Citations

Introduction: While the molecular target of immunomodulators such as pomalidomide (POM) and lenalidomide (LEN) has been identified, the mechanisms underlying therapeutic resistance remain incompletely understood. The uniformly emerging resistance to therapy over time in the absence of identifiable cereblon pathway mutations in the majority of patients raises questions about alternative mechanisms including aberrant gene expression. Methods: We performed gene expression profiling using an Affymetrix GeneChip Human Genome U133 Plus 2.0 microarray on CD138+ bone marrow cells from patients with relapsed / refractory (RRMM) and newly diagnosed (NDMM) multiple myeloma prior to initiating treatment. Patients were treated on two phase II clinical trial protocols (MC0789: POM ± dexamethasone in RRMM; MC0884: LEN ± dexamethasone in NDMM) between 2007 and 2012. We categorized patients based on their IMWG response as non-responders (SD) and responders (VGPR+). We selected 15 responders and 15 non-responders from MC0789 (n = 30) and compared overall survival, gene expression patterns, and involved cellular pathways between the two groups. We selected 5 responders and 5 non-responders from MC0884 (n = 10) for targeted validation of differentially expressed candidate genes. After data quality control and normalization of gene expression values, differential gene expression was estimated using limma. Statistical significance was adjusted for multiple testing in the discovery set using a false discovery rate-based approach for genome-wide experiments (q-value). We used Gene Ontology and PANTHER pathway analysis for functional annotation of differentially expressed genes. Overall survival estimates were calculated using the Kaplan-Meier method. Computation and visualization were performed in R. Results: Median age at treatment initiation on MC0789 was 65 years (40 - 82), 65% of the patients were male. Pomalidomide resistance was associated with an increase in mortality (median overall survival 1.6 versus 6.4 years, p = 0.009, Kaplan-Meier plot). There were 1076 differentially regulated genes between responders and non-responders (521 up- and 555 down-regulated, q < 0.050 for all genes, volcano plot). Expression of CRBN was 1.5-fold down-regulated in non-responders (q = 0.005). Supervised hierarchical clustering of the top 500 differentially expressed genes demonstrated distinct patterns in pomalidomide-resistant disease (heatmap). Gene ontology analysis revealed protein synthesis as one of the most enriched biological processes (bar graph). Pathway analysis showed a 6-fold enrichment (FDR = 0.007) of the ubiquitin proteasome pathway in pomalidomide-resistant disease. Differentially expressed genes involved key protein degradation pathways, epigenetic modifiers, and transcription factors. Targeted validation in MC0884 revealed 13 common genes with at least 1.5-fold differential expression (5 up- and 8 down-regulated), 12 of which have previously been implicated in the regulation of apoptosis, tumor glucose metabolism, Rho and Wnt signaling, miRNA-driven resistance to chemotherapy, and ubiquitin-dependent protein degradation (Table and Sankey diagram). The most up-regulated gene in non-responders was MYRIP, a gene coding for a vesicle trafficking protein associated with platinum resistance and suppression of pro-apoptotic BCL-2 family members in solid malignancies. The most down-regulated gene was FRZB, a gene coding for a negative regulator of Wnt signaling, previously implicated in the progression of monoclonal gammopathy of undetermined significance to multiple myeloma. Conclusions: Overall survival of patients with pomalidomide-resistant RRMM remains poor. Pomalidomide resistance was associated with differential gene expression in several potentially targetable cellular pathways beyond the known drug target cereblon. Targeted validation of candidate genes revealed common cellular pathways in immunomodulator-resistant disease. Elucidating the exact molecular mechanisms underlying immunomodulator resistance is of considerable interest for biomarker development and the identification of novel therapeutic targets and warrants further exploration. Figure Disclosures Lacy: Celgene: Research Funding. Dispenzieri:Celgene: Research Funding; Alnylam: Research Funding; Intellia: Consultancy; Janssen: Consultancy; Pfizer: Research Funding; Akcea: Consultancy; Takeda: Research Funding. Kumar:Takeda: Research Funding; Celgene: Consultancy, Research Funding; Janssen: Consultancy, Research Funding.

