Abstract: Immune therapies, including CAR‐T cells, bispecific antibodies, and antibody‐drug conjugates, are revolutionizing the treatment of multiple myeloma. In this review, we discuss clinical trial design considerations relevant to immune therapies. We first examine issues pertinent to specific populations, including elderly, patients with renal impairment, high‐risk/extramedullary disease, and prior immune therapies. We then highlight trial designs to optimize the selection of dose and schedule, explore rational combination therapies based on preclinical data, and evaluate the nuances of commonly used endpoints. By exploiting their pharmacokinetic/pharmacodynamic profiles and utilizing novel translational insights, we can optimize the use of immune therapies in multiple myeloma.

Abstract: FLCr ≥100 and BMPC ≥60% identify high-risk SMM, although with more modest median TTP and 2-year PD than previously published. Baseline immunoparesis, eMP, eHb, and edFLC can help identify an ultra-high-risk SMM cohort.

Abstract: T-cell redirection therapy using chimeric antigen receptor (CAR) T cells and bispecific antibodies (BiAbs) has shown promising efficacy in heavily pretreated patients with relapsed/refractory multiple myeloma (RRMM), leading to the approval of 2 CAR T-cell products and numerous BiAb trials. Data on the outcomes after relapse following BiAbs are urgently required to develop strategies for sequencing salvage therapies. We identified 58 patients progressing after a BiAb trial at Mount Sinai Hospital. Progression-free survival (PFS) to the first salvage (PFS1), second salvage therapy (PFS2), and overall survival (OS) were estimated using the Kaplan-Meier method. The median age of the patients was 67 years, and 78% had high-risk cytogenetics. They had a median of 6 prior therapy lines, 89% were triple-class refractory, and 44% were penta-drug refractory. After the BiAb trial, patients were followed for a median of 30.5 months and received a median of 2 additional salvage therapies (range, 1-9). The most common first salvage was T-cell redirection in 19 patients (10 BiAb and 9 CAR T cells). Ten patients underwent T-cell redirection as a second salvage treatment. T-cell redirection therapy as first or second salvage was feasible and associated with a median PFS1 of 28.9 months, PFS2 of 30.9 months, and an OS of 62% at 2 years. The sequential use of different T-cell redirection therapies is possible and may lead to deep and durable responses following the relapse after BiAb therapy in RRMM.

Abstract: Selinexor is the first selective inhibitor of nuclear export to be approved for the treatment of relapsed or refractory multiple myeloma (MM). Currently, there are no known genomic biomarkers or assays to help select MM patients at higher likelihood of response to selinexor. Here, we aimed to characterize the transcriptomic correlates of response to selinexor-based therapy. METHODS We performed RNA sequencing on CD138+ cells from the bone marrow of 100 patients with MM who participated in the BOSTON study, followed by differential gene expression and pathway analysis. Using the differentially expressed genes, we used cox proportional hazard models to identify a gene signature predictive of response to selinexor, followed by validation in external cohorts. RESULTS The three-gene signature predicts response to selinexor-based therapy in patients with MM in the BOSTON cohort. Then, we validated this gene signature in 64 patients from the STORM cohort of triple-class refractory MM and additionally in an external cohort of 35 patients treated in a real-world setting outside of clinical trials. We found that the signature tracks with both depth and duration of response, and it also validates in a different tumor type using a cohort of pretreatment tumors from patients with recurrent glioblastoma. Furthermore, the genes involved in the signature, WNT10A, DUSP1, and ETV7, reveal a potential mechanism through upregulated interferon-mediated apoptotic signaling that may prime tumors to respond to selinexor-based therapy. CONCLUSION In this study, we present a present a novel, three-gene expression signature that predicts selinexor response in MM. This signature has important clinical relevance as it could identify patients with cancer who are most likely to benefit from treatment with selinexor-based therapy.

