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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 (>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.

Background: Ciltacabtagene autoleucel (cilta-cel; JNJ-68284528; LCAR-B38M CAR-T cells) is a chimeric antigen receptor T (CAR-T) cell therapy with 2 B-cell maturation antigen-targeting single-domain antibodies designed to confer avidity. In the phase 1 LEGEND-2 study in China, LCAR-B38M yielded deep, durable responses with a manageable safety profile in patients (pts) with relapsed/refractory multiple myeloma (R/R MM). The phase 1b/2 CARTITUDE-1 study (NCT03548207) is further evaluating cilta-cel in this pt population in the US. We present updated data from the phase 1b portion along with initial phase 2 data. Methods: Eligible pts (aged ≥18 y) were diagnosed with MM per International Myeloma Working Group (IMWG) criteria and had measurable disease, Eastern Cooperative Oncology Group performance status ≤1, received ≥3 prior regimens or were double-refractory to a proteasome inhibitor and immunomodulatory drug, and received an anti-CD38 antibody. After apheresis, bridging therapy was permitted. Cyclophosphamide 300 mg/m2 and fludarabine 30 mg/m2 daily for 3 d were used for lymphodepletion. A single infusion of cilta-cel at a target dose of 0.75×106 (range 0.5-1.0×106) CAR+ viable T cells/kg was administered 5-7 d after start of lymphodepletion. The primary objective of the phase 1b portion was to characterize cilta-cel safety and establish the recommended phase 2 dose; the primary objective of the phase 2 portion was to evaluate cilta-cel efficacy. Response was assessed per IMWG criteria and minimal residual disease (MRD) by next-generation sequencing. Adverse events (AEs) were graded using CTCAE v5.0. In the phase 1b portion, cytokine release syndrome (CRS) was graded by Lee et al (Blood 2014) and neurotoxicity by CTCAE v5.0; in the phase 2 portion, CRS and neurotoxicity were graded by American Society for Transplantation and Cellular Therapy (ASTCT) criteria. In this combined analysis, Lee et al and CTCAE v5.0 were mapped to ASTCT criteria for CRS and immune effector cell-associated neurotoxicity syndrome (ICANS), respectively. Results: As of the May 20, 2020 clinical cutoff, 97 pts (58.8% male; median age 61.0 y [range 43-78]) with R/R MM received cilta-cel (29 in phase 1b; 68 in phase 2). Median follow-up duration was 8.8 mo (range 1.5-20.4). Pts had received a median of 6 prior lines of therapy (range 3-18); 83.5% were penta-exposed, 87.6% were triple-refractory, 41.2% were penta-refractory, and 97.9% were refractory to last line of therapy. Overall response rate per independent review committee (primary endpoint) was 94.8% (95% CI 88.4-98.3), with a stringent complete response rate of 55.7% (95% CI 45.2-65.8), very good partial response rate of 32.0% (95% CI 22.9-42.2), and partial response rate of 7.2% (95% CI 3.0-14.3). All pts achieved a reduction in M-protein. Median time to first response was 1.0 mo (range 0.9-5.8; 80.4% ≤1.0 mo), and median time to complete response or better was 1.8 mo (range 0.9-12.5; 74.1% ≤3.0 mo); responses deepened over time (Figure). Median duration of response was not reached (NR). Of 52 MRD-evaluable pts, 94.2% were MRD-negative at 10-5. The 6-mo progression-free survival (PFS) and overall survival (OS) rates (95% CI) were 87.4% (78.9-92.7) and 93.8% (86.7-97.2), respectively; median PFS and OS were NR. Ten deaths occurred during the study; 8 were due to AEs (both related and unrelated; CRS/hemophagocytic lymphohistiocytosis, neurotoxicity, respiratory failure, sepsis, septic shock, pneumonia, lung abscess, and acute myelogenous leukemia [n=1 each]), and 2 due to progressive disease. AEs reported in >70% of pts were CRS (94.8%; grade [gr] 3/4 4.1%), neutropenia (90.7%; gr 3/4 90.7%), anemia (81.4%; gr 3/4 68.0%), and thrombocytopenia (79.4%; gr 3/4 59.8%). Median time to CRS onset was 7.0 d (range 1-12) and median duration 4.0 d (range 1-27, excluding n=1 with 97 d). CAR-T cell-related neurotoxicity was reported in 20.6% of pts (gr 3/4 10.3%). Cilta-cel CAR+ T cells showed maximum peripheral expansion at 14 d (range 9-43). Among pts with 6 mo' individual follow-up, 67% had cilta-cel CAR+ T cells below the level of quantification (2 cells/µL) in peripheral blood. Conclusions: Preliminary phase 1b/2 data from CARTITUDE-1 indicate a single low-dose infusion of cilta-cel leads to early, deep, and durable responses in heavily pretreated pts with MM with a safety profile consistent with LEGEND-2. Further investigation of cilta-cel in other MM populations is underway. Disclosures Madduri: Celgene: Consultancy, Honoraria; AbbVie: Consultancy, Honoraria; Foundation Medicine: Consultancy, Honoraria; Takeda: Consultancy, Honoraria; Janssen: