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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.

Expression of cell type markers in CITE-seq and comparison of cell subset abundance between technical replicates and across different centers

supplementary table 1 shows cell type annotation markers, clinical information of patients, cross-technique comparison of cell subset abundance and expression of cell type marker genes in CITE-seq and CyTOF

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.

The standard treatment for metastatic prostate cancer, androgen deprivation therapy (ADT), is designed to suppress androgen receptor (AR) activity. However, men invariably progress to castration-resistant prostate cancer (CRPC), and AR reactivation contributes to progression in most cases. To identify mechanisms that may drive CRPC, we examined a VCaP prostate cancer xenograft model as tumors progressed from initial androgen sensitivity prior to castration to castration resistance and then on to relapse after combined therapy with further AR-targeted drugs (abiraterone plus enzalutamide). AR activity persisted in castration-resistant and abiraterone/enzalutamide–resistant xenografts and was associated with increased expression of the AR gene and the AR-V7 splice variant. We then assessed expression of individual AR-regulated genes to identify those that persisted, thereby contributing to tumor growth, versus those that decreased and may therefore exhibit tumor suppressor activities. The most significantly decreased AR target gene was dipeptidyl peptidase 4 (DPP4), which encodes a membrane-anchored protein that cleaves dipeptides from multiple growth factors, resulting in their increased degradation. DPP4 mRNA and protein were also decreased in clinical CRPC cases, and inhibition of DPP4 with sitagliptin enhanced the growth of prostate cancer xenografts following castration. Significantly, DPP4 inhibitors are frequently used to treat type 2 diabetes as they increase insulin secretion. Together, these results implicate DPP4 as an AR-regulated tumor suppressor gene whose loss enhances growth factor activity and suggest that treatment with DPP4 inhibitors may accelerate emergence of resistance to ADT.Significance: These findings identify DPP4 as an AR-stimulated tumor suppressor gene that is downregulated during progression to castration-resistant prostate cancer, warning that treatment with DPP4 inhibitors, commonly used to treat type 2 diabetes, may accelerate prostate cancer progression following androgen deprivation therapy. Cancer Res; 78(22); 6354–62. ©2018 AACR.

Suppl. Table 2. Log2 Fold Changes (FC) of some genes differentially expressed in BRAFV600E-PTC vs BRAFWT-PTC

Supplementary Figures 1-12 Legends

Supplementary Methods

Suppl. Table 3. Log2 Fold Changes (FC) of some genes differentially expressed in PTC with BRAFV600E and hTERT mutation vs BRAFV600E-PTC

Suppl. Figure 1 Drug dose-effect analysis in BRAFWT/V600E-KTC1 cells Suppl.Figure 2 Dose-effect analysis of vemurafenib and sorafenib on pericyte viability. Suppl. Figure 3 BRAFWT/WT-TPC1 cells viability assay. Suppl. Figure 4 ELISA analysis of cytokines secretion in pericytes, and PTC cells harboring the heterozygous BRAFWT/V600E mutation or with BRAFWT/WT. Suppl. Figure 5 Effects of pericyte secretome in BRAFWT/WT-TPC1 cells. Suppl. Figure 6 Analysis of secreted TSP-1 levels in PTC cells harboring the heterozygous BRAFWT/V600E mutation or with BRAFWT/WT treated with shTSP-1 or shGFP (control) pericyte secretome. Suppl. Figure 7 Effects of shTSP-1 pericyte secretome in BRAFWT/WT-TPC1 cells. Suppl. Figure 8 Viability assay in the presence of SRI31277 in PTC cells and pericytes. Suppl. Figure 9 Effects of the small molecule SRI31277 in PTC cells with BRAFWT/WT. Suppl. Figure 10 THBS1 (TSP-1) mRNA expression levels in PTC samples harboring both BRAFWT/V600E and hTERT mutations compared to BRAFWT/V600E-PTC samples. Suppl. Figure 11 THBS1 (TSP-1) mRNA expression levels in PTC samples harboring both BRAFWT/V600E and hTERT mutations compared to BRAFWT/WT-PTC samples. Suppl. Figure 12 THBS1 (TSP-1) gene regulatory networks pathways in PTC harboring BRAFWT/V600E and hTERT mutations compared to BRAFWT/WT-PTC clinical samples.

