Kumar, Shaji

Introduction: Waldenström macroglobulinemia (WM) is characterized by circulating monoclonal IgM and bone marrow infiltration with lymphoplasmacytic lymphoma (LPL) cells. Presently, there is limited data to guide prognosis or treatment options of WM based on the proportion of lymphocytes or plasma cells in the LPL population on the pathology. In this study, we aim to look at differing clinical features and outcomes of patients with WM based on the plasma cell (PC) percentage in the diagnostic bone marrow biopsy. Methods Patients with WM seen at Mayo Clinic, Rochester, MN between January 2001 and December 2017, with a bone marrow biopsy and a corresponding serum free light chain (FLC) examination at diagnosis of WM were included. The percentage of lymphocytes and PCs in the marrow biopsy was determined based on morphology ± immunohistochemistry. On immunohistochemistry, CD138 and/or MUM-1 positivity was used to quantify PCs, whereas CD20 and/or PAX5 were used to quantify lymphocytes. Patients with ≥10% PC component in the lymphoplasmacytic (LPL) cells on bone marrow biopsy were considered to have WM with excess plasma cells (WMEPC), while those with <10% PCs were considered to have lymphocytic WM (LWM). Continuous variables were compared using Wilcoxon test and categorical variables were compared using Chi square test or Fisher's exact test. Time-to-next therapy (TTNT) was defined as the period from initiation of first line therapy to start of second line therapy or death due to any cause before second line therapy. Time-to-event analyses were conducted using Kaplan Meier method. Results: Of 228 evaluable patients, 147 (64%) had ≥ 10% PCs in the initial marrow biopsy. The median follow-up was 3.4 years (95% CI: 2.3-4.3 years) for LWM and 4.3 years (95% CI: 3.4-4.8 years) for WMEPC (p=0.33). The baseline characteristics of WMEPC versus LWM are depicted in Table 1. Patients with WMEPC had lower bone marrow involvement by LPL (30%) compared to LWM (60%; p<0.001) at diagnosis, but the rate of symptomatic hyperviscosity was higher in WMEPC during the disease course (Table 1). The TTNT was shorter for WMEPC [2.6 years (95% CI: 1.8-4.5 years)] compared to LWM [4.5 years (95% CI 3.2-8.2)], although it did not reach statistical significance (p=0.09, Figure 1). There was a trend towards shorter OS for WMEPC [Not reached (95% CI 7.2- NR)] compared to LWM [10.7 years (95% CI 7.3 -12.1 years); p=0.28], Figure 2. Of the 228 patients, 79% (n=182) had data available for systemic therapy. The rate of PR or better with frontline therapy was higher in rates in LWM compared to WMEPC (61 % vs 42% p=0.02, Table 1). Among WMEPC cohort, patients receiving either a proteasome inhibitor (PI)-based or an immunomodulatory (IMiD)-based combination as frontline therapy (n=16) had higher rates of partial response or better (68%) compared to non-PI/non-IMiD based therapy (n=114), p=0.04. Conclusion: WM with excess plasma cells at diagnosis is associated with a trend towards shorter TTNT and OS compared to WM with <10% plasma cells in the lymphoplasmacytic population at diagnosis. Our preliminary data shows higher rates of partial response or better with the use of PI/IMiD based therapy, but the small cohort size limits the strength of this finding which needs to be validated externally. Disclosures Ansell: Bristol-Myers Squibb: Research Funding; Trillium: Research Funding; Bristol-Myers Squibb: Research Funding; Bristol-Myers Squibb: Research Funding; LAM Therapeutics: Research Funding; Mayo Clinic Rochester: Employment; Regeneron: Research Funding; Trillium: Research Funding; Bristol-Myers Squibb: Research Funding; Affimed: Research Funding; Seattle Genetics: Research Funding; Regeneron: Research Funding; Bristol-Myers Squibb: Research Funding; Affimed: Research Funding; LAM Therapeutics: Research Funding; Trillium: Research Funding; Mayo Clinic Rochester: Employment; Seattle Genetics: Research Funding; Mayo Clinic Rochester: Employment; Bristol-Myers Squibb: Research Funding; Seattle Genetics: Research Funding; Mayo Clinic Rochester: Employment; Trillium: Research Funding; LAM Therapeutics: Research Funding; Affimed: Research Funding; Affimed: Research Funding; Bristol-Myers Squibb: Research Funding; Regeneron: Research Funding; Mayo Clinic Rochester: Employment; Trillium: Research Funding; Bristol-Myers Squibb: Research Funding; Seattle Genetics: Research Funding; Regeneron: Research Funding; LAM Therapeutics: Research Funding; LAM Therapeutics: Research Funding; Regeneron: Research Funding; Mayo Clinic Rochester: Employment; Affimed: Research Funding; Mayo Clinic Rochester: Employment; Trillium: Research Funding; Regeneron: Research Funding; Regeneron: Research Funding; Seattle Genetics: Research Funding; Affimed: Research Funding; Seattle Genetics: Research Funding; Mayo Clinic Rochester: Employment; Trillium: Research Funding; Trillium: Research Funding; Bristol-Myers Squibb: Research Funding; Trillium: Research Funding; LAM Therapeutics: Research Funding; LAM Therapeutics: Research Funding; Regeneron: Research Funding; Regeneron: Research Funding; Affimed: Research Funding; Seattle Genetics: Research Funding; LAM Therapeutics: Research Funding; Seattle Genetics: Research Funding; Seattle Genetics: Research Funding; Affimed: Research Funding; Mayo Clinic Rochester: Employment; LAM Therapeutics: Research Funding; Affimed: Research Funding. Gertz:Spectrum: Honoraria, Research Funding; Janssen: Honoraria; Celgene: Honoraria; Prothena: Honoraria; Alnylam: Honoraria; Ionis: Honoraria. Dingli:alexion: Consultancy; Janssen: Consultancy; Millenium: Consultancy; Rigel: Consultancy; Karyopharm: Research Funding. Nowakowski:Selvita: Membership on an entity's Board of Directors or advisory committees; NanoString: Research Funding; MorphoSys: Consultancy, Research Funding; Genentech, Inc.: Research Funding; F. Hoffmann-La Roche Ltd: Research Funding; Curis: Research Funding; Bayer: Consultancy, Research Funding; Celgene: Consultancy, Research Funding. Dispenzieri:Alnylam: Research Funding; Celgene: Research Funding; Takeda: Research Funding; Pfizer: Research Funding; Janssen: Consultancy; Intellia: Consultancy; Akcea: Consultancy. Lacy:Celgene: Research Funding. Kumar:Celgene: Consultancy, Research Funding; Janssen: Consultancy, Research Funding; Takeda: Research Funding. Kapoor:Janssen: Research Funding; Cellectar: Consultancy; Glaxo Smith Kline: Research Funding; Amgen: Research Funding; Takeda: Honoraria, Research Funding; Sanofi: Consultancy, Research Funding; Celgene: Honoraria.

