Nandakumar, Bharat

e19519 Background: NSMM is a rare subtype of myeloma that has not been well characterized in the era of novel agent induction therapy. Thus, we evaluated the clinical and cytogenetic features of patients with newly diagnosed NSMM evaluated at our institution. Methods: We evaluated all NSMM patients seen at the Mayo Clinic, Rochester from 2008-2017 based on the absence of a detectable monoclonal protein in their serum and urine electrophoresis and having a concurrent involved serum immunoglobulin light chain of less than 5 mg/dL. Survival analysis was performed by the Kaplan-Meier method and clinical and cytogenetic features were reported descriptively. Results: This cohort consisted of 30 consecutive patients with NSMM with a median age of 62 years (40-79) of which 16 (53%) were male. The median bone marrow plasma cell involvement was 70% (Range: 0 – 100). There were 3/25 (12%) patients who presented with a creatinine of 2 mg/dL or greater and 6/23 (26%) patients who presented with hypercalcemia (11 mg/dL or greater). The ISS classification of this cohort is as follows: Stage 1- 8 (36%) patients, Stage 2- 3 (14%) patients and Stage 3- 11 (50%) patients. There were 6 (21%) out of 28 patients with high risk cytogenetics (either del 17p, t(4;14), t(14;16) or t(14;20). The distribution of primary cytogenetic abnormalities among this cohort was as follows: t(11;14) – 16 (57%), t(4;14) – 1 (4%), trisomies – 6 (21%) and other - 5 (13%). The median overall survival (OS) for patients in this cohort was 59 months (95% CI: 34 – 64). In comparison to a control cohort of newly diagnosed secretory myeloma patients matched for age, gender and year of diagnosis in a 1:2 ratio, the median OS was worse for the NSMM cohort but this was not statistically significant (59 vs. 92 months, p = 0.257). Conclusions: Patients diagnosed with NSMM had a predisposition towards having a t(11;14) primary cytogenetic abnormality. In the era of novel agent induction therapy, they may have a survival outcome that may be worse than newly diagnosed secretory myeloma patients.

8039 Background: Treatment of MM has evolved significantly over the past decade, with increasing use of multi-drug combinations for initial therapy. In addition, supportive care approaches have also improved. We examined how these improvements have translated to survival outcomes in patients with newly diagnosed MM. Methods: Patients (n=3449) with a diagnosis of MM made between 2004 and 2017 and seen at Mayo Clinic within six months of the diagnosis, were included in this analysis. Patients were divided into three groups based on the year of diagnosis; group 1- 2004-07 (n=831), group 2-2008-12 (n=1161), and group 3-2013-17 (n=1457). Survival of the groups were estimated using Kaplan-Meier method, and compared using log rank test. Results: The median age was 64 years (22 to 96); 60% were male and 40% were female. 14% were >75 years, 33% were aged 65-75 and 53% were <65 years. The median overall survival for the whole cohort was 5.7 years (95%CI; 5.4, 6.3). The median OS for the groups 1, 2 and 3 were 3.9, 6.3 and NR, respectively; p<0.001. The 4-year survival estimates were 50%, 62%, and 75%, respectively. We then explored the improvements in patient subgroups. While all patients experienced improvements in OS over time, improvement in group 3 was most prominent for those >75 years. In patients <65 years, the 4-yr OS for groups 1, 2 and 3 were 57, 71, and 79% respectively. In patients 65-75 years of age, the 4-yr OS for groups 1, 2 and 3 were 48, 60, and 75% respectively. In patients >75 years, the 4-yr OS for groups 1, 2 and 3 were 24, 35, and 56% respectively. While patients with high-risk disease did not see as much benefit in the earlier period, substantial progress was seen in the last group. The 3-yr OS for patients with high-risk cytogenetics were 52, 55, and 73% for groups 1, 2 and 3 compared to 67, 75, and 85% for standard-risk cytogenetics respectively. 2067 patients were staged according to the International Staging System and the median OS for stages 1, 2 and 3 were 6.5, 4.6 and 2.4 in group 1; 9.2, 6.6 and 3.5 in group 2 and NR for any of the stages in group 3. Conclusions: The results confirm continued improvement in survival of newly diagnosed multiple myeloma patients, including elderly and high-risk MM.

