Nandakumar, Bharat

BackgroundSarcopenia increases with age and is associated with poor survival outcomes in patients with cancer. By using a deep learning–based segmentation approach, clinical computed tomography (CT) images of the abdomen of patients with newly diagnosed multiple myeloma (NDMM) were reviewed to determine whether the presence of sarcopenia had any prognostic value.MethodsSarcopenia was detected by accurate segmentation and measurement of the skeletal muscle components present at the level of the L3 vertebrae. These skeletal muscle measurements were further normalized by the height of the patient to obtain the skeletal muscle index for each patient to classify them as sarcopenic or not.ResultsThe study cohort consisted of 322 patients of which 67 (28%) were categorized as having high risk (HR) fluorescence in situ hybridization (FISH) cytogenetics. A total of 171 (53%) patients were sarcopenic based on their peri‐diagnosis standard‐dose CT scan. The median overall survival (OS) and 2‐year mortality rate for sarcopenic patients was 44 months and 40% compared to 90 months and 18% for those not sarcopenic, respectively (p < .0001 for both comparisons). In a multivariable model, the adverse prognostic impact of sarcopenia was independent of International Staging System stage, age, and HR FISH cytogenetics.ConclusionsSarcopenia identified by a machine learning–based convolutional neural network algorithm significantly affects OS in patients with NDMM. Future studies using this machine learning–based methodology of assessing sarcopenia in larger prospective clinical trials are required to validate these findings.

110 Background: Sarcopenia or a loss of muscle mass increases with aging and is associated with increased overall mortality in patients with cancer. Recent advances in machine learning–based CNN algorithms have allowed for the rapid processing of digital images to produce image classifications of body composition. Since incidence of MM is highly associated with aging, we sought to determine if the presence of sarcopenia, as determined by utilizing this machine learning–based CNN algorithm on CT images, had prognostic value in patients with NDMM. Methods: We identified all patients with NDMM from January 2003 to July 2019 who had a standard-dose CT scan that included the L3 vertebral level performed within 6 months of diagnosis. Using a machine learning–based CNN-algorithm, abdominal CT images were analyzed to measure muscle area. These measurements were normalized by dividing the area values by the height of the patient squared (m2) to obtain skeletal muscle index (SMI) values. Patients were categorized as sarcopenic according to international gender-specific consensus cutoffs for SMI (male: < 55 cm2/m2 and female: < 39 cm2/m2). Patients with the following FISH cytogenetics were considered high risk (HR): t(4;14), t(14;16), t(14;20), and deletion 17p/monosomy 17 whereas the remainder were standard risk (SR). Survival analysis was performed using the Kaplan-Meier method and compared via the log-rank method. Results: The study cohort consisted of 344 patients. 68 (20%) were categorized as HR based on FISH cytogenetics.187 (54%) patients were sarcopenic based on their peri-diagnosis standard-dose CT scan. Sarcopenic patients were more likely to have ISS-3 disease (45% vs. 30%; p =.023), be male (73% vs. 48%; p <.001), and be ≥ age 75 (27% vs. 14%; p =.002) compared to non-sarcopenic patients. The median OS for patients with HR FISH and ISS 2 / 3 disease was 40 months and 57 months respectively compared to 90 months and 119 months for those with SR FISH and ISS-1 disease respectively (FISH: p <.004; ISS: p <.001). The median OS for sarcopenic patients was 44 months compared to 90 months for those not sarcopenic (p <.001). The time to next therapy (TTNT) for sarcopenic patients was 39 months compared to 45 months for those not sarcopenic (p =.05). In a multivariable model, the presence of sarcopenia (HR 1.64, 95% CI, 1.05–2.56; p =.03) retained significance in the presence of HR FISH, ISS 2 / 3 disease, and age ≥ 75. Conclusions: Gender-specific sarcopenia identified by a machine learning–based CNN algorithm significantly affects OS in patients with NDMM and is independent of age, ISS stage, and cytogenetic status. Future studies utilizing this machine learning–based methodology of assessing sarcopenia in larger prospective clinical trials are required to validate these findings.

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.

