental CN features: i) absolute CN of segments, ii) difference in CN in adjacent segments, iii) breakpoints per 10 Mb, iv) breakpoints per chromosome arm, v) lengths of oscillating CN segment chains, and vi) the size of segments. The optimal number of categories in each CNV feature was established using a mixed effect model (mclust R package). Using CoMMpass low-coverage WGS, de novo extraction using the hierarchical dirichlet process defined 5 signatures, 2 of which (CNV-SIG 4 and CNV-SIG 5) contain features associated with chromothripsis: longer lengths of oscillating CN states, higher numbers of breakpoints / chromosome arm, and higher total numbers of small segments of CN change. Next, we demonstrate that CNV signatures are highly predictive of chromothripsis (average area-under-the-curve /AUC = 0.9, based on 10-fold cross validation). Chromothripsis-associated CNV signatures are correlated with biallelic TP53 inactivation (p= 0.01) and gain1q21 (p As WGS is currently too expensive and computationally intensive to employ in routine practice, we investigated if CNV signatures can predict chromothripsis without using WGS. First, we performed de novo signature extraction using whole exome data from 865 CoMMpass samples. CNV signatures extracted without reference to WGS produced an AUC = 0.81 for predicting chromothripsis (in those with WGS to confirm; n =752), and the chromothriptic-signatures confirmed the association with a shorter PFS (HR=7.2, 95%CI 1.32-39.4, p = 0.022). Second, we applied CNV signature analysis to NDMM having either the myTYPE targeted sequencing panel (n= 113; Yellapantula, Blood Can J 2019) or a single nucleotide polymorphism (SNP) array (n= 217). CNV signature assessment by each technology was predictive of clinical outcome, likely due to the detection of chromothripsis. As with WGS, multivariate analysis confirmed CNV signatures to be independently prognostic (myTYPE; p = 0.003, SNP; p = 0.004). Overall, we demonstrate that CNV signature analysis in NDMM provides a highly accurate prediction of chromothripsis. CNV signature assessment remains reliable by multiple surrogate measures, without requiring WGS. Chromothripsis-associated CNV signatures are an independent and adverse prognostic factor, potentially allowing refinement of standard prognostic scores for NDMM patients and providing a more accurate risk stratification for clinical trials. Disclosures Hultcrantz: Amgen: Research Funding; Daiichi Sankyo: Research Funding; GSK: Research Funding; Intellisphere LLC: Consultancy. Dogan:Takeda: Consultancy; National Cancer Institute: Research Funding; Roche: Consultancy, Research Funding; Seattle Genetics: Consultancy; AbbVie: Consultancy; EUSA Pharma: Consultancy; Physicians Education Resource: Consultancy; Corvus Pharmaceuticals: Consultancy. Morgan:Bristol-Myers Squibb: Consultancy, Honoraria; Janssen: Research Funding; Karyopharm: Consultancy, Honoraria; Amgen: Consultancy, Honoraria; Takeda: Consultancy, Honoraria; Celgene: Consultancy, Honoraria, Research Funding; Roche: Consultancy, Honoraria; GSK: Consultancy, Honoraria. Landgren:Cellectis: Consultancy, Honoraria; Takeda: Other: Independent Data Monitoring Committees for clinical trials, Research Funding; BMS: Consultancy, Honoraria; Adaptive: Consultancy, Honoraria; Takeda: Other: Independent Data Monitoring Committees for clinical trials, Research Funding; Glenmark: Consultancy, Honoraria, Research Funding; Seattle Genetics: Research Funding; Binding Site: Consultancy, Honoraria; Karyopharma: Research Funding; Merck: Other; BMS: Consultancy, Honoraria; Karyopharma: Research Funding; Merck: Other; Pfizer: Consultancy, Honoraria; Celgene: Consultancy, Honoraria, Research Funding; Seattle Genetics: Research Funding; Juno: Consultancy, Honoraria; Juno: Consultancy, Honoraria; Janssen: Consultancy, Honoraria, Other: Independent Data Monitoring Committees for clinical trials, Research Funding; Celgene: Consultancy, Honoraria, Research Funding; Janssen: Consultancy, Honoraria, Other: Independent Data Monitoring Committees for clinical trials, Research Funding; Pfizer: Consultancy, Honoraria; Amgen: Consultancy, Honoraria, Research Funding; Cellectis: Consultancy, Honoraria; Glenmark: Consultancy, Honoraria, Research Funding; Binding Site: Consultancy, Honoraria.

