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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 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.

Introduction Despite recent improvement in outcomes for de novo disease, pediatric T-cell acute lymphoblastic leukemia (T-ALL) remains challenging to treat at relapse. Investigation into genomic markers of treatment response and therapy resistance offers an opportunity to further enhance outcomes for these patients. We previously identified a T-ALL blast-associated gene signature at diagnosis (Dx) and characterized the immune microenvironment in Dx T-ALL marrow samples using single cell transcriptome analysis (Bhasin et al. Blood 2020(ASH)). This approach allowed us to generate a granular expression map of both the T-ALL landscape and the Dx bone marrow (BM) immune microenvironment. Here we expand this work by evaluating samples collected from the same patients Dx and End of Induction (EOI) BM samples from pediatric T-ALL patients. The use of paired samples provides insight into treatment-induced changes in the microenvironment. Further, the inclusion of both minimal residual disease (MRD) positive and MRD negative samples allowed us to compare differences between these groups. Methods Using the 10X genomics platform, we profiled the single cell transcriptome of ~18,000 BM and immune microenvironment cells from viably frozen samples collected from T-ALL patients at Dx or EOI. Five paired Dx and EOI samples and one EOI sample from a patient with relapsed T-ALL were evaluated, for a total of 11 samples. Three paired samples were MRD positive at EOI and two were MRD negative; the relapsed sample was MRD negative. Cell clustering was performed using the Seurat package and differential expression analysis was performed using R/Bioconductor packages (Hao et al. Cell 2021). Cell communication analysis was conducted using the CellChat R tool (v 1.0.0) to infer cell-cell communication within the EOI MRD positive and MRD negative subsets and compare their communication networks (Jin et al. Nature Comm 2021). Results Using our previously described blast-associated gene signature (Bhasin et al. ASH 2020) we were able to identify residual blast populations at EOI in MRD-positive samples. Comparative analysis of gene profiles at Dx and EOI showed significant changes in the microenvironment cell populations with highest increase in erythroid cell populations after induction therapy. The gene expression profiles were significantly different for immune cells at Dx and EOI and the relapsed sample had greater similarity to the Dx samples indicating a persistent immunosuppressive environment. Clustering analysis of the EOI samples (3 MRD positive and 2 MRD negative) demonstrated the presence of patient specific blast cells in MRD positive samples that retained patient-specific transcriptomeheterogeneity at EOI (Fig.1A). Analysis of communication networks between different cell types based on receptor and ligand expression levels between different cell types identified a CD34 + cluster of stem cells that had different interactions with other immune populations in the MRD positive and negative subsets. Differential expression analysis between the MRD positive and MRD negative cells in this CD34 + stem cell cluster identified higher expression of myeloid associated genes such as CEBPB, CEBPD, AZU1 in the MRD negative group relative to the MRD positive cells, which showed higher expression of B-cell related genes such as IGHM, VPREB1, CD79A/ B along with upregulation of P13K signaling in B-lymphocytes, B-cell receptor signaling and autophagy pathways. Analysis of upstream regulators based on the differential gene signature between the MRD positive and MRD negative group demonstrated upregulation of MYC and TCF3 activity and inhibition of TGFB1, CSF3 and CEBPA in MRD positive compared to MRD negative samples (Fig.1B). Conclusions: Leukemic blasts exhibit patient-specific gene expression signatures that are present at EOI in MRD positive samples. Exploration of the impact of minimal residual disease at EOI revealed differential gene expression patterns in stem cells from MRD positive samples, characterized by activation of B cell related signaling pathways and regulators such as MYC and TCF3. In contrast, a more myeloid-like expression signature was observed in stem cells from MRD negative samples. These findings open the avenues for exploration of therapeutic targets of T-ALL progression. Figure 1 Figure 1. Disclosures DeRyckere: Meryx: Other: Equity ownership. Graham: Meryx: Membership on an entity's Board of Directors or advisory committees, Other: Equity ownership.

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.