Sarcina ventriculi is a rare gram-positive coccus increasingly reported in patients with a history of delayed gastric emptying or gastric outlet obstruction and is sometimes seen in association with emphysematous gastritis and perforation. We report a case of a 67-year-old male who presented with epigastric pain. CT imaging and cholangiopancreatography were concerning for pancreatic neoplasia. Upper endoscopic ultrasound-guided fine needle aspiration cytology of a perigastric lymph node confirmed metastatic adenocarcinoma of pancreatic origin, and cocci arranged in a tetrad fashions characteristic of Sarcina ventriculi were noted. To our knowledge, this is the first reported case of Sarcina ventriculi in an FNA of metastatic pancreatic carcinoma in a perigastric lymph node. These organisms likely represent carry-through contaminants from the transgastric approach of the endoscopic FNA.

Abstract Background: There has been a steady improvement in the survival of patients with multiple myeloma (MM) due to better understanding of the pathophysiology, use of novel agents, and risk-adapted therapy. These improvements were largely observed in clinical trials and tertiary center populations and questions remain about the generalizability of these findings to patients in community settings. Here, we compared the experience of patients seen at Mayo Clinic, Rochester (MAYO) and those followed in the Surveillance, Epidemiology, and End Results Program (SEER), to gain a better understanding of current trends in survival. Methods: We studied all patients with MM diagnosed between 01/2004 and 12/2018 seen at MAYO and those followed in SEER (18 registry research data 2000-2018, 11/2020 submission). For comparison with SEER, MAYO patients who were alive at the end of SEER follow-up (12/31/2018) were censored. For the estimation of relative survival, patients alive at the end of 2019 were censored to align with the published United States general population rate tables. Overall survival (OS) estimates were calculated using the method described by Kaplan and Meier. Multivariable-adjusted proportional hazards regression models were used to assess the association between the 5-year interval of diagnosis and overall survival. Expected OS estimates were calculated based on United States general population rate tables from the 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 trends in SMRs over time. Results: The median age at diagnosis was 3 years lower in patients seen at MAYO compared to patients followed in SEER. The proportion of patients 75 years and older at the time of diagnosis was higher in SEER (26.3%) compared to MAYO (15.3%). Median OS was 6.7 years (95% CI 6.3-7.1) for MAYO compared to 4.6 years (95% CI 4.5-4.7) in SEER (Figure 1). When stratifying OS by year of diagnosis (in 5-year-intervals: 2004-2008, 2009-2013, 2014-2018), improvements were seen in both patient populations over time, albeit to a greater extent in patients seen at MAYO (Figures 2 and 3). The results were consistent after adjusting for age and sex in both MAYO and SEER. There were improvements in OS across all three 5-year intervals and age groups in both patient populations. In MAYO, these improvements were most pronounced during the second 5-year interval for younger patients (&lt; 65 years) and more pronounced during the last 5-year interval for older patients (&gt; 65 years). In SEER, the improvements were more gradual across the three 5-year intervals, but it was noted that the relative improvements in younger patients were greater than in older patients during the last 5-year interval. Early mortality decreased over time in both patient populations: from 11% to 6% at MAYO and from 23% to 16% in SEER (Figure 4). To examine the observed improvements in the context of the survival trends of the general population, we stratified the SMRs by patient population, age at diagnosis, and year of diagnosis, and a lack of improvement in excess mortality was observed among older patients (&gt;75 years) in both populations over time (Figure 5). Three- and five-year overall survival estimates were higher in patients seen at MAYO compared to patients followed in SEER, but the survival advantage diminished with increasing follow-up time (Figure 6). On further sub-group analysis within MAYO patients, excess mortality