BACKGROUND: Anemia, neutropenia, and thrombocytopenia are expected complications of autologous hematopoietic cell transplant (AHCT) for multiple myeloma (MM). However, prolonged cytopenias may predispose patients to other infectious, cardiovascular, and/or hematological toxicities. Various factors have been implicated in the length of post-AHCT cytopenias including stem cell dose, marrow microenvironment, and clonal hematopoiesis. We hypothesize that the length of post-transplant anemia, neutropenia, and thrombocytopenia may negatively impact hospital length-of-stay (LOS) and in-hospital complications. METHODS: This is a single-center observational study of MM patients who underwent AHCT. Patients were part of a larger cohort with detailed single nucleotide polymorphism (SNP) array cytogenetic data. Exclusion criteria were concurrent amyloidosis and tandem transplant. Demographic data, comorbidities, cytogenetic features (presence of TP53 deletion, and translocation status of MMSET, CCDN3, CCDN1, MAF, and MAFB), therapy line, peri-transplant laboratory values, and clinical outcome were collected retrospectively. LOS was calculated from transplant day -2 to hospital discharge. Predictive factors for LOS were calculated with multiple linear regression. Multiple logistic regression was then used to calculate associating factors for in-hospital complications. Grade III and IV post-transplant cardiovascular, infectious, and hematological complications were collected following the Common Terminology Criteria for Adverse Events (CTCAE). Post-transplant anemia was defined as sustained hemoglobin (Hb) RESULTS: 158 AHCT cases of MM patients were identified. 95 patients received AHCT as frontline treatment, 35 patients were transplanted in the salvage setting, and 14 patients received both frontline and subsequent-line transplant. The most commonly used conditioning regimen was melphalan 200 mg/m2 in 123 cases, followed by melphalan 140 mg/m2 in 23 patients. 50.6% (80/158) developed grade III anemia with a median onset of 9 days after transplant (IQR 5-11 days) and median length of 2 days (IQR 1-6 days). Neutropenia had higher incidence of 96.8% (153/158) with an earlier onset of 5 days (IQR 5-6 days) and median length of 5 days (IQR 2-7 days). Grade III thrombocytopenia occurred in 98.1% (155/158), with median onset of 7 days (IQR 5-7 days) and a median duration of 8 days (IQR 6-10 days). When comparing patients who received frontline transplant vs subsequent line transplants, no statistical significance was observed between onset or length of cytopenias (p=0.40). Median LOS was 16 days (IQR 15-19 days). The most frequent post-AHCT complications in our cohort were neutropenic fever (N=27), followed by engraftment syndrome (N=12), pneumonia (N=4), urinary tract infection (N=2), cellulitis (N=2), and bacteremia (N=1). Cardiovascular events were uncommon (N=3) and included pericarditis, new onset atrial fibrillation, and new onset supraventricular tachycardia. Pulmonary embolism (N=1) and deep vein thrombosis (N=3) were recorded. No major bleeding was observed. Longer LOS was independently associated with post-AHCT anemia (p=0.03) and thrombocytopenia (p=0.0005). Notably, LOS and in-hospital complication rates were not significantly associated with demographic data, pre-existing comorbidities, pre-transplant cytopenias, or cytogenetic abnormalities. CONCLUSION: In our cohort, longer duration of post-transplant anemia and thrombocytopenia were statistically associated with longer hospital LOS, but not with post-transplant complications. Prospective validation in an independent cohort is warranted. Disclosures Landgren: Karyopharma: Research Funding; Binding Site: Consultancy, Honoraria; Takeda: Other: Independent Data Monitoring Committees for clinical trials, Research Funding; Janssen: Consultancy, Honoraria, Other: Independent Data Monitoring Committees for clinical trials, Research Funding; Merck: Other; Seattle Genetics: Research Funding; Celgene: Consultancy, Honoraria, Research Funding; Adaptive: Consultancy, Honoraria; Juno: Consultancy, Honoraria; Amgen: Consultancy, Honoraria, Research Funding; Celgene: Consultancy, Honoraria, Research Funding; Cellectis: Consultancy, Honoraria; Juno: Consultancy, Honoraria; Glenmark: Consultancy, Honoraria, Research Funding; Seattle Genetics: Research Funding; Pfizer: Consultancy, Honoraria; Merck: Other; Karyopharma: Research Funding; Binding Site: Consultancy, Honoraria; BMS: Consultancy, Honoraria; Cellectis: Consultancy, Honoraria; BMS: Consultancy, Honoraria; Pfizer: Consultancy, Honoraria; Janssen: Consultancy, Honoraria, Other: Independent Data Monitoring Committees for clinical trials, Research Funding; Takeda: Other: Independent Data Monitoring Committees for clinical trials, Research Funding; Glenmark: Consultancy, Honoraria, Research Funding. Chung:Genentech: Research Funding.