Introduction: Pediatric mixed phenotype acute leukemia (MPAL), a rare subgroup of leukemia, contains features of both myeloid and lymphoid lineage blasts, which makes the disease more difficult to diagnose/treat. More information is needed to understand the origins of the major pediatric MPAL subtypes, B/Myeloid (B-MPAL) and T/Myeloid (T-MPAL), and how they relate to other leukemias. Single-cell RNA sequencing (scRNA-seq) analysis of bone marrow (BM) can provide in-depth information about the leukemia microenvironment and reveal differences/similarities between MPAL subtypes and other types of leukemia that could be exploited to develop novel diagnostics/therapies. Methods: We analyzed ~16,000 cells from five pediatric MPAL BM samples to generate a transcriptomic landscape of B-MPAL and T-MPAL blasts and associated microenvironment cells. Samples collected at the time of diagnosis (Dx) were used to generate scRNA-Seq data using a droplet-based barcoding technique (Panigrahy et al. JCI 2019, Tellechea et al. JID, 2020). After data normalization, cell clusters were identified using principal component analysis (PCA) and Uniform Manifold Approximation and Projection (UMAP) approach (Becht et al. Nat. Biotech 2018). Meta-analysis was performed using single cell samples from ongoing studies in the Bhasin lab (Bhasin, et al. Blood 2020 (ASH), Thomas et al. Blood 2020 (ASH)) and publicly available single cell data from GEO biorepository. Unsupervised analysis using UMAP and PCA was performed to determine the overall relationship among B-MPAL, T-MPAL and other leukemias (acute myeloid leukemia (AML), B-cell acute lymphoblastic leukemia (B-ALL), T-cell ALL (T-ALL)). Supervised differentially expressed gene (DEG) analysis was performed to identify B- and T- MPAL blast cell signatures (P value < 0.001 and log2 FC > 0.5). Transcriptomic profiles in MPAL samples and normal BM stem and immune cells were compared using data from the Human Cell Atlas Data Portal (humancellatlas.org). Gene set enrichment analysis (GSEA) was performed, and significantly enriched pathways were compared in MPAL subtypes (P value < 0.001). Results: PCA analysis showed transcriptome similarity between B-MPAL and both B-ALL and AML, while T-MPAL transcriptome correlated with T-ALL and AML (Fig. 1A). B- and T-MPAL subtype blasts clustered separately from each other in UMAP analyses, with T-MPAL blasts clustering with T-ALL blasts, and B-MPAL somewhat overlapping with B-ALL blasts. Subtype DEG analysis of leukemia blasts and healthy BM revealed distinct significantly upregulated gene signatures in B-MPAL (YBX3, SOCS2, BCL11A, and HIST1H1C) and T-MPAL (ITM2A, HPGD, PDLIM1, and TRDC) blasts (Fig. 1B). Pathway analysis showed upregulated gene activity related to TNFA signaling via NFKB, B-cell survival, and the AP1, FRA, and NGF transcription factors in B-MPAL blasts. In contrast, IL-17 and IL-12, T-cell apoptosis, and Stathmin pathways were upregulated in T-MPAL blasts (Fig. 1C). T-MPAL T-cells also expressed higher levels of T-cell exhaustion markers compared to T-cells in B-MPAL samples and healthy bone marrow. After filtering out genes that are significantly expressed in immune cells, we identified genes that are differentially expressed at diagnosis in MPAL blasts from patients that relapsed after treatment (Dx1) versus patients that achieved remission (Dx2). These genes are potential prognostic markers for B-MPAL and T-MPAL relapse/remission. These include MDM2 and NEIL1 from Dx1 and FOSL2 and CDKN1A in Dx2 B-MPAL blasts. In T-MPAL, expression of HES4 and SPINK2 is associated with Dx1 blasts and GNAQ and ITGA4 with Dx2 blasts. Pathway enrichment analysis on B-MPAL blasts revealed upregulation of interferon gamma and PD-1 signaling in Dx1 samples and increased HSP27 and Cell Cycle pathways in the Dx2 subset. T-MPAL Dx1 associated pathways included prostaglandin synthesis and IL-17, while cell-cell junction and extracellular matrix interactions were increased in T-MPAL Dx2 samples (Fig. 1D). Conclusion: Single-cell profiling was used to characterize the molecular landscapes of MPAL blasts and the bone marrow microenvironment and identified gene signatures and pathways that are specifically enriched in B- and T-MPAL subtypes. Figure 1 Figure 1. Disclosures DeRyckere: Meryx: Other: Equity ownership. Graham: Meryx: Membership on an entity's Board of Directors or advisory committees, Other: Equity ownership.

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 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.

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.