Consultancy, Honoraria; Legend: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Speaking Engagement, Speakers Bureau; Kinevant: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Speaking Engagement, Speakers Bureau; GSK: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Speaking Engagement, Speakers Bureau. Berdeja:Teva: Research Funding; Bluebird: Research Funding; Bioclinica: Consultancy; Celgene: Consultancy, Research Funding; EMD Sorono: Research Funding; Kite Pharma: Consultancy; Prothena: Consultancy; Cellularity: Research Funding; Karyopharm: Consultancy; Servier: Consultancy; Legend: Consultancy; Poseida: Research Funding; Lilly: Research Funding; Acetylon: Research Funding; CURIS: Research Funding; Janssen: Consultancy, Research Funding; Genentech, Inc.: Research Funding; Glenmark: Research Funding; Takeda: Consultancy, Research Funding; BMS: Consultancy, Research Funding; Constellation: Research Funding; CRISPR Therapeutics: Consultancy, Research Funding; Vivolux: Research Funding; Abbvie: Research Funding; Amgen: Consultancy, Research Funding; Kesios: Research Funding; Novartis: Research Funding. Usmani:Celgene: Other; BMS, Celgene: Consultancy, Honoraria, Other: Speaking Fees, Research Funding; GSK: Consultancy, Research Funding; Pharmacyclics: Research Funding; Merck: Consultancy, Research Funding; Abbvie: Consultancy; Sanofi: Consultancy, Honoraria, Research Funding; Takeda: Consultancy, Honoraria, Other: Speaking Fees, Research Funding; Janssen: Consultancy, Honoraria, Other: Speaking Fees, Research Funding; SkylineDX: Consultancy, Research Funding; Seattle Genetics: Consultancy, Research Funding; Incyte: Research Funding; Array Biopharma: Research Funding; Amgen: Consultancy, Honoraria, Other: Speaking Fees, Research Funding. Jakubowiak:Adaptive, Juno: Consultancy, Honoraria; AbbVie, Amgen, BMS/Celgene, GSK, Janssen, Karyopharm: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees. Cohen:Celgene: Membership on an entity's Board of Directors or advisory committees; Takeda,: Membership on an entity's Board of Directors or advisory committees; Janssen: Membership on an entity's Board of Directors or advisory committees; GlaxoSmithKline: Membership on an entity's Board of Directors or advisory committees; Kite Pharma: Membership on an entity's Board of Directors or advisory committees; Oncopeptides: Membership on an entity's Board of Directors or advisory committees; Seattle Genetics: Membership on an entity's Board of Directors or advisory committees; AstraZeneca: Membership on an entity's Board of Directors or advisory committees; Genentech/Roche: Membership on an entity's Board of Directors or advisory committees; Bristol-Myers Squibb: Membership on an entity's Board of Directors or advisory committees, Research Funding; Novartis: Other: Patents/Intellectual property licensed, Research Funding. Stewart:Janssen, BMS, Sanofi-Aventis, GSK: Honoraria; Tempus, Inc., Genomics England LLC: Membership on an entity's Board of Directors or advisory committees. Hari:Amgen: Consultancy; BMS: Consultancy; GSK: Consultancy; Janssen: Consultancy; Takeda: Consultancy; Incyte Corporation: Consultancy. Htut:City of Hope Medical Center: Current Employment. Munshi:OncoPep: Consultancy, Current equity holder in private company, Membership on an entity's Board of Directors or advisory committees, Patents & Royalties; BMS: Consultancy; Janssen: Consultancy; Adaptive: Consultancy; Legend: Consultancy; Amgen: Consultancy; Karyopharm: Consultancy; Takeda: Consultancy; AbbVie: Consultancy; C4: Current equity holder in private company. Deol:Novartis: Consultancy; Kite, a Gilead Company: Consultancy. Lesokhin:BMS: Consultancy, Honoraria, Research Funding; Genentech: Research Funding; Janssen: Research Funding; Juno: Consultancy, Honoraria; Takeda: Consultancy, Honoraria; Serametrix Inc.: Patents & Royalties; GenMab: Consultancy, Honoraria. Singh:Janssen: Current Employment. Zudaire:Janssen: Current Employment. Yeh:Janssen: Current Employment. Allred:Janssen: Current Employment. Olyslager:Janssen: Current Employment. Banerjee:Janssen: Current Employment. Goldberg:Johnson & Johnson: Current Employment, Current equity holder in publicly-traded company. Schecter:Janssen: Current Employment. Jackson:Janssen: Current Employment; Memorial Sloan Kettering Cancer Center: Consultancy. Deraedt:Janssen: Current Employment, Current equity holder in publicly-traded company. Zhuang:Janssen: Current Employment. Infante:Janssen: Current Employment. Geng:Legend Biotech USA Inc.: Current Employment. Wu:Legend Biotech USA Inc.: Current Employment. Carrasco:Legend Biotech USA Inc.: Current Employment. Akram:Legend Biotech USA Inc.: Current Employment. Hossain:Legend Biotech USA Inc.: Current Employment. Rizvi:Legend Biotech USA Inc.: Current Employment. Fan:Legend Biotech USA Inc.: Current Employment. Jagannath:BMS, Janssen, Karyopharm, Legend Biotech, Sanofi, Takeda: Consultancy. Lin:Kite, a Gilead Company: Consultancy, Research Funding; Janssen: Consultancy, Research Funding; Merck: Research Funding; Legend BioTech: Consultancy; Juno: Consultancy; Bluebird Bio: Consultancy, Research Funding; Celgene: Consultancy, Research Funding; Novartis: Consultancy; Vineti: Consultancy; Takeda: Research Funding; Gamida Cells: Consultancy; Sorrento: Consultancy, Membership on an entity's Board of Directors or advisory committees. Martin:AMGEN: Research Funding; Seattle Genetics: Research Funding; Janssen: Research Funding; GSK: Consultancy; Sanofi: Research Funding.

