Cho, Hearn Jay

As part of the Multiple Myeloma Research Foundation (MMRF) immune atlas pilot project, we compared immune cells of multiple myeloma bone marrow samples from 18 patients assessed by single-cell RNA sequencing (scRNA-seq), mass cytometry (CyTOF), and cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) to understand the concordance of measurements among single-cell techniques. Cell type abundances are relatively consistent across the three approaches, while variations are observed in T cells, macrophages, and monocytes. Concordance and correlation analysis of cell type marker gene expression across different modalities highlighted the importance of choosing cell type marker genes best suited to particular modalities. By integrating data from these three assays, we found International Staging System stage 3 patients exhibited decreased CD4+ T/CD8+ T cells ratio. Moreover, we observed upregulation of RAC2 and PSMB9, in natural killer cells of fast progressors compared with those of nonprogressors, as revealed by both scRNA-seq and CITE-seq RNA measurement. This detailed examination of the immune microenvironment in multiple myeloma using multiple single-cell technologies revealed markers associated with multiple myeloma rapid progression which will be further characterized by the full-scale immune atlas project. Significance: scRNA-seq, CyTOF, and CITE-seq are increasingly used for evaluating cellular heterogeneity. Understanding their concordances is of great interest. To date, this study is the most comprehensive examination of the measurement of the immune microenvironment in multiple myeloma using the three techniques. Moreover, we identified markers predicted to be significantly associated with multiple myeloma rapid progression.

8055 Background: Multiple myeloma (MM) is characterized by somatic mutations involving cancer-associated genes. The most commonly mutated genes are N- and K-Ras, which increase in prevalence as the disease progresses. Case reports and retrospective data suggest efficacy of targeting the MAPK pathway in N/K-Ras mutated MM. The MyDRUG trial was initiated to explore the efficacy of specific molecularly-targeted therapies in combination with standard therapies in MM. Methods: MyDRUG (NCT03732703) is a genomically-guided umbrella trial for patients with functional high-risk MM, defined as early relapse following primary therapy (3 years for transplant with maintenance, 18 months without), with specific genetic abnormalities. Subjects undergo molecular profiling of their MM cells and are assigned to a targeted arm if a variant allele frequency (VAF) over 25% is identified. The targeted mutated genes and respective agents (approved for non-MM indications and with a known phase 2 dose) are: KRAS/NRAS/BRAF (cobimetinib), FGFR3 (enasidenib), IDH (erdafitinib), CDKN2C (abemaciclib), t(11;14) (venetoclax). Patients receive the investigational drug for 2 cycles as a single agent followed by addition of an active MM combination (ixazomib, pomalidomide and dexamethasone, IPd). Limited dose escalation was performed with the single agent followed by dose assessment in combination with IPd. Here we present the results of the dose escalation portion of C1 arm exploring cobimetinib in patients with N/K-RAS or BRAF mutations. Cobimetinib was administered at 40 mg daily in combination with standard doses of IPd. Results: Eleven subjects with BRAF/RAS mutations were screened between August 2019 and October 2020, with 4 screen failures. Seven were enrolled, 5 males, median age 65 years, and median time from diagnosis of 30 months. N-RAS, K-RAS or BRAF mutations were seen in 4, 2, and 1 subject(s), respectively, with VAF ranging from 33-93%. Median number of prior lines of therapy was 1 (1-3), 3 patients had extramedullary disease, and 1 patient had high risk cytogenetics. Median duration of therapy was 12 months. One patient was not evaluable for dose limiting toxicity. All but 1 patient had at least one cycle delayed due to adverse events (AEs), but no dose reductions were required. No dose limiting toxicities were observed across the cycles, either during single agent therapy (Cycles 1-2) or in combination with IPd (Cycles 3 and 4). Six patients responded to therapy (4 PR, 2 VGPR). One patient was not response evaluable. Fatigue was the most common non-hematological AE followed by diarrhea. Conclusions: Here we report on the feasibility of genomically-guided, precision medicine therapy in K/N-RAS/BRAF-mutated MM. The MEK inhibitor cobimetinib in combination with IPd appears safe in functionally high-risk patients. Ongoing study will provide more information regarding the efficacy of this approach. Clinical trial information: NCT03732703.