was observed for all International staging system (ISS) stages and patients with high-risk (HR) cytogenetics. Stratifying the SMRs by age and risk group, decreases in excess mortality were most pronounced among older patients, particularly those with indolent disease (Figure 7). Conclusions: Survival outcomes in MM continued to improve in both patient populations. These improvements were more pronounced in MAYO compared to SEER, which may be partially explained by differences in access to specialized care. The most pronounced improvements were observed among younger patients and those with favorable disease characteristics. Future efforts need to focus on improving therapies for patients with unfavorable disease characteristics and on increasing access to specialized care in community settings. Figure 1 Figure 1. Disclosures Kapoor: Glaxo SmithKline: Research Funding; Takeda: Research Funding; Sanofi: Research Funding; Amgen: Research Funding; Sanofi: Consultancy; Pharmacyclics: Consultancy; Ichnos Sciences: Research Funding; BeiGene: Consultancy; Regeneron Pharmaceuticals: Research Funding; AbbVie: Research Funding; Karyopharm: Research Funding; Karyopharm: Consultancy; Cellectar: Consultancy. Dingli: Alexion: Consultancy; Apellis: Consultancy; Janssen: Consultancy; Novartis: Research Funding; GSK: Consultancy; Sanofi: Consultancy. Gertz: Ionis Pharmaceuticals: Other: Advisory Board; Aurora Biopharma: Other: Stock option; AbbVie Inc, Celgene Corporation: Other: Data Safetly & Monitoring; Akcea Therapeutics, Ambry Genetics, Amgen Inc, Celgene Corporation, Janssen Biotech Inc, Karyopharm Therapeutics, Pfizer Inc (to Institution), Sanofi Genzyme: Honoraria; Akcea Therapeutics, Alnylam Pharmaceuticals Inc, Prothena: Consultancy. Lin: Novartis: Consultancy; Celgene: Consultancy, Research Funding; Juno: Consultancy; Takeda: Research Funding; Gamida Cell: Consultancy; Vineti: Consultancy; Merck: Research Funding; Bluebird Bio: Consultancy, Research Funding; Legend: Consultancy; Sorrento: Consultancy; Janssen: Consultancy, Research Funding; Kite, a Gilead Company: Consultancy, Research Funding. Dispenzieri: Sorrento Therapeutics: Consultancy; Janssen: Consultancy, Research Funding; Takeda: Research Funding; Oncopeptides: Consultancy; Alnylam: Research Funding; Pfizer: Research Funding. Kumar: Tenebio: Research Funding; Amgen: Consultancy, Research Funding; Oncopeptides: Consultancy; Bluebird Bio: Consultancy; KITE: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Merck: Research Funding; Novartis: 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, Research Funding; Astra-Zeneca: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Roche-Genentech: Consultancy, Research Funding; Beigene: Consultancy; Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Carsgen: Research Funding; Antengene: Consultancy, Honoraria; BMS: Consultancy, Research Funding; Abbvie: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Sanofi: Research Funding.

Background: The absence of residual monoclonal protein in the serum and urine by IFE using specific antibodies to individual heavy and light chains has been an important part of response assessment and is required for categorizing patients as in complete response. MASS FIX has been shown to be more sensitive in picking up small amounts of monoclonal protein and represents an advancement in the field. Ongoing studies are examining the clinical significance of this increased sensitivity. We designed the following study using clinically obtained test results to examine this question. Patients and methods: The clinical laboratory at Mayo Clinic changed the process from IFE to MASS FIX in July 2018. We identified patients who had sequential testing before and after this date and collected test results from the last IFE prior to the date of transition and the first MASS FIX after the date of transition. Patients were grouped based on the paired results and outcomes compared between groups. Time to next therapy (TTNT) was defined as the time from the first MASS FIX date to the start of next therapy. Survival was estimated by the Kaplan-Meier method and these curves were compared using log rank test. Results: Overall, 739 patients with MM, who had a MASS FIX within 12 months of the IFE were studied; 380 (51%) patients had a negative IFE prior to