Evans, Laura A.

AbstractAltered energy metabolism and changes in glycolytic and oxidative phosphorylation pathways are hallmarks of all cancer cells. The expression of select genes associated with the production of various enzymes and proteins involved in glycolysis and oxidative phosphorylation were assessed in the clonal plasma cells derived from patients with newly diagnosed multiple myeloma (NDMM) enrolled in the Multiple Myeloma Research Foundation (MMRF) CoMMpass data set. A scoring system consisting of assigning a point for every gene where their fragments per kilobase of transcript per million (FPKM) was above the median yielded a minimum of 0 and a maximum of 12 for the set of genes in the glycolytic and oxidative phosphorylation pathways to create a total energy metabolism molecular signature (EMMS) score. This EMMS score was independently associated with worse progression free survival (PFS) and overall survival (OS) outcomes of patients with NDMM. A higher EMMS score was more likely to be present in clonal plasma cells derived from Multiple myeloma (MM) patients than those from patients with monoclonal gammopathy of undetermined significance (MGUS). This was functionally confirmed by the clonal plasma cells from MM patients having a higher rate of mitochondrial and glycolysis‐derived ATP formation than clonal plasma cells from MGUS patients. Thus, this study provides evidence for the effect of energy metabolism within clonal plasma cells on pathogenesis and outcomes of patients with MM. Exploiting the energy‐producing metabolic pathways within clonal plasma cells for diagnostic and therapeutic purposes in MM should be explored in the future.

Abstract Introduction: Altered cellular metabolism is a hallmark of every cancer cell. Aerobic glycolysis ("The Warburg Effect") is one of the earliest recognized metabolic abnormalities in cancer cells whereby extracellular glucose is preferentially metabolized and eventually processed to generate lactate and energy in the form of ATP before the former is released extracellularly, irrespective of oxygen availability. While extracellular lactate produced and released from cancer cells has traditionally been considered a waste metabolic by-product, recent understanding of cell metabolism suggests that it can also serve as a primary metabolic fuel for cancer cells via uptake by monocarboxylate transporters (MCTs). Our goal was to evaluate this "Reverse Warburg Effect" phenomenon in multiple myeloma (MM) cells and determine if it can be exploited for therapeutic purposes. Methods: All HMCLs, MM1S, RPMI-8226 and U266, were grown in RPMI-1640 cell culture medium containing 11 mM glucose and supplemented with 10% dialyzed fetal bovine serum (FBS) and 2 mM Glutamine. Primary MM cells were extracted using magnetic bead CD138 positive selection from MM patient bone marrow aspirates. For 13C-labeling experiments, HMCLs and primary MM cells were suspended in RPMI-1640 cell culture media containing 13C-labeled isotopes. Isotopomer analysis of glycolytic and tricarboxylic acid (TCA) cycle metabolites from HMCL and primary MM cell pellets was performed using Agilent Technologies 5975C gas chromatography-mass spectrometry. Small molecule inhibitors, AZD3965 and syrosingopine, were purchased from Selleck Chemicals and Sigma respectively. Cellular viability and proliferation were measured using 3-(4, 5-dimethylthiazol-2-yl)-2, 5-diphenyl tetrasodium bromide (MTT) and CCK-8 assays respectively. MCT-1 and MCT-4 antibodies for western blotting were utilized to evaluate their cell membrane expression on HMCLs. Results: The HMCLs, MM1S and RPMI-8226 as well as primary CD138+ cells from MM patient bone marrow were cultured in cell culture media containing physiological levels (1 mM) of U-13C-Lactate. The incorporation of extracellular 13C into the intracellular glycolytic and TCA cycle metabolite pool was observed (Fig 1) based on the expected isotopomeric patterns, demonstrating the Reverse Warburg Effect in MM cells. The relative contribution of carbon substrate by extracellular lactate compared to extracellular glucose was assessed in the following HMCLs: MM1S, RPMI-8226 and U266 cells by culturing in cell culture media containing 3-13C-Lactate and U-13C-Glucose. Extracellular lactate (yellow bar) contribution to the formation of TCA metabolites equaled that