Summary: The multiple myeloma treatment landscape has changed dramatically. This change, paralleled by an increase in scientific knowledge, has resulted in significant improvement in survival. However, heterogeneity remains in clinical outcomes, with a proportion of patients not benefiting from current approaches and continuing to have a poor prognosis. A significant proportion of the variability in outcome can be predicted on the basis of clinical and biochemical parameters and tumor-acquired genetic variants, allowing for risk stratification and a more personalized approach to therapy. This article discusses the principles that can enable the rational and effective development of therapeutic approaches for high-risk multiple myeloma.

Abstract Introduction: Multiple myeloma (MM) is a complex hematological malignancy with the heterogenous immune bone marrow (BM) environment contributing to tumor growth, drug resistance, and immune escape. T-Cells play a critical role in the clearance of malignant plasma cells from the tumor environment. However, T-Cells in multiple myeloma demonstrate impaired cytotoxicity, proliferation, and cytokine production due to the activation of immune inhibitory receptors from ligands produced by the myeloma cells. In this study, we investigate the behavior of T-Cells in MM patients by using single-cell RNA-Seq (scRNA-Seq) to compare the transcriptomic profiles of BM T-Cells of patients with rapid progressing (FP; PFS < 18mo) and non-progressing (NP; PFS > 4yrs) disease. Methods: Newly diagnosed MM patients (n=18) from the Multiple Myeloma Research Foundation (MMRF) CoMMpass study (NCT01454297) were identified as either rapid progressors or non-progressors based on their progression free survival since diagnosis. To capture transcriptomic data, scRNA-Seq was performed on 48 aliquots of frozen CD138-negative BM cells at three medical centers/universities (Beth Israel Deaconess Medical Center, Boston, Washington University in St. Louis, and Mount Sinai School of Medicine, NYC). Samples were collected at diagnosis prior to treatment. Surface marker expression for 29 proteins was captured for at least one sample per patient using CITE-Seq. After integration and batch correction, clustering was performed to identify cells of T or NK lineage. Uniform Manifold Approximation and Projection (UMAP) and differential expression were used to identify T-Lymphoid subtypes, and differences in NP and FP samples. Results: In this study, single cell transcriptomic profiles were identified for ~102,207 cells from 48 samples of 18 MM patients. 40,328 T (CD3+) and NK (CD3-, NKG7+) cells were isolated, and subclustered for further analysis (Fig 1A). Using differentially expressed markers for each cluster, the T-Lymphoid subset was refined into seven subtypes, consisting of various CD4+ T-Cells, CD8+ T-Cells, and NK cells (Fig 1B). The CD8+ cells were divided into three distinct phenotypes, namely a GZMK-, GZMB- CD8+ T-Cell cluster, a GZMK+ CD8+ Exhausted T-Cell cluster enriched in TIGIT and multiple chemokines (CCL3, CCL4, XCL2), and a GZMB+ NkT cluster enriched in cytolytic markers (PRF1, GNLY, NKG7) (Fig 1C). Differential expression between NP and FP samples in this CD8+ subset showed enrichment of the NkT cytotoxic markers in NP samples, while FP samples were enriched in the CD8+ Exhausted chemokine markers (Fig 1D). Furthermore, the proportion of CD8+ Exhausted T-Cells was enriched in FP samples (p.val < 0.05) (Fig 1E). Exhaustion markers were measured through both RNA and surface marker levels. In RNA, TIGIT was uniquely associated with the FP-enriched CD8+ Exhausted T-Cell cluster, and CD160 was uniquely expressed in FP samples (Fig 1F). CITE-Seq surface marker expression confirms enrichment of both TIGIT and PD1 in the CD8+ Exhausted T-Cell cluster, and along with more exhaustion in FP samples (p.val < 0.01). Conclusion: In this study, we have identified significant differences in T-Cell activity in patients with non-progressing and rapid-progressing multiple myeloma. T-Cells in rapid progressing patients appear to be in a suppressed state, with low cytolytic activity and enriched exhaustion markers. This GZMK+ T-Cell population shows strong similarities with an aging-associated subtype of effector memory T-Cells found to be