Abstract: Multiple myeloma (MM) is an incurable plasma cell malignancy accounting for more than 10,000 deaths in the US each year. Novel therapeutic approaches for relapsed MM are urgently needed. Tumor-specific mutations are ideal targets for cancer immunotherapy as they can be potentially recognized as neo-antigens by mature T-cells. Targeting tumor-specific antigens harboring somatic mutations presented on major histocompatibility complex class I molecules (MHC-I) with peptides could personalize the therapeutic approach for relapsed patients. To test this possibility, we examined 6 relapsed MM tumor samples from Mount Sinai, NY to predict in silico patient-specific tumor mutations that may activate the patient's immune systems. This is the first study to utilize Whole-Exome Sequence data (WES) from relapsed MM patients to show the feasibility of using exome sequencing to identify mutation derived neo-antigens that are patient-specific. DNA and RNA from six MM patients were extracted from sorted CD138+ cells from bone marrow aspirates. At the time of sample collection all patients had relapsed following at least five lines of therapy including Autologous Stem Cell Transplantation. The exome capture for DNA sequencing was carried out using the Agilent human whole-exome SureSelect assay. RNA-seq libraries were prepared using Illumina mRNA-seq protocol. All libraries were sequenced on an Illumina HiSeq2500 to generate 100 nucleotide reads. RNA reads were aligned to human reference genome (hg19) and assembled into transcripts using Bowtie-TopHat-Cufflinks. WES data was mapped to reference genome by BWA and then processed by MuTect to detect somatic mutations. Patient-specific alleles were determined using Seq2HLA. The identified mutations lead to candidate antigenic peptides that were filtered by tumor expression level (FPKM 〉 2) using RNA sequence data. Candidate peptides of 8-11 character long were then ranked based on peptide-MHC binding affinity prediction (IC50nM) performed in silico using NetMHCpan. We identified a total of 340 tumor-specific nonsynonymous somatic mutations expressed in the context of patient specific HLA type. 263 (77%) genes were strong binders (IC50 〈 150nM) and 77 (23%) genes were moderate/weak binders (IC50150-500nM). The number of mutated genes that were immunogenic per patient ranged from a minimum of 6 genes to 147 genes. Further, Database for Annotation, Visualization and Integrated Discovery (DAVID) tool was used to identify potentially enriched biological processes among the 340 genes using Gene Ontology (GO) terms. Enrichment analysis of 263 genes showed that they are mainly involved in myeloid cell activation during immune response (eg.LAT2, MYO1F), cell cycle process (CDK1, CHEK2, DNM2, EP300, SETD8), cellular response to stress (RAD21, HDAC2, MAT2B), chromatin silencing (SIRT2, SMARCA4), cell apoptosis and signal transduction (KRAS, NLRP1, ING4, IGF2R). Similarly enrichment analysis of 77 genes revealed their involvement mainly in B cell activation and leukocyte differentiation (LRRC8A, CD3E, PRKDC). Examples of some of these significantly mutated genes with binding affinity and predicted peptides are shown in Table 1. In this study, we show for the first time a correlation between tumor mutations and the epitope landscape by in silico data, suggesting that somatic mutations in MM are immunogenic and could potentially confer antitumor vaccine activity. Our results support an approach in creating cancer vaccines that use tumor-specific immunogenic mutations for the development of personalized vaccines for MM patients. Table 1. Immunogenic mutations in Multiple Myeloma # Patient Specific alleles Peptides IC50 Mutated Genes Effect Patient#1 1 HLA-C*14:02 CYGHTMVAF 57.75 LZTR1 p.R284C 2 HLA-C*14:02 LYFFGMHVQEY 29.75 EP300 p.C1372Y 3 HLA-C*14:02 TFNEPSSEYF 114.21 SMARCA4 p.G1146S Patient#2 4 HLA-B*15:01 MSLHNLGTVF 26.2 BCR p.A1160G 5 HLA-A*31:01 SIISDSPR 149.38 FcRL5 p.V269I 6 HLA-A*31:01 HYFMHLLK 37.16 SIRT2 p.R116H Patient#3 7 HLA-C*05:01 ITDFGHSEIL 25.18 CHEK2 p.K344E Patient#4 8 HLA-A*11:01 VVGARGVGK 121.47 KRAS p.G12R 9 HLA-B*41:01 LEIDQLFRI 132.84 CDK1 p.S208L Patient#5 10 HLA-A*31:01 AVGCGFRRARR 106.68 MAT2B p.P65R Patient#6 11 HLA-A*30:01 HQRVLYIEI 93.61 HDAC2 p.D145E 12 HLA-A*30:01 FTRCLTPLL 63.19 RAD21 p.V397L Disclosures Chari: Array Biopharma: Consultancy, Other: Institutional Research Funding, Research Funding; Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Millennium/Takeda: Consultancy, Research Funding; Biotest: Other: Institutional Research Funding; Novartis: Consultancy, Research Funding; Onyx: Consultancy, Research Funding. Jagannath:BMS: Honoraria; Janssen: Honoraria; MERCK: Honoraria; Novartis Pharmaceuticals Corporation: Honoraria; Celgene: Honoraria. Dudley:NuMedii, Inc: Patents & Royalties; Janssen Pharmaceuticals: Consultancy; GlaxoSmithKline: Consultancy; Personalis: Patents & Royalties; Ayasdi, Inc: Other: Equity; Ecoeos, Inc: Other: Equity. Hammerbacher:Cloudera: Membership on an entity's Board of Directors or advisory committees; Bay Sensors: Other: Equity; Cambrian Genomics: Other: Equity; Genome Compiler Corporation: Other: Equity; Science Exchange: Other: Equity; Transcriptic: Other: Equity; Pymetrics: Other: Equity. Schadt:Pacific Biosciences: Consultancy; Berg Pharma: Other: Scientific Advisory Board; GNS Healthcare: Other: Scientific Advisory Board; Clinical Gene Networks AB: Other: Equity. Bhardwaj:Dynavax Technologies Corporation: Consultancy; Crucell: Other: Equity; Dendreon Corporation: Other: Scientific Advisory Board; Merck & Co., Inc.: Other: Scientific Advisory Board; Neostem, Inc.: Other: Equity.

Abstract: HLA-E is a non-classical major histocompatibility complex (MHC) class I molecule characterized by limited polymorphism in contrast to its class I counterparts HLA-A, -B, and -C. HLA-E elicits an inhibitory signal via its interactions with NKG2A on NK cells (and subsets of T cells) and is considered the most dominant inhibitory signal for an NK cell. Analyses of genetic variation in HLA-A, -B and -C genes indicate that human populations are split into two roughly equal groups with ~40% expressing high levels of HLA-E and the remaining individuals expressing low levels of HLA-E. A recent analysis of 9,763 treatment naïve, HIV-infected individuals demonstrated that higher levels of HLA-E expression profoundly correlates with immunosuppression and higher viral loads. The objective of this study was to measure the prognostic power of HLA-E expression on tumor cells in newly diagnosed, treatment-naïve multiple myeloma (MM) patients. We hypothesized that expression of HLA-E will significantly influence NK cell and reactivity to tumors and their capacity to potentiate tumor-specific CD8 T cells as well as distinguish patients with enhanced natural ability to control the disease. This hypothesis is supported by recent evidence showing that knocking down HLA-E on B16 melanoma cells conferred 100% protection after challenge in mice treated with anti-PD-1 and the GM-CSF-secreting tumor vaccine (GVAX). In humans, however, emerging data suggest that the breadth and magnitude of NK cell reactivity vary strongly depending on education (HLA-E vs HLA-ABC). Thus, we further hypothesized that individuals with higher levels of HLA-E expression on their germline tissue would have repertoires of NK cells strongly educated/trained through interactions of HLA-E and NKG2A and a significantly higher threshold for NK cell activation in response to MM plasma cells. We investigated the effect and prognostic impact of HLA-E expression in a cohort of 436 newly diagnosed, treatment naïve MM patients enrolled in the Multiple Myeloma Research Foundation (MMRF) CoMMpass study. We computationally inferred HLA-E expression based on HLA-A and -B genotypes determined using Optitype on Whole-Exome samples from matched germline and tumor tissue. To infer HLA-E expression, we used pre-determined expression scores for HLA-A that was generated from a large analysis of healthy and HIV-infected individuals determined by measuring the logistic regression of mean mRNA expression of HLA-A. We then determined whether the alleles of HLA-B encode a methionine (M) and/or threonine (T) at residue -21 of their leader sequence peptide. Using this approach, we then inferred whether an individual had low, medium or high expression of HLA-E. Finally, we stratified MM patients in three groups based on their HLA-E high (n=32), medium (n=296) and low (n=120) expression. Using mass cytometry and a panel of 40 antibodies targeting broad phenotypes of major cell lineages known to reside in the circulation and in the bone marrow (BM), we performed ex vivo analyses of BM-derived CD138-negative cells from a subset of patients in our cohort. Cox regression analysis showed that patients in the HLA-E high group had a significantly shorter duration of progression-free survival (PFS) compared to patients with medium