Background: Multiple myeloma (MM) treatment has advanced considerably with proteasome inhibitors, immunomodulatory drugs (IMiDs), and, most recently, monoclonal antibodies. However, treatment response is quite heterogeneous, and many patients still progress even with novel combination therapies. Thus, understanding the factors that underlie response to treatment is a priority. Several somatic genomic aberrations and mutations predict poor response to therapy. However, germline variation has not previously been investigated as a predictor of treatment response in MM. We used genome-wide association and transcriptome-wide association approaches to identify germline genetic predictors of treatment response in MM. Methods: We included 510 MM patients from Mayo Clinic and 324 MM patients from UCSF diagnosed from 1999-2015. Basic demographics, laboratory values and pathology at diagnosis, type of initial therapy, duration of therapy, and follow-up were ascertained by chart review. Patients were grouped into categories of treatment based on their first line therapy: proteasome inhibitor-based regimen, IMiD based regimen, combination proteasome and IMiD based regimen, or other. Response was assessed using International Myeloma Working Group (IMWG) response criteria after 4-6 cycles of induction. As such, response was categorized into progressive disease (PD), minimal response (MR), partial response (PR), very good partial response (VGPR), or complete response (CR). Germline samples were genotyped using Illumina or Affymetrix arrays and were imputed based on the Haplotype Reference Consortium (HRC). For the genome-wide association study (GWAS), we included all SNPs with minor allele frequency >0.01 and imputation r2 of >0.5. We tested each SNP for association with treatment response using a linear regression model that adjusted for age, gender, and genetic ancestry (from principal components analysis (PCA)). To perform the transcriptome-wide association study (TWAS), we calculated predicted gene expression data using PREDIXCAN software on a reference cohort of 922 individuals with genotype and RNA expression data from peripheral blood. We then tested the association between predicted gene expression and response to therapy using linear regression models. Both the GWAS and TWAS were performed on subgroups of patients who received either proteasome inhibitors or IMiD therapies. The analyses of patients on proteasome inhibitors were adjusted for IMiD use as a covariate and analyses of patients on IMiDs were adjusted for proteasome inhibitor use. The threshold for genome-wide significance loci was set at P = 5 x 10-8 and the threshold for suggestive loci was set at P = 10-6. The threshold for significance for TWAS was set at P = 4 x 10-6 by using a Bonferroni correction for the number of genes for which genetic models of expression could be developed by PREDIXCAN. Results: Overall, 42.7% (59 of 138) of patients on proteasome inhibitors alone, 32.5% (66 of 203) of patients on IMiDs alone, and 58.1% (50 of 86) of patients on combination achieved at least a VGPR. There were no significant differences in response across centers in analyses that adjusted for age, sex and types of therapy. There were no genome wide significant loci to predict for response. We identified 8 suggestive SNPs associated with proteasome inhibitor response and 4 suggestive SNPs associated with IMiD response. TWAS identified ZNF622 as a candidate genetic modifier of proteasome inhibitor effect that was significant after correction for multiple hypothesis testing (P = 1.6 x 10-6). Higher genetically predicted expression was associated with improved response to proteasome inhibitor therapy. Among patients above the median of predicted expression of ZNF622 on proteasome inhibitors, 62.6% achieved at least VGPR; among patients at or below the median of expression, only 37.6% achieved at least VGPR. Conclusions: We identified an association between predicted expression of ZNF622 and clinical response to proteasome inhibitor therapy among MM patients. ZNF622 is a zinc finger binding protein which is known to be co-activator of B Myb activity and to affect apoptosis in response to oxidative stress. Our work highlights the potential importance of pharmacogenetic modifiers of treatment response in MM. Disclosures Shah: Nkarta: Consultancy, Membership on an entity's Board of Directors or advisory committees; Indapta Therapeutics: Equity Ownership; University of California, San Francisco: Employment; Celgene, Janssen, Bluebird Bio, Sutro Biopharma: Research Funding; Genentech, Seattle Genetics, Oncopeptides, Karoypharm, Surface Oncology, Precision biosciences GSK, Nektar, Amgen, Indapta Therapeutics, Sanofi: Membership on an entity's Board of Directors or advisory committees; Amgen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Poseida: Research Funding; Bristol-Myers Squibb: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Kite: Consultancy, Membership on an entity's Board of Directors or advisory committees; Teneobio: Consultancy, Membership on an entity's Board of Directors or advisory committees. Wong:Celgene: Research Funding; Janssen: Research Funding; Roche: Research Funding; Fortis: Research Funding. Martin:Roche and Juno: Consultancy; Amgen, Sanofi, Seattle Genetics: Research Funding. Kumar:Celgene: Consultancy, Research Funding; Takeda: Research Funding; Janssen: Consultancy, Research Funding.