enriched in older populations (Mogilenko et al, Immunity 54, 2021). These findings will be further validated in an expanded study, consisting both of a larger number of samples, and multiple samples at different timepoints from the same patient. Figure 1 Figure 1. Disclosures Jayasinghe: MMRF: Consultancy; WUGEN: Consultancy. Vij: BMS: Research Funding; Takeda: Honoraria, Research Funding; Sanofi: Honoraria, Research Funding; BMS: Honoraria; GSK: Honoraria; Oncopeptides: Honoraria; Karyopharm: Honoraria; CareDx: Honoraria; Legend: Honoraria; Biegene: Honoraria; Adaptive: Honoraria; Harpoon: Honoraria. Kumar: Carsgen: Research Funding; KITE: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Beigene: Consultancy; Bluebird Bio: Consultancy; Janssen: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Tenebio: Research Funding; Oncopeptides: Consultancy; Antengene: Consultancy, Honoraria; Roche-Genentech: Consultancy, Research Funding; Merck: Research Funding; Astra-Zeneca: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene: 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; Novartis: Research Funding; Amgen: Consultancy, Research Funding; Abbvie: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; BMS: Consultancy, Research Funding; Adaptive: Membership on an entity's Board of Directors or advisory committees, Research Funding; Sanofi: Research Funding. Avigan: Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; Pharmacyclics: Research Funding; Kite Pharma: Consultancy, Research Funding; Juno: Membership on an entity's Board of Directors or advisory committees; Partner Tx: Membership on an entity's Board of Directors or advisory committees; Karyopharm: 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; Aviv MedTech Ltd: Membership on an entity's Board of Directors or advisory committees; Takeda: Membership on an entity's Board of Directors or advisory committees; Legend Biotech: Membership on an entity's Board of Directors or advisory committees; Chugai: Membership on an entity's Board of Directors or advisory committees; Janssen: Consultancy; Parexcel: Consultancy; Takeda: Consultancy; Sanofi: Consultancy.

Abstract Multiple Myeloma (MM) is the second most common hematologic malignancy, marked by uncontrolled clonal expansion of plasma cells. MM genome is complex and heterogeneous, with a high frequency of structural variants (SVs) and copy-number abnormalities (CNAs). Single-cell sequencing technologies offer advantages over traditional bulk methods in cancer genomics research for evaluating cellular heterogeneity and investigating evolution of cellular subpopulations within the tumor. Thus, there is an impetus to translate the growing understanding of the genomic landscape of MM into dissecting the tumor heterogeneity using scRNA-seq. So far, there are no reported studies systematically comparing tumor and immune populations differences between MM NON-progressors (NPs) and RAPID-progressor (RPs) and investigating how clonal plasma cells contribute to progression in a large cohort. In addition, the bone marrow microenvironment plays an important role in the evolution of premalignant MM and MM progression. A previous study of single-cell transcriptomics analysis of Monoclonal gammopathy of undetermined significance (MGUS) and MM tumor microenvironment (TME) revealed that natural killer (NK) cell abundance is frequently increased in the early stages and associated with altered chemokine receptor expression. This study shed the light on the role of immune cells on disease progression from asymptomatic MGUS to symptomatic MM. However to date, how immune cells influence disease progression within symptomatic MM is still unclear. Here, we subjected 344 CD138-negative Bone Marrow Mononuclear cells (BMMC) samples to scRNA-seq. To control for the first line therapy, 272 patients were treated with RVD and Autologous stem cell transplant (ASCT) and were stratified into 3 groups based on progression: 200 NON-progressors (NPs) (PFS> 5 years), 7 Intermediate Progressors (IPs, PFS 2-4 years) and 38 RAPID-progressors (RPs) (PFS<=18 months). In addition, there are 72 subjects treated with RVD but without ASCT, including 34 NPs, 2 IPs and 36 RPs. From MMRF CoMMpass study (NCT01454297), we also have whole exome sequencing (WES) and bulk RNA-seq