Introduction: Emerging evidence supports that cytogenetic abnormalities (CAs) drive myelomagenesis and heterogeneity (e.g., clinical presentation, response to therapy, and prognosis) of multiple myeloma (MM). Diversity of CAs, including primary (IgH translocations and trisomies) and secondary CAs (copy number abnormalities) argues that MM is not a "single disease". Thus, identification of CAs at diagnosis is essential for risk stratification, guiding treatment, and prognostic estimation in daily practice. However, although frequency, configuration, and significance of CAs have been well documented in the Western countries, this information is lacking in the Asian population. To this end, a multi-center retrospective analysis was carried out to examine epidemiology and prognostic significance of CAs alone or in combination in a cohort of Chinese patients with newly-diagnosed MM (NDMM). Materials and Methods: A total of 1015 NDMM patients who had the baseline information of CAs detected by FISH at four institutes nationwide were included. According to the IMWG consensus updated in 2016, 1q gain, del(17p), t[4;14], and t[14;16] were defined as high-risk CAs (HRCAs), while another HRCA t[14;20] was not tested routinely in a majority of these patients. In addition, del(1p) and del(13q14) were considered as an adverse CA. According to the mSMART3.0 proposed by Mayo Clinic in 2018, double-hit (DHMM) and triple-hit MM (THMM) were defined as co-occurrence of 2 or >= 3 HRCAs, respectively. The Kaplan-Meier approach was used to estimate progression-free survival (PFS) and overall survival (OS). Results: In this cohort, the median age of 1015 patients was 61 years; 63.5% were male. The type of IgG, light chain, IgA, IgD, non/oligosecretory, IgM, or IgE accounted for 42.9%, 26.2%, 24.5%, 3.4%, 2.5%, 0.4%, and 0.1%, respectively. Del(13q) (46.4%) and 1q gain (46.1%; 3 copies = 73.8%, >= 4 copies = 26.2%) represented the most common CAs, followed by t(4;14) (14.0%), t(11;14) (11.8%), del(1p) (11.5%), del(17p) (9.9%; 20-50% cells = 35.6%, > 50% cells = 64.4%), and t(14;16) (5.1%). While none of these CAs was detected in 23.8% of cases, the frequency of patients who carried 1 - 5 CAs was 31.9%, 28.0%, 13.4%, 2.0%, and 0.9%, respectively. In the 1q+ cases, 36.4% patients carried second CA(s), including del(13q) (61.1%), t(4;14) (20.3%), del(1p) (14.8%), del(17p) (10.7%), t(11;14) (10.4%), and t(14;16) (8.1%). In the del(17p) cases, 57.5% patients had additional CA(s), including del(13q) (75.2%), 1q gain (49.5%), del(1p) (21.6%), t(4;14) (19.6%), t(14;16) (8.7%), and t(11;14) (2.2%). In the cases bearing IgH translocations, 26.0% patients also carried other CA(s), including del(13q) and 1q gain (61.9% for each), and del(17p) (9.3%). In the cases harboring HRCAs, the percentage of patients who carried 1 - 4 HRCAs was 70.2%, 24.8%, 3.7%, and 1.1%, respectively. Overall, 14.3% and 2.9% patients had DHMM or THMM, of whom 65.0%, 18.0%, 12.0%, and 5.0% had 2 - 5 HRCAs, respectively. While there was no significant difference in PFS between the cases carrying 1 and 2 CAs (P = 0.209), the patients who had 3 or more CAs displayed a sharp reduction in median PFS (P = 0.022 and P = 0.003 for 3 vs 1 or 2 CAs). Although multiple CAs was associated with shorter median OS, no statistical significance was observed for each comparison (P > 0.05). However, patients who carried >= 2 HRCAs had significantly shorter median PFS (12.1 months; P = 0.0004) and OS (29.3 months; P = 0.027) than those who had one single HRCA (32.2 and 65.6 months for median PFS and OS). Conclusion: In comparison with the Western countries, the incidence of secondary HRCAs (e.g., 1q gain and del(17p)) is relatively higher in Chinese patients at diagnosis, while the standard-risk CA such as t(11;14) is clearly less frequent. The proportion of Chinese patients who carry multiple CAs (up to 5) or HRCAs appear to be greater as well. In this context, patients carrying two or more HRCAs, so called DHMM or THMM, exhibit significantly worse outcome than those carrying only one HRCA. Together, this study builds up an up-to-date profile of CAs for Chinese patients, which might lay a foundation for revising the criteria for risk stratification and the guideline for treatment that is more feasible and practicable in China. It also provides the information about frequency and configuration of MM-driven CAs, which might be more relevant to the Asian population. Disclosures Kumar: Takeda: Research Funding; Celgene: Consultancy, Research Funding; Janssen: Consultancy, Research Funding.