implementation of MASS FIX. These patients had a MASS FIX test performed at a median of 3.7 (0.03-12) months from the last available IFE. The gap between IFE and MASS FIX was &lt;6 months and &lt; 3 months in 71% and 54% of the patients. Among the patients with a negative IFE, 130 (34%) tested positive on the subsequent MASS FIX. Limiting the analysis to those with a MASS FIX within 6 and 3 months yielded 32% in each group that turned positive from previous negative IFE. The median TTNT from the MASS FIX date was significantly shorter for those who became positive on MASSFIX; median was not reached for either group (P=0.002) (Figure). We then limited the analysis to those patients with a gap of &lt;6 months and &lt;3 months between the tests. The results were consistent: P= 0.012 for those with a gap of &lt; 6 months and P=0.01 for those with a gap &lt; 3 months. Conclusion: The results suggest that the additional sensitivity offered by MASS FIX have clinical significance in patients with MM, given that patients with residual disease identified by MASS FIX have an earlier relapse. The data supports the use of the more sensitive MASS FIX in the laboratory compared to the conventional IFE approaches. The study has the disadvantage that the samples were not tested simultaneously and some of the samples turning positive may reflect an early evidence of relapse, but the relative consistency of the finding irrespective of the gap between the tests argues against that. Ongoing studies are examining MASS FIX on the samples that were called to be negative by IFE initially. Figure Disclosures Murray: The Binding Site: Patents & Royalties: Patent Use of Mass Spec to identify monoclonal proteins licensed to The Binding Site. Dispenzieri:Takeda: Research Funding; Celgene: Research Funding; Janssen: Research Funding; Intellia: Research Funding; Pfizer: Research Funding; Alnylam: Research Funding. Kapoor:Cellectar: Consultancy; Sanofi: Consultancy, Research Funding; Janssen: Research Funding; Celgene: Honoraria; Takeda: Honoraria, Research Funding; GlaxoSmithKline: Research Funding; Amgen: Research Funding. Gertz:Janssen: Other: personal fee; Spectrum: Other: personal fee, Research Funding; Springer Publishing: Patents & Royalties; Abbvie: Other; Appellis: Other: personal fee; Annexon: Other: personal fee; Celgene: Other; Amgen: Other: personal fee; Physicians Education Resource: Other: personal fee; Medscape: Other: personal fee, Speakers Bureau; Research to Practice: Other; Sanofi: Other; Teva: Speakers Bureau; Johnson and Johnson: Speakers Bureau; DAVA oncology: Speakers Bureau; Proclara: Other; Aurora Bio: Other; Prothena: Other: personal fee; Alnylam: Other: personal fee; Ionis/Akcea: Other: personal fee. Dingli:Karyopharm Therapeutics: Research Funding; Bristol Myers Squibb: Research Funding; Rigel: Consultancy; Sanofi-Genzyme: Consultancy; Millenium: Consultancy; Alexion: Consultancy; Janssen: Consultancy; Apellis: Consultancy. Lin:Janssen: Consultancy, Research Funding; Kite, a Gilead Company: Consultancy, Research Funding; Takeda: Research Funding; Gamida Cells: Consultancy; Sorrento: Consultancy, Membership on an entity's Board of Directors or advisory committees; Vineti: Consultancy; Legend BioTech: Consultancy; Merck: Research Funding; Novartis: Consultancy; Celgene: Consultancy, Research Funding; Bluebird Bio: Consultancy, Research Funding; Juno: Consultancy. Kumar:Dr. Reddy's Laboratories: Honoraria; AbbVie: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; Carsgen: Other, Research Funding; BMS: Consultancy, Research Funding; Karyopharm: Consultancy; Tenebio: Other, Research Funding; Celgene/BMS: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; Adaptive Biotechnologies: Consultancy; Kite Pharma: Consultancy, Research Funding; Sanofi: Research Funding; Takeda: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; Janssen Oncology: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; Genentech/Roche: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; Merck: Consultancy, Research Funding; MedImmune: Research Funding; Cellectar: Other; Genecentrix: Consultancy; Oncopeptides: Consultancy, Other: Independent Review Committee; IRC member; Amgen: Consultancy, Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments, Research Funding; Novartis: Research Funding.