and low HLA-E expression (Log-Rank p 〈 0.01, Fig. 1A). Semi-supervised clustering analyses were performed on mass cytometry data using the Phenograph function (Fig. 1B and 1C). We clustered on 40 markers targeting major cell lineages, including B cells, T cells, NK cells, monocytes, macrophages, neutrophils and DCs. In the group with low/medium HLA-E expression, we observed expansions of effector memory CD4 T cells, CD57+ NK cells and three subsets of effector memory CD8 T cells. We also observed an opposite trend in the group with high HLA-E, where we found expansions of effector CD4 and CD8 T cells expressing the highest levels of PD-1 and TIGIT as well as a subset of macrophages and regulatory T cells (Tregs). In conclusion, we demonstrated that HLA-E expression correlates with worse PFS in newly diagnosed MM patients. Our data suggests that HLA-E-mediated inhibition of NKG2A-expressing NK cells and T cells is a significant factor in host immune responses and clinical outcome in MM. We are currently analyzing a larger cohort of patient samples by mass cytometry to look more closely at phenotypes and functions of T cells, NK cells and myeloid cells. Disclosures Chari: Janssen: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Array Biopharma: Research Funding; Bristol Myers Squibb: Consultancy; Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Novartis: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Amgen: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Pharmacyclics: Research Funding; The Binding Site: Consultancy; Adaptive Biotechnology: Membership on an entity's Board of Directors or advisory committees; Seattle Genetics: Membership on an entity's Board of Directors or advisory committees. Cho:BMS: Membership on an entity's Board of Directors or advisory committees; F. Hoffmann-La Roche Ltd: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Genentech Inc: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Agenus Inc.: Research Funding; J & J: Consultancy; Janssen: Consultancy. Barlogie:Millenium: Consultancy, Research Funding; Myeloma Health, LLC: Patents & Royalties: : Co-inventor of patents and patent applications related to use of GEP in cancer medicine licensed to Myeloma Health, LLC; Multiple Myeloma Research Foundation: Other: travel stipend; ComtecMed- World Congress on Controversies in Hematology: Other: travel stipend; European School of Haematology- International Conference on Multiple Myeloma: Other: travel stipend; International Workshop on Waldenström's Macroglobulinemia: Other: travel stipend; Celgene: Consultancy, Research Funding; Dana Farber Cancer Institute: Other: travel stipend. Jagannath:Novartis: Consultancy; Merck: Consultancy; Bristol-Myers Squibb: Consultancy; Celgene: Consultancy; Medicom: Speakers Bureau; Multiple Myeloma Research Foundation: Speakers Bureau.

Abstract: Introduction: Lenalidomide (R) maintenance therapy in multiple myeloma (MM) has been shown to improve progression-free survival (PFS) and overall survival (OS) after autologous stem-cell transplantation (ASCT). Even in the transplant ineligible population, R until progression is associated with improved PFS. The ever-increasing use of R maintenance therapy, however, eventually leads to refractoriness to R at maintenance doses. Moreover, clinical trials with len-dex (Rd) backbone regimens including daratumumab, elotuzumab, ixazomib, and carfilzomib have all excluded such patients (pts). This is particularly an issue for elotuzumab and ixazomib, which have no single agent approval. There are currently no published data on the outcomes of full dose Rd or Rd backbone containing regimens in pts refractory to R maintenance. A prospective randomized trial would be difficult to perform given variability in pt factors (i.e. R tolerance, age, renal function) and disease factors (i.e. molecular risk and clinical vs biochemical progression). We therefore performed a retrospective study to characterize outcomes of pts on R maintenance therapy. Methods: This is a single-institution, retrospective study in which we reviewed the records of all consecutive pts with a diagnosis of MM at the Mount Sinai Hospital between February 2010 and October 2016. There were 465 pts identified who had maintenance R as a single agent or in combination with low-dose dexamethasone or prednisone. Pts were excluded if insufficient data were available or 〈 3 month (mo) follow up from time of initiation of R maintenance. Time to progression (TTP) on R maintenance, next line of therapy, and PFS on next line of therapy were determined using Kaplan Meyer analysis. Results: A total of 350 pts were included in this study. Baseline characteristics are summarized in Table 1. The median follow up time was 59 mos and median time on R maintenance was 21.0 mos. 172 pts (49%) progressed while on R maintenance or within 60 days of R discontinuation. 