of glucose (red bar) based on the expected isotopomer patterns, suggesting the relative importance of extracellular lactate as an essential nutrient like glucose (Fig 2). Since MCT-1 and MCT-4 are key bidirectional cell membrane transporters of lactate in and out of cells, we explored the clinical significance of their gene expression level on clinical outcomes using the COMMPASS dataset provided by the Multiple Myeloma Research Foundation (MMRF). When MM patients were dichotomized at above or below the median of the expression levels of fragments per kilobase of transcript per million (FPKM), MCT-1 and MCT-4 overexpression conferred a worse progression free survival and overall survival (Fig 3). The MCT-1/MCT-4 protein expression was detectable across the various HMCLs: MM1S, U266 and RPMI-8226 (Fig 4). Inhibition of MCT-1 by specific inhibitor AZD3965 was able to reduce proliferation but not affect viability of HMCLs at 48 hours (Fig 5). However, dual inhibition of MCT-1/MCT-4 using syrosingopine was able to significantly reduce proliferation and decrease viability of HMCLs in a dose dependent fashion (Fig 6). Finally, dual inhibition of MCT-1/MCT-4 using syrosingopine reduced the utilization of extracellular lactate into the TCA cycle pool by HMCLs in media containing 3-13C-Lactate (Fig 7). Conclusion: Utilization of extracellular lactate via Reverse Warburg Effect phenomenon appears highly active in MM cells. Disrupting the utilization of extracellular lactate by dual inhibition of both MCT-1 and MCT-4 appears therapeutic. In the future, dual inhibition of MCT-1/MCT-4 in combination with other anti-MM therapies should be evaluated to determine synergistic therapeutic potential. Figure 1 Figure 1. Disclosures Kumar: Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; KITE: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Carsgen: Research Funding; Sanofi: Research Funding; Novartis: Research Funding; Antengene: Consultancy, Honoraria; Beigene: Consultancy; Bluebird Bio: Consultancy; Adaptive: Membership on an entity's Board of Directors or advisory committees, Research Funding; Tenebio: Research Funding; Janssen: Consultancy, 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; Oncopeptides: Consultancy; Astra-Zeneca: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Merck: Research Funding; Roche-Genentech: Consultancy, Research Funding; BMS: Consultancy, Research Funding; Abbvie: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Amgen: Consultancy, Research Funding.

Abstract Introduction: Multiple gene expression profile assays have successfully prognosticated the overall survival (OS) outcomes of patients with Newly Diagnosed Multiple Myeloma (NDMM) and are now available for use in clinical practice. The genes included in these profiles encompass several aspects of cancer biology broadly such as cell-cycle biology, energy metabolism, migration, adhesion, immune evasion etc. Since altered energy metabolism is one of the hallmarks of all cancer cells, our goal was to take a deep-dive into specific genes associated with intracellular central carbon energy metabolism (CCEM) pathways such as oxidative phosphorylation (OxPhos) as well as glycolysis and lactate fermentation (Glyco-Lac) in MM cells to determine their impact on the pathogenesis and prognosis of MM patients. Methods: Gene expression level data via RNA sequencing from the COMMPASS dataset (made available by the Multiple Myeloma Research Foundation (MMRF)) was utilized. The expression levels of all genes associated with the OxPhos (N = 118) and Glyco-Lac (N = 34) pathways were evaluated in CD138+ cells derived from patients with NDMM. Each gene was dichotomized into low and high expression groups -based on their expression level above and below the median fragments per kilobase of transcript per million (FPKM), respectively. The prognostic significance of dichotomizing each of the 152 genes in this NDMM cohort was evaluated by levels of their hazard ratios (HRs) for OS determined by the log-rank method. Genes from the OxPhos and Glyco-Lac pathways were selected based on their significant negative impact on OS, wide representation of their biologic activity throughout the pathway and high independence from each other. This resulted in the selection of 12 distinct genes of interest. A scoring system consisting of assigning