Background: The past two decades have seen an explosion in the development of new drugs for the treatment of myeloma, and previous studies have shown that these advances have translated into improved survival. It is unclear if we have continued to maintain the pace of improvement in the recent years. We designed this study to examine the patterns of survival in patients with newly diagnosed MM, specifically examining the impact on patient subgroups by age and disease risk. Patients and Methods: We included patients with newly diagnosed MM, who were seen at Mayo Clinic, Rochester between 2004 and 2018. We grouped them into three 5-year groups: group 1 (2004-2008), group 2 (2009-2013) and group 3 (2014-2018) to assess the trends over time. Only those who were seen within 6 months of diagnosis were included in the current cohort. Fluorescence in situ hybridization (FISH) was performed using clg based approach as previously described and high risk (HR) was defined as those with t(4;14), t(14;16), t(14;20) and del17p. Survival was estimated using the Kaplan-Meier method and curves were compared using the log rank test. The Cox proportional hazards model was used to examine the prognostic impact of various clinical and laboratory factors. Results: The study cohort had 3783 patients: 61% were male, median age was 64 years (range 22-96), 47% were ≥65 and 14% ≥75 years. There was no change over time in the median age or the gender distribution. No clear trends were evident across the groups in terms of the disease characteristics including high risk status. The patients in the cohort were divided into three 5 year groups: Group 1(2004-2008; n=1045), Group 2(2009-2013; n=1237) and Group 3(2014-2018; n=1501). The initial treatment regimen changed over time to include proteasome inhibitors (PI) and immunomodulatory drug (IMiD) based regimens with a PI-IMiD combination being 2%, 18% and 55% respectively in groups 1, 2 and 3 (Figure 1). The median follow-up for the entire cohort was 6.8 years (95% CI; 6.5,6.9), and for groups 1, 2 and 3 were 13 years, 8 years and 3.2 years respectively. The median OS for the entire group was 6.8 years (95% CI; 6.4, 7.2). The median OS from diagnosis has improved over time with the groups 1, 2, and 3 having a median OS (95% CI) of 5.5 years (5.1, 6.1), 7.3 years (6.7, 8.0) and NR respectively(P=0.0001) (Figure 2). We specifically looked at the 1, 2 and 3-year survival over the years and found a steady trend for improvement (Figure 3). When examining trends by age, those under 65 years had improvement in survival from group 1 to 2; median OS was 7.3 years, 10 years and NR for groups 1, 2 and 3 respectively (P=0.0001) . Those above 65 had major improvement from group 2 to 3; median OS was 4.4 years, 5.2 years and NR for groups 1,2 and 3 respectively (P=0.0001). When examined by FISH risk status, improvement in OS was seen among with Standard Risk (SR) status, especially during the last 5-year period. The median OS for patients with HR status was 3.5 years, 4.5 years and NR (P=0.07); and 7.6 years, 8.6 years and NR in patients with SR status (P=0.0012) for groups 1, 2 and 3 respectively. Conclusion: In a large cohort of patients with newly diagnosed MM, we have been able to demonstrate sustained improvement in OS over the years, likely driven by introduction of new drugs and continued use of transplant. The impact of newer generation of drugs, especially monoclonal antibodies is not reflected in this analysis and will likely become obvious over the next five years. The data will help identify the areas that require focus for continued improvements in order to derive the maximum impact on outcomes. Disclosures Kapoor: Celgene: Honoraria; Amgen: Research Funding; Sanofi: Consultancy, Research Funding; Janssen: Research Funding; Cellectar: Consultancy; GlaxoSmithKline: Research Funding; Takeda: Honoraria, Research Funding. Gertz:Alnylam: Other: personal fee; Prothena: Other: personal fee; Teva: Speakers Bureau; Johnson and Johnson: Speakers Bureau; DAVA oncology: Speakers Bureau; Proclara: Other; Aurora Bio: Other; Springer Publishing: Patents & Royalties; Sanofi: Other; Research to Practice: Other; Celgene: Other; Physicians Education Resource: Other: personal fee; Medscape: Other: personal fee, Speakers Bureau; Amgen: Other: personal fee; Appellis: Other: personal fee; Annexon: Other: personal fee; Spectrum: Other: personal fee, Research Funding; Janssen: Other: personal fee; Ionis/Akcea: Other: personal fee; Abbvie: Other. Dispenzieri:Intellia: Research Funding; Alnylam: Research Funding; Takeda: Research Funding; Pfizer: Research Funding; Janssen: Research Funding; Celgene: Research Funding. Dingli:Rigel: Consultancy; Bristol Myers Squibb: Research Funding; Apellis: Consultancy; Alexion: Consultancy; Sanofi-Genzyme: Consultancy; Karyopharm Therapeutics: Research Funding; Millenium: Consultancy; Janssen: Consultancy. Russell:Imanis: Other: Equity Ownership. Lin:Juno: Consultancy; Bluebird Bio: Consultancy, Research Funding; Celgene: Consultancy, Research Funding; Novartis: Consultancy; Takeda: Research Funding; Legend BioTech: Consultancy; Merck: Research Funding; Vineti: Consultancy; Janssen: Consultancy, Research Funding; Kite, a Gilead Company: Consultancy, Research Funding; Sorrento: Consultancy, Membership on an entity's Board of Directors or advisory committees; Gamida Cells: Consultancy. Kumar:Carsgen: Other, Research Funding; Adaptive Biotechnologies: Consultancy; Tenebio: Other, Research Funding; Celgene/BMS: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; Cellectar: Other; Genecentrix: Consultancy; Dr. Reddy's Laboratories: Honoraria; Janssen Oncology: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; Novartis: Research Funding; Kite Pharma: Consultancy, Research Funding; Takeda: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; MedImmune: Research Funding; Sanofi: Research Funding; Karyopharm: Consultancy; Oncopeptides: Consultancy, Other: Independent Review Committee; IRC member; BMS: Consultancy, Research Funding; Genentech/Roche: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; Merck: Consultancy, Research Funding; Amgen: Consultancy, Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments, Research Funding; AbbVie: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments.