Purpose: Multiple myeloma (MM) is a plasma cell (PC) malignancy with an increasing incidence in the US. Epidemiological studies demonstrate a 2-3 fold higher incidence of the pre-malignant monoclonal gammopathy of undetermined significance (MGUS) and MM with a ~4-year younger age of onset among African Americans (AA) compared to European Americans (EAs) (Fonseca, Leukemia, 2017). With equal access to care, AAs have better overall survival compared to EAs (Waxman, Blood, 2010). This disparity may be explained by ancestral-associated genetic predisposition of AAs to development of monoclonal gammopathies and to specific acquired, cytogenetically-defined subtypes. Using calculated genetic ancestry data, we have previously identified a higher prevalence of IgH translocations t(11;14), t(14;16) and t(14;20) in individuals with >80% African ancestry (Baughn, BCJ, 2018). Since SNP rs9344 encoding the CCND1 870G>A polymorphism has been reported in association with increased risk of t(11;14) (Weinhold, Nature Genetics, 2013), we investigated whether rs9344 correlates with African ancestry and with t(11;14) in our cohort of patients with plasma cell dyscrasias. Methods: We studied 898 patients with monoclonal gammopathies who had undergone uniform testing to identify MM-specific cytogenetic abnormalities. DNA from bone marrow samples was genotyped on the Precision Medicine Research Array and biogeographical ancestry was assessed using the Geographic Population Structure Origins tool (Elhaik, Nat Commun, 2014). Plasma cell proliferative disorder FISH of immunoglobulin (cIg)-stained positive plasma cells was performed as described (Baughn, BCJ, 2018). Individuals were divided into three ancestral groups: 1. EAs (<0.1% African ancestry and <30% Asian ancestry); 2. AAs (> 80% African ancestry); and 3. Other. Chi-squared test was used to determine the overall comparison between the 3 ancestral groups and also between ancestral groups 1 vs 2, and 2 vs 3 using pairwise comparison. All tests were two-sided with alpha level set at 0.05 for overall statistical significance. Pairwise comparison was considered statistically significant when p<0.025 based on Bonferroni method for multiple comparisons. Results: We identify increased risk of development of either a t(11;14), t(14;16) or t(14;20) in AAs (48.8%) compared to EAs (33.6%) (p-value=0.0051). To explore the genetic basis of increased t(11;14) specifically (37.4% AAs vs. 27.3% p-value=0.049), we evaluated the frequency of the G risk allele of rs9344 in relation to African ancestry. The frequency of the G risk allele was higher in AAs (0.81) compared to EAs (0.59) (p-value <0.0001) and also higher in t(11;14) cases (0.73) compared to non-t(11;14) controls (0.58) (p-value <0.0001). A multivariate model identified only rs9344 as significantly associated with t(11;14) after adjusting for age, gender and race group suggesting that it plays a role in the development of t(11;14) (p-value <0.001 for GG, p-value=0.005 for AG). To test if these results are replicable, we studied the MMRF CoMMpass cohort. This cohort includes individuals with newly diagnosed MM along with self-report race information and translocation data from long insert whole genome sequencing. Although t(11;14) was not enriched in self-reported Black individuals from this cohort (17.5% Black vs. 20.4% White, p-value=0.47), there was association between the rs9344 and self-reported Black race (p-value <0.0001) and with the presence of t(11;14) when the t(11;14) was analyzed by long insert whole genome sequencing (p-value=0.0001). Conclusions: To our knowledge, this study includes the largest group of African individuals with an abnormal plasma cell clone along with uniformly collected FISH, genotyping and ancestry data. We have identified in this diverse population the association of the CCND1 870G>A polymorphism (rs9344) with African ancestry and with t(11;14) suggesting that it plays a role in the development of t(11;14) plasma cell dyscrasias. Disclosures Elhaik: DNA Diagnostics Center: Consultancy. Baird:DNA Diagnostics Center: Employment. Cerhan:Janssen: Membership on an entity's Board of Directors or advisory committees; Celgene: Research Funding; NanoString: Research Funding. Bergsagel:Celgene: Consultancy; Ionis Pharmaceuticals: Consultancy; Janssen Pharmaceuticals: Consultancy. Dispenzieri:Alnylam: Research Funding; Akcea: Consultancy; Intellia: Consultancy; Janssen: Consultancy; Pfizer: Research Funding; Takeda: Research Funding; Celgene: Research Funding. Kumar:Janssen: Consultancy, Research Funding; Celgene: Consultancy, Research Funding; Takeda: Research Funding.

Introduction: Patients who develop therapy-related myeloid neoplasms (t-MN) have dismal outcomes. Previous studies reported the incidence and risk factors associated with t-MN development. Lenalidomide, in the setting of oral, but not intravenous, melphalan is associated with a higher risk of t-MN (Palumbo et. al, Lancet Oncology, 2014). We carried out this study to evaluate the clinical and pathologic features of t-MN, therapies employed, and factors that predict long-term survival after diagnosis. Patients and methods: We identified patients who received the first ASCT 1998-2016 at our institution. t-MN was defined per the WHO 2016 classification. Median overall survival (OS) was calculated from the time of t-MN diagnosis to last follow-up or death. Statistical analyses were performed using SAS (JMP v14.1) or GraphPad Prism (v7). Results: Out of 2115 patients that underwent at least one ASCT, 53 (2.5%) developed t-MN. Thirty-five of 53 (66%) patients who developed t-MN had received lenalidomide. Among 2062 patients that did not develop t-MN, 916 (44.4%) patients received lenalidomide. Lenalidomide exposure was associated with development of t-MN (χ2 with Yate's correction 8.9, p=0.003). Ten patients were excluded from further analyses due to lack of follow up. Clinical characteristics are shown in Table 1a (N=43). Median age at t-MN diagnosis was 70 years (range 44-79). Median time from ASCT to t-MN was 5 years (range 1-15). After a median follow-up of 70 months (95% CI, 38-134), the median OS was 12 months (95% CI, 9-17, Figure). Primary causes of death were t-MN (71%), MM (12%), both (6%), and other including infection, GVHD, and unknown (12%). Seven (16%) had t-AML and 36(84%) had t-MDS. Three (42%) of 7 patients with t-AML had pure erythroid phenotype. At the time of last follow-up, 9 (21%) were alive. Seven (17%) underwent two ASCT, 16 (36%) received more than 2 years cumulative dose of lenalidomide. Median number of cycles of alkylator therapy including high-dose melphalan (HDM) used for ASCT was 2 (range 1-6). On univariate analysis, factors predicting OS from t-MN diagnosis were ≥ 2 alkylator vs. < 2 cycles (11 vs. 27 months, p=0.02), ≥10% vs. <10% blast at the time of t-MN diagnosis (5.5 vs. 17 months, p=0.01), and the presence of complex karyotype (CK) vs. not (11 vs. 17 months, p=0.03). There was no difference in survival among t-MDS patients who transformed to t-AML vs. those who did not, those who received 1 vs. 2 ASCT, and those with lenalidomide duration ≥ 2 vs. <2 years. On multivariate analysis, the number of alkylator therapies and CK, but not % blasts predicted OS from t-MN diagnosis (Figure). Eight (19%) patients proceeded to receive an autologous (n=4) or allogeneic (n=4) transplant for t-MN (Table 1b). There was no survival advantage for patients who underwent SCT for t-MN, compared to those who did not (17 vs. 10 months, p=0.3). Similarly, there was no survival advantage for patients who received allo SCT compared to auto SCT (21 vs. 16 months, p=0.7). Survival for t-MDS and t-AML was also not different (15 vs. 6 months, p=0.2). There is only one long-term survivor in the transplant group (s/p allogeneic SCT). Four of seven (57%) patients died of progressive t-MN, 1 died of progressive MM, progressive MM and t-MN, and TRM each. Conclusions: Lenalidomide, in the setting of HDM, is associated with an increased risk of t-MN. Increased exposure to alkylators, a higher tumor burden, and the presence of CK predict poor survival in t-MN. As HDM/ASCT followed by lenalidomide maintenance remains the standard of care for eligible patients, minimizing exposure to other alkylator regimens may be prudent. While considered the standard, only a minority undergo transplant; and post-transplant outcomes remain poor. Studies to identify prognostic genetic and epigenetic risk factors are ongoing. Disclosures Al-Kali: Astex Pharmaceuticals, Inc.: Research Funding. Kumar:Celgene: Consultancy, Research Funding; Janssen: Consultancy, Research Funding; Takeda: Research Funding. Patnaik:Stem Line Pharmaceuticals.: Membership on an entity's Board of Directors or advisory committees.