a point for every gene where their FPKM was above the median yielded a minimum of 0 and a maximum of 12 for the set of genes in the OxPhos and Glyco-Lac pathways to create an energy metabolism score (EMS). Patients were then categorized into "low" (0-8) or "high" (9-12) EMS groups. In addition, patients were designated as having high-risk cytogenetics if they had any of the following: t(4;14), t(14;16), t(14;20), del17p, and Amp 1q. Survival analysis was performed using the Kaplan-Meier survival analysis and compared via the log-rank method. Finally, the distribution of the EMS score among patients within a spectrum of plasma cell disorders such as, monoclonal gammopathy of undetermined significance (MGUS), smoldering multiple myeloma and MM as well as normal plasma cells was assessed using a previously published transcriptome dataset (GSE6477). Results: A total of 766 patients were included in this study. The selected genes included in this scoring system and their biological relevance in the OxPhos and Glyco-Lac pathways are depicted in Figure A. The distribution of low and high EMS groups across ISS stage and cytogenetic risk are depicted in Figure B and C respectively. The median OS (mOS) and PFS for the high (N = 248) vs. low (N = 518) EMS groups was 55 vs. 94 months (HR: 2.41; (95%CI: 1.85-3.14); P <0.0001) (Figure D) and 24 vs. 42 months (HR: 1.74; (95%CI: 1.42-2.13); P <0.0001). Patients with ISS 2 stage disease categorized as having a high or low EMS score had a similar mOS to patients with ISS 3 and ISS 1 stage disease respectively (Figure E). Similarly, the mOS was similar among patients with standard risk and high-risk cytogenetics when categorized by low and high EMS (Figure F). Finally, the relative expression of most of the 12 select CCEM genes (Figure G) and the probability of a high EMS score increased among patients along the spectrum of plasma cell disorders (Normal plasma cell -> MGUS -> SMM -> MM) (Figure H). Conclusions: The expression levels of specific genes associated with the activity of intracellular CCEM pathways such as glycolysis, lactate fermentation and oxidative phosphorylation in plasma cells provides evidence for the impact of cellular energy metabolism on the pathogenesis and survival outcomes of MM. This provides an opportunity for developing future studies aimed at understanding the energy metabolism pathways of plasma cell malignancies in greater detail so as to exploit them for diagnostic and therapeutic purposes in MM. Figure 1 Figure 1. Disclosures Kumar: Novartis: Research Funding; Amgen: Consultancy, Research Funding; Beigene: Consultancy; Carsgen: Research Funding; Merck: Research Funding; Antengene: Consultancy, Honoraria; Janssen: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Astra-Zeneca: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Oncopeptides: Consultancy; Roche-Genentech: Consultancy, Research Funding; KITE: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Tenebio: Research Funding; Bluebird Bio: Consultancy; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; BMS: Consultancy, Research Funding; Abbvie: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Adaptive: Membership on an entity's Board of Directors or advisory committees, Research Funding; Sanofi: Research Funding. Dispenzieri: Alnylam: Research Funding; Sorrento Therapeutics: Consultancy; Oncopeptides: Consultancy; Pfizer: Research Funding; Takeda: Research Funding; Janssen: Consultancy, Research Funding. Kapoor: AbbVie: Research Funding; Takeda: Research Funding; Karyopharm: Consultancy; Cellectar: Consultancy; BeiGene: Consultancy; Pharmacyclics: Consultancy; Sanofi: Consultancy; Amgen: Research Funding; Ichnos Sciences: Research Funding; Regeneron Pharmaceuticals: Research Funding; Glaxo SmithKline: Research Funding; Karyopharm: Research Funding; Sanofi: Research Funding. Dingli: Apellis: Consultancy; Sanofi: Consultancy; Janssen: Consultancy; GSK: Consultancy; Novartis: Research Funding; Alexion: Consultancy. Lin: Gamida Cell: Consultancy; Takeda: Research Funding; Kite, a Gilead Company: Consultancy, Research Funding; Juno: Consultancy; Novartis: Consultancy; Legend: Consultancy; Janssen: Consultancy, Research Funding; Celgene: Consultancy, Research Funding; Bluebird Bio: Consultancy, Research Funding; Sorrento: Consultancy; Merck: Research Funding; Vineti: Consultancy. Gertz: Akcea Therapeutics, Ambry Genetics, Amgen Inc, Celgene Corporation, Janssen Biotech Inc, Karyopharm Therapeutics, Pfizer Inc (to Institution), Sanofi Genzyme: Honoraria; Aurora Biopharma: Other: Stock option; AbbVie Inc, Celgene Corporation: Other: Data Safetly & Monitoring; Ionis Pharmaceuticals: Other: Advisory Board; Akcea Therapeutics, Alnylam Pharmaceuticals Inc, Prothena: Consultancy. Fonseca: Aduro: Consultancy; Kite: Consultancy; Juno: Consultancy; Merck: Consultancy; Takeda: Consultancy; Caris Life Sciences: Membership on an entity's Board of Directors or advisory committees; Patent: Prognosticaton of myeloma via FISH: Patents & Royalties; AbbVie: Consultancy; Scientific Advisory Board: Adaptive Biotechnologies: Membership on an entity's Board of Directors or advisory committees; OncoTracker: Consultancy, Membership on an entity's Board of Directors or advisory committees; Mayo Clinic in Arizona: Current Employment; Amgen: Consultancy; BMS: Consultancy; Celgene: Consultancy; Bayer: Consultancy; Janssen: Consultancy; Novartis: Consultancy; Pharmacyclics: Consultancy; Sanofi: Consultancy; GSK: Consultancy.

e20016 Background: An elevated serum LDH level is an adverse prognostic factor in NDMM. However, this category includes quantitative serum LDH levels that range from just over the upper limit of normal (ULN) to levels that may be 2 or more-fold higher than the ULN. This binary classification of serum LDH level of “normal versus elevated” fails to discriminate between the different disease biology that exists among NDMM patients with elevated serum LDH levels. Thus, we attempted to further stratify NDMM patients by the level of their serum LDH and determine its impact on OS. Methods: The cohort included patients diagnosed with NDMM from the Mayo Clinic, Rochester from 2003 - 2017 who were treated with novel agent induction therapy and had serum LDH levels measured at the time of diagnosis. The serum LDH levels were stratified into three levels: Normal (LDH < 222 U/L), Elevated (LDH 223-444 U/L), and Very Elevated (LDH >444 U/L or >2x upper limit of normal). Survival analysis was performed using the Kaplan-Meier survival analysis and compared via the log-rank method. Results: The cohort consists of 1,196 NDMM patients with a median age of 65 (22 – 95). R-ISS classification and cytogenetic risk were available for 968 and 970 patients respectively. The median serum LDH level was (162 U/L (3- 1260)) and an elevated LDH was present in 199 patients (17%). The median OS for patients with normal (N = 997; 83%), elevated (N = 170; 13%) and very elevated (N = 29; 3%) LDH levels were 76 months, 57 months and 23 months respectively (P < 0.001). The impact of these different levels of LDH on OS by R-ISS stage and cytogenetic risk is shown in the Table. Conclusions: A very small subset of NDMM patients has very elevated LDH levels that confer an exceptionally poor OS irrespective of R-ISS stage and cytogenetic risk. Future studies elucidating their disease biology responsible for such poor OS outcomes are warranted.[Table: see text]

AbstractThe diagnosis of primary plasma cell leukemia (pPCL) has been made by quantifying circulating plasma cells (cPCs) morphologically on a peripheral blood (PB) smear. However, this technique is not sufficiently sensitive. Multiparametric flow cytometry (MFC) provides a readily available and highly sensitive method to identify and quantify cPCs that could complement PB smear assessment. However, an optimal quantitative cutoff for cPCs by MFC to identify pPCL has not been established. Thus, a total of 591 patients newly diagnosed multiple myeloma (NDMM) patients who had their PB samples evaluated morphologically by PB smear, and immunophenotypically by MFC prior to beginning therapy were evaluated. The presence of ≥200 cPCs/μL by MFC (N = 25 or 5% of the total population) was chosen to identify patients with ≥5% cPCs by PB smear with a specificity of 99% and a sensitivity of 77%. For patients with ≥200 cPCs/μL by MFC compared to the remainder of the cohort, the median Time to next therapy (TTNT) was 18 vs 30 months and the median OS was 38 vs 70 months respectively. Thus, MFC assessment of PB can be utilized in conjunction with the morphological assessment of a PB smear to aid in improving the identification of pPCL among NDMM patients.