51 pts (15%) remain on R maintenance as of last follow up. The remaining 127 pts (36%) discontinued R for reasons other than progression and either progressed after 60 days (median 658 days, range 91-2053 days) or have not progressed. The median TTP on R maintenance was 34.2 mos (Fig 1A) and the majority of these were characterized by the treating physician as biochemical (65% during maintenance and 56% after R discontinuation). Of the patients with serologic and symptomatic progression, the majority were by bone disease (24% and 37%, respectively). 234 pts had data available on next line of therapy and the median PFS on this next line was 16.8 mo (95% CI: 13.2-20.1), however the PFS was shorter for those who had progressed while on R maintenance versus those who had progressed after R maintenance had been discontinued (13.2 mos vs. 28.9 mos, respectively, p 0.0001). The median PFS according to next line of therapy for those who received an increase in R dose + dex vs 3rd agent added to Rd backbone vs total change in therapy was 9.5 mos vs 21.0 mos vs 14.2 mos, respectively (Fig 1B). The most common drugs added to an Rd backbone were bortezomib and elotuzumab with an associated PFS of 19.0 and 40.1 mos, respectively. The majority of those receiving elotuzumab + Rd had progressed on R maintenance (15/18 = 83%). The most common regimens for those with a total change in therapy are summarized in Table 2. Conclusions: The median TTP on R maintenance was 34.2 mos and while most progression was felt to be biochemical, of those with symptomatic progression as well, the primary manifestation was bone disease (approximately 30% of patients), highlighting the importance of surveillance osseous imaging in MM. While an increase in R dose with steroids was associated with an additional 9.5 mos PFS and a total change in regimen with 14.2 mos PFS, those who received an Rd containing triplet had impressive results. In particular, Rd + elotuzumab resulted in a PFS of 40.1 mos. Multivariate analysis accounting for the potential confounding patient and disease factors inherent to treatment selection in retrospective studies will be presented at the meeting. Disclosures Cho: BMS: Consultancy; GSK: Consultancy; Takeda: Research Funding; The Multiple Myeloma Research Foundation: Employment; Genentech: Honoraria, Research Funding; Agenus: Research Funding; Celgene: Honoraria, Research Funding. Jagannath:Celgene: Consultancy; Novartis: Consultancy; Merck: Consultancy; Medicom: Speakers Bureau; Multiple Myeloma Research Foundation: Speakers Bureau; BMS: Consultancy. Madduri:Abbvie: Consultancy; Takeda: Consultancy; Celgene: Consultancy; undation Medicine: Consultancy. Parekh:Celgene Corporation: Research Funding; Karyopharm Inc.: Research Funding; Foundation Medicine Inc.: Consultancy. Richter:Adaptive Biotechnologies: Membership on an entity's Board of Directors or advisory committees; Amgen: Consultancy, Speakers Bureau; Bristol-Meyers Squibb: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Celgene: Consultancy, 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; Karyopharm: Membership on an entity's Board of Directors or advisory committees; Oncopeptides: Membership on an entity's Board of Directors or advisory committees; Sanofi: Membership on an entity's Board of Directors or advisory committees; Takeda: Membership on an entity's Board of Directors or advisory committees. Chari:Millennium/Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Amgen: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Karyopharm: Consultancy, Membership on an entity's Board of Directors or advisory committees; Array Biopharma: Research Funding; GlaxoSmithKline: Research Funding; Novartis Pharmaceuticals: Research Funding; Oncoceutics: Research Funding; Pharmacyclics: Research Funding; Sanofi: Membership on an entity's Board of Directors or advisory committees; Seattle Genetics: Membership on an entity's Board of Directors or advisory committees, Research Funding; Bristol-Myers Squibb: Consultancy; Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding.