from CD138-positive fraction of BMMC samples. Our preliminary analysis of scRNA-seq of 6 samples treated by the same first-line therapies (RVD but without ASCT) revealed that plasma cells (PCs) from two patients (MMRF2550 and MMRF2562) with chromosome 13q deletion clustered together, whereas PCs from a patient (MMRF2187) with t(8;14) Myc translocation formed a distinct cluster. Interestingly, PCs from a patient (MMRF2271) with both chromosome 13q deletion and t(8;14) formed another independent cluster. These observations highlight the important role of genetic drivers in transcriptome profiles of plasma cells. In addition, we further investigated how progression features correlate with the immune microenvironment and identified differentially expressed genes between NK cells from RPs versus those from NPs, which could potentially serve as MM progression markers. Interestingly, several cytotoxicity genes are significantly upregulated in FPs, such as GZMB and KLRB1 (log Fold Change=1.44, and 8.41 respectively). Overall, our preliminary results provide a small glimpse of the interconnected nature of driver genetic alterations and progression features in MM tumor and TME. This study will provide a sufficiently broad, deep, and diverse vast dataset for accurately characterizing MM at single-cell resolution to help interrogate how genetic alterations and disease progression interplay MM tumor and TME. We hope this study could identify novel candidate targets for therapeutic approaches, and ultimately stratify patients by risk of progression for early intervention in the clinic. Disclosures Kumar: Roche-Genentech: Consultancy, Research Funding; Merck: Research Funding; Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; KITE: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Amgen: Consultancy, Research Funding; Novartis: Research Funding; Abbvie: 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; Beigene: Consultancy; Carsgen: Research Funding; Bluebird Bio: Consultancy; Astra-Zeneca: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; Oncopeptides: Consultancy; Tenebio: Research Funding; BMS: Consultancy, Research Funding; Antengene: Consultancy, Honoraria; Adaptive: Membership on an entity's Board of Directors or advisory committees, Research Funding; Sanofi: Research Funding. Oh: Abbvie: Membership on an entity's Board of Directors or advisory committees; Blueprint Medicines: Membership on an entity's Board of Directors or advisory committees; Celgene Bristol Myers Squibb: Membership on an entity's Board of Directors or advisory committees; Constellation: Membership on an entity's Board of Directors or advisory committees; CTI Biopharma: Membership on an entity's Board of Directors or advisory committees; Disc Medicine: Membership on an entity's Board of Directors or advisory committees; Geron: Membership on an entity's Board of Directors or advisory committees; Incyte: Membership on an entity's Board of Directors or advisory committees; Kartos Therapeutics: Membership on an entity's Board of Directors or advisory committees; PharamaEssentia: Membership on an entity's Board of Directors or advisory committees; Sierra Oncology: Membership on an entity's Board of Directors or advisory committees. Vij: BMS: Research Funding; Takeda: Honoraria, Research Funding; Sanofi: Honoraria, Research Funding; BMS: Honoraria; GSK: Honoraria; Oncopeptides: Honoraria; Karyopharm: Honoraria; CareDx: Honoraria; Legend: Honoraria; Biegene: Honoraria; Adaptive: Honoraria; Harpoon: Honoraria. Avigan: Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; Pharmacyclics: Research Funding; Kite Pharma: Consultancy, Research Funding; Juno: Membership on an entity's Board of Directors or advisory committees; Partner Tx: Membership on an entity's Board of Directors or advisory committees; Karyopharm: 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; Aviv MedTech Ltd: Membership on an entity's Board of Directors or advisory committees; Takeda: Membership on an entity's Board of Directors or advisory committees; Legend Biotech: Membership on an entity's Board of Directors or advisory committees; Chugai: Membership on an entity's Board of Directors or advisory committees; Janssen: Consultancy; Parexcel: Consultancy; Takeda: Consultancy; Sanofi: Consultancy.