Introduction: Multiple myeloma (MM) is a genetically complex and clinically heterogeneous disease. Disease biology and phenotype is heavily influenced by the tumor microenvironment and the interaction between the immune milieu and malignant plasma cell population. Understanding the molecular profile of tumor along with the immune ecosystem can provide insights into key pathways that are important in disease pathobiology. Therefore, in this study, we have used single-cell RNA-Seq (scRNA-Seq) to compare the detailed maps of the bone marrow microenvironment of patients with rapid progressing disease (PFS < 18 months) with those whose disease had not progressed at the time of analysis (PFS < 4 years) Methods: MM patients (n=18) with rapid and no progression were identified from the Multiple Myeloma Research Foundation (MMRF) CoMMpass study, a longitudinal genomic study of patients with newly diagnosed, active multiple myeloma (NCT01454297). To generate a robust scRNA-Seq profile with minimal false positive, we profiled multiple technical replicates/aliquots of viably frozen CD138-negative bone marrow cells from each patient at three medical centers/universities (Beth Israel Deaconess Medical Center, Boston, Washington University in St. Louis and Mount Sinai School of Medicine, NYC using droplet-based single-cell barcoding technique. After batch correction and normalization, the cellular clusters were identified using principal component analysis and Uniform Manifold Approximation and Projection (UMAP) approach (Becht et al, 2018). Differential expression, pathways and systems biology analysis between rapid and non-progressors revealed differences for specific cell clusters (Panigrahy, Gartung et al. 2019). To determine association of plasma cell overexpressed genes with survival in CoMMpass study, survival analysis was performed using Kaplan-Meier (K-M) approach. Results: In this study, comparative analysis was performed of the bone marrow microenvironment of patients with aggressive and indolent disease by generating single-cell profiles of ~102,207 cells from 48 samples of 18 patients with MM. The UMAP approach identified multiple transcriptionally diverse clusters of plasma (CD138+), immune (PTPRC+) and erythroid (GYPA1/2+) cells (Fig 1a). Interestingly, the analysis identified CD138+ plasma/tumors cells clusters in a subset of samples from patients with rapid -progression and these clusters depicted a high degree of inter-patient heterogeneity (Fig 1a). Further characterization of plasma tumor cells depicted significant activation (Z score >2 and P-value <.001) of pathway related to "Unfolded protein response", epithelial-mesenchymal transition (EMT), and "p38 MAPK Signaling". These rapid progressions associated with plasma cells overexpressing multiple genes (e.g., Hazard ratio (HR) CCL3=1.9 95% CI= (1.5-3.9) log-rank P=0.0004, HSPA5 HR=1.4 (1-2.6), P=0.03) that are associated with poor outcome in multiple myeloma based CoMMpass data. The bone marrow microenvironment cells formed 22 clusters, comprising of cells from myeloid, macrophages, T cells, B cells, dendritic cells, Natural Killer T (NKT) cells, and erythroid lineages. The Non-progressive patients depicted enrichment of GZMB+ T and NKT cells with overexpression of genes associated with "Natural Killer Cell Signaling", "CD28 Signaling in T Helper Cells", "NF-kB Signaling" and "Th17 Activation Pathway" (Fig1b, c). Systems biology analysis depicted significant activation of TNF, STAT4, and NFATC2 regulatory signatures in NKT cells. The analysis also observed enrichment of macrophages, several types of monocytes, and myeloid cells in the samples from patients with non-progressive disease (Fig 1d). The myeloid/monocytes cluster depicted significant activation of multiple metabolic (i.e., Glycolysis, Gluconeogenesis) and immune response (i.e. IL8) pathways (Fig 1e). In summary, this multi-site study provides insights into potentially significant differences in the transcriptomic landscape of multiple myeloma patients with rapid and non-progression of disease. The non-progressive patients depict significant enrichment of activated T cells and myeloid lineage populations, suggesting