Abstract: Background: Multiple myeloma is typically characterized by clonal expansion of malignant plasma cells within the bone marrow compartment. Presence of extramedullary disease (EMD) either in the form of soft tissue plasmacytoma or circulating plasma cells can be seen at diagnosis but is more common in the relapsed and refractory patients. Presence of EMD is typically associated with a poor prognosis, both in the newly diagnosed and relapsed setting, but trials designed specifically for patients with EMD are sparse. Treatment of EMD can be challenging and the responses even when seen are often short lasting, highlighting the need for developing specific treatment approaches aimed at these patients. Based on initial trials suggesting activity of pomalidomide in the setting of EMD and the increased tissue distribution with the oral proteasome inhibitor ixazomib, we designed this trial to examine if the all oral combination of ixazomib, pomalidomide and dexamethasone can be effective in the setting of EMD. Patients and Methods: Patients with previously treated multiple myeloma, with adequate hematologic and organ function were enrolled if there was evidence, at study entry, of EMD defined as one or more plasmacytomas, outside the bone marrow that were non-contiguous with a bone lesion and had a single diameter of ≥2 cm OR as plasma cell leukemia, with circulating plasma cells & gt; 5% of peripheral blood leukocytes or at least 0.5 X 109/L or 200 cells/150000 events by flowcytometry. Patients were treated on 28-day cycles, with ixazomib 4 mg on days 1, 8, and 15 along with pomalidomide 4 mg PO daily on days 1-21 and dexamethasone 40 milligrams weekly until disease progression or unacceptable toxicity. The goals of the study were to determine confirmed response rate (≥ PR), toxicities of this combination, differential response rates and progression-free survival (PFS). Confirmed response rate, differential response rates and PFS were estimated using Duffy and Santner approach, exact binomial distributions and Kaplan Meier curves, respectively. The study was designed to accrue 30 patients. Results: This study was designed to enroll up to 30 patients but was closed after enrolling 17 patients due to slow accrual. The baseline characteristics are as indicated in Table 1. Eleven patients were enrolled with an extramedullary plasmacytoma while the remaining six patients had plasma cell leukemia. Median number of lines of prior therapy was 3 (range: 1, 7). Overall, 10 (58.8%) patients had progressive disease and 11 (64.7%) patients have died, median (range) follow-up for the live patients is 22.2 (2.1, 37.9) months. Patients were treated for a median of 2 (range 1-34) cycles. A grade 3+ AE, at least possibly attributed, was seen in 53%. Grade 3+, at least possibly related, hematologic toxicity was noted in 41%, with 29% experiencing grade 3+ neutropenia. Notable grade 3+ non-hematologic toxicities (regardless of attribution), with & gt;10% incidence rate were anemia, hypoxia, infections and lung infection. The confirmed response rate in evaluable patients was 35% (1 CRs and 5 PRs), with a 90% CI of 6% - 37%. While 33% (4/12; 95% CI: 1-8%) of patients reported a biochemical response, 40% (2/5; 95% CI: 0-4%) of patients reported an extramedullary response. The median PFS was 4.5 (95% CI: 2-11.8) months. Conclusions: The combination of ixazomib, pomalidomide and dexamethasone resulted in disease response in a third of this group of high-risk patients with EMD; however, the responses were not very durable, with a median PFS of only 4.5 months. The trial highlights the feasibility of doing clinical trials specifically targeted towards this high-risk patient population. Future trials should explore combinations of novel agents, including monoclonal antibodies through multicenter collaborative efforts. Disclosures Kansagra: Alnylam Pharmaceuticals, Bristol Myers Squibb /Celgene, GlaxoSmithKline, Janssen, Pharmacyclics, Takeda Pharmaceuticals, Pfizer, Karyopharm Therpeutics: Other: Advisory Board. Witzig:Spectrum: Consultancy; Immune Design: Research Funding; Karyopharm Therapeutics: Research Funding; Acerta: Research Funding; Incyte: Consultancy; AbbVie: Consultancy; MorphSys: Consultancy; Celgene: Consultancy, Research Funding. Kumar:Oncopeptides: Consultancy, Other: Independent Review Committee; IRC member; Genecentrix: Consultancy; Carsgen: Other, Research Funding; Cellectar: Other; Celgene/BMS: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; Dr. Reddy's Laboratories: Honoraria; AbbVie: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; Takeda: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; Janssen Oncology: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; Merck: Consultancy, Research Funding; Amgen: Consultancy, Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments, Research Funding; Kite Pharma: Consultancy, Research Funding; Novartis: Research Funding; Adaptive Biotechnologies: Consultancy; Genentech/Roche: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; BMS: Consultancy, Research Funding; Karyopharm: Consultancy; MedImmune: Research Funding; Sanofi: Research Funding; Tenebio: Other, Research Funding.