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: Advances in the understanding of disease biology, the introduction of new drugs, and better supportive care have improved outcomes in multiple myeloma (MM). Most improvements have been observed in clinical trial and tertiary referral center populations but questions remain about the generalizability of these findings to patients treated in the community. Methods: We studied all patients diagnosed with MM between 01/01/2001 and 12/31/2015 who had complete demographic and overall survival (OS) data available and were seen at Mayo Clinic (MAYO) or followed in the Surveillance, Epidemiology, and End Results Program (SEER, 18 registry data 2000 - 2016, 11/2018 submission). We retrieved age at diagnosis, sex, date of diagnosis, date of last follow-up, and OS for all patients. OS was defined as the time from diagnosis to death from any cause. Patients who were alive at the end of follow-up (12/31/ 2016) were censored. OS estimates were calculated using the Kaplan-Meier method. Age- and sex-adjusted Cox models were used to assess the association between the 5-year interval of diagnosis and OS. Expected OS estimates were calculated based on United States general population rate tables (Human Mortality Database) using a conditional approach. Standardized mortality ratios (SMR) were calculated by dividing the number of observed deaths by the number of expected deaths in age- and sex-matched general United States population controls. Linear regression models were fit to test for linear trends in early mortality and SMR over time (per 5-year interval). P-values below 0.05 were considered statistically significant. Results: The median age at diagnosis was 3 years lower in patients at MAYO (64 years, 15% ≥ 75 years, 60% male, n = 3293) compared to SEER (67 years, 29% ≥ 75 years, 55% male, n = 61779). After a median follow-up of 2.8 years the median OS was longer in MAYO compared to SEER (5.4 versus 4.0 years, HR 0.82, 95% CI 0.78 - 0.86, p &lt; 0.001) and remained statistically significant after adjusting for age and sex (HR 0.91, 95% CI 0.86 - 0.95, p &lt; 0.001). Early mortality (1-year mortality) decreased between 2001-2005 and 2011-2015: From 20% to 11% at MAYO and from 26% to 19% in SEER. When grouping OS by year of diagnosis (in 5-year-intervals) improvements were seen in both populations (A) and remained statistically significant after adjusting for age and sex. The relative improvements were similar comparing the 5-year period after the introduction of the novel therapies (2006 - 2010) to 2001 - 2005 and more pronounced in MAYO for the most recent 5-year interval (2011 - 2015, A). This trend was reflected in a steady temporal improvement in 5-year OS estimates in MAYO including the most recent 5-year interval (2011 - 2015, B bottom left). In SEER there was a comparable increase between the first two 5-year intervals but a lesser improvement in more recently (2011 - 2015, B bottom left). A diagnosis of MM remained associated with significant excess mortality in all age groups over time in both populations (B top). There was a decrease in excess mortality over time at MAYO (SMR decline per 5-year interval 1.3, 95% CI 0.9 - 1.8, p &lt; 0.001) while there was little change in SEER (SMR decline 0.0, 95% CI -0.3 - 0.3, p = 0.917, B bottom right). Further stratifying by age at diagnosis, the decrease in excess mortality was observed mostly in patients &lt; 75 years at MAYO (SMR decline per 5-year interval 1.7, 95% CI 1.5 - 2.0, p &lt; 0.001, C bottom left) and to a lesser extent in older patients (SMR decline 0.4, 95% CI 0.2 - 0.6, p &lt; 0.001, C bottom right). No such trends towards improvement were observed in either age group in SEER (C bottom). In older patients, early mortality remained approximately 30% in both populations despite continued improvements, while the 5-year OS estimates for the most recent 5-year interval (2011 - 2015) were 37% at MAYO and 26% in SEER (C top). Conclusions: Both early mortality and long-term survival have improved over time. Reductions in excess mortality were largely confined to younger patients with access to specialized care. Patients ≥ 75 years represent more than a quarter