Introduction: While advances in front-line conventional chemotherapy have increased the likelihood of attaining remission in pediatric AML, relapse rates remain high (25-35%), and novel therapies are needed (Zhang, Savage et al. 2019). The clinical and molecular heterogeneity of AML makes it complex to study and creates challenges for the development of novel therapies (Bolouri, Farrar et al. 2018). It is important to identify cells and pathways underlying relapse to facilitate development of novel therapies. Single-cell RNA Sequencing (scRNA-Seq) allows in-depth analysis of the heterogeneous AML landscape to provide a detailed view of the tumor microenvironment, revealing populations of blasts and immune cells which may be relevant to relapse or complete remission. Methods: We analyzed ~36,500 cells from 14 pediatric AML bone marrow samples in our institutional biorepository, spanning different AML subtypes and 3 healthy children to generate a comprehensive scRNA-Seq landscape of immature AML-associated blasts and microenvironment cells. Samples collected at the time of diagnosis (Dx), end of induction (EOI), and relapse (Rel) were used to generate scRNA-Seq data using a droplet-based barcoding technique (Panigrahy, Gartung et al. 2019). After normalization of scRNA-Seq data, the cell 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 relapsed and remission patients reveal differences for specific cell clusters (Panigrahy, Gartung et al. 2019). To determine the clinical outcome association of our AML blast specific markers, survival analysis was performed on AML TARGET data (https://ocg.cancer.gov/programs/target) using cox proportional hazard survival approach. To characterize AML blast cells with high accuracy, we used support vector machine (SVM), an Artificial Intelligence based feature extraction and model development approach (Bhasin, Ndebele et al. 2016). Results:ScRNA-Seq analysis of paired Dx and EOI samples using UMAP identified three blast cell clusters with significant gene expression differences among different patients, indicating heterogeneity of AML blast cells (Fig 1a, b). Comparative analysis of the three Dx enriched blast cell clusters with other cells identified a "core blast cell signature" with overexpression of genes like AZU1, CLEC11A, FLT3, and NREP (Fig 1c). These core AML-blast genes were linked to significant activation of the Wnt/Ca2+, Phospholipase C, and integrin signaling pathways (Z score >2 and P-value The scRNA-Seq of AML specific blast cells from relapsed and remission samples exhibited a different clustering pattern indicating different transcriptome landscapes. Relapse-associated AML cell clusters expressed high levels of AZU1, S100A4, LGALS1, and GRK2 genes (Fig 2a). Analysis of the non-AML tumor microenvironment demonstrated enrichment of T/NK in relapsed samples, with differential expression of T cell regulatory/activation genes (Fig 2b, c). ScRNA-Seq showed enrichment of monocyte/macrophage cell clusters in remission samples with distinct relapse- and remission-specific clusters. Remission associated macrophage/monocyte clusters showed overexpression of S100A10, FTH1, CST3 and IFITM2 genes (Fig 2d). Similarly, enrichment of T cell and monocyte/macrophage clusters was observed in relapse and remission samples respectively during EOI. Conclusions: Using single cell transcriptomics we developed a novel potential gene signature to characterize heterogenous AML blast populations with high sensitivity. These genes and the pathways they regulate implicate potential therapeutic targets in pediatric AML. Single cell transcriptome analysis also enabled identification of cell clusters with modulated gene expression at both Dx and EOI that may be useful in predicting relapse/remission. Disclosures Bhasin: Canomiiks Inc: Current equity holder in private company, Other: Co-Founder.

Introduction: T-cell acute lymphoblastic leukemia (T-ALL) is characterized by proliferation of immature T-cells and accounts for ~15% of pediatric ALL. T-ALL blasts are phenotypically diverse and are sub-classified into pro-, pre-, cortical and mature T-ALL based on the stage of differentiation of the leukemic clone. Early T precursor ALL (ETP-ALL) is a T-ALL subtype associated with higher risk of relapse (Raetz and Teachey, 2016). Bulk sequencing approaches have revealed valuable information about modulated genes in T-ALL; however, little is understood about the interplay between tumor cells and the immune microenvironment. We present a comprehensive single cell RNA sequencing (scRNASeq) analysis of T-ALL samples with the purpose of characterizing the heterogenous tumor and microenvironment cells in order to identify dysregulated genes in leukemia cells and investigate oncogenic signaling pathways. Methods: We profiled 16,280 cells from 5 diagnostic pediatric T-ALL bone marrow samples using the Chromium single cell transcriptomics platform (10x Genomics, Pleasanton, CA). To compare T-ALL versus healthy bone marrow profiles and identify T-ALL-specific malignant blast cell populations, we included data from 3 healthy pediatric bone marrow samples from a recent study (Caron et al, 2020). Dimension reduction using the Uniform Manifold Approximation and Projection (UMAP) approach was used to identify unique cell type clusters (Becht et al, 2018). Using the TARGET dataset (https://ocg.cancer.gov/programs/target), we further evaluated the prognostic significance of identified malignant blast-specific genes by performing Kaplan-Meier survival analysis and compared gene expression patterns and tumor microenvironment makeup between ETP-ALL and non-ETP T-ALL patient samples. Results: We successfully characterized leukemic blasts (CD7+, CD99+ and CD3D+) and other major immune cell types (T cells, B cells, monocytes, erythroid precursors) using the expression of established marker genes (Fig 1A, C). Clustering analysis revealed patient-specific leukemia blast cell clusters (Fig 1B). Differential expression analysis between CD3D+ patient-specific leukemia clusters and CD3D+ clusters comprised of normal T-cells identified a set of 385 promiscuous genes that are significantly differentially expressed between malignant and normal T-cells (p-value 1.3, p-value Conclusions: We identified a gene signature characterizing heterogenous T-ALL blast populations. External validation using the TARGET dataset identified genes within the signature that are associated with poor outcomes in T-ALL. Differences in the composition of the immune microenvironment in ETP-ALL versus non-ETP T-ALL samples provide a promising area for future study. Further studies will be carried out with relapsed and non-relapsed T-ALL samples to validate this leukemia signature. Disclosures Bhasin: Canomiiks Inc: Current equity holder in private company, Other: Co-Founder.

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.