Abstract: The application of novel single-cell technologies provides personalized profiles on patient samples to improve standard of healthcare and evolve the field of precision medicine. In the field of Immuno-oncology, application of single cell RNA-seq (scRNA-seq) to evaluate gene expression profiles has provided new insights on the cancer subpopulation dynamic, network reconstruction, and cell trajectory inferences. Here we present a standardized and validated scRNA-seq analytical workflow applied to a series of common cryopreserved Multiple Myeloma (MM) bone marrow samples obtained from subjects enrolled in the Multiple Myeloma Research Foundation (MMRF) CoMMpass study (NCT01454297). scRNA-seq data was generated from common samples at three different academic research centers. While the high variability of scRNA-seq data raises computational challenges in data analysis, we have tested different quality control, alignment, batch correction, clustering and annotation methods to prove that the results are consistent for subsets of cells, independent of the center generating the data. To evaluate differences in cell type composition and cross-center differences, an in-depth analysis was performed on four CD138- sorted samples ( & gt;18,000 total cells) that were subject to scRNA-sequencing at 3 different centers. We tested three different batch correction methods including: Harmony, Seurat Merge and Seurat Anchor. Immune cell types (CD4 T, CD8 T, NK, Monocytes, Macrophages and pDCs), exhibited similarities in clustering structure and shared complementary differentially expressed genes while Plasma and B cells exhibited distinct center variation when using Seurat Merge. The evaluation of batch effects across samples generated at 3 centers identified subtle batch effects without significant impact of cellular clusterings. For data concordance analyses, Seurat Merge was used for batch effect correction and data normalization and a beta-variational autoencoder and random forest classifier was utilized to assign cell types. In comparing the proportion of each cell type identified across centers using dead cell removal bead depletion, the overall population of plasma and B cells were found to be dissimilar for a subset of samples which could be attributed to differences in bone marrow aliquot sampling. We further explored how the application of dead cell removal altered the distribution of tumor and immune populations for the same sample of interest. Samples that underwent dead cell removal have consistently higher NK and CD8+T cell proportion, while CD4+T is higher more often in non-treated samples. In summary, dead cell removal ultimately improved the overall data quality (more viable cells) without significantly altering the gene expression signatures or proportions of each cell type. Further comparative analysis of transcriptome signatures of various cell types (e.g., B cells, T cells, Plasma cells) across 3 centers depicted significant similarity in transcriptome profile. This suggests even if three centers captured different numbers of various cell types due to variation in protocols and aliquots, the cells still have significantly similar transcriptome profiles that might be helpful in producing the same biological results across three sites. Our in-depth cross-institutional assessment of tumor and immune cell types in MM will provide a valuable resource and thorough analysis strategy to the broader scientific community. Disclosures Dhodapkar: Celgene/BMS: Membership on an entity's Board of Directors or advisory committees, Other; Janssen: Membership on an entity's Board of Directors or advisory committees, Other; Kite: Membership on an entity's Board of Directors or advisory committees, Other; Lava Therapeutics: Membership on an entity's Board of Directors or advisory committees, Other; Roche/Genentech: Membership on an entity's Board of Directors or advisory committees, Other; Amgen: Membership on an entity's Board of Directors or advisory committees, Other.