Background: Despite treatment advances, multiple myeloma (MM) remains a condition with high unmet medical need as patients (pts) relapse and become resistant to therapy. MM pts experience a range of serious disease-related symptoms and side effects impacting their functioning and health-related quality of life (HRQL). The median overall survival (OS) has improved with the advent of new treatments in the last two decades, however, the impact of newer combination treatments on pt quality of life remains unknown (Beckner, N et al. Cancer Res. 2011;183:25; Bharat, N et al. J Clin Oncol 2019;37:15(suppl):8039). Venetoclax is a novel, orally bioavailable small molecule inhibitor of BCL-2 activity in MM. In the Phase 3 double-blind, randomized clinical trial (M14-031, BELLINI) progression free survival (PFS) was studied in relapsed/refractory (RR) MM pts receiving venetoclax or placebo in combination with bortezomib and dexamethasone. In addition, patient-reported outcomes (PRO) data focused on physical functioning, pain, and fatigue from BELLINI were evaluated to better understand the overall impact on pt HRQL of adding venetoclax to bortezomib and dexamethasone-based doublet therapy. Methods: The BELLINI Phase 3 study (NCT01794520) enrolled patients ≥18 y old with R/R MM who had received 1-3 prior lines of therapy. The study met its primary endpoint of PFS (HR 0.63, 95% CI=0.44-0.89) but higher risk of death (HR 2.027, 95% CI=1.04-3.95) was observed in the treatment arm compared with placebo, although the OS data are not yet mature (Kumar, S et al. EHA Library. Jun 16 2019;273254:LB2601). The secondary HRQL endpoints included in this study were pain severity, fatigue, physical functioning (PF), and global health status, which were measured using the Brief Pain Inventory-Short Form (BPI-SF), PROMIS Fatigue Short Form surveys, and the European Organization for the Research and Treatment of Cancer Quality of Life Questionnaire (EORTC QLQ-C30). Change from baseline to day 1 of each cycle visit (up to 25 cycles or last treatment visit) was assessed for each measure in both the treatment and placebo groups. Summary analyses compared mean and median scores between arms at each cycle. The treatment effect was evaluated in a mixed effect model controlling for the patient's baseline score, total visits (or cycles of treatment), age (<65 or ≥65), prior therapy lines, and prior proteasome inhibitor exposure. The model also included an interaction term examining treatment by visits. Results: The intention-to-treat (ITT) dataset included 194 pts (median age: 66y; 53% with 2 or 3 prior lines of therapy) in the treatment group and 97 pts (median age: 65y; 55% with 2 or 3 prior lines of therapy) in the placebo group (2:1 randomization). There was no clear evidence for an independent effect of treatment on PF (P=0.522). Pain scores across multiple measures showed a similar trend, with a statistically non-significant improvement of unadjusted average "Worst Pain" scores for the venetoclax group (P=0.289; Figure B). Non-inferiority in pain results were replicated by the pain subscale of the QLQ-30 (P=0.778), the BPI pain severity index (P=0.418), and the related BPI pain interference subscale score (P=0.603). Fatigue scores likewise demonstrated no significant differences between treatment arms whether using PROMIS Cancer Fatigue t-scores (P=0.442), raw scores (P=0.568), or the 3-item QLQ-C30 subscale (P=0.959), although there was a positive trend across cycle summary scores for the treatment group compared with the placebo group. Unadjusted QLQ-C30 PF subscale scores showed declines from baseline that were generally worse in the placebo group across 25 treatment cycles, although differences were smaller and non-significant in the adjusted model (Figure A). Conclusions: Comparable HRQL in pain severity, fatigue, and PF was observed in RRMM pts receiving venetoclax in combination with bortezomib and dexamethasone compared to pts receiving bortezomib and dexamethasone. The summary scores showed outcomes that were comparable or slightly improved for those randomized to the venetoclax combination. Results of concurrent measures of the same or associated concepts reinforced confidence in these results. Additional analyses of subgroups are ongoing (eg, t(11;14), BCL-2 expression status) and may provide further insights surrounding HRQL in these populations. Figure Disclosures Gasparetto: BMS: Consultancy, Honoraria, Other: Travel, accommodations, or other expenses paid or reimbursed ; Celgene: Consultancy, Honoraria, Other: Travel, accommodations, or other expenses paid or reimbursed ; Janssen: Consultancy, Honoraria, Other: Travel, accommodations, or other expenses paid or reimbursed . Popat:Takeda: Honoraria, Other: travel, accommodations, expenses; GSK: Consultancy, Honoraria; Celgene Corporation: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: travel, accommodations, expenses; Janssen: Honoraria, Other: travel support to meetings; AbbVie: Consultancy, Membership on an entity's Board of Directors or advisory committees. Kumar:Celgene: Consultancy, Research Funding; Takeda: Research Funding; Janssen: Consultancy, Research Funding. Cavo:celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: travel accommodations, Speakers Bureau; janssen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: travel accommodations, Speakers Bureau; AbbVie: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; takeda: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; amgen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; novartis: Honoraria; sanofi: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; bms: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau. de