of all patients in the community, a third of them died within one year of the diagnosis, and only one in four was alive five years later. Older patients with MM remain a vulnerable population and have derived only limited benefit from recent advances in the field. Safe and effective therapies for older patients with MM remain an unmet need. Figure Disclosures Gertz: Ionis/Akcea: Consultancy; International Waldenstrom Foundation: Research Funding; 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; DAVA oncology: 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. Dispenzieri:Alnylam: Research Funding; Akcea: Consultancy; Takeda: Research Funding; Pfizer: Research Funding; Janssen: Consultancy; Intellia: Consultancy; Celgene: Research Funding. Lacy:Celgene: Research Funding. Maurer:Nanostring: Research Funding; Morphosys: Membership on an entity's Board of Directors or advisory committees; Celgene: Research Funding. Dingli:alexion: Consultancy; Janssen: Consultancy; Millenium: Consultancy; Rigel: Consultancy; Karyopharm: Research Funding. Kapoor:Takeda: Honoraria, Research Funding; Celgene: Honoraria; Janssen: Research Funding; Amgen: Research Funding; Sanofi: Consultancy, Research Funding; Glaxo Smith Kline: Research Funding; Cellectar: Consultancy. Leung:Takeda: Research Funding; Prothena: Membership on an entity's Board of Directors or advisory committees; Aduro: Membership on an entity's Board of Directors or advisory committees; Omeros: Research Funding. Kumar:Celgene: Consultancy, Research Funding; Janssen: Consultancy, Research Funding; Takeda: Research Funding.

AbstractOur prior studies identified the prognostic significance of quantifying cPCs by multiparametric flow cytometry (MFC) in newly diagnosed multiple myeloma (NDMM) patients. We evaluated if a similar quantification of cPCs could add prognostic value to the current R‐ISS classification of 556 consecutive NDMM patients seen at the Mayo Clinic, Rochester from 2009 to 2017. Those patients that had ≥5 cPCs/μL and either R‐ISS stage I or stage II disease were re‐classified as R‐ISS IIB stage for the purposes of this study. The median time to next therapy (TTNT) and overall survival (OS) for patients with ≥5 cPCs/μL at diagnosis was as follows: R‐ISS I (N = 110) ‐ 40 months and not reached; R‐ISS II (N = 69) ‐ 30 and 72 months; R‐ISS IIB (N = 96) ‐ 21 and 45 months and R‐ISS III (N = 281) ‐ 20 and 47 months respectively. Finally, ≥ 5 cPCs/μL retained its adverse prognostic significance in a multivariable model for TTNT and OS. Hence, quantifying cPCs by MFC can potentially enhance the R‐ISS classification of a subset of NDMM patients with stage I and II disease by identifying those patients with a worse than expected survival outcome.

BACKGROUND: pPCL is a rare and aggressive form of multiple myeloma (MM) with poor survival outcomes compared to that experienced by MM patients. It has been traditionally defined by the presence of an absolute plasma cell count of > 2000/microL or >20% circulating plasma cells (cPCs) in the peripheral blood. However, several recent studies have validated the optimal cutoff for defining pPCL to be >5% cPCs on a peripheral blood smear due to equally poor outcomes in this less restrictive cohort. Hence, given the rarity of this disease entity, we evaluated the clinical outcomes and cytogenetic features of patients diagnosed with pPCL at our institution with the new proposed definition of pPCL by >5% cPCs on peripheral blood smear that were treated with novel agent induction therapies. METHODS: We evaluated all patients with pPCL diagnosed between 2000 - 2018 evaluated at Mayo Clinic in Rochester, Minnesota. Data regarding these patients were extracted from a prospectively maintained database and from the review of the electronic medical records. Patients were categorized as having high risk cytogenetics if any of the following abnormalities were present: del 17p, t(4;14), t(14;16) or t(14;20). Chi-square tests and Fisher exact tests were used to compare differences between the sub-groups of interest. Survival analysis was performed by the Kaplan-Meier method and differences assessed using the log rank test. RESULTS: This cohort consisted of 71 patients