their role toward better outcomes. These findings will be further expanded by ongoing single cell analyses of the CoMMpass tissue bank under the MMRF Immune Atlas initiative. Figure 1 Disclosures Bhasin: Canomiiks Inc: Current equity holder in private company, Other: Co-Founder. Dhodapkar: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; 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. Kumar:Merck: Consultancy, Research Funding; Adaptive Biotechnologies: Consultancy; Genecentrix: Consultancy; Tenebio: Other, Research Funding; Celgene/BMS: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; Genentech/Roche: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; Oncopeptides: Consultancy, Other: Independent Review Committee; IRC member; Kite Pharma: Consultancy, Research Funding; Novartis: Research Funding; Sanofi: Research Funding; MedImmune: Research Funding; Karyopharm: Consultancy; BMS: Consultancy, Research Funding; Cellectar: Other; Carsgen: Other, Research Funding; Dr. Reddy's Laboratories: Honoraria; Janssen Oncology: 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; AbbVie: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; Amgen: Consultancy, Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments, Research Funding.

Compared with traditional bulk sequencing technologies, single-cell technologies have advantages to evaluate cellular heterogeneity and investigate the evolution of cellular subpopulations from the tumor and microenvironment. Application of single-cell sequencing in Multiple Myeloma (MM) is especially beneficial given MM is a highly heterogeneous disease with uncontrolled clonal expansion of plasma cells. Single-cell RNA sequencing (scRNA-seq) has been previously utilized to understand this hematopoietic malignancy in both tumor and immune populations in MM (Ledergor G. et al., 2018, Zavidij, O. et al., 2020). Mass cytometry (CyTOF) has also been used to identify the expansion of novel memory B cells in MM. (Hansmann, L. et al., 2015). Among the various single cell techniques, cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) is a multimodal approach with simultaneous quantification of single-cell transcriptomes and surface proteins. Since these three single-cell approaches enable the identification of cell types, cell states and characterization of cellular heterogeneity at transcriptomic and/or protein levels, understanding the concordance of the measurements among these three modalities is of great interest. We applied scRNA-seq, CyTOF and CITE-seq on four baseline samples of CD138-negative 'immune cell' fractions from patients enrolled in the MMRF CoMMpass study (NCT01454297). Two subjects were fast progressors (PFS < 18 months) and two subjects were non-progressors (PFS not reached). With multi-center collaboration coordinated by Multiple Myeloma Research Foundation (MMRF), each sample was subject to scRNA-seq and CyTOF by 3 independent centers. All sites received aliquots of the same sample. On average, 1,060 immune cells were detected in each sample using scRNA-seq and >64K CD45+ cells were detected using CyTOF. CITE-seq was performed in one center and 4,805 CD138-negative immune cells were identified on average. To compare cell type abundance between scRNA-seq, CyTOF and CITE-seq, we calculated the cell subset frequency of each immune population relative to the CD45+ populations. Overall, all three approaches were concordant while there is a stronger concordance between scRNA-seq and CyTOF. Cell type abundance is especially consistent for B cells, monocytes/macrophages, and plasmacytoid dendritic cells (pDC) among the 3 methods. For the same patient of interest, natural killer (NK) cell frequency was detected at the lowest level in CyTOF relative to scRNA and CITE-seq. The T cell population showed the highest discrepancy among techniques, with highest abundance in scRNA-seq followed by CyTOF and lowest abundance in CITE-seq. Interestingly, CITE-seq detected far less CD4 T cells compared to the other two techniques while CD8 T cell frequency did not show drastic differences. In addition to cell type abundance, we further examined the concordance of expression of cell type signature genes between scRNA-seq and CyTOF. Overall, expression between RNA-level and protein-level is positively correlated with typical cell type markers highly expressed in both techniques, especially in the following cell populations: CD8+ T cells (CD3, CD8), NK cells (CD56, GranzymeB/GZMB, NKG2A/KLRC1), B cells (CD19, CD38), Monocytes (CD14, CD11c/ITGAX, CD33). To note, although the concordance in CD4+ T cells is generally good, we found the expression of CD4 is higher in CyTOF compared to scRNA-seq whereas CD127/IL7R tends to be overexpressed in scRNA-seq. This could explain why IL7R is often identified as a differentially expressed