Background: Patients with newly diagnosed MM can be quite symptomatic as a result of end organ damage related to MM as well as presence of other comorbidities in this relatively older patient population. Prior studies have shown that the performance score as well as other quality of life (QOL), frailty and comorbidities scores can have prognostic value in these patients. However, many of these questionnaires are lengthy and complex, and not practical in a busy outpatient practice. We implemented a three question symptom assessment score in 2010 in our outpatient practice. Here we present the results of the analysis looking at the impact of this measurement in patients with newly diagnosed MM. Patients and Methods: Patients seen in the outpatient Hematology practice at Mayo Clinic, Rochester MN, were asked to grade the symptoms of fatigue, pain, and QOL on a 0 to 10 scale during the past 7 days, with 10 being the worst and the QOL on a similar scale but with 10 being the best. For the purpose of the current analysis the QOL score was reversed.These three scores were added for a total score. We looked at the impact of the baseline score as well as the score after four cycles of therapy, at the time of best response, and at the time of the first progression, wherever they were available. Survival curves were generated by the Kaplan-Meier method and compared using the log rank test. The Cox proportional hazards model was used for assessment of the impact of different prognostic variables. Results: Data from 735 patients with newly diagnosed MM seen at Mayo Clinic between 2011 and 2018 were available from baseline. The median total score was 10 (0-30), and for fatigue, pain and QOL were 4, 3 and 7 respectively, all with range of 0-10. Based on the baseline scores, patients were grouped into tertiles (Group 1 with score &lt; 8, group 2 with score 8 to 14 and group 3 with a score ≥ 15). There were 256 (34%), 272 (37%) and 207 (28%) patients in groups 1, 2 and 3 respectively. The median overall survival (OS) (95% CI) from diagnosis was NR (7.9, NR), 7.7 (6.2, NR) and 5.4 (4.2, 6.9), respectively for groups 1, 2 and 3 (P &lt; 0.0001) (Figure 1a). The prognostic value of the score was independent of age, FISH based risk status and the ISS stage. When evaluated after 4 cycles (n=325), the OS was significantly different for the 3 groups (P&lt;0.01) (figure 1b). We then examined the impact of the score at best response to initial therapy (n=446) on OS from that timepoint and saw similar trend, but the results were not statistically significant (P=0.09) (Figure 1c). When evaluated at the time of first disease progression, the score again identified three groups with different OS from progression (P&lt;0.001) (Figure 1d). Conclusions:Patient-reported outcomes are associated with survival in patients with MM. This simple, easy to complete patient-reported outcome 3-item scale can be easily completed in a busy practice and is a clinically useful tool to assess potential outcomes of patients at various disease stages, including at diagnosis and at relapse. Disclosures Kumar: Adaptive Biotechnologies: Consultancy; Amgen: Consultancy, Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments, Research Funding; AbbVie: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; Genentech/Roche: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; Kite Pharma: Consultancy, Research Funding; Novartis: Research Funding; Sanofi: Research Funding; MedImmune: Research Funding; Karyopharm: Consultancy; Tenebio: Other, Research Funding; BMS: Consultancy, Research Funding; Janssen Oncology: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; Carsgen: Other, Research Funding; Takeda: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; Cellectar: Other; Genecentrix: Consultancy; Merck: Consultancy, Research Funding; Oncopeptides: Consultancy, Other: Independent Review