with pPCL with a median age of 62 years (range: 34-91) of which 33 (46%) were male. The median follow up was 46 months (95% CI: 41 - 90). The median bone marrow plasma cell content was 84% (Range: 10 - 100) and the median plasma cell percentage on the peripheral smear was 23% (range: 5 - 93). There were 48 (68%) patients with >20% cPCs in their peripheral blood smear. Only 23 (32%) patients had concurrent information on the number of cPCs by multiparametric flow cytometry at diagnosis and the median number of cPCs detected was 32,807 per 150,000 events analyzed (Range: 354 - 132,256). There were 15/54 (28%) patients who presented with a creatinine of 2 mg/dL or greater and 17/55 (31%) pts who presented with hypercalcemia (11 mg/dL or greater). Data for ISS classification was available in 45 patients and is as follows: Stage 1- 3 (7%) patients, Stage 2- 10 (22%) patients and Stage 3- 32 (71%) patients. Data on the presence of primary cytogenetic abnormalities were available in 60 (85%) patients of which 29 (49%) had high risk cytogenetics. The distribution of primary cytogenetic abnormalities among this cohort was as follows: t(11;14) - 27 (45%), t(4;14) - 5 (8%), t(14;16) - 8 (13%), t(14;20)- 3(5%) and del 17p- 15(25%). All patients received novel agent induction therapy with 40 (57%) of them having received at least one autologous stem cell transplant and 1 patient undergoing an allogeneic stem cell transplant. The median overall survival (OS) for all pPCL patients in this cohort was 23 months (95% CI: 19 - 38). The OS was similar in patient with 5-19% cPCs vs. >20% cPCs (24 vs. 23 months, P = 0.999). However, when the entire cohort was stratified by cytogenetic risk, the median OS was 51 months for standard risk vs. 19 months for high risk (P = 0.005) (FIGURE). There were only 17 (30%) patients in this cohort who were alive for 48 months or longer since diagnosis (i.e. twice the median OS of this cohort) and they were more likely to not have high risk cytogenetics at diagnosis (P = 0.05). CONCLUSION: In this analysis of one of the largest contemporary cohorts of pPCL patients, the outcomes of patients diagnosed with pPCL remain poor despite the use of novel agent induction therapy. However, some patients appear to do better than expected and this phenomenon may be influenced by the absence of high risk cytogenetics. Disclosures Kumar: Celgene: Consultancy, Research Funding; Janssen: Consultancy, Research Funding; Takeda: Research Funding. Dispenzieri:Akcea: Consultancy; Intellia: Consultancy; Janssen: Consultancy; Pfizer: Research Funding; Takeda: Research Funding; Celgene: Research Funding; Alnylam: Research Funding. Dingli:Karyopharm: Research Funding; Rigel: Consultancy; Millenium: Consultancy; Janssen: Consultancy; alexion: Consultancy. Lacy:Celgene: Research Funding. Kapoor:Glaxo Smith Kline: Research Funding; Sanofi: Consultancy, Research Funding; Amgen: Research Funding; Takeda: Honoraria, Research Funding; Cellectar: Consultancy; Celgene: Honoraria; Janssen: Research Funding. Russell:Imanis: Equity Ownership. Leung:Omeros: Research Funding; Takeda: Research Funding; Prothena: Membership on an entity's Board of Directors or advisory committees; Aduro: Membership on an entity's Board of Directors or advisory committees. Gertz:Celgene: Consultancy; Prothena Biosciences Inc: Consultancy; Appellis: Consultancy; Amgen: Consultancy; Springer Publishing: Patents & Royalties; Amyloidosis Foundation: Research Funding; International Waldenstrom Foundation: Research Funding; Janssen: Consultancy; Ionis/Akcea: Consultancy; Alnylam: Consultancy; Annexon: Consultancy; Abbvie: Other: personal fees for Data Safety Monitoring board; Research to Practice: Consultancy; Teva: Speakers Bureau; Medscape: Consultancy, Speakers Bureau; Physicians Education Resource: Consultancy; Pharmacyclics: Membership on an entity's Board of Directors or advisory committees; Proclara: Membership on an entity's Board of Directors or advisory committees; Spectrum: Consultancy, Research Funding; i3Health: Other: Development of educational programs and materials; Johnson and Johnson: Speakers Bureau; DAVA oncology: Speakers Bureau.

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.