Figure S1. Targeted siRNA knockdown of DPP4 decreases DPP4 antibody positivity in western blots and IHC; Figure S2. Immunoscoring of AR, AR-V7, P-AR(S81), and PSA protein expression in serially biopsied VCaP xenografts; Figure S3. Ingenuity Pathway, Hierarchical Clustering, and Principal Component analysis of Pre-Cx, CRPC, and AER, serial biopsies; Figure S4. DPP4 is an AR-stimulated gene; Figure S5. DPP4 expression is decreased in Post-ADT therapy samples in several clinical and one preclinical data set compared to Pre-Cx; Figure S6. DPP4 expression is decreased in clinical CRPC sections, despite normal levels of PSA; Figure S7. CpG islands in the DPP4 promoter and exon 1 are unmethylated; Figure S8. lncRNA-OIS1 is expressed at similar levels in Pre-Cx and AER VCaP tumors; Figure S9. Androgen treatment of VCaP CRPC xenografts restores DPP4 expression; Figure S10. The volume ratio (End Volume/Starting Volume) of VCaP tumors shows a trend toward positive correlation with tumor DPP4 protein levels.

The file contains supplemental details related to real-time quantitative PCR, RNA sequencing data analysis, meta-analysis of DDP4 expression and AR score across multiple prostate cancer datasets, immunohistochemistry analysis (AR, AR-V7, pAR(S81), and PSA expression), and promoter methylation analysis. It also contains 9 references related to these methods.

Purpose:The BRAFV600E oncogene modulates the papillary thyroid carcinoma (PTC) microenvironment, in which pericytes are critical regulators of tyrosine-kinase (TK)-dependent signaling pathways. Although BRAFV600E and TK inhibitors are available, their efficacy as bimodal therapeutic agents in BRAFV600E-PTC is still unknown.Experimental Design:We assessed the effects of vemurafenib (BRAFV600E inhibitor) and sorafenib (TKI) as single agents or in combination in BRAFWT/V600E-PTC and BRAFWT/WT cells using cell-autonomous, pericyte coculture, and an orthotopic mouse model. We also used BRAFWT/V600E-PTC and BRAFWT/WT-PTC clinical samples to identify differentially expressed genes fundamental to tumor microenvironment.Results:Combined therapy blocks tumor cell proliferation, increases cell death, and decreases motility via BRAFV600E inhibition in thyroid tumor cells in vitro. Vemurafenib produces cytostatic effects in orthotopic tumors, whereas combined therapy (likely reflecting sorafenib activity) generates biological fluctuations with tumor inhibition alternating with tumor growth. We demonstrate that pericytes secrete TSP-1 and TGFβ1, and induce the rebound of pERK1/2, pAKT and pSMAD3 levels to overcome the inhibitory effects of the targeted therapy in PTC cells. This leads to increased BRAFV600E-PTC cell survival and cell death refractoriness. We find that BRAFWT/V600E-PTC clinical samples are enriched in pericytes, and TSP1 and TGFβ1 expression evoke gene-regulatory networks and pathways (TGFβ signaling, metastasis, tumor growth, tumor microenvironment/ECM remodeling functions, inflammation, VEGF ligand–VEGF receptor interactions, immune modulation, etc.) in the microenvironment essential for BRAFWT/V600E-PTC cell survival. Critically, antagonism of the TSP-1/TGFβ1 axis reduces tumor cell growth and overcomes drug resistance.Conclusions:Pericytes shield BRAFV600E-PTC cells from targeted therapy via TSP-1 and TGFβ1, suggesting this axis as a new therapeutic target for overcoming resistance to BRAFV600E and TK inhibitors.