Introduction Multiple myeloma (MM), relapsed or refractory (R/R) to standard of care therapies, represents a treatment indication with a significant unmet clinical need. Transcriptional activation of c-MYC through bromodomain and extra-terminal (BET) proteins contributes to the malignant phenotype of the disease. RO is a novel thienodiazepine, small molecule, non-covalent inhibitor of the BET family of bromodomains. Preclinical studies with BET inhibitors have demonstrated significant single agent in vivo activity in myeloma, accompanied by down-regulation of MYC and MYC target gene expression. We report results of a monotherapy phase 1b study of RO in R/R MM (NCT03068351). Methods Eligible pts with R/R MM included those treated with at least three prior lines of multiple myeloma therapy, including a proteasome inhibitor and an immunomodulatory agent, or who are double-refractory to a proteasome inhibitor and an immunomodulatory agent. Primary refractory myeloma pts were only allowed in the dose escalation portion of the study. In the dose escalation (part I), pts received subcutaneous (SC) escalating doses of RO (0.30-0.65 mg/kg) in a standard 3 + 3 design on days 1 - 14 of 21-day cycles to determine both the maximum tolerated dose (MTD) and recommended dose (RD). In the expansion cohort (part II), pts received RO as monotherapy at the RD level. Primary endpoint was safety (DLT, MTD, RD) and secondary endpoints included evaluation of pharmacodynamics (CD11b expression) and preliminary efficacy assessments based on IMWG criteria. Results Between June 2017 and April 2019, a total of 24 pts were enrolled in the US, the UK and Australia. 13 pts were enrolled in the dose escalation and 11 pts in the expansion part. The median age of pts was 65.5 years (range: 46 - 82 years). The study population was heavily pretreated with a median of 6 (3-9) prior therapies. Pts were refractory to immunomodulatory drugs (63%), proteasome inhibitors (46%), both (46%), or daratumumab (42%). 57 total cycles were administered, with a median of 2 (1-6) cycles per patient (pt). Two DLTs occurred in one pt in the 0.65 mg/kg cohort (thrombocytopenia grade 4, angina pectoris grade 3). The recommended dose is 0.65 mg/kg. Grade 3 treatment emergent AEs in ≥ 5% of pts were thrombocytopenia (11 pts [45.8%]), anemia (7 pts [29.2%]), fatigue (3 pts [12.5%]), injection site reaction, malaise, decreased appetite, and hyponatremia (2 pts [8.3%] each). 3 pts (12.5%) experienced a total of 4 AEs leading to discontinuation of the study treatment; these AEs were left ventricular dysfunction, fatigue, sepsis, and staphylococcal bacteremia (the latter two AEs occurred in the same pt). A total of 8 deaths (33.3%) were reported in the study, all as a consequence of progressive disease. The best overall response recorded was partial response in 4 pts (16.7 %), 3 of whom having received prior daratumumab. One pt experienced a minimal response, and stable disease was reported in 12 out of 24 (50%). There was no evidence for a dose-related increase in efficacy, though data are very limited (Table). Responses obtained during treatment with RO6870810 were short-lived and lasted for appr. 6 weeks. As evidence of target engagement, pharmacodynamic profiling demonstrated decreases in CD11b levels in peripheral blood mononuclear cells (Figure). Conclusions Treatment of MM pts with the BET inhibitor RO as monotherapy resulted in a high incidence of cytopenias, especially grade 3-4 thrombocytopenia and grade 3 anemia. However, none of these events led to study drug discontinuation. Cytopenias are a known side effect of BET inhibitors, with thrombocytopenia frequently reported as a DLT in various pt populations. Pts with NUT carcinoma, other solid tumors, or diffuse large B-cell lymphoma treated in the First in Man trial of RO had a low rate of cytopenias, indicating that the underlying disease and the extensive pre-treatment in MM may play a role in their occurrence (publication