la Rubia:Janssen: Consultancy; AbbVie: Consultancy; Celgene: Consultancy; Amgen: Consultancy; Takeda: Consultancy. Hungria:Janssen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; BMS: Consultancy, Honoraria, Speakers Bureau; Janssen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Abbvie: Consultancy, Membership on an entity's Board of Directors or advisory committees; Amgen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Amgen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Abbvie: Consultancy, Membership on an entity's Board of Directors or advisory committees; BMS: Consultancy, Honoraria, Speakers Bureau; Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Takeda: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Takeda: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau. Salwender:Sanofi: Honoraria, Other: Travel or accommodations; Amgen: Honoraria, Other: Travel or accommodations; Janssen Cilag: Honoraria, Other: Travel or accommodations; AbbVie: Honoraria; Bristol-Myers Squibb: Honoraria, Other: Travel or accommodations; Celgene: Honoraria, Other: Travel or accommodations; Takeda: Honoraria, Other: Travel or accommodations. Suzuki:Ono: Research Funding; BMS: Honoraria, Research Funding; Takeda: Honoraria; Janssen: Honoraria; Celgene: Honoraria. Maciel:Amgen: Other: funding for travel, teaching, participation in advisory boards; Janssen: Other: funding for travel, teaching, participation in advisory boards. Moreau:Takeda: Consultancy, Honoraria; AbbVie: Consultancy, Honoraria; Celgene: Consultancy, Honoraria; Janssen: Consultancy, Honoraria; Amgen: Consultancy, Honoraria. Harrison:Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Takeda: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Novartis: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen Cilag: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; GSK: Consultancy, Research Funding; AbbVie: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: investigator on studies, Research Funding; Amgen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding. Jia:AbbVie: Employment, Other: Stock/stock options. Karve:AbbVie: Employment, Other: stock/stock options. Ma:Genentech: Employment, Other: stock options; AbbVie: Other: Alliance Partner. Devine:Genentech: Employment, Other: stock options; AbbVie: Other: Alliance Partner. Benjamin:AbbVie: Employment, Other: stock/stock options.

Background: Two-year follow-up from ZUMA-1 trial for axicabtagene ciloleucel (axi-cel) CD19 chimeric antigen receptor T-cell (CART) in aggressive non-Hodgkin lymphoma (NHL) demonstrated that patients (pts) achieving complete remission (CR) as their best response have the longest progression free survival (PFS), while PFS remained poor for others. Majority of the best response are achieved by month 3 post-infusion. Moreover, a subset of pts who do not achieve CR by month 1 may still achieve CR while others will progress within months. Having a clinically available test to predict for durable response can assist clinicians in selecting pts who can be observed for continued response vs. those who may need intervention earlier. Peak CAR-T expansion has been correlated with best clinical response to axi-cel. However there is no clinically available test to quantify CART cells post treatment. Given the prolonged lymphotoxic effect of the lymphodepletion (LD) chemotherapy administered before CART infusion, we hypothesize that the peak expansion of absolute lymphocyte count (ALC, ALCpeak) after infusion likely corresponds to CART expansion. We examined for correlation between ALCpeak and clinical response at month 3 post-infusion for pts who received axi-cel. Methods: Data was collected from pts who received axi-cel between June 2016 and March 2019 at Mayo Clinic Rochester (NCT02348216, NCT03153462, and standard of care). All infused pts were included in the intention to treat analysis for response and event-free survival (EFS). Lugano 2014 criteria were used for clinical response. EFS was defined as the date of infusion until progression or death due to any cause and evaluated using Kaplan Meier curves with log-rank test. Comparison between subgroups was investigated using Fisher's exact test and Wilcoxon test for categorical and continuous variables, respectively. Statistical analysis performed using JMP Pro 14.1. Results: Of the thirty-six NHL pts treated with axi-cel, the median age was 56 yrs (range 26-65); 78% (28) were male; 44% (16) had DLBCL; the median number of prior therapies was 3 (2-6), 47% (17) had stem cell transplant. Nineteen (53%) pts received bridging therapy after leukapheresis. All pts received fludarabine and cyclophosphamide for LD chemotherapy and achieved ALC nadir by the day of CART infusion (median 0.03 x 10^9/L, range 0.01-0.11). As CART expansion were found to peak within the first two weeks in ZUMA-1 trial, we examined ALC trend over 15 days post infusion and found that ALCpeak generally happened within the same timeframe (median ALCpeak day: 9, range 6-15). At 3 months post infusion, 33.3% (12/36) of pts achieved CR, 8.3% (3/36) pts were in partial remission (PR). No correlation was found between CR response and clinical characteristics: age >60 years, stage III/IV, B symptoms, elevated LDH, IPI score 3-4, number of previous treatments, use of bridging therapy, peak CRP and ferritin. However, the ALCpeak was significantly different between CR and non-CR pts (median, range: 1.09, 0.35-2.55; 0.67, 0.15-1.03; p=0.04). Using receiver operator curve (ROC)-derived area under the curve (AUC) estimates, ALCpeak 1.08 x 10^9/L was identified as the best threshold to discriminate patients at different