Supplementary Table 1 Antibodies list and experimental conditions for immunohistochemistry (IHC)

Suppl. Table 4. Log2 Fold Changes (FC) of some genes differentially expressed in PTC wih BRAFV600E and hTERT mutation vs BRAFWT-PTC

Despite advancements in understanding the pathophysiology of Multiple Myeloma (MM), the cause of rapid progressing disease in a subset of patients is still unclear. MM’s progression is facilitated by complex interactions with the surrounding bone marrow (BM) cells, forming a microenvironment that supports tumor growth and drug resistance. Understanding the immune microenvironment is key to identifying factors that promote rapid progression of MM. To accomplish this, we performed a multi-center single-cell RNA sequencing (scRNA-seq) study on 102,207 cells from 48 CD138- BM samples collected at the time of disease diagnosis from 18 patients with either rapid progressing (progression-free survival (PFS) 4 years) disease. Comparative analysis of data from three centers demonstrated similar transcriptome profiles and cell type distributions, indicating subtle technical variation in scRNA-seq, opening avenues for an expanded multicenter trial. Rapid progressors depicted significantly higher enrichment of GZMK+ and TIGIT+ exhausted CD8+ T-cells (P = 0.022) along with decreased expression of cytolytic markers (PRF1, GZMB, GNLY). We also observed a significantly higher enrichment of M2 tolerogenic macrophages in rapid progressors and activation of pro-proliferative signaling pathways, such as BAFF, CCL, and IL16. On the other hand, non-progressive patients depicted higher enrichment for immature B Cells (i.e., Pre/Pro B cells), with elevated expression for markers of B cell development (IGLL1, SOX4, DNTT). This multi-center study identifies the enrichment of various pro-tumorigenic cell populations and pathways in those with rapid progressing disease and further validates the robustness of scRNA-seq data generated at different study centers.

A major cause of cancer recurrence following chemotherapy is cancer dormancy escape. Taxane-based chemotherapy is standard of care in breast cancer treatment aimed at killing proliferating cancer cells. Here, we demonstrate that docetaxel injures stromal cells, which release protumor cytokines, IL-6 and G-CSF, that in turn invoke dormant cancer outgrowth both in vitro and in vivo. Single-cell transcriptomics shows a reprogramming of awakened cancer cells including several survival cues such as stemness, chemoresistance, as well as an altered tumor microenvironment with augmented pro-tumor immune signaling. IL-6 plays a role in cancer cell proliferation, whereas G-CSF mediates tumor immunosuppression. Pathways and differential expression analyses confirmed MEK as the key regulatory molecule in cancer cell outgrowth and survival. Antibody targeting of protumor cytokines (IL-6, G-CSF) or inhibition of cytokine signaling via MEK/ERK pathway using selumetinib prior to docetaxel treatment prevented cancer dormancy outgrowth suggesting a novel therapeutic strategy to prevent cancer recurrence.

We have developed a personalized vaccine in which patientderived leukemia cells are fused to autologous dendritic cells, evoking a polyclonal T-cell response against shared antigens and neoantigens. We postulated that the dendritic cell/acute myeloid leukemia fusion vaccine would demonstrate synergy with checkpoint blockade by expanding tumor antigen-specific lymphocytes that would provide a critical substrate for checkpoint blockade-mediated activation. Using an immunocompetent murine leukemia model, we examined the immunological response to and therapeutic efficacy of vaccination in conjunction with checkpoint blockade with respect to leukemia engraftment, disease burden, survival and the induction of tumor-specific immunity. Mice treated with checkpoint blockade alone had rapid progression of leukemia and only a modest extension of survival. Vaccination with dendritic cell/acute myeloid leukemia fusions resulted in the expansion of tumor-specific lymphocytes and disease eradication in a subset of animals, while the combination of vaccination and checkpoint blockade induced a fully protective tumor-specific immune response in all treated animals. Vaccination followed by checkpoint blockade resulted in upregulation of genes regulating activation and proliferation in memory and effector T cells. Long-term survivors exhibited increased T-cell clonal diversity and were resistant to subsequent tumor challenge. The combination of dendritic cell/acute myeloid leukemia fusion vaccine and checkpoint blockade treatment offers unique synergy, inducing durable activation of leukemia-specific immunity, protection from lethal tumor challenge and the selective expansion of tumor-reactive clones.