gene (DEG) in CD4+ T cell population while CD4 is barely seen in DEG list in scRNA-seq analysis. It is also interesting to notice that the expression of most NK cell markers has strong concordance between scRNA-seq and CyTOF except NKG2D/KLRK1, which has much higher expression in CyTOF relative to scRNA-seq. Our preliminary results suggested good concordance of immune cell type abundance identified in CyTOF, scRNA-seq and CITE-seq as well as concordant expression of some canonical cell type markers between RNA level and protein level. This work provides the field with reference data sets and shows more detailed examination of NK and T cell subsets is needed when handling single cell sequencing with different modalities. Disclosures Dhodapkar: Roche/Genentech: 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; Kite: 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; Celgene/BMS: 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. Kumar:MedImmune: Research Funding; Amgen: Consultancy, Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments, Research Funding; AbbVie: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; Janssen Oncology: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; Carsgen: Other, Research Funding; Merck: Consultancy, Research Funding; Adaptive Biotechnologies: Consultancy; Celgene/BMS: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; Genentech/Roche: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; Oncopeptides: Consultancy, Other: Independent Review Committee; IRC member; Kite Pharma: Consultancy, Research Funding; Novartis: Research Funding; Sanofi: Research Funding; Takeda: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; Tenebio: Other, Research Funding; Karyopharm: Consultancy; BMS: Consultancy, Research Funding; Genecentrix: Consultancy; Cellectar: Other; Dr. Reddy's Laboratories: Honoraria. Gnjatic:Neon Therapeutics: Consultancy, Membership on an entity's Board of Directors or advisory committees; OncoMed: Consultancy, Membership on an entity's Board of Directors or advisory committees; Bristol-Myers Squibb: Research Funding; Genentech: Research Funding; Immune Design: Research Funding; Agenus: Research Funding; Janssen R&D: Research Funding; Pfizer: Research Funding; Takeda: Research Funding; Regeneron: Research Funding; Merck: Consultancy, Membership on an entity's Board of Directors or advisory committees. Bhasin:Canomiiks Inc: Current equity holder in private company, Other: Co-Founder.

Outcomes in multiple myeloma (MM) have improved dramatically in the last two decades with the advent of novel therapies including immunomodulatory agents (IMiDs), proteasome inhibitors and monoclonal antibodies. In recent years, immunotherapy for the treatment of MM has advanced rapidly, with the approval of new targeted agents and monoclonal antibodies directed against myeloma cell-surface antigens, as well as maturing data from late stage trials of chimeric antigen receptor (CAR) T cells. Therapies that engage the immune system to treat myeloma offer significant clinical benefits with durable responses and manageable toxicity profiles, however, the appropriate use of these immunotherapy agents can present unique challenges for practicing physicians. Therefore, the Society for Immunotherapy of Cancer convened an expert panel, which met to consider the current role of approved and emerging immunotherapy agents in MM and provide guidance to the oncology community by developing consensus recommendations. As immunotherapy evolves as a therapeutic option for the treatment of MM, these guidelines will be updated.

BackgroundExtracorporeal photopheresis (ECP) is an immunomodulatory cellular therapy which has been shown to induce a tolerogenic state in patients with acute and chronic graft‐vs‐host disease. ECOG‐ACRIN explored the activity of ECP as a part of a reduced intensity conditioning regimen in two multicenter trials in patients with MDS (E1902) and lymphomas (E1402). While both studies closed before completing accrual, we report results in 23 patients (17 MDS and 6 lymphoma).Study Design and MethodsPatients received 2 days of ECP followed by pentostatin 4 mg/m2 /day for two consecutive days, followed by 600 cGy of total body irradiation prior to stem cell infusion. Immunosuppression for aGVHD was infusional cyclosporine A or tacrolimus and methotrexate on day +1, +3, with mycophenolate mofetil starting on day 100 for chronic GVHD prophylaxis.ResultsAll patients engrafted, with median time to neutrophil and platelet engraftment of 15‐18 days and 10‐18 days respectively. Grade 3 or 4 aGVHD occurred in 13% and chronic extensive GVHD in 30%.ConclusionsThese studies demonstrate that ECP/pentostatin/TBI is well tolerated and associated with adequate engraftment of neutrophils and platelets in patients with lymphomas and MDS.