likelihood to achieve CR at month 3. Pts achieving ALCpeak ≥ 1.08 showed a strong increased likelihood of achieving CR at month 3 (OR 2.8; 95% CI, 1.12-6.99; P=0.02). There was no statistical difference between the ALCpeak high and low groups in terms of use of bridging therapy, occurrence of any grade or higher grade cytokine release syndrome or neurotoxicity, or the use of tocilizumab or steroid. Interestingly, for pts who had changes in clinical response from month 1 to month 3, all 3 pts who converted to CR by month 3 had ALCpeak≥1.08, while the 13 of the 15 pts who had progressed by month 3 had ALCpeak<1.08. In addition, EFS was significantly different between pts who had ALCpeak above and below 1.08 (6 months EFS rate 75% vs. 25%, respectively, Figure 1). Conclusions: We found that the ALC expansion occurred in the first 15 days post axi-cel infusion, consistent with the known time frame for CART expansion. Higher ALCpeak level in this timeframe correlated with higher likelihood of achieving CR, and is associated with conversion to CR. This study is limited by the small sample size and limited follow-up, but the results are promising for further prospective investigation in a larger cohort of patients. Disclosures Paludo: Verily Life Sciences: Research Funding; Verily Life Sciences: Research Funding; Celgene: Research Funding; Celgene: Research Funding. Ansell:Mayo Clinic Rochester: Employment; Bristol-Myers Squibb: Research Funding; Seattle Genetics: Research Funding; LAM Therapeutics: Research Funding; Regeneron: Research Funding; LAM Therapeutics: Research Funding; Mayo Clinic Rochester: Employment; Seattle Genetics: Research Funding; Affimed: Research Funding; Affimed: Research Funding; Trillium: Research Funding; Mayo Clinic Rochester: Employment; Trillium: Research Funding; Seattle Genetics: Research Funding; Seattle Genetics: Research Funding; Seattle Genetics: Research Funding; Mayo Clinic Rochester: Employment; Trillium: Research Funding; Mayo Clinic Rochester: Employment; Trillium: Research Funding; Trillium: Research Funding; Mayo Clinic Rochester: Employment; Seattle Genetics: Research Funding; Regeneron: Research Funding; Regeneron: Research Funding; Bristol-Myers Squibb: Research Funding; Affimed: Research Funding; Affimed: Research Funding; Mayo Clinic Rochester: Employment; Seattle Genetics: Research Funding; LAM Therapeutics: Research Funding; Bristol-Myers Squibb: Research Funding; Bristol-Myers Squibb: Research Funding; Regeneron: Research Funding; LAM Therapeutics: Research Funding; Affimed: Research Funding; Seattle Genetics: Research Funding; Bristol-Myers Squibb: Research Funding; Trillium: Research Funding; Regeneron: Research Funding; Affimed: Research Funding; LAM Therapeutics: Research Funding; Bristol-Myers Squibb: Research Funding; Trillium: Research Funding; Mayo Clinic Rochester: Employment; LAM Therapeutics: Research Funding; Regeneron: Research Funding; LAM Therapeutics: Research Funding; Affimed: Research Funding; Trillium: Research Funding; Regeneron: Research Funding; Trillium: Research Funding; LAM Therapeutics: Research Funding; Affimed: Research Funding; Regeneron: Research Funding; Bristol-Myers Squibb: Research Funding; LAM Therapeutics: Research Funding; Affimed: Research Funding; Bristol-Myers Squibb: Research Funding; Mayo Clinic Rochester: Employment; Regeneron: Research Funding; Bristol-Myers Squibb: Research Funding; Seattle Genetics: Research Funding. Bennani:Purdue Pharma: Other: Advisory board; Purdue Pharma: Other: Advisory board; Seattle Genetics: Other: Advisory board; Adicet Bio: Other: Advisory board; Bristol-Myers Squibb: Research Funding; Kite Pharma: Other: Advisory board; Kite Pharma: Other: Advisory board; Adicet Bio: Other: Advisory board; Seattle Genetics: Other: Advisory board; Adicet Bio: Other: Advisory board; Bristol-Myers Squibb: Research Funding; Kite Pharma: Other: Advisory board; Bristol-Myers Squibb: Research Funding; Seattle Genetics: Other: Advisory board; Purdue Pharma: Other: Advisory board. Dingli:alexion: Consultancy; Janssen: Consultancy; Millenium: Consultancy; Karyopharm: Research Funding; Rigel: Consultancy. Kapoor:Celgene: Honoraria; Janssen: Research Funding; Cellectar: Consultancy; Sanofi: Consultancy, Research Funding; Amgen: Research Funding; Takeda: Honoraria, Research Funding; Glaxo Smith Kline: Research Funding. Kenderian:Novartis: Patents & Royalties, Research Funding; Tolero: Research Funding; Humanigen: Other: Scientific advisory board , Patents & Royalties, Research Funding; Lentigen: Research Funding; Morphosys: Research Funding; Kite/Gilead: Research Funding. Kumar:Takeda: Research Funding; Janssen: Consultancy, Research Funding; Celgene: Consultancy, Research Funding.

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 : Daratumumab (DARA) is a human CD38 antibody approved in combination with standard-of-care (SoC) regimens in pts with NDMM who are TIE. DARA-based treatments showed significantly improved progression-free survival (PFS) in pts with TIE NDMM in ongoing phase 3 studies ALCYONE (DARA + bortezomib/melphalan/prednisone [D-VMP] vs VMP) and MAIA (DARA + lenalidomide/dexamethasone [D-Rd] vs Rd continuous). An NMA allows estimation of the relative efficacy of regimens not compared in head-to-head trials. A previous NMA examined D-VMP and D-Rd vs comparators (Facon T et al., EHA 2019), excluding the SWOG S0777 study (bortezomib/lenalidomide/dexamethasone [VRd]; Durie BG, et al. Lancet 2017) as it enrolled both transplant-eligible (TE) and TIE pts, and data for pts who are TIE only (~50% [aged ≥65 y and frail]) were unavailable. In this NMA, D-Rd and D-VMP had the highest probabilities of being more effective