Introduction: The randomized, open-label, multicenter, phase 3 CANDOR study compared carfilzomib, dexamethasone, and daratumumab (KdD) to carfilzomib and dexamethasone (Kd) in patients with multiple myeloma who have relapsed after 1-3 prior lines of therapy (ClinicalTrials.gov, NCT03158688). In the previously reported primary analysis (Dimopoulos et al, Lancet 2020), a significant progression-free survival (PFS) benefit was demonstrated in patients treated with KdD vs patients treated with Kd (hazard ratio [HR], 0.63 [95% CI, 0.46-0.85]; two-sided P=0.0027). However, after a median follow-up of 16.9 months, median PFS was not reached in the KdD arm. Here, we report updated efficacy and safety outcomes from the CANDOR study. Methods: Adult patients with relapsed or refractory multiple myeloma (RRMM) received 28-day cycles of KdD or Kd (randomized 2:1). In the primary analysis, PFS was the primary endpoint and overall survival (OS) a key secondary endpoint. In this prespecified interim OS analysis, statistical testing was based on the actual number of OS events observed by the data cutoff (approximately 36 months after enrollment of the first patient); PFS was summarized descriptively. Disease progression was determined locally by investigators in an unblinded manner and centrally by the sponsor using a validated computer algorithm (Onyx Response Computer Algorithm [ORCA]) in a blinded manner. PFS and OS were compared between the KdD and Kd arms using a stratified log-rank test, and HRs were estimated by a stratified Cox proportional-hazards model. Results: Patients were randomized to KdD (n = 312) and Kd (n = 154). Of all randomized patients, median age was approximately 64 years; 42% received previous lenalidomide, and 33% were lenalidomide refractory; 90% received previous bortezomib, and 29% were bortezomib refractory. At the data cutoff date of June 15, 2020, 199 (63.8%) patients in the KdD arm and 88 (57.1%) in the Kd arm remained on study. Among patients treated with KdD and Kd, 140 (44.9%) and 85 (55.2%) had PFS events, respectively; median follow-up was 27.8 months (KdD) and 27.0 months (Kd). Median PFS by ORCA was 28.6 months for the KdD arm versus 15.2 months for the Kd arm (HR, 0.59 [95% CI, 0.45-0.78]; Figure). OS data were not mature and will be updated at a future prespecified analysis. Median treatment duration was 79.3 weeks with KdD versus 40.3 weeks with Kd. Grade ≥3 adverse events (AEs) occurred in 87.0% and 75.8% of patients in the KdD and Kd arms, respectively, and fatal AEs occurred in 8.8% and 4.6%; one fatal AE in the KdD arm (due to arrhythmia) and one fatal AE in the Kd arm (due to COVID-19 pneumonia) had occurred since the primary analysis. Carfilzomib treatment discontinuation rates due to AEs were 26.0% with KdD and 22.2% with Kd. Exposure-adjusted AE rates per 100 patient years were: 171.2 and 151.9 for grade ≥3 AEs and 6.9 and 5.6 for fatal AEs in the KdD and Kd arms, respectively. Updated data by key subgroups will be presented. Conclusion: With approximately 11 months of additional follow-up, a 13.4-month improvement in median PFS was observed in patients treated with KdD (28.6 months) versus patients treated with Kd (15.2 months; HR, 0.59 [95% CI, 0.45-0.78]). Safety was consistent with previously reported results. KdD continues to show a favorable benefit-risk profile and represents an efficacious treatment option for patients with RRMM. Figure 1 Disclosures Dimopoulos: Takeda: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Personal fees, Research Funding, Speakers Bureau; BMS: Consultancy, Membership on an entity's Board of Directors or advisory committees, Other: Personal fees; Amgen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Personal fees, Research Funding, Speakers Bureau; Janssen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Personal fees, Research Funding, Speakers Bureau; Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Personal fees, Speakers Bureau. Quach:GlaxoSmithKline, Karyopharm, Amgen, Celgene, Janssen Cilag: Consultancy; Amgen, Celgene, karyopharm, GSK, Janssen Cilag, Sanofi.: Membership on an entity's Board of Directors or advisory committees; Amgen, sanofi, celgene, Karyopharm, GSK: Research Funding; GlaxoSmithKline, Karyopharm, Amgen, Celgene, Janssen Cilag: Honoraria. Mateos:EDOMundipharma: Consultancy; Adaptive: Consultancy; Pharmamar: Consultancy; GlaxoSmithKline: Consultancy; AbbVie: Consultancy; Takeda: Consultancy; Amgen: Consultancy; Celgene: Consultancy; Janssen: Consultancy. Landgren:Pfizer: Consultancy, Honoraria; Merck: Other; Cellectis: Consultancy, Honoraria; Juno: Consultancy, Honoraria; Glenmark: Consultancy, Honoraria, Research Funding; BMS: Consultancy, Honoraria; Binding Site: Consultancy, Honoraria; Celgene: Consultancy, Honoraria, Research Funding; Pfizer: Consultancy, Honoraria; Karyopharma: Research Funding; Merck: Other; Janssen: Consultancy, Honoraria, Other: Independent Data Monitoring Committees for clinical trials, Research Funding; Takeda: Other: Independent Data Monitoring Committees for clinical trials, Research Funding; Glenmark: Consultancy, Honoraria, Research Funding; Juno: Consultancy, Honoraria; Seattle Genetics: Research Funding; Cellectis: Consultancy, Honoraria; Seattle Genetics: Research Funding; Takeda: Other: Independent Data Monitoring Committees for clinical trials, Research Funding; BMS: Consultancy, Honoraria; Adaptive: Consultancy, Honoraria; Amgen: Consultancy, Honoraria, Research Funding; Celgene: Consultancy, Honoraria, Research Funding; Binding Site: Consultancy, Honoraria; Janssen: Consultancy, Honoraria, Other: Independent Data Monitoring Committees for clinical trials, Research Funding; Karyopharma: Research Funding. Leleu:Incyte: Honoraria; Merck: Honoraria; Novartis: Honoraria; Amgen: Honoraria; GSK: Honoraria; Sanofi: Honoraria; BMS-celgene: Honoraria; Janssen: Honoraria; Oncopeptide: Honoraria; AbbVie: Honoraria; Carsgen: Honoraria; Karyopharm: Honoraria. Siegel:Janssen: Consultancy, Honoraria, Speakers Bureau; Merck: Consultancy, Honoraria, Speakers Bureau; Amgen: Consultancy, Honoraria, Speakers Bureau; Celulatiry: Consultancy; Karyopharma: Consultancy, Honoraria; Takeda: Consultancy, Honoraria, Speakers Bureau; BMS: Consultancy, Honoraria, Speakers Bureau. Weisel:Takeda: Consultancy, Honoraria; Amgen: Consultancy, Honoraria, Research Funding; Karyopharm: Consultancy, Honoraria; Adaptive: Consultancy, Honoraria; Bristol-Myers Squibb: Consultancy, Honoraria; GlaxoSmithKline: Honoraria; Sanofi: Consultancy, Honoraria, Research Funding; Janssen: Consultancy, Honoraria, Research Funding; Celgene: Consultancy, Honoraria, Research Funding; Abbvie: Consultancy, Honoraria; Roche: Consultancy, Honoraria. Gavriatopoulou:Takeda: Consultancy, Honoraria; Janssen: Consultancy, Honoraria; Genesis Pharma: Consultancy, Honoraria; Karyopharm: Consultancy, Honoraria; Amgen: Consultancy, Honoraria. Oriol:Janssen: Consultancy; Celgene: Consultancy, Speakers Bureau; Amgen: Consultancy, Speakers Bureau. Rabin:Janssen, BMS/Celgene, Takeda, Karyopharm, Amgen: Consultancy; Janssen, BMS/Celgene, Takeda: Other: Travel; Jansse, BMS/Celgene, Takeda: Speakers Bureau. Nooka:GlaxoSmithKline: Consultancy, Honoraria, Other: Personal Fees: Travel/accomodations/expenses, Research Funding; Karyopharm Therapeutics, Adaptive technologies: Consultancy, Honoraria, Research Funding; Spectrum Pharmaceuticals: Consultancy; Celgene: Consultancy, Honoraria, Research Funding; Amgen: Consultancy, Honoraria, Research Funding; Oncopeptides: Consultancy, Honoraria; Janssen: Consultancy, Honoraria, Research Funding; Bristol-Myers Squibb: Consultancy, Honoraria, Research Funding; Sanofi: Consultancy, Honoraria; Adaptive Technologies: Consultancy, Honoraria; Takeda: Consultancy, Honoraria, Research Funding. Ding:Amgen: Current Employment. Zahlten-Kumeli:Amgen: Current Employment, Current equity holder in publicly-traded company. Usmani:Celgene: Other; GSK: Consultancy, Research Funding; Pharmacyclics: Research Funding; Array Biopharma: Research Funding; Seattle Genetics: Consultancy, Research Funding; Merck: Consultancy, Research Funding; Incyte: Research Funding; SkylineDX: Consultancy, Research Funding; Takeda: Consultancy, Honoraria, Other: Speaking Fees, Research Funding; Sanofi: Consultancy, Honoraria, Research Funding; Abbvie: Consultancy; BMS, Celgene: Consultancy, Honoraria, Other: Speaking Fees, Research Funding; Amgen: Consultancy, Honoraria, Other: Speaking Fees, Research Funding; Janssen: Consultancy, Honoraria, Other: Speaking Fees, Research Funding.

PURPOSE : The World Trade Center (WTC) attack of September 11, 2001 created an unprecedented environmental exposure to known and suspected carcinogens. A higher incidence of multiple myeloma (MM) and precursor disease has been reported among first responders to the WTC disaster compared to the unexposed population (Landgren, 2018). To expand on prior screening studies, and to characterize the genomic impact of the exposure to known and potential carcinogens in the WTC debris, we were motivated to perform whole genome sequencing (WGS) of WTC first responders and recovery workers who were diagnosed with a plasma cell disorder after the attack. PATIENTS AND METHODS: We performed WGS of 9 CD138-positive bone marrow mononuclear samples from patients who were diagnosed with plasma cell disorders after exposure to the WTC disaster: 4 monoclonal gammopathy of undetermined significance (MGUS), 2 smoldering multiple myeloma (SMM), 2 MMs, and 1 patient with plasma cell leukemia (PCL). Eight patients (88%) were first responders and one was a recovery worker. Peripheral blood mononuclear cells were used as normal match. Median coverage for tumor and normal samples was 50.9X (range 47-76) and 37X (range 35-41), respectively. The landscape of genomic drivers and complex structural events was compared to 752 MM patients enrolled in the CoMMpass trial with available whole exome and low-coverage long-insert WGS data (IA15; NCT01454297). To characterize the mutational signature landscape we combined the WTC cohort with 110 whole genomes from 56 patients with multiple myeloma and myeloma precursor disease (Rustad et al. 2020; Landau et al. 2020) and we ran our three-step workflow: de novo extraction (i.e. sigprofiler), assignment, and fitting (i.e. mmsig). To exclude contribution of any environmental agents in the WTC debris with known mutational signatures (Kucab et al., 2019), we ran our fitting algorithm mmsig in each post-WTC case, including and forcing the extraction of these mutational signatures. RESULTS: No significant differences were observed in comparing the post-WTC driver and mutational signatures landscape with 110 previously published WGS from 56 patients with MM and the CoMMpass WGS cohort (n=752). Likewise, we did not observe any new or distinct mutational signatures among WTC-exposed patients. Following forced extraction of 5 mutational signatures associated with environmental agents detected in the WTC debris (e.g. PAHs), we did not find significant contributions from any of these described environmental mutational signatures. To reconstruct the temporal activity of each mutational process we divided all single nucleotide variants into subclonal and clonal. Clonal mutations were further subdivided into duplicated (acquired before a chromosomal gain) and unduplicated (Rustad et al. 2020). WTC-exposed patients had differing patterns in mutational signature timelines of AID and APOBEC activity. Overall, the mutational signature activity over time in post-WTC plasma cell dyscrasia reflects what has been previously observed in multiple myeloma without WTC-exposure (Rustad et al., 2020). Finally, leveraging constant activity of the clock-like single base substitution mutational signatures 1 and 5 over time and our molecular time workflow (Rustad et al., 2020), we estimated the age at which each evaluable patient acquired a tumor-initiating chromosomal gain and found that they were windowed to both pre- and post-WTC exposure across neoplasms (Figure 1). In some cases, clonal multi-chromosomal gain events were acquired decades before both the