TPS8057 Background: Multiple myeloma (MM) is the second most prevalent blood cancer, representing approximately 1% of all cancers. Although overall survival has improved in recent years due to new approved agents, the vast majority of MM patients (pts) ultimately stop responding to treatment. Moreover, about a quarter of MM pts characterized as “high risk” experience limited benefit from existing treatments. Seminal genomic sequencing research efforts, such as the MMRF CoMMpass study, have highlighted that a large number of MM cases harbor potentially actionable oncogenic molecular alterations and published reports on small numbers of cases suggest that Precision Medicine (PM) interventions clinically targeting such actionable drivers may benefit MM pts. These results suggest that PM approaches in MM are possible and should be further studied clinically. To that end, we have launched MyDRUG, a master protocol aimed at developing new myeloma regimens based on individual pt’s genomics. Methods: This study is a Phase I-II, multicenter master protocol in functional high risk MM patients (i.e patients having progressed from baseline treatment within 18 months without maintenance or 3 years on maintenance) with 1-3 prior lines of therapy. Patients are screened for actionable genomic alterations on the CLIA-grade MI-ONCOSEQ platform. Patients with actionable alterations are assigned to the appropriate targeted agent used in combination with a backbone regimen ixazomib, pomalidomide and dexamethasone (IPd), whereas patients without such alterations go on an immune arm (see Table). Inclusion criteria include measurable disease (as measured by M-protein and FLC) and acceptable hematologic and metabolic functions. A maximum of 12 evaluable pts will be accrued onto the phase I portion of each arm and an additional 21 evaluable phase II pts will be accrued onto each arm for a total of 27 evaluable pts at the MTD combination dose. The primary objective of the study is to evaluate ORR per IMWG consensus criteria. Secondary objectives are to assess adverse events (AEs), progression-free survival, and overall survival (OS). Experimental correlative aims include assessing molecular or clonal response, molecular and immune signatures of resistance/response, MRD. Clinical trial information: NCT03732703. [Table: see text]

Abstract Abstract 906 Background: Single-arm Phase 2 trials of DF in the treatment of severe VOD/MOF post-SCT demonstrated a complete response (CR) in 36–46% of patients (pts). A subsequent Phase 3 trial enrolling pts with more severe VOD (28%/24% with ventilator/dialysis dependence at study entry vs 5%/7%) demonstrated a CR of 24% compared to 9% in a matched Historical Control (HC); p=0.0148; Richardson ASH 2009). Given the life-threatening nature of VOD/MOF, DF was made available in the United States through a Treatment IND protocol (T-IND) in December 2007 to gather additional safety and response data. Methods: Pts were required to have a diagnosis of VOD based upon Baltimore criteria (total bilirubin ≥ 2.0 mg/dL with ≥ 2 of the following: hepatomegaly, ascites or 5% weight gain) with MOF (either renal and/or pulmonary failure). Pts without a Baltimore diagnosis could enroll with biopsy-proven evidence of VOD. Eligibility criteria were expanded to permit patients with late onset VOD (VOD diagnosed after 21 days (d) or VOD/MOF after 28d post-SCT). Key exclusion criteria included clinically significant bleeding or need for >1 pressor to maintain BP. CR was defined as bilirubin < 2 mg/dL + resolution of MOF. Mortality was assessed at D+100. DF was given at 6.25 mg/kg IV q6h (25 mg/kg/d); treatment duration was recommended for at least 21d. Results: This interim analysis is based on 104 pts enrolled between14Dec2007-29Sep2009 and treated at 36 institutions. Most sites (n=23) enrolled only 1–2 pts. Nearly all pts (n=98) had undergone prior SCT; 6 had prior chemotherapy only; and 29 pts had late onset VOD post-SCT. Of the SCT pts, 69 pts enrolled in the T-IND met eligibility criteria for the original Phase 3 trial. Baseline demographics included median age 17 years (range 0.1 – 66); 59% male; and 16% with prior SCT. The most common diagnosis was leukemia (30% AML; 22% ALL; 7% other leukemia). The majority of pts had undergone allogeneic SCT (88%) with conditioning consisting of cyclophosphamide (76%), busulfan (42%) and/or TBI (41%). Mean onset of VOD post-SCT was 17 days. At entry, 20% of pts were dialysis dependent; 28% were ventilator dependent. Mean number of days of DF administration was 20 (range 2–88). Of the 104 pts, 36 (35%) achieved a CR by D+100. For the various sub-populations, the CR by D+100 rate was 24% for pts with late-onset VOD (n=29); 35% for those pts undergoing SCT (n=98); and 39% for