Introduction: Recent advancements in the treatment of multiple myeloma (MM) have centered on engaging the immune system to target multiple myeloma cells. Although these therapies are being combined with immunomodulatory imide drugs (IMiDs) and corticosteroids, the individual contributions of these drugs on the immune system of MM patients has not been examined in the upfront setting. In this study, we examined the peripheral blood immunophenotypes of newly diagnosed multiple myeloma (NDMM) patients receiving the IMiD lenalidomide with or without the corticosteroid dexamethasone. Methods: To characterize immunophenotypes, we utilized flow cytometry to profile white blood cell populations from 35 patients enrolled in a clinical trial testing the efficacy of lenalidomide with and without dexamethasone in NDMM (NCT00772915). In this trial, all patients were initiated on single-agent lenalidomide. Dexamethasone was initiated in patients that did not meet desirable responses or for disease progression. At each cycle, peripheral blood was stained with a 17-marker antibody panel against several immune lineages and functional surface markers. We grouped patients into two groups: 1) lenalidomide alone or 2) lenalidomide with dexamethasone according to their treatment regimen at each cycle timepoint. Results: First we confirmed anti-myeloma cell activity for both groups by measuring a steady decline in circulating plasma cells in both groups. Examining peripheral blood immunophenotypes showed an expected decrease in T cells and a smaller decrease in B cells in both groups of patients. Closer inspection of B cell populations revealed a switch towards a more immature B cell phenotype in both treatment groups. This was measured as a switch from CD19-CD20+ cells to CD19+CD20- B cells. Inspection of T cell subsets revealed that patients receiving single-agent lenalidomide had a sustained decrease in the levels of CD4+ T cells and increase in the levels of CD8+ T cells. This was seen in both naïve and regulatory T cells evidenced by a decrease in the CD4/CD8 ratio among CD28+ T cells as well as CD25+ T cells. Importantly, this alteration did not lead to sustained alterations in the overall level of CD25+ or CD28+ T cells, and the addition of dexamethasone reverses this trend. In addition to the effects seen on T and B cell numbers, we detected expansions of NK cell populations in patients receiving lenalidomide alone. This expansion is detected as an overall increase in CD56+ mononuclear cells with the majority of cells being CD56+CD3- cells. Conclusions: Our data show that lenalidomide and dexamethasone therapy have shared but distinct effects on peripheral blood immunophenotypes in NDMM. Both drugs alter B cells numbers and populations leading to an expansion of CD19+CD20- B cells. However, lenalidomide alone decreases the CD4/CD8 T cell ratio; and, lenalidomide more strongly expands NK cell populations. The addition of dexamethasone reverses this trend and leads to a restoration of the CD4/CD8 ratio. This suggests that lenalidomide without dexamethasone might be counterproductive in immunotherapies intended to recruit CD4+ T cells. Conversely, lenalidomide alone could increase the efficacy of immunotherapies dependent on NK cell recruitment such as antibody-dependent cellular cytotoxicity (ADCC). This information may benefit future investigations of immune responses in MM patients and improve the adoption of immunotherapies to MM patients. Figure Disclosures Kumar: Celgene: Consultancy, Research Funding; Janssen: Consultancy, Research Funding; Takeda: Research Funding.