INTRODUCTION: Primary plasma cell leukemia (pPCL) is the most aggressive form of multiple myeloma (MM). Recently, pPCL has been defined by as few as >5% circulating plasma cells (cPCs) detected morphologically on a peripheral blood smear. However, this technique may not be sufficiently sensitive and is dependent on inter-observer bias to properly identify and quantify cPCs. Multiparametric flow cytometry (MFC) provides a readily available and highly sensitive method to identify and quantify cPCs. However, an optimal quantitative cutoff for cPCs by MFC to define the presence of pPCL has not been established to date. Thus, this is the first study to date that determines an optimal cutoff of cPCs/ microliter that could identity patients with pPCL at diagnosis. METHODS: We retrospectively evaluated all newly diagnosed MM patients seen at the Mayo Clinic, Rochester from January 2007 to December 2017 who had their peripheral blood samples evaluated morphologically by peripheral blood smear and immunophenotypically by MFC prior to beginning therapy. Patients with a peripheral blood smear detecting >5% cPCs were classified as having pPCL. Six-color MFC was performed on peripheral blood mononuclear cells by isolated by Ficoll gradient, and stained with antibodies to CD45, CD19, CD38, CD138 and cytoplasmic Kappa and Lambda immunoglobulin light chains. The data was collected using Becton Dickinson FacsCanto II instruments collecting 150,000 events and analyzed using the BDFacs DIVA Software. The gating strategy employed first used the expression of CD38, CD138, and cytoplasmic immunoglobulin light chains to identify all plasma cells in the specimen. The cPCs were then discriminated from polyclonal/normal plasma cells based on differential CD19 and CD45 expression. The cPCs detected were reported as the number of clonal events/150,000 collected total events and converted to the absolute number of cPCs/microliter using their absolute lymphocyte count and monocyte count determined at the time of the blood draw for cPC MFC assessment. A receiver operator curve (ROC) was performed to determine the absolute cPCs/microliter by MFC that best identified those patients with >5% cPC on their peripheral blood smear. Survival analysis was performed by the Kaplan-Meier method and differences assessed using the log rank test. RESULTS: A total of 591 patients were included in this analysis of which 59% were male and the median age was 66 years (range: 27 - 95) with 22% being 75 years of age or older. From this cohort, 133 (24%) patients had high risk FISH cytogenetics, 172 (30%) were classified as ISS 3 at diagnosis, 83 (15%) had an elevated LDH level and 61 (17%) had a plasma cell labeling index (PCLI) of 2% or greater. There were 65 (11%) patients with any amount of cPCs detectable by peripheral blood smear of which only 22 (4% of the total population) met criteria for pPCL with >5% cPCs. With MFC, 403 (68%) patients had detectable cPCs. The median number of cPCs per microliter was 0.81 (0 - 10,455) with 273 (46%) having >1 cPC/microliter. A ROC analysis determined that the presence of >50 cPCs/microliter by MFC was the optimal cutoff to