the 69 pts who matched the entry criteria for the Phase 3 trial. Comparison of this group of 69 pts to the Phase 3 HC showed a statistically improved outcome (p=0.0007). Similar to the Phase 3 results, the CR rate was higher in pts who were not on dialysis/ventilator at study entry (41% vs 23%). Although children had previously been noted to have a higher CR rate in prior studies, the CR rate in the T-IND was similar in children compared to adults (35% and 34%, respectively). Overall D+100 mortality rate equaled 61% (n=104). Comparing the 69 pts who met Phase 3 entry criteria, D+100 mortality was 58% (p=0.0465 compared to the HC using Kaplan Meier estimates by log-rank analysis). Toxicity proved manageable: 11 pts experienced a total of 13 drug-related AEs including pulmonary hemorrhage (n=5), GI hemorrhage (n=3), hypotension (n=2), rectal hemorrhage, cerebral hemorrhage and pulmonary alveolar hemorrhage (1 pt each). Overall, 10% of pts experienced a drug-related toxicity that led to discontinuation. Similar to the observation of decreased GvHD in pediatric pts undergoing SCT who received prophylactic DF compared to best supportive care, the incidence of all grade GvHD was relatively low (8%). Conclusions: In 104 pts with severe VOD/MOF enrolled at 36 US institutions, use of DF demonstrated a 35% CR by D+100. Comparing those pts who met criteria for the Phase 3 trial to the HC used in that study, D+100 CR and survival were significantly improved in this study population. DF-associated toxicities are consistent with prior studies. Following an amendment in 30Sep2009, entry criteria for the T-IND study were expanded to permit enrollment of pts without SCT (VOD post chemotherapy) as well as pts without MOF to evaluate earlier intervention. Analysis of this group will be performed separately. Enrollment into the T-IND study continues. Disclosures: Richardson: Gentium: Membership on an entity's Board of Directors or advisory committees. Massaro:Gentium: Consultancy. Hannah:Gentium: Consultancy. Iacobelli:Gentium: Employment.

Abstract Introduction Programmed cell death-1 (PD-1) is an immune checkpoint receptor that inhibits T cell activation upon interaction with its ligands PD-L1 or PD-L2. Increased PD-L1 expression has been reported in various lymphoid malignancies, and may allow these tumors to circumvent host anti-tumor immunity. Nivolumab, a fully human IgG4 monoclonal PD-1 receptor blocking antibody, potentiates T cell activity, and has clinical efficacy in various solid tumors. We hypothesized that nivolumab might also have clinically important anti-tumor activity in patients with lymphoid malignancies. Methods This open-label study enrolled patients with relapsed or refractory lymphoid malignancies including B-cell non-Hodgkin lymphoma (B-NHL), T-cell NHL (T-NHL), multiple myeloma (MM), and classical Hodgkin lymphoma (cHL). Patients were treated using a dose escalation design (1 mg/kg and 3 mg/kg) of nivolumab administered every two weeks for up to two years. Responses were assessed using standard criteria. The primary endpoint was safety; key secondary endpoints included anti-tumor activity and expression of immunomodulatory proteins in tumor biopsies. The preliminary results for the cHL patients will be reported separately. Results Twenty-nine patients with B-NHL, 2 patients with primary mediastinal B-cell lymphoma; 23 patients with T-NHL; 27 patients with MM; and 1 with chronic myelogenous leukemia were enrolled. Patients were heavily pretreated with 67%, 69%, and 78% of MM, B-NHL, and T-NHL patients, respectively, having received ≥ 3 prior treatment regimens. Previous autologous stem cell transplantation was reported for 56% of MM, 14% of B-NHL, and 9% of T-NHL patients. Prior brentuximab treatment was reported in 7% of B-NHL and 26% of T-NHL patients. When this pre-planned interim analysis was performed, six patients had been treated at the 1 mg/kg dose with 2 dose-limiting toxicities (DLTs) occurring in the same patient: grade 3 pneumonia and pneumonitis. At the 3mg/kg dose, seven patients were treated with one patient experiencing two DLTs: grade 3 eosinophilia and diplopia. Additional patients were enrolled in the cohort expansion at 3 mg/kg. Drug-related adverse events (AEs) occurred in 72%, 65%, and 52% of B-NHL, T-NHL, and MM patients, respectively. Serious AEs in B-NHL patients were pneumonitis (7%), acute respiratory distress syndrome, dermatitis, diplopia, enteritis, eosinophilia, mucosal inflammation, pyrexia and vomiting, each occurring in 3%. In the T-NHL patients, serious AEs were pneumonitis, rash, and sepsis, each occurring in 4%, and in MM patients, serious AEs were pneumonitis, myositis, and increased creatine phosphokinase, each occurring in 4%. The incidence and severity of