diagnosis and the WTC exposure. Specifically, of 6 patients with large clonal chromosomal gains, 1 MM case, 1 SMM, and 1 MGUS showed evidence of a pre-existing clone prior to WTC exposure, two MGUS showed evidence of multi-gain events following the exposure, and one MM case had a 1q gain in the same time window as the attack. CONCLUSIONS: Post-WTC plasma cell neoplasms had similar genomic landscapes to non-exposed cases. Although limitations in sample size preclude any definitive conclusions, our findings suggest that the observed increased incidence of plasma cell neoplasms in this population is due to complex and heterogeneous effects of the WTC exposure that may have initiated or contributed to progression of malignancy. The existence of pre-malignant clonal entities at time of WTC exposure may therefore be relevant for future WTC-related study. Figure 1 Disclosures Hultcrantz: Intellisphere LLC: Consultancy; Amgen: Research Funding; Daiichi Sankyo: Research Funding; GSK: Research Funding. Shah:Physicians Education Resource: Honoraria; Celgene/BMS: Research Funding. Iacobuzio-Donahue:BMS: Research Funding. Papaemmanuil:Isabl: Current equity holder in private company, Membership on an entity's Board of Directors or advisory committees; Novartis: Consultancy, Honoraria; Illumina: Consultancy, Honoraria; Kyowa Hakko Kirin: Consultancy, Honoraria; Celgene: Consultancy, Honoraria, Research Funding; Prime Oncology: Consultancy, Honoraria; MSKCC: Patents & Royalties. Verma:BMS: Consultancy, Research Funding; acceleron: Consultancy, Honoraria; stelexis: Current equity holder in private company; Janssen: Research Funding; Medpacto: Research Funding. Dogan:National Cancer Institute: Research Funding; EUSA Pharma: Consultancy; Takeda: Consultancy; Seattle Genetics: Consultancy; Corvus Pharmaceuticals: Consultancy; Physicians Education Resource: Consultancy; Roche: Consultancy, Research Funding; AbbVie: Consultancy. Landgren:Celgene: Consultancy, Honoraria, Research Funding; Seattle Genetics: Research Funding; Cellectis: Consultancy, Honoraria; Juno: Consultancy, Honoraria; Pfizer: Consultancy, Honoraria; Merck: Other; Karyopharma: Research Funding; Janssen: Consultancy, Honoraria, Other: Independent Data Monitoring Committees for clinical trials, Research Funding; Pfizer: Consultancy, Honoraria; Merck: Other; Karyopharma: Research Funding; Binding Site: Consultancy, Honoraria; Takeda: Other: Independent Data Monitoring Committees for clinical trials, Research Funding; Seattle Genetics: Research Funding; Takeda: Other: Independent Data Monitoring Committees for clinical trials, Research Funding; Janssen: Consultancy, Honoraria, Other: Independent Data Monitoring Committees for clinical trials, Research Funding; Celgene: Consultancy, Honoraria, Research Funding; BMS: Consultancy, Honoraria; Juno: Consultancy, Honoraria; Amgen: Consultancy, Honoraria, Research Funding; Adaptive: Consultancy, Honoraria; Cellectis: Consultancy, Honoraria; Glenmark: Consultancy, Honoraria, Research Funding; Binding Site: Consultancy, Honoraria; BMS: Consultancy, Honoraria; Glenmark: Consultancy, Honoraria, Research Funding.

Introduction The incidence of lymphoproliferative disorders (including plasma cell disorders) (LPD) and diabetes mellitus (DM) increase with age. The metabolic link between DM and multiple myeloma (MM) or LPD has been researched in other epidemiologic studies with a small number of cases suggesting an increased risk (Dankner et al Am J Epi 2016, Castillo et al Blood 2012). However, this link has not yet been studied in patients with related disorders such as monoclonal gammopathy of unknown significance (MGUS), AL amyloidosis (AL), or Waldenstrom macroglobulinemia (WM). This is the first study to evaluate the risk of DM on LPD in a large Swedish population-based case-control study. Methods We conducted a large population-based matched case-control study to evaluate the impact of a preceding diagnosis of DM on the development of a LPD. We included all cases of MM, WM, AL and other LPs (OLP) in the nationwide Swedish Cancer Registry and cases of AL and chronic lymphocytic leukemia (CLL) in the Swedish patient registry from 1987-2013. MGUS was acquired from a network of oncology and hematology clinics in Sweden. OLPs included the diagnoses of Hodgkin lymphoma, non-Hodgkin lymphoma, CLL, T cell lymphoma and other lymphocytic leukemias. For each case, up to four controls matched by age, gender, and county of residence from the general population were included. Cases with no controls were excluded. Diagnoses of DM were acquired from the Swedish patient registry where they were coded using ICD 9 and 10 codes. Conditional logistic regression was then performed controlling for the matching variables estimating the odds ratio (OR) of each LPD for a diagnosis of DM before the diagnosis of the LPD. OR of each LPD for the diagnosis of DM greater than one year and less than one year prior to the diagnosis of LPD was also calculated. For MGUS cases, we also assessed the risk of progression to LPD. To avoid immortal time bias we include DM as a time dependent covariate in a Cox-model adjusting for age, sex, and year of MGUS diagnosis. All analyses were performed in R using the survival, and survminer packages. Results Patients with a diagnosis of MGUS (OR: 1.58; p 1 year prior to the diagnosis were included (Table 1). However, patients with (vs. without) a diagnosis of DM are not more likely to progress from MGUS to MM, WM, AL or OLP (HR 0.89; p=0.11) (Table 2). Discussion The strength of our study lies in its large sample size and the availability of 4 matched controls for each case. The diagnosis of DM is associated with