than reference treatment Rd continuous in terms of PFS. Recently, VRd received EMA approval for treatment of TIE NDMM based on SWOG S0777, but results specific to TIE pts are still unavailable. Recent NMAs of TIE pts (Blommestein HM et al., Haematologica, 2019; Weisel et al., EHA 2019; Cao Y et al., Clin Lymphoma Myeloma Leuk, 2019) have included data from the the intent-to-treat (ITT) population of SWOG S0777, though its inclusion violates the similarity assumption. Here, we present a sensitivity analysis of the previously-conducted NMA including all pts who received VRd in SWOG S0777 for a comprehensive view of the comparative effectiveness of DARA-based treatments in TIE NDMM. Methods: VRd was included as part of a sensitivity analysis of the previous NMA based on the ITT population, reporting PFS and overall survival (OS). The Guyot method was used to estimate OS hazard ratios (HRs) for pts aged ≥65 y (proxy for TIE) from SWOG S0777 (Durie BG et al., ASH 2018). Median follow-up in ALCYONE and MAIA was 27.8 months and 28.0 months, respectively. Based on a systematic literature review conducted through January 2019, both PFS and OS were extracted and synthesized in Bayesian NMAs. Choice of fixed- or random-effects (FE or RE) model was based on lowest deviance information criterion (DIC) and/or presence of heterogeneity in the network. Rd continuous was selected as reference, as it was commonly included in guidelines across regions. For PFS and OS, HR <1 indicates the comparison is not in favor of Rd continuous. In addition to the previous NMA, we also report an NMA sensitivity analysis including only comparators approved by the EMA that are relevant for the European, Middle East and/or Africa (EMEA) region (EMEA NMA). Results: Global NMA: RE model was used for both PFS and OS. Compared with Rd continuous, the PFS HR for VRd in the sensitivity analysis was 0.74 (95% credible interval [CrI], 0.42-1.30), which was similar to SWOG S0777 (0.71; [96% Wald confidence interval [CI], 0.56-0.91]) and higher than HRs for D-Rd (0.55; 95% CrI, 0.31-0.97) and D-VMP (0.58; 95% CrI, 0.20-1.62; Table). For OS, the SWOG S0777 HR estimated by Guyot algorithm for pts aged ≥65 y was 0.80 (95% CI, 0.56-1.16). Compared with Rd continuous, the OS HR for VRd in the sensitivity analysis was 0.80 (95% CrI, 0.51-1.26), which was similar to SWOG S0777 and higher than D-Rd (0.78; 95% CrI, 0.51-1.18). EMEA NMA: FE model was used for both PFS and OS. Compared with Rd continuous, the PFS HR for VRd in the sensitivity analysis was 0.74 (95% CrI, 0.60-0.92), which was higher than HRs of D-Rd (0.55; 95% CrI, 0.43-0.71) and D-VMP (0.64; 95% CrI, 0.41-1.02). Compared with Rd continuous, the OS HR for VRd in the sensitivity analysis was 0.80 (95% CrI, 0.55-1.17), which was higher than for D-Rd (0.78; 95% CrI, 0.56-1.09). These results are consistent with the Global NMA, but differ slightly as the EMEA NMA has fewer evidence-loops (only EMEA SoC regimens were included). Conclusions: This study demonstrated favorable efficacy outcomes for DARA-based regimens vs other relevant frontline options, such as VRd, for pts with NDMM who are TIE. Limitations of the sensitivity analysis include lack of comparative trial data and demographic differences between pts from SWOG S0777 and trials of frontline MM treatments for pts who are TIE included in our network. The majority of SWOG S0777 pts are TE (<65 y), who often have better prognoses than TIE pts (≥65 y), and thus violates the similarity assumption of NMAs. However, DARA-based regimens still showed a comparative clinical advantage. Disclosures Facon: Takeda: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Celgene: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Janssen: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Sanofi: Membership on an entity's Board of Directors or advisory committees; Amgen: Membership on an entity's Board of Directors or advisory committees. San-Miguel:Amgen, Bristol-Myers Squibb, Celgene, Janssen, MSD, Novartis, Roche, Sanofi, and Takeda: Consultancy, Honoraria. Usmani:Amgen, Bristol-Myers Squibb, Celgene, Janssen, Merck, SkylineDX, Takeda: Other: Consultant/Advisor; Amgen Array Biopharma, Bristol-Myers Squibb, Celgene, Janssen, Merck, Pharmacyclics, Sanofi, Takeda: Other: Research Grant; Amgen, Celgene, Janssen, Sanofi, Takeda: Speakers Bureau. Dimopoulos:Sanofi Oncology: Research Funding. Kumar:Janssen: Consultancy, Research Funding; Takeda: Research Funding; Celgene: Consultancy, Research Funding. Mateos:Adaptive: Honoraria; EDO: Membership on an entity's Board of Directors or advisory committees; Pharmamar: Membership on an entity's Board of Directors or advisory committees; Takeda: Honoraria, Membership on an entity's Board of Directors or advisory committees; Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees; Amgen: Honoraria, Membership on an entity's Board of Directors or advisory committees; GSK: Membership on an entity's Board of Directors or advisory committees; Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees; Abbvie: Membership on an entity's Board of Directors or advisory committees. Cavo:Janssen, Celgene, Amgen, Abbvie: Consultancy, Membership on an entity's Board of Directors or advisory committees; Celgene, Janssen, Amgen, BMS, Abbvie, Takeda: Honoraria; Janssen, Celgene: Other: Travel Accommodations; Janssen, Celgene: Speakers Bureau. Heeg:Ingress-Health: Employment. Van Beekhuizen:Ingress-health: Consultancy. Pisini:Janssen: Employment, Equity Ownership. Nair:Janssen: Employment, Equity Ownership. Lam:Janssen: Employment, Equity Ownership. Slavcev:Janssen: Employment, Equity Ownership. He:Janssen: Employment, Equity Ownership.