identify patients with >5% cPCs by blood smear with approximately a specificity of 94% and a sensitivity of 86%. The presence of >50 cPCs/microliter by MFC was associated with higher presence of high risk FISH cytogenetics, ISS 3 classification, elevated LDH and PCLI > 2%. The median time to next treatment (TTNT) for patients with and without >5% cPCs by peripheral blood smear was 18 months vs. 30 months (P < 0.001). The median TTNT for patients with and without >50 cPCs/microliter by MFC was 19 months vs. 30 months (P < 0.01). The median overall survival (OS) for patients with and without >5% cPCs by peripheral blood smear was 25 months vs. 71 months (P < 0.001; Fig 1A). The median OS for patients with and without >50 cPCs/microliter by MFC was 38 months vs. 71 months (P < 0.01; Fig 1B). CONCLUSION: This is the largest study that attempts to identify an optimal cutoff of cPCs/microliter by MFC for diagnosing pPCL patients. The presence of >50 cPCs/microliter is an optimal cutoff by MFC that best predicts for the presence of >5% cPCs by peripheral blood smear. However, there remains discordance in the OS outcomes between the two cutoffs by different methodologies for pPCL. This could signify the presence of biological processes associated with the presence of >5% cPCs when detected by peripheral blood smear that are not captured by MFC. Disclosures Dispenzieri: Pfizer: Research Funding; Janssen: Consultancy; Intellia: Consultancy; Akcea: Consultancy; Alnylam: Research Funding; Takeda: Research Funding; Celgene: Research Funding. Dingli:Karyopharm: Research Funding; Rigel: Consultancy; Millenium: Consultancy; Janssen: Consultancy; alexion: Consultancy. Lacy:Celgene: Research Funding. Kapoor:Amgen: Research Funding; Takeda: Honoraria, Research Funding; Glaxo Smith Kline: Research Funding; Sanofi: Consultancy, Research Funding; Cellectar: Consultancy; Janssen: Research Funding; Celgene: Honoraria. Leung:Aduro: Membership on an entity's Board of Directors or advisory committees; Omeros: Research Funding; Prothena: Membership on an entity's Board of Directors or advisory committees; Takeda: Research Funding. Russell:Imanis: Equity Ownership. Gertz:DAVA oncology: Speakers Bureau; Ionis/Akcea: Consultancy; Alnylam: Consultancy; Prothena Biosciences Inc: Consultancy; Celgene: Consultancy; Janssen: Consultancy; Spectrum: Consultancy, Research Funding; Annexon: Consultancy; Appellis: Consultancy; Amgen: Consultancy; Medscape: Consultancy, Speakers Bureau; Physicians Education Resource: Consultancy; Abbvie: Other: personal fees for Data Safety Monitoring board; Research to Practice: Consultancy; Teva: Speakers Bureau; Johnson and Johnson: Speakers Bureau; Pharmacyclics: Membership on an entity's Board of Directors or advisory committees; Proclara: Membership on an entity's Board of Directors or advisory committees; i3Health: Other: Development of educational programs and materials; Springer Publishing: Patents & Royalties; Amyloidosis Foundation: Research Funding; International Waldenstrom Foundation: Research Funding. Kumar:Takeda: Research Funding; Celgene: Consultancy, Research Funding; Janssen: Consultancy, Research Funding.