Background: Multiple myeloma (MM) is a heterogeneous disease with varying survival outcomes depending on the presence of certain genetic abnormalities. Common abnormalities include trisomies, translocations involving the chromosome 14, and amplifications or deletions of chromosomes 1, 13, and 17. t(11;14), occurring in 15% of patients with myeloma, had been considered a standard risk abnormality, but recent data suggest inferior outcome. This is important as new therapeutic options such as the BCL-2 inhibitor venetoclax has been shown to be particularly effective in t(11;14) patients. Methods: This was a multicenter study to identify the outcomes of patients with t(11;14), using a retrospectively assembled cohort. Patients with MM diagnosed between 2005 and 2015 with t(11;14) identified on FISH performed within six months of diagnosis, and with treatment details available and if alive, a minimum of 12 months of follow up, were enrolled. Results: The current analysis includes 1216 patients; median age of 62.56 years; 58.7% male. The median follow-up from diagnosis for the entire cohort was 51.9 months; 69.1% of the patients were alive at the last follow up. ISS stage distribution included: Stage I (35.7%), Stage II (34.0%) and Stage III (15.1%), data was missing for the rest. The distribution of concurrent FISH abnormalities included: trisomies (3.5%), del 13q (13.3%), 1q amp (8.8%), and del 17p or monosomy 17 (5.8%). Initial regimen included: 27.2% had an immunomodulatory (IMiD), 45.9% had a proteasome inhibitor (PI), 17.7% had both, and 9.0% had no novel agent. The drug classes by line of therapy are shown in Table 1. An early stem cell transplant (defined as within 12 months of start of first line treatment) was used in 49.4% of patients. The median time to next treatment (TTNT) after starting initial treatment was 26.6 (95% CI: 23.9 to 29.2) months. The median overall survival (OS) from diagnosis for the entire cohort was 95.1 (95% CI: 85.9 to 105.9) months; 4-year estimates for those diagnosed from January 2005 to December 2009, and from January 2010 to December 2014 were 77.5% and 78.6%, respectively. The median OS for those with any one high risk FISH lesion (del 17p/ 1q amp) was 67.5 (55.2, 97.1) versus 101.7 (89.7, 107.3) months. Patients with early SCT (within 12 months of diagnosis) had better OS: 108.3 (103.8, 133.0) vs. 69.8 (61.5, 80.3) months. Conclusion: Patients with t(11;14) without high risk FISH abnormalities have an excellent survival. Patients receiving a PI + IMiD combination and those receiving autologous SCT as part of initial therapy had best survival. Though numbers are limited, patients in the later lines receiving newer drugs such as venetoclax and daratumumab had high response rates and durable responses. Disclosures Kumar: Celgene: Consultancy, Research Funding; Janssen: Consultancy, Research Funding; Takeda: Research Funding. Bittrich:Celgene: Other: Travel Funding, Research Funding; Else Kröner Fresenius Foundation: Research Funding; Otsuka Pharmaceuticals Europe: Other: N/A; SANOFI Aventis: Membership on an entity's Board of Directors or advisory committees, N/A, Research Funding; University Hospital Wuerzburg: Employment; Bristol Myers Squibb: Research Funding; Pfizer: Other: Travel Funding; AMGEN: Other: Travel Funding; JAZZ Pharmaceuticals: Other: Travel Funding; Wilhelm Sander Foundation: Research Funding; German Research Foundation (DFG): Other: N/A; University of Würzburg: Other: N/A. Goldschmidt:Mundipharma: Research Funding; Takeda: Membership on an entity's Board of Directors or advisory committees, Research Funding; Adaptive Biotechnology: Membership on an entity's Board of Directors or advisory committees; John-Hopkins University: Research Funding; Dietmar-Hopp-Stiftung: Research Funding; Janssen: Consultancy, Research Funding; Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Sanofi: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; MSD: Research Funding; Molecular Partners: Research Funding; John-Hopkins University: Research Funding; Amgen: Consultancy, Research Funding; Bristol-Myers Squibb: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Chugai: Honoraria, Research Funding; Novartis: Membership on an entity's Board of Directors or advisory committees, Research Funding. Reece:Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Otsuka: Research Funding; Amgen: Consultancy, Honoraria, Research Funding; BMS: Research Funding; Karyopharm: Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Takeda: Consultancy, Honoraria, Research Funding; Merck: Research Funding. Mateos: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; 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; Takeda: Honoraria, Membership on an entity's Board of Directors or advisory committees; Pharmamar: Membership on an entity's Board of Directors or advisory committees; GSK: Membership on an entity's Board of Directors or advisory committees; Adaptive: Honoraria; EDO: Membership on an entity's Board of Directors or advisory committees. Ludwig:Celgene: Speakers Bureau; Amgen: Research Funding, Speakers Bureau; Takeda: Research Funding, Speakers Bureau; PharmaMar: Consultancy; Janssen: Speakers Bureau; BMS: Speakers Bureau. Mangiacavalli:celgene: Consultancy; Amgen: Consultancy; Janssen cilag: Consultancy. Dimopoulos:Sanofi Oncology: Research Funding. Kastritis:Amgen: Honoraria, Research Funding; Janssen: Honoraria, Research Funding; Takeda: Honoraria; Pfizer: Honoraria; Prothena: Honoraria; Genesis: Honoraria. Yee:Amgen: Consultancy, Honoraria; Celgene: Consultancy, Honoraria, Research Funding; Takeda: Consultancy; Bristol-Myers Squibb: Consultancy, Research Funding; Karyopharm: Consultancy; Adaptive: Consultancy. Raje:Amgen Inc.: Consultancy; Bristol-Myers Squibb: Consultancy; Celgene Corporation: Consultancy; Takeda: Consultancy; Janssen: Consultancy; Merck: Consultancy. Rosta:Cornerstone Research Group: Employment. Haltner:Cornerstone Research Group: Employment. Cameron:Cornerstone Research Group: Employment, Equity Ownership. Durie:Amgen, Celgene, Johnson & Johnson, and Takeda: Consultancy.