The TAM family receptor tyrosine kinases TYRO3, AXL and MERTK are potential therapeutic targets in a variety of cancers. In our previous studies, MERTK inhibition in the leukemia microenvironment significantly prolonged survival in a syngeneic B-cell acute leukemia (B-ALL) model, implicating MERTK as a promising immuno-oncology target. Strikingly, Mertk-/- mice were almost completely protected against leukemia. Here, we probed the mechanisms of anti-leukemia immunity mediated by MERTK inhibition and evaluated roles for TYRO3 and AXL in the leukemia microenvironment. Single cell RNA sequencing and flow cytometry studies revealed an increase in CD8α+ dendritic cells (DCs) with enhanced antigen-presentation capacity in naïve and leukemia-bearing Mertk-/- mice. These cells were also increased in leukemic wild type (WT) mice treated with the MERTK inhibitor MRX-2843 (currently in phase I/Ib clinical trials). Additionally, CD8+ T cells expressing high levels of the T cell exhaustion marker Tox were decreased in mice treated with MRX-2843, implicating a CD8α+ DC – T cell axis in the anti-leukemia immune response stimulated by MERTK inhibition. Indeed, combined depletion of CD8+ T cells and CD8α+ DCs completely abrogated the anti-leukemia response in Mertk-/- mice, while immunity remained partially intact in mice with selective depletion of CD8+ T cells. Similarly, protection from leukemia was abrogated in Mertk-/- scid mice, which lack functional B and T cells and have defects in DC function. These data demonstrate a critical immunosuppressive role for MERTK in DCs of the leukemia microenvironment. Similar to Mertk-/- mice, B-ALL growth was almost completely prevented in Tyro3-/- mice, while Axl-/- did not impact leukemogenesis, implicating TYRO3, but not AXL, as an additional immunotherapeutic target. However, in contrast to Mertk-/- mice, CD8α+ DCs with enhanced antigen-presentation capacity were not significantly increased in Tyro3-/- mice compared to WT, indicating differences in the underlying mechanisms by which MERTK and TYRO3 contribute to the immunosurveillance of leukemia cells. Indeed, in vivo depletion experiments confirmed differential roles for MERTK and TYRO3 in the leukemia microenvironment. In contrast to Mertk-/- mice, selective depletion of CD8+ T cells completely abrogated protection from leukemia in Tyro3-/- mice, indicating a mechanism less dependent on DCs. Together, these findings reveal novel and distinct mechanistic insights into the immunosuppressive roles for MERTK and TYRO3 in the leukemia microenvironment, demonstrate a critical role for MERTK in DC activity, and validate a similar mechanism mediated by MRX-2843. Thus, these studies provide strong rationale for development of MERTK and/or TYRO3-targeted immunotherapies for treatment of acute leukemia. Citation Format: Justus M Huelse, Swati S Bhasin, Kristen M Jacobsen, Beena E Thomas, Madison L Chimenti, Travon A Baxter, Xiaodong Wang, Stephen V Frye, Curtis J Henry, H. Shelton Earp, Manoj Bhasin, Deborah DeRyckere, Douglas K Graham. MERTK inhibition induces an anti-leukemia dendritic cell – T cell axis while TYRO3 inhibition protects by a separate mechanism [abstract]. In: Proceedings of the AACR Special Conference: Tumor Immunology and Immunotherapy; 2022 Oct 21-24; Boston, MA. Philadelphia (PA): AACR; Cancer Immunol Res 2022;10(12 Suppl):Abstract nr A35.

The genomics data-driven identification of gene signatures and pathways has been routinely explored for predicting cancer survival and making decisions related to targeted treatments. A large number of packages and tools have been developed to correlate gene expression/mutations to the clinical outcome but lack the ability to perform such analysis based on pathways, gene sets, and gene ratios