Committee; IRC member; Celgene/BMS: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; Dr. Reddy's Laboratories: Honoraria. Kapoor:Janssen: Research Funding; Cellectar: Consultancy; Sanofi: Consultancy, Research Funding; Amgen: Research Funding; Takeda: Honoraria, Research Funding; GlaxoSmithKline: Research Funding; Celgene: Honoraria. Gertz:Proclara: Other; Springer Publishing: Patents & Royalties; Abbvie: Other; Celgene: Other; Aurora Bio: Other; Medscape: Other: personal fee, Speakers Bureau; Sanofi: Other; Teva: Speakers Bureau; Johnson and Johnson: Speakers Bureau; DAVA oncology: Speakers Bureau; Amgen: Other: personal fee; Appellis: Other: personal fee; Annexon: Other: personal fee; Spectrum: Other: personal fee, Research Funding; Janssen: Other: personal fee; Prothena: Other: personal fee; Physicians Education Resource: Other: personal fee; Research to Practice: Other; Ionis/Akcea: Other: personal fee; Alnylam: Other: personal fee. Dispenzieri:Alnylam: Research Funding; Intellia: Research Funding; Janssen: Research Funding; Takeda: Research Funding; Celgene: Research Funding; Pfizer: Research Funding. Dingli:Rigel: Consultancy; Millenium: Consultancy; Alexion: Consultancy; Janssen: Consultancy; Apellis: Consultancy; Sanofi-Genzyme: Consultancy; Karyopharm Therapeutics: Research Funding; Bristol Myers Squibb: Research Funding. Lin:Novartis: Consultancy; Celgene: Consultancy, Research Funding; Bluebird Bio: Consultancy, Research Funding; Juno: Consultancy; Legend BioTech: Consultancy; Merck: Research Funding; Takeda: Research Funding; Gamida Cells: Consultancy; Sorrento: Consultancy, Membership on an entity's Board of Directors or advisory committees; Vineti: Consultancy; Janssen: Consultancy, Research Funding; Kite, a Gilead Company: Consultancy, Research Funding.

e20510 Background: pPCL is a rare and aggressive form of multiple myeloma (MM) with dismal survival outcomes compared to the remainder of MM patients. Several studies have validated the optimal cutoff for defining pPCL to be >5% circulating plasma cells (cPCs) on a peripheral blood smear due to equally poor outcomes in this less restrictive cohort. We evaluated the clinical outcomes and cytogenetic features of patients diagnosed with pPCL at our institution that were treated with novel agent induction therapies. Methods: We evaluated patients with pPCL diagnosed between 2000 – 2018 (using the 5% cut off) at Mayo Clinic, Minnesota. Data was extracted from a prospectively maintained database and from the review of electronic medical records. Patients were categorized as having high risk (HR) 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 sub-groups. Survival analysis was performed by the Kaplan-Meier method and differences assessed using the log rank test. Results: This cohort consisted of 67 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 involvement was 84% (Range: 10 – 100) and the median cPCs percentage on the peripheral blood was 23% (range: 5 - 93). Data on primary cytogenetic abnormalities were available in 60 (85%) patients and the distribution 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 36 (54%) having received autologous stem cell transplant and 4 undergoing an allogeneic stem cell transplant. The median time to next therapy (TTNT) and overall survival (OS) for all patients was 13 months (95% CI: 9 – 17) and 24 months (95% CI: 19 – 40) respectively; 16 months and 51 months for standard risk (SR) vs. 10 months and 19 months for HR (P = 0.005 for OS), when stratified by cytogenetic risk. There were only 17 (31%) patients who were alive for ≥48 months since diagnosis (i.e. twice the median OS of this cohort); The absence of high risk cytogenetics (P = 0.049) and non-elevated LDH level (P = 0.047) at diagnosis predicted for achieving this survival goal. Conclusions: 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 presence of HR cytogenetics.