Background CD38 is highly expressed on MM cells, hence anti-CD38 agents are of interest as a therapeutic approach in MM. The anti-CD38 monoclonal antibody, dara, as mono- or combination therapy, has substantially improved efficacy outcomes in RRMM, including in heavily pretreated pts. However, many pts relapse and new therapeutic approaches are needed, particularly for pts R/R to dara. TAK-169 is a dimeric fusion protein of an anti-CD38 antibody single chain variable fragment fused to a modified Shiga-like toxin-A subunit. Its unique mechanism of action involves specific binding to CD38+ cells, forced internalization into the target cells, retrograde transport to the cytosol, and irreversible, enzymatic inactivation of target cell ribosomes to cause apoptosis. TAK-169 showed potent, rapid in vitro activity on CD38-expressing cell lines and in vivo efficacy in MM mouse xenograft tumor models with a wide CD38 expression range when administered intravenously (IV) once weekly (QW) or once every 2 weeks (Q2W). Activity was also shown in ex vivo assays from 6 pt bone marrow aspirate (BMA) samples, including from a dara-resistant pt (EC50 5 nM). TAK-169 retained in vitro activity in the presence of dara with modest shifts in EC50 noted at higher dara concentrations. In contrast to dara, TAK-169 has direct tumor cell kill activity that is independent of a pt's immune function status and may therefore be effective in a dara-resistant setting. Accordingly, this phase 1 study will assess safety, tolerability, preliminary efficacy, pharmacokinetics (PK) and pharmacodynamics (PD) of TAK-169 in RRMM. Methods This first-in-human, multicenter, open-label study (NCT04017130; Figure) comprises dose escalation (Part 1) and expansion (Part 2). Eligible pts must be aged ≥18 years with a confirmed diagnosis of MM. Pts enrolled in Part 1 should not be candidates for available therapies known to confer clinical benefit in RRMM. In part 2, pts enrolled should have received ≥3 prior lines of therapy, or ≥2 if one included a PI and IMiD combined. Prior anti-CD38 therapy (including dara) is permitted, except in the Part 2 anti-CD38 therapy-naive expansion cohort. In Part 1, the primary objective is to assess safety/tolerability of TAK-169 in pts with RRMM, and determine maximum tolerated dose/recommended Phase 2 dose (MTD/RP2D). Secondary objectives include efficacy, PK, PD and immunogenicity of TAK-169. Approximately 60 pts are planned to be enrolled to receive IV TAK-169 QW on Days 1/8/15/22 in 28-day cycles. Dosing will start at 50 μg/kg, with subsequent dose levels of 100, 200, 335, 500, 665 μg/kg. A 2nd Q2W dose escalation cohort may be initiated, starting at the QW MTD. The Bayesian Logistic Regression Model with overdose control will be used for dose escalation cohort 2 and for all subsequent dose escalation cohorts, and other non-DLT-safety, clinical, PK, and PD data will be considered. In Part 2, the primary objective is preliminary evaluation of TAK-169 clinical activity in RRMM. Secondary objectives include further evaluating safety, efficacy, PK, PD, and immunogenicity of TAK-169. Part 2 will enroll approximately 54 pts into dara RR (QW and Q2W dosing) and anti-CD38 therapy-naïve (QW dosing) cohorts. TAK-169 will be administered at the MTD/RP2D (QW and Q2W) determined in Part 1. Response will be monitored longitudinally using orthogonal approaches. M-protein levels in serum and urine will be measured each cycle to determine objective responses by International Myeloma Working Group (IMWG) criteria. Minimal residual disease status in pts suspected to be at complete response will be assessed. Circulating free DNA (cfDNA) will be collected from longitudinal plasma samples for disease monitoring. Baseline cytogenetic data will be collected, and TAK-169 efficacy will be assessed in pts with high-risk cytogenetics. Serial blood samples will be used to characterize PK and PD of TAK-169 after IV administration, and to assess the presence of antidrug antibodies. BMA samples collected at screening