an increased risk of MGUS, MM, AL and OLP. This risk is greatest for DM diagnosis in the year prior to LPD diagnosis. This may be due to DM induced hyperglycemia leading to epigenetic changes that increase the odds of developing/accelerating early stage cancer given that they may have prediabetes/undiagnosed DM for many years prior (Huang et al Diabetologia 2014). It is also plausible that patients with DM for >1 year have been on metformin which has been shown to have a protective effect and are less likely to progress suggesting the role of hyperglycemia in its progression (Chang et al Lancet Haematol 2015). Alternatively, patients are likely to have DM diagnosed in the year prior due to similar symptoms of fatigue or weight loss, or a detection bias due to increased physician visits and laboratory testing. This lack of increased progression from MGUS in DM may be explained by the small numbers of patients that progressed in this dataset or the inability to control for factors such as DM treatment. Some other limitations include the lack of granular information related to DM, body mass index as well as prognostic information for the LPD. Conclusions Although, smaller prior studies have suggested a link between DM and cancer including MM and LPD, this is the largest study in patients with LPD. This is also the first epidemiologic study establishing a link between DM and MGUS as well as AL. Research into further understanding this association would enable us to provide patients with better treatment options in the future. Disclosures Shah: Physicians Education Resource: Honoraria; Celgene/BMS: Research Funding. Hultcrantz:Intellisphere LLC: Consultancy; Amgen: Research Funding; Daiichi Sankyo: Research Funding; GSK: Research Funding. Hassoun:Takeda: Research Funding; Celgene: Research Funding; Novartis: Consultancy. Korde:Astra Zeneca: Other: Advisory Board; Amgen: Research Funding. Lesokhin:BMS: Consultancy, Honoraria, Research Funding; Takeda: Consultancy, Honoraria; GenMab: Consultancy, Honoraria; Serametrix Inc.: Patents & Royalties; Janssen: Research Funding; Genentech: Research Funding; Juno: Consultancy, Honoraria. Mailankody:Physician Education Resource: Honoraria; PleXus Communications: Honoraria; Takeda Oncology: Research Funding; Janssen Oncology: Research Funding; Allogene Therapeutics: Research Funding; Juno Therapeutics, a Bristol-Myers Squibb Company: Research Funding. Landgren:Adaptive: Consultancy, Honoraria; Takeda: Other: Independent Data Monitoring Committees for clinical trials, Research Funding; Binding Site: Consultancy, Honoraria; Karyopharma: Research Funding; Janssen: Consultancy, Honoraria, Other: Independent Data Monitoring Committees for clinical trials, Research Funding; Seattle Genetics: Research Funding; BMS: Consultancy, Honoraria; Cellectis: Consultancy, Honoraria; Glenmark: Consultancy, Honoraria, Research Funding; Juno: Consultancy, Honoraria; Seattle Genetics: Research Funding; Pfizer: Consultancy, Honoraria; Merck: Other; Karyopharma: Research Funding; Binding Site: Consultancy, Honoraria; BMS: Consultancy, Honoraria; Cellectis: Consultancy, Honoraria; Juno: Consultancy, Honoraria; Pfizer: Consultancy, Honoraria; Merck: Other; Amgen: Consultancy, Honoraria, Research Funding; Celgene: Consultancy, Honoraria, Research Funding; Janssen: Consultancy, Honoraria, Other: Independent Data Monitoring Committees for clinical trials, Research Funding; Takeda: Other: Independent Data Monitoring Committees for clinical trials, Research Funding; Glenmark: Consultancy, Honoraria, Research Funding; Celgene: Consultancy, Honoraria, Research Funding.

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

Background. Consensus from prior studies shows that the use of maintenance therapy after completion of combination therapy leads to longer progression-free survival (PFS) for patients with multiple myeloma with some studies showing an overall survival (OS) benefit. Currently, lenalidomide is the standard of care; however, there are limited published data on long-term use regarding ability to sustain minimal residual disease (MRD)-negativity and late toxicities. We were motivated to develop a study focusing on continuous, induction-agnostic lenalidomide maintenance with integration of clinical and correlative data. Here, we report formal results of this phase II study with focus on MRD dynamics and tolerability. Methods. This single arm, phase II trial enrolled 100 evaluable patients. Lenalidomide 10 mg is given days 1-21 on a 28-day cycle. Per protocol, patients underwent bone marrow biopsies and aspirates as well as PET/CT at baseline, annually, at progression/end of study; blood work was done every 3 months. The study was statistically powered for the primary endpoint of PFS at 36 months. Correlative assays included MRD testing (10-color single-tube flow cytometry and IGHV sequencing; sensitivity ≤10-5), genomic characterization of detectable disease, and profiling of the bone marrow microenvironment performed on serially banked samples. Results. 100 evaluable patients were enrolled (63% males) between September 2015 and January 2019. Baseline characteristics include median age 63 years (range 38-87 years) and median ECOG score 1 (range 0-1). Prior to enrollment, 22 (30%) patients had high-risk FISH/SNP signature defined as one or more of: 1q+, t(4;14), t(14;16), t(14;20), and 17p- and 48 patients had undergone autologous hematopoietic cell transplantation (AHCT). At abstract submission, median cycles delivered is 39 (range 9-62). 74% of patients have completed ³24 cycles and 55% have completed ³36 cycles. Overall PFS at 36 months was 77% (95% CI: 0.69-0.87) and PFS at 60 months was 63% (95% CI: 0.51-0.78). All patients had MRD testing at least once. 46% were MRD-negative at enrollment. 