Abstract: Background: Bispecific antibodies (BiAbs) are a novel off-the-shelf class of drugs currently being investigated in clinical trials for patients with relapsed/refractory multiple myeloma (RRMM) with promising efficacy in heavily pretreated patients. BiAbs simultaneously bind two antigens, thereby engaging CD3+ T cells with myeloma cells expressing specific antigens such as BCMA, GPRC5D, FcRH5 or CD38. However, the outcome of myeloma patients after relapse on BiAbs is unknown and effective approaches for salvage therapy are needed. Methods: Demographics, disease characteristics and post-clinical trial outcomes were collected retrospectively on RRMM patients who relapsed after BiAb therapy at the Tisch Cancer Institute (The Mount Sinai Hospital, New York). We identified a total of 116 patients who were enrolled on trials with BiAbs targeting either BCMA or GPRC5D. Of these, 69 patients were no longer enrolled on the trials due to disease progression (including 5 patients who died on the trial). Clinical data was collected up until July of 2021. This retrospective study was approved by the institutional review board (IRB) and follows the Declaration of Helsinki and International Conference on Harmonization Guidelines for Good Clinical Practice (IRB: GCO#: 11-1433). Survival and response duration were calculated by Kaplan-Meier estimation. Results: The 64 RRMM patients had a median age of 58.5 years (range: 46-82) at time of disease progression following BiAbs therapy, and 48% were male. Median time from diagnosis to initiation of BiAbs therapy was 5 years (range: 1.6-16.3) and patients had a median follow-up of 24.9 months from time of relapse from BiAb therapy. Fifty patients (78%) had high-risk cytogenetics, including gain1q21, del17p, t(4;14), t(14;16) and t(14;20). Most patients were highly pretreated with a median of 7 prior lines (range: 3-17) and 54 patients (84%) had received an autologous stem cell transplant (ASCT) prior to receiving BiAbs. Three patients were treated with chimeric antigen receptor (CAR) T cell therapy prior to BiAb and 5 patients were exposed to a BCMA antibody-drug conjugate prior to the BiAb. Furthermore, 89% of patients were triple-class refractory while 44% were penta-refractory. Following treatment with a BiAb, 2 patients were lost to follow up, 1 patient decided to be monitored off treatment and 61 patients received a median of 2 lines of therapy (range: 1-8). Most common therapies included a second BiAb (n=20; 33%), CAR T cells (n=15; 26%) or intensive chemotherapy (n=36; 59%) such as melphalan, carmustine or VDPACE with stem cell rescue (n=13) or DCEP (n=23). Best response to initial treatment following the BiAb varied widely and included 12 complete responses, 5 very good partial responses, 17 partial responses, 2 minimal responses, 10 stable disease and 13 progressed disease for an overall response rate (ORR) of 58%. Encouraging responses were seen in 10 patients who directly transitioned from one BiAb to another and 8 patients who directly transitioned to CAR T cell therapy. The progression-free survival of those 18 patients who directly transitioned to a T cell directed therapy was 28.9 months (95% CI: 21.6-NE) and their median overall survival was not reached. Furthermore, the overall survival for the whole cohort of patients was 17.6 months (95% CI: 12.0-NE). Conclusion: Our data suggests that heavily pretreated, predominantly triple-class refractory, patients relapsing after BiAbs may still have good outcomes when sequentially treating with other immunological/T cell-directed therapeutics such as BiAbs and CAR T cells. Studying the appropriate sequence of these treatments is of paramount importance as BiAbs are expected to become part of the standard of care for RRMM patients. Disclosures Richard: Karyopharm, Janssen: Honoraria. Richter: Celgene: Speakers Bureau; Adaptive Biotechnologies: Speakers Bureau; Celgene: Consultancy; Janssen: Consultancy; BMS: Consultancy; Karyopharm: Consultancy; Antengene: Consultancy; Sanofi: Consultancy; X4 Pharmaceuticals: Consultancy; Oncopeptides: Consultancy; Adaptive Biotechnologies: Consultancy; Janssen: Speakers Bureau; Secura Bio: Consultancy; Astra Zeneca: Consultancy. Chari: Janssen Pharmaceuticals: Consultancy, Research Funding; Bristol Myers Squibb: Consultancy, Research Funding; Novartis Pharmaceuticals: Consultancy, Research Funding; Amgen: Consultancy, Research Funding; Pharmacyclics: Research Funding; Seattle Genetics: Consultancy, Research Funding; Takeda Pharmaceutical Company: Consultancy, Research Funding; Karyopharm: Consultancy; Sanofi Genzyme: Consultancy; Oncopeptides: Consultancy; Antegene: Consultancy; Glaxosmithkline: Consultancy; Secura Bio: Consultancy. Parekh: Foundation Medicine Inc: Consultancy; Amgen: Research Funding; PFIZER: Research Funding; CELGENE: Research Funding; Karyopharm Inv: Research Funding.