will be used to evaluate baseline expression of candidate biomarkers. Toxicity will be evaluated using National Cancer Institute Common Terminology Criteria for Adverse Events (NCI CTCAE) version 5. Efficacy analyses will use descriptive statistics with a 95% confidence interval and the Kaplan-Meier method. PK parameters and immunogenicity status will be summarized using descriptive statistics. Recruitment is ongoing. Figure Disclosures Kumar: Celgene: Consultancy, Research Funding; Janssen: Consultancy, Research Funding; Takeda: Research Funding. Cornell:Takeda: Consultancy; KaryoPharm: Consultancy. Landgren:Amgen: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Merck: Other: IDMC; Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Takeda: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Theradex: Other: IDMC; Adaptive: Honoraria, Membership on an entity's Board of Directors or advisory committees; Abbvie: Membership on an entity's Board of Directors or advisory committees; Sanofi: Membership on an entity's Board of Directors or advisory committees; Karyopharm: Membership on an entity's Board of Directors or advisory committees; Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding. Ailawadhi:Cellectar: Research Funding; Pharmacyclics: Research Funding; Janssen: Consultancy, Research Funding; Celgene: Consultancy; Amgen: Consultancy, Research Funding; Takeda: Consultancy. Higgins:Molecular Templates, Inc.: Employment, Equity Ownership. Willert:Molecular Templates: Employment. Waltzman:Molecular Templates, Inc.: Employment, Equity Ownership. Lin:Millennium Pharmaceuticals, Inc., Cambridge, MA, USA, a wholly owned subsidiary of Takeda Pharmaceutical Company Limited: Employment, Equity Ownership. Zhang:Millennium Pharmaceuticals, Inc., Cambridge, MA, USA, a wholly owned subsidiary of Takeda Pharmaceutical Company Limited: Employment. Lublinsky:Millennium Pharmaceuticals, Inc., Cambridge, MA, USA, a wholly owned subsidiary of Takeda Pharmaceutical Company Limited: Employment. Dash:Millennium Pharmaceuticals, Inc., Cambridge, MA, USA, a wholly owned subsidiary of Takeda Pharmaceutical Company Limited: Employment. Hanley:Millennium Pharmaceuticals, Inc., Cambridge, MA, USA, a wholly owned subsidiary of Takeda Pharmaceutical Company Limited: Employment. Manoharan:Millennium Pharmaceuticals, Inc., Cambridge, MA, USA, a wholly owned subsidiary of Takeda Pharmaceutical Company Limited: Employment. Leichter:Millennium Pharmaceuticals, Inc., Cambridge, MA, USA, a wholly owned subsidiary of Takeda Pharmaceutical Company Limited: Employment. Ottinger:Millennium Pharmaceuticals, Inc., Cambridge, MA, USA, a wholly owned subsidiary of Takeda Pharmaceutical Company Limited: Employment. Labotka:Millennium Pharmaceuticals, Inc., Cambridge, MA, USA, a wholly owned subsidiary of Takeda Pharmaceutical Company Limited: Employment. Newcomb:Millennium Pharmaceuticals, Inc., Cambridge, MA, USA, a wholly owned subsidiary of Takeda Pharmaceutical Company Limited: Employment, Equity Ownership. Vorog:Millennium Pharmaceuticals, Inc., Cambridge, MA, USA, a wholly owned subsidiary of Takeda Pharmaceutical Company Limited: Employment. OffLabel Disclosure: TAK-169 is a dimeric fusion protein of an anti-CD38 antibody single chain variable fragment fused to a modified Shiga-like toxin-A subunit

Background: Combinations of alkylating agents with proteasome inhibitors have demonstrated efficacy in newly diagnosed and relapsed multiple myeloma (MM), with melphalan or cyclophosphamide combinations being some of the commonly used regimens for initial treatment of MM. Ixazomib (Ixa) is an oral proteasome inhibitor that is approved for use in combination with lenalidomide for patients with relapsed MM. We examined if Ixa can be effectively combined with cyclophosphamide (Ctx) in order to develop a less expensive, all oral regimen for patients with relapsed MM. Patients and Methods: Patients with relapsed MM, who were proteasome inhibitor naïve OR have received less than 6 cycles of therapy with bortezomib