drug related AEs were similar across tumor types. Efficacy results are shown for each tumor type in the table. The overall response rate (ORR) and complete response (CR) rate in patients with B-NHL were 28% and 7%, respectively, including an ORR of 36% in patients with diffuse large B-cell lymphoma (DLBCL), and 40% in patients with follicular lymphoma (FL). In patients with T-NHL, ORR was 17% (no CR), including an ORR of 40% in the 5 patients with peripheral T cell lymphoma. No objective responses were observed in MM. Analysis of PD-L1 expression and correlation to clinical outcome is being performed and will be presented. Conclusions Nivolumab administered at a dose of 3 mg/kg every two weeks was tolerable and the safety profile was similar to that of the agent in solid tumors. Objective responses were observed in DLBCL, FL, mycosis fungoides (MF), and peripheral T cell lymphoma (PTCL). Durable stable disease was observed in relapsed MM. The results of this phase 1 study have led to phase 2 studies in DLBCL and FL, which are ongoing. Table: Efficacy Results Tumor N Complete Response n (%) Partial Response n (%) Stable Disease (SD) n (%) Progression Free Survival Rate at 24 Weeks (%) Diffuse Large B Cell Lymphoma (DLBCL) 11 1 (9) 3 (27) 3 (27) (24) Follicular Lymphoma (FL) 10 1 (10) 3 (30) 6 (60) (68) Other B Cell Lymphoma 8 0 0 5 (63) (38) Primary Mediastinal B Cell Lymphoma 2 0 0 2 (100) (0) Mycosis Fungoides (MF) 13 0 2 (15) 9 (69) (39) Peripheral T Cell Lymphoma (PTCL) 5 0 2 (40) 0 (30) Other T Cell Lymphoma 5 0 0 1 (20) (0) Multiple Myeloma (MM) 27 0 0 18 (67) (15) Chronic Myelogenous Leukemia 1 0 0 1 (100) (100) Disclosures Lesokhin: Bristol-Myers Squibb: Consultancy, Research Funding. Ansell:Bristol-Myers Sqibb: Research Funding. Armand:Merck: Consultancy. Cohen:Celgene: Member, Independent Response Adjudication Committee Other; Onyx: Advisory Board, Advisory Board Other; Bristol-Myers Squibb: Advisory Board, Advisory Board Other, Research Funding; Janssen: Advisory Board, Advisory Board Other. Lebovic:Genentech, Allos, Celgene, Onyx, Millennium: Consultancy, Research Funding, Speakers Bureau. Rodig:Bristol-Myers Squibb: Research Funding. Zhu:Bristol-Myers Squibb: Employment. Grosso:Bristol-Myers Squibb: Employment, Equity Ownership. Kim:Bristol-Myers Squibb: Employment. Shipp:Merck: Membership on an entity's Board of Directors or advisory committees; Pharmacyclics: Membership on an entity's Board of Directors or advisory committees; Bristol-Myers Squibb: Membership on an entity's Board of Directors or advisory committees, Research Funding; Gilead: Consultancy, Membership on an entity's Board of Directors or advisory committees; Bayer: Membership on an entity's Board of Directors or advisory committees; Sanofi: Research Funding; Janssen R&D: Membership on an entity's Board of Directors or advisory committees. Borrello:Bristol-Myers Squibb: Research Funding. Timmerman:Bristol-Myers Squibb: Research Funding.

Purpose Cancer cells can exploit the programmed death-1 (PD-1) immune checkpoint pathway to avoid immune surveillance by modulating T-lymphocyte activity. In part, this may occur through overexpression of PD-1 and PD-1 pathway ligands (PD-L1 and PD-L2) in the tumor microenvironment. PD-1 blockade has produced significant antitumor activity in solid tumors, and similar evidence has emerged in hematologic malignancies. Methods In this phase I, open-label, dose-escalation, cohort-expansion study, patients with relapsed or refractory B-cell lymphoma, T-cell lymphoma, and multiple myeloma received the anti–PD-1 monoclonal antibody nivolumab at doses of 1 or 3 mg/kg every 2 weeks. This study aimed to evaluate the safety and efficacy of nivolumab and to assess PD-L1/PD-L2 locus integrity and protein expression. Results Eighty-one patients were treated (follicular lymphoma, n = 10; diffuse large B-cell lymphoma, n = 11; other B-cell lymphomas, n = 10; mycosis fungoides, n = 13; peripheral T-cell lymphoma, n = 5; other T-cell lymphomas, n = 5; multiple myeloma, n = 27). Patients had received a median of three (range, one to 12) prior systemic treatments. Drug-related adverse events occurred in 51 (63%) patients, and most were grade 1 or 2. Objective response rates were 40%, 36%, 15%, and 40% among patients with follicular lymphoma, diffuse large B-cell lymphoma, mycosis fungoides, and peripheral T-cell lymphoma, respectively. Median time of follow-up observation was 66.6 weeks (range, 1.6 to 132.0+ weeks). Durations of response in individual patients ranged from 6.0 to 81.6+ weeks. Conclusion Nivolumab was well tolerated and exhibited antitumor activity in extensively pretreated patients with relapsed or refractory B- and T-cell lymphomas. Additional studies of nivolumab in these diseases are ongoing.