submitted). In this heavily pretreated R/R MM population, we have established 0.65 mg/kg as the recommended monotherapy dose. Pharmacodynamics effects were evident at this dose, but as monotherapy in this R/R MM cohort, response rates were low and less durable. Future drug combination approaches may result in an improved benefit / risk ratio. Disclosures Ramasamy: Janssen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; BMS: Consultancy, Research Funding, Speakers Bureau; Takeda: Consultancy, Research Funding, Speakers Bureau; Amgen: Consultancy, Honoraria, Research Funding; Abbvie: Consultancy, Honoraria; Sanofi: Consultancy, Honoraria; Oncopeptides: Consultancy, Honoraria. Nooka:Adaptive Technologies: Consultancy, Honoraria; Oncopeptides: Consultancy, Honoraria; Spectrum Pharmaceuticals: Consultancy; GlaxoSmithKline: Consultancy, Honoraria, Other: Personal Fees: Travel/accomodations/expenses, Research Funding; Takeda: Consultancy, Honoraria, Research Funding; Celgene: Consultancy, Honoraria, Research Funding; Janssen: Consultancy, Honoraria, Research Funding; Amgen: Consultancy, Honoraria, Research Funding; Bristol-Myers Squibb: Consultancy, Honoraria, Research Funding; Sanofi: Consultancy, Honoraria; Karyopharm Therapeutics, Adaptive technologies: Consultancy, Honoraria, Research Funding. Quach:Amgen, Celgene, karyopharm, GSK, Janssen Cilag, Sanofi.: Membership on an entity's Board of Directors or advisory committees; GlaxoSmithKline, Karyopharm, Amgen, Celgene, Janssen Cilag: Consultancy; GlaxoSmithKline, Karyopharm, Amgen, Celgene, Janssen Cilag: Honoraria; Amgen, sanofi, celgene, Karyopharm, GSK: Research Funding. Htut:City of Hope Medical Center: Current Employment. Popat:AbbVie: Consultancy, Honoraria; Celgene: Consultancy, Honoraria; Takeda: Consultancy, Honoraria, Other: Travel support, Research Funding; Bristol Myers Squibb: Consultancy, Honoraria; GSK: Consultancy, Honoraria, Other: TRAVEL, ACCOMMODATIONS, EXPENSES (paid by any for-profit health care company); Janssen: Consultancy, Honoraria, Other: TRAVEL, ACCOMMODATIONS, EXPENSES (paid by any for-profit health care company). Liedtke:Jazz Pharmaceuticals: Membership on an entity's Board of Directors or advisory committees; GSK: Membership on an entity's Board of Directors or advisory committees; Pfizer: Honoraria; Janssen: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees; Caelum: Membership on an entity's Board of Directors or advisory committees; Adaptive: Membership on an entity's Board of Directors or advisory committees. Tuchman:Caelum: Honoraria; Sanofi: Honoraria, Research Funding; Amgen: Research Funding; Janssen: Research Funding; Oncopeptides: Consultancy; Roche: Research Funding; Karyopharm: Honoraria, Research Funding; Celgene: Honoraria, Research Funding, Speakers Bureau. Hertzberg:Gilead: Membership on an entity's Board of Directors or advisory committees; MSD: Membership on an entity's Board of Directors or advisory committees; Abbvie: Honoraria; BMS: Honoraria; Takeda: Honoraria, Membership on an entity's Board of Directors or advisory committees; Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees; Roche: Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Support of parent study and funding of editorial support. Demario:BioNTech SE: Current Employment, Current equity holder in publicly-traded company; Hoffmann-La Roche Ltd.: Current equity holder in publicly-traded company, Ended employment in the past 24 months. Nueesch:Hoffmann-La Roche Ltd.: Current Employment, Current equity holder in publicly-traded company. Chesne:Hoffmann-La Roche Ltd.: Current Employment, Current equity holder in publicly-traded company. Franjkovic:Hoffmann-La Roche Ltd.: Current Employment, Current equity holder in publicly-traded company. Lechner:Roche Diagnostics GmbH: Current Employment, Current equity holder in publicly-traded company. Kornacker:Hoffmann-La Roche Ltd.: Current Employment, Current equity holder in publicly-traded company.