7 patients who were MRD+ at enrollment converted to MRD-negative. At median follow up 39.4 months (range 7-56 months), 20/100 patients (20%) have progressed. In consideration of the entire follow-up time from initial MRD-negativity, 44 (of 95 tested; 46%) and 37 (of 73 tested; 51%) achieved sustained MRD-negativity at 1 and 2 years, respectively. 22 patients were MRD-negative at 3 years (of 51 tested; 43%). Among those who sustained MRD-negativity for 2 years, with median follow-up of 19 months past the 2-year landmark analysis (max 120 months), there were no progression events. Age, induction regimen, and MRD status at enrollment were the only significant variables associated with PFS regardless of cytogenetic risk or transplant status. At 1 and 2-year landmark analysis, MRD-negativity superseded all else as the most significant factor associated with PFS with HR 0.06(p=0.0004) and HR 1/Inf (p=0.015), respectively. Toxicities (grade 3) included neutrophil count decrease (20%), hypertension (3%), diarrhea (3%), lung infection (2%), and maculo-papular rash (2%), and toxicities (grade 4) include sepsis (2%) and platelet count decrease (7%). The most common non-grade 3/4 toxicities were diarrhea (55%), fatigue (36%), and upper respiratory infection (30%). 7% developed a secondary malignancy on study: 3 adenocarcinoma, 1 squamous cell carcinoma, 1 CMML, 1 MDS, 1 ALL, and 1 glioblastoma. One evaluable patient required dose reduction due to toxicities/tolerability. Conclusions. This prospective study of continuous lenalidomide maintenance, agnostic to induction regimen or AHCT usage, was designed to evaluate the dynamics of MRD-negativity in relation to PFS. It expands on the importance of MRD as a predictor of outcome and illustrates how continuous maintenance therapy can deepen and sustain MRD-negative responses achieved with modern combination therapy. For this cohort, MRD-negativity at each landmark profoundly outweighed the impact of all other variates. Among those who had sustained MRD-negativity at 2 years (37% of the cohort), regardless of MRD status at enrollment, none have had progression events at median 43 months. Our results support cross-sectional MRD testing as a surrogate endpoint for drug approval, and the use of longitudinal MRD tracking in clinical management. Disclosures Korde: Amgen: Research Funding; Astra Zeneca: Other: Advisory Board. Lesokhin:Genentech: Research Funding; Janssen: Research Funding; GenMab: Consultancy, Honoraria; Juno: Consultancy, Honoraria; Takeda: Consultancy, Honoraria; Serametrix Inc.: Patents & Royalties; BMS: Consultancy, Honoraria, Research Funding. Smith:Precision Biosciences: Consultancy; Bristol Myers Squibb: Consultancy, Patents & Royalties, Research Funding; Fate Therapeutics: Consultancy. Shah:Physicians Education Resource: Honoraria; Celgene/BMS: Research Funding. Mailankody:Physician Education Resource: Honoraria; PleXus Communications: Honoraria; Takeda Oncology: Research Funding; Janssen Oncology: Research Funding; Allogene Therapeutics: Research Funding; Juno Therapeutics, a Bristol-Myers Squibb Company: Research Funding. Hultcrantz:Intellisphere LLC: Consultancy; Amgen: Research Funding; Daiichi Sankyo: Research Funding; GSK: Research Funding. Hassoun:Takeda: Research Funding; Celgene: Research Funding; Novartis: Consultancy. Scordo:McKinsey & Company: Consultancy; Angiocrine Bioscience, Inc.: Consultancy, Research Funding; Omeros Corporation: Consultancy; Kite - A Gilead Company: Other: Ad-hoc advisory board. Chung:Genentech: Research Funding. Shah:Amgen: Research Funding; Janssen Pharmaceutica: Research Funding. Lahoud:MorphoSys: Other: Advisory Board. Thoren:Sebia: Research Funding; The Binding Site: Research Funding. Ho:Invivoscribe, Inc.: Honoraria. Dogan:AbbVie: Consultancy; EUSA Pharma: Consultancy; Takeda: Consultancy; Seattle Genetics: Consultancy; Corvus Pharmaceuticals: Consultancy; Physicians Education Resource: Consultancy; Roche: Consultancy, Research Funding; National Cancer Institute: Research Funding. Giralt:MILTENYI: Consultancy, Research Funding; ACTINUUM: Consultancy, Research Funding; KITE: Consultancy; OMEROS: Consultancy, Honoraria; NOVARTIS: Consultancy, Honoraria, Research Funding; CELGENE: Consultancy, Honoraria, Research Funding; JAZZ: Consultancy, Honoraria; AMGEN: Consultancy, Research Funding; TAKEDA: Research Funding. Landgren:Takeda: Other: Independent Data Monitoring Committees for clinical trials, Research Funding; Celgene: Consultancy, Honoraria, Research Funding; Binding Site: Consultancy, Honoraria; Karyopharma: Research Funding; Janssen: Consultancy, Honoraria, Other: Independent Data Monitoring Committees for clinical trials, Research Funding; Seattle Genetics: Research Funding; Glenmark: Consultancy, Honoraria, Research Funding; Takeda: Other: Independent Data Monitoring Committees for clinical trials, Research Funding; Janssen: Consultancy, Honoraria, Other: Independent Data Monitoring Committees for clinical trials, Research Funding; Pfizer: Consultancy, Honoraria; Merck: Other; Cellectis: Consultancy, Honoraria; BMS: Consultancy, Honoraria; Juno: Consultancy, Honoraria; Glenmark: Consultancy, Honoraria, Research Funding; Pfizer: Consultancy, Honoraria; Seattle Genetics: Research Funding; Karyopharma: Research Funding; Merck: Other; Adaptive: Consultancy, Honoraria; Amgen: Consultancy, Honoraria, Research Funding; Celgene: Consultancy, Honoraria, Research Funding; Binding Site: Consultancy, Honoraria; BMS: Consultancy, Honoraria; Cellectis: Consultancy, Honoraria; Juno: Consultancy, Honoraria.