Background: Based on results from the phase 3 trials, maintenance therapy after autologous stem cell transplantation has become the standard of care. While lenalidomide is typically used as the maintenance agent, patients with high risk MM are often maintained on proteasome inhibitor-based maintenance treatment. We examined the trends in maintenance use and outcomes in a large group of patients transplanted at a single institution over two decades spanning the years when maintenance gradually became standard of care. Methods: We included patients who underwent their first autologous stem cell transplantation between 2000 and 2016 in the current study. Patients who received an allogeneic SCT or a planned tandem SCT were excluded. Clinical data was extracted from the medical records or from a prospectively maintained database. Early transplant was defined as SCT within 12 months of diagnosis. High risk (HR) myeloma was defined as per IMWG criteria, based on presence of high-risk FISH abnormalities. Progression free survival and overall survival were estimated from either diagnosis as well as from the date of SCT. Results: We included 1961 patients with MM who were transplanted between January 2000 and August 2016 in the current analysis. Majority (n=1496; 76%) had an early SCT while the remaining patients were transplanted beyond 12 mos from diagnosis. Maintenance was employed in 31% of the patients, 33% of the early SCT and 23% of the delayed SCT. Proportion of patients receiving maintenance increased over the years: <1% in 2000 to 85% in 2016. The most common maintenance approach was an IMiD (56%), followed by a PI (33%) and a PI+IMiD (8%). FISH risk was available in 875 patients, all assessed within 6 months of diagnosis, and 22% were HR. Overall, maintenance was more commonly used in HR patients (64% vs. 36%), and a PI containing maintenance was more commonly used in HR (42% vs. 12%). The PFS was superior for those receiving a maintenance therapy (70 vs. 23 mos, P<0.001); NR vs. 27 mos for early SCT and 25 vs. 16 mos for delayed SCT. The impact of maintenance therapy was more evident in the HR group HR vs SR risk ratio was 2.3 without maintenance, and 1.5 with maintenance. Among the HR patients, PI based maintenance had a trend towards better outcome, while the PFS was better with non-PI maintenance among the SR patients. OS from diagnosis was also better among the early SCT group who received maintenance (99 vs. 88 mos; p=0.01); however, the OS advantage appeared most evident among the HR patients. Conclusions: The experience with maintenance therapy post autologous SCT for myeloma in a non-clinical trial setting appear to confirm the findings from the phase 3 trials. Maintenance provided benefit in patients who had a delayed SCT in our experience. The benefit appear to be particularly relevant for high risk patients, who appear to benefit from PI based regimens. In contrast, SR patients appear to benefit primarily from IMiD based regimen. Disclosures Gertz: Spectrum: Consultancy, Research Funding; Johnson and Johnson: Speakers Bureau; Amgen: Consultancy; Ionis/Akcea: Consultancy; Research to Practice: Consultancy; Appellis: Consultancy; Celgene: Consultancy; Teva: Speakers Bureau; Alnylam: Consultancy; Janssen: Consultancy; Annexon: Consultancy; Abbvie: Other: personal fees for Data Safety Monitoring board; Medscape: Consultancy, Speakers Bureau; Prothena Biosciences Inc: Consultancy; Physicians Education Resource: Consultancy. Lacy:Celgene: Research Funding. Dispenzieri:Akcea: Consultancy; Intellia: Consultancy; Janssen: Consultancy; Pfizer: Research Funding; Takeda: Research Funding; Celgene: Research Funding; Alnylam: Research Funding. Dingli:Millenium: Consultancy; Rigel: Consultancy; Janssen: Consultancy; alexion: Consultancy; Karyopharm: Research Funding. Kapoor:Amgen: Research Funding; Takeda: Honoraria, Research Funding; Glaxo Smith Kline: Research Funding; Celgene: Honoraria; Janssen: Research Funding; Cellectar: Consultancy; Sanofi: Consultancy, Research Funding. Kumar:Takeda: Research Funding; Celgene: Consultancy, Research Funding; Janssen: Consultancy, Research Funding.

Background: The treatment of Multiple Myeloma (MM) has evolved significantly in the past decade with the introduction of novel agents and drug combinations, thus enhancing treatment efficacy and allowing more patients to achieve complete response (CR). This has created a need to identify surrogates for depth of treatment response. Serum free light chain (sFLC) ratio normalization has been shown to be prognostic for progression free survival as well as overall survival in patients achieving a complete response to therapy. Consequently, it has been incorporated as a defining feature for stringent CR, along with lack of clonal plasma cells by immunohistochemistry (IHC) or low sensitivity flow cytometry. The routine use of multiparametric flow cytometry with higher sensitivity to detect residual disease than IHC or the older 4-color flow cytometry, has raised the question as to whether sFLC ratio is still a valid indicator of response depth. Moreover, in nearly half of the patients with an abnormal sFLC ratio after treatment, the abnormality is secondary to suppression of one or both serum light chains. Therefore, we designed a retrospective study to address these issues. Patients and Methods: This is a retrospective study using the Multiple Myeloma Database at Mayo Clinic, Rochester. We included patients who, after any line of therapy, had negative serum and urine immunofixation and absence of clonal bone marrow plasma cells by flow cytometry (PC-PRO), which has a sensitivity of >10-4. Simultaneous sFLC data was also extracted. Patients were grouped into three categories based on their sFLC ratios: 1) normal ratio (normal), 2) abnormal ratio due to suppression of the uninvolved light chain (LC), involved LC, or both (Abn-suppressed) and 3) abnormal ratio due to elevation of the involved LC (Abn-inv elevated). The primary endpoint was the median time to next treatment (TTNT), defined as the time from sample collection to the time of initiation of the subsequent therapy or time of last follow up if a subsequent line of treatment was not initiated. Results: The cohort consisted of 510 patients. 285 (56%) were males and 225 (44%) females. Median age was 61 years (IQR: 55-67). Median Follow-up was 41 months. The last treatments administered prior to data collection included stem cell transplant (SCT) (with or without maintenance) in 290 (57%) patients, and non-SCT regimens in the others. The sFLC ratio was normal in 337 (66%) and abnormal in 173 (34%) patients. Among the patients with abnormal sFLC ratios, 81 had elevated involved LC, 25 had suppression of the involved LC, 45 had suppression of the uninvolved LC and 22 had suppression of both LCs. We first examined the TTNT for the three groups and found that the TTNT was identical for those with a normal ratio and those with an abnormal ratio due to suppression of one or both light chains (Figure 1). So, we combined these two groups (Normal-Abn suppressed) and compared their outcomes to the patients with abnormal sFLC ratio due to elevated involved LC. The Abn-inv elevated group had a shorter TTNT as shown in Figure 2 (log-rank 0.06, Wilcoxon <0.01). The Abn-inv elevated group also had decreased overall survival compared to the other group (log-rank: 0.05, Wilcoxon: 0.01) (Figure 3). Conclusion: This study provides 2 important observations. First, patients with an abnormal ratio due to suppression of one or both LCs have outcomes similar to those with a normal ratio, suggesting a need to clarify the current definition of stringent CR. Second, the study suggests an important prognostic value for an abnormal sFLC ratio due to elevated involved LC, suggesting this as an important surrogate for depth of response. Disclosures Kapoor: Janssen: Research Funding; Takeda: Honoraria, Research Funding; Cellectar: Consultancy; Celgene: Honoraria; Sanofi: Consultancy, Research Funding; Amgen: Research Funding; Glaxo Smith Kline: Research Funding. Dispenzieri:Akcea: Consultancy; Intellia: Consultancy; Janssen: Consultancy; Pfizer: Research Funding; Takeda: Research Funding; Celgene: Research Funding; Alnylam: Research Funding. Gertz:Ionis: Honoraria; Alnylam: Honoraria; Prothena: Honoraria; Celgene: Honoraria; Janssen: Honoraria; Spectrum: Honoraria, Research Funding. Lacy:Celgene: Research Funding. Dingli:Karyopharm: Research Funding; Rigel: Consultancy; Millenium: Consultancy; Janssen: Consultancy; alexion: Consultancy. Leung:Takeda: Research Funding; Aduro: Membership on an entity's Board of Directors or advisory committees; Prothena: 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.