and had a better than PR with no progression at the time of discontinuation, were enrolled. The primary objective was to determine overall response rate (ORR). Treatment consisted of Ixa 4mg PO days 1, 8, 15; Ctx 300 mg/m2 PO days 1, 8, 15, 22 and dexamethasone (Dex) 40 mg PO weekly in a 28-day cycle. Overall, 37 patients were accrued; data on 33 eligible patients were available for analysis as of July 18, 2019. Results: The median age was 71 (48-89), 61% were male and the median duration from diagnosis was 46 months (mos). Median number of prior lines of therapy was 4 (range 1-5), 76%, 42% and 67% respectively had a prior IMiD, proteasome inhibitor or stem cell transplant, respectively. At data cutoff, 22 (67%) had progressed, 4 (12%) had died and the median follow up of those alive was 21.3 mos. Fourteen patients are still receiving treatment, with median of 8.5 cycles. Most common reason for treatment discontinuation was disease progression (10 pts; 53%). The ORR was 60% including 6% CR and 24% VGPR. The median event free survival was 11.3 mos (95%CI: 9.0 - 26.8). Overall, 401 cycles have been administered across the study, with dose modifications/ hold required for Ixa, Ctx, and Dex in 9 (27%), 14 (42%), and 22 (67%) patients respectively, the most common reason being hematologic toxicity. A grade 3 or higher adverse event at least possibly attributed to the study drugs was seen in 77% of patients, hematologic in 67% and non-hematologic in 30%. (Table 1) The most commonly observed hematologic toxicities included thrombocytopenia, neutropenia, lymphopenia and anemia; for non-hematologic was nausea, diarrhea, peripheral neuropathy toxicity and fatigue. Conclusions: The combination of Ixa, Ctx and Dex (ICd) offers a convenient, all oral regimen for treatment of relapsed disease not refractory to proteasome inhibitors. The regimen has good efficacy in this group f heavily pretreated patients, with an acceptable toxicity profile. Disclosures Lacy: Celgene: Research Funding. Gertz:Ionis: Honoraria; Spectrum: Honoraria, Research Funding; Janssen: Honoraria; Celgene: Honoraria; Prothena: Honoraria; Alnylam: Honoraria. Ailawadhi:Takeda: Consultancy; Janssen: Consultancy, Research Funding; Cellectar: Research Funding; Pharmacyclics: Research Funding; Amgen: Consultancy, Research Funding; Celgene: Consultancy. Bergsagel:Janssen Pharmaceuticals: Consultancy; Celgene: Consultancy; Ionis Pharmaceuticals: Consultancy. Fonseca:AbbVie, Amgen, Bayer, Celgene, Kite, Janssen, Juno, Merck, Pharmacylics, Sanofi, Takeda: Other: Consultant/Advisor; Prognosticatin of MM based on Genetic Categorization by FISH: Patents & Royalties; Adaptive Biotechnologies: Other: Scientific Advisory Board. Dingli:alexion: Consultancy; Janssen: Consultancy; Millenium: Consultancy; Rigel: Consultancy; Karyopharm: Research Funding. Kapoor:Amgen: Research Funding; Takeda: Honoraria, Research Funding; Glaxo Smith Kline: Research Funding; Sanofi: Consultancy, Research Funding; Celgene: Honoraria; Cellectar: Consultancy; Janssen: Research Funding. Chanan-Khan:AbbVie: Research Funding; Pharmacyclics: Research Funding; Xencor: Research Funding; Merck: Research Funding; Jansen: Research Funding; Mayo Clinic: Employment; Ascentage: Research Funding; Millennium: Research Funding. Larsen:Janssen Oncology: 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. Russell:Imanis: Equity Ownership. Stewart:Roche: Consultancy; Seattle Genetics: Consultancy; Takeda: Consultancy; Ionis: Consultancy; Janssen: Consultancy, Research Funding; Oncopeptides: Consultancy; Ono: Consultancy; Amgen: Consultancy, Research Funding; Bristol Myers-Squibb: Consultancy; Celgene: Consultancy, Research Funding. Kumar:Celgene: Consultancy, Research Funding; Janssen: Consultancy, Research Funding; Takeda: Research Funding.

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 &lt;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&lt;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 &lt;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.