Introduction Multiple myeloma (MM) is consistently preceded by an asymptomatic expansion of clonal plasma cells, clinically recognized as monoclonal gammopathy of undetermined significance (MGUS) or smoldering multiple myeloma (SMM). Here, we present the first comprehensive whole-genome sequencing (WGS) analysis of patients with MGUS and SMM. Methods To characterize the genomic landscape of myeloma precursor disease (i.e. SMM and MGUS) we performed WGS of CD138-positive bone marrow mononuclear samples from 32 patients with MGUS (N=18) and SMM (N=14), respectively. For cases with low cellularity resulting in low amounts of extracted DNA (N=15), we used the low-input enzymatic fragmentation-based library preparation method (Lee-Six et al, Nature 2019). Myeloma precursor disease samples were compared with 80 WGS of patients with MM. All WGSs (N=112) were investigated using computational tools available at the Wellcome Sanger Institute. Results After a median follow up of 29 months (range: 2-177), 17 (53%) patients with myeloma precursor disease progressed to MM (13 SMM and 4 MGUS). To interrogate the genomic differences between progressive versus stable myeloma precursor disease we first characterized the single base substitution (SBS) signature landscape. Across the entire cohort of plasma cell disorders, all main MM mutational signatures were identified: aging (SBS1 and SBS5), AID (SBS9), SBS8, SBS18, and APOBEC (SBS2 and SBS13). Interestingly, only 2/15 (13%) stable myeloma precursor disease cases showed evidence of APOBEC activity, while 14/17 (82%) and 68/80 (85%) patients with progressive myeloma precursor disease (p=0.0058) and MM (p=0.004), respectively, had APOBEC mutational activity. The two stable cases with detectable APOBEC were characterized by a high APOBEC3A:3B ratio, a feature which defines a group of MAF-translocated MM patients whose pathogenesis is characterized by intense and early APOBEC activity (Rustad et al Nat Comm 2020) and is distinct from the canonical ~1:1 APOBEC3A:3B mutational activity observed in most cases. When exploring the cytogenetic landscape, no differences were found between progressive myeloma precursor disease and MM cases. Compared to progressors and to MM, patients with stable myeloma precursor disease were characterized by a significantly lower prevalence of known recurrent MM aneuploidies (i.e. gain1q, del6q, del8p, gain 8q24, del16q) (p Conclusions In summary, we were able to comprehensively interrogate for the first time the whole genome landscape of myeloma precursor disease. We provide novel evidence of two biologically and clinically distinct entities: (1) progressive myeloma precursor disease, which represents a clonal entity where most of the genomic drivers have been already acquired, conferring an extremely high risk of progression to MM; and (2) stable myeloma precursor disease, which does not harbor most of the key genomic MM hallmarks and follows an indolent clinical outcome. Disclosures Hultcrantz: Intellisphere LLC: Consultancy; Amgen: Research Funding; Daiichi Sankyo: Research Funding; GSK: Research Funding. Dogan:Roche: Consultancy, Research Funding; Corvus Pharmaceuticals: Consultancy; Physicians Education Resource: Consultancy; Seattle Genetics: Consultancy; Takeda: Consultancy; EUSA Pharma: Consultancy; National Cancer Institute: Research Funding; AbbVie: Consultancy. Landgren:Pfizer: Consultancy, Honoraria; Adaptive: Consultancy, Honoraria; Janssen: Consultancy, Honoraria, Other: Independent Data Monitoring Committees for clinical trials, Research Funding; Juno: Consultancy, Honoraria; Cellectis: Consultancy, Honoraria; Merck: Other; Seattle Genetics: Research Funding; Celgene: Consultancy, Honoraria, Research Funding; Glenmark: Consultancy, Honoraria, Research Funding; Takeda: Other: Independent Data Monitoring Committees for clinical trials, Research Funding; Janssen: Consultancy, Honoraria, Other: Independent Data Monitoring Committees for clinical trials, Research Funding; Binding Site: Consultancy, Honoraria; Celgene: Consultancy, Honoraria, Research Funding; Amgen: Consultancy, Honoraria, Research Funding; Karyopharma: Research Funding; Binding Site: Consultancy, Honoraria; BMS: Consultancy, Honoraria; BMS: Consultancy, Honoraria; Takeda: Other: Independent Data Monitoring Committees for clinical trials, Research Funding; Merck: Other; Seattle Genetics: Research Funding; Glenmark: Consultancy, Honoraria, Research Funding; Karyopharma: Research Funding; Cellectis: Consultancy, Honoraria; Juno: Consultancy, Honoraria; Pfizer: Consultancy, Honoraria. Bolli:Celgene: Honoraria; Janssen: Honoraria.