Introduction: Several patient and disease biology characteristics that impact overall survival (OS) outcomes of patients with newly diagnosed multiple myeloma (NDMM) have been well described. However, several prior population-based registry studies in the US such as SEER and NCDB databases have demonstrated the negative prognostic impact of socio-economic (SE) factors such as low median household income based on US national census bureau data for zip code of residence as well as absence of marital support on the OS of NDMM patients. Whether such SE factors are independent of established host and disease biology related factors remains unknown due to the absence of detailed patient-level data in population-based cancer registries. Thus, we evaluated whether such SE factors independently affected the OS of NDMM patients in our institution when accounting for patient and disease biology related factors. Methods: We analyzed a retrospective cohort of consecutive NDMM patients seen at the Mayo Clinic from January 2005 to December 2015. Median household income for each patient's area of residence was estimated by matching their home zip code at diagnosis with data available through the US National Census Bureau. Median household income < $63,000 for a zip code were used to classify patients as having low income based on the National Cancer Database classification of the bottom three quartiles. Absence of marital support was considered in patients divorced, single or widowed at the time of diagnosis. Survival estimates were calculated using the Kaplan-Meier method. Patient and disease biology related factors taken into consideration included age, FISH cytogenetics, ISS stage and LDH levels. Survival estimates were compared between groups using the log-rank test. Multivariable adjusted proportional hazards regression models were fit for the association between known prognostic factors and OS. Results: A total of 2,621 MM patients were analyzed from the Mayo Clinic cohort of which 60% were male and the median age was 64 (22 - 96) years with 14% being 75 years of age or older. In this cohort, 541 (33%) patients had ISS 3 disease and 370 (22%) had high risk cytogenetics by FISH. The median OS based on estimated average household income was 65 months for the group of patients with low household income (N = 32%) and 80 months for the remainder (P = 0.001) (Fig 1A). The median OS based on marital support was 59 months for those without marital support at diagnosis (N = 21%) versus 76 months for remainder (P = 0.003) (Fig 1B). However, in a multivariable analysis that included the aforementioned SE factors and patient and disease biology characteristics , only age of 75 years or older (p < 0.001), the presence of high-risk FISH cytogenetics (p < 0.001), presence of ISS 3 disease (p < 0.001) and elevated LDH (p = 0.005) retained independent statistical significance in predicting for worse OS. Lower household income (p = 0.264) and absence of marital support (p = 0.054) were not independent predictors of worse OS. Conclusions: With the caveat of patient selection bias in a tertiary academic center, SE factors such as estimated average household income and marital support at diagnosis do not appear to impact OS of NDMM patients when considered in the context of traditional host and disease biology risk factors. These findings are in contrast to those suggested by population based registry studies; nevertheless, similar patient-level assessments should be considered in patient cohorts from non-tertiary medical centers as these results may likely be different. Disclosures Dispenzieri: Akcea: Consultancy; Intellia: Consultancy; Janssen: Consultancy; Pfizer: Research Funding; Takeda: Research Funding; Celgene: Research Funding; Alnylam: Research Funding. Dingli:alexion: Consultancy; Janssen: Consultancy; Millenium: Consultancy; Rigel: Consultancy; Karyopharm: Research Funding. Lacy:Celgene: Research Funding. Kapoor:Amgen: Research Funding; Glaxo Smith Kline: Research Funding; Sanofi: Consultancy, Research Funding; Celgene: Honoraria; Janssen: Research Funding; Cellectar: Consultancy; Takeda: Honoraria, Research Funding. Leung:Prothena: Membership on an entity's Board of Directors or advisory committees; Aduro: Membership on an entity's Board of Directors or advisory committees; Takeda: Research Funding; Omeros: Research Funding. Russell:Imanis: Equity Ownership. Gertz:Medscape: Consultancy, Speakers Bureau; Research to Practice: Consultancy; Springer Publishing: Patents & Royalties; Ionis/Akcea: Consultancy; Pharmacyclics: Membership on an entity's Board of Directors or advisory committees; Alnylam: Consultancy; Prothena Biosciences Inc: Consultancy; Celgene: Consultancy; Janssen: Consultancy; Spectrum: Consultancy, Research Funding; Annexon: Consultancy; Appellis: Consultancy; Amgen: Consultancy; Physicians Education Resource: Consultancy; Abbvie: Other: personal fees for Data Safety Monitoring board; Teva: Speakers Bureau; Johnson and Johnson: Speakers Bureau; DAVA oncology: Speakers Bureau; Proclara: Membership on an entity's Board of Directors or advisory committees; i3Health: Other: Development of educational programs and materials; Amyloidosis Foundation: Research Funding; International Waldenstrom Foundation: Research Funding. Kumar:Celgene: Consultancy, Research Funding; Janssen: Consultancy, Research Funding; Takeda: Research Funding.