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: 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 > 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 >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: Title: Architecture of sample preparation and data governance of Immuno-genomic data collected from bone marrow and peripheral blood samples obtained from multiple myeloma patients In multiple myeloma (MM), the interactions between malignant plasma cells and the bone marrow microenvironment is crucial to fully understand tumor development, disease progression, and response to therapy. The core challenge in understanding those interactions has been the establishment of a standard process and a standard model for handling the data quality workflow and the underlying data models. Here we present the Platform (Figure 1), an integrated data flow architecture designed to create data inventory and process tracking protocols for multi-dimensional and multi-technology immune data files. This system has been designed to inventory and track peripheral blood and bone marrow samples from multiple myeloma subjects submitted for immune analysis under the MMRF Immune Atlas initiative (figure 2), and the processing and storage of Single Cell RNA-seq (scRNA-seq) and Mass Cytometry time-of-flight (CyTOF) data files derived from these immune analyses. While these methods have been previously applied on both tumor and immune populations in MM [2,3], this level of multi-institutional and multi-technology is unique. The Cloud Immune-Precision platform contains standardized protocols and bioinformatics workflows for the identification and categorization of immune cell populations and functional states based upon scRNA-seq gene signatures (ref: Bioinformatics manuscript in submission) and CyTOF protein signatures. Upon further expansion, it will contain high dimensional scRNAseq and CyTOF immune data from both bone marrow and peripheral blood samples from myeloma patients enrolled in the Multiple Myeloma Research Foundation (MMRF) CoMMpass study (NCT01454297) [1] (Figure 3). The architecture covers the automation of data governance protocols, data transformation and ETL model developments that will create an immune proteomic and profiling database and its integration into clinical and genomics databases: e.g. the MMRF CoMMpass clinical trial. This large-scale data integration will establish a cutting-edge Immune-Precision central platform supporting large scale, immune-focused advanced analytics in multiple myeloma patients. This platform will allow researchers to interrogate the relationships between immune transcriptomic and proteomic signatures and tumor genomic features, and their impact on clinical outcomes, to aid in the optimization of therapy and therapeutic sequencing. Furthermore, this platform also promotes the potential to (further) elucidate the mechanisms-of-action of approved and experimental myeloma therapies, drive biomarker discovery, and identify new targets for drug discovery. Figure 1: Cloud Immune-Precision Platform (Integrated data flow architecture designed to create data inventory and process tracking protocols for multi-dimensional and multi-technology immune data files) Figure 2: Sample tracking process architecture Figure 3: Data file creation and repository process tracking References: 1- Settino, Marzia et al. "MMRF-CoMMpass Data Integration and Analysis for Identifying Prognostic Markers." Computational Science - ICCS 2020: 20th International Conference, Amsterdam, The Netherlands, June 3-5, 2020, Proceedings, Part III vol. 12139 564-571. 22 May. 2020, doi:10.1007/978-3-030-50420-5_42 2- Ledergor, Guy et al. "Single cell dissection of plasma cell heterogeneity in symptomatic and asymptomatic myeloma." Nature medicine vol. 24,12 (2018): 1867-1876. doi:10.1038/s41591-018-0269-2 3- Hansmann, Leo et al. "Mass cytometry analysis shows that a novel memory phenotype B cell is expanded in multiple myeloma." Cancer immunology research vol. 3,6 (2015): 650-60. doi:10.1158/2326-6066.CIR-14-0236-T Figure 1 Disclosures Bhasin: Canomiiks Inc: Current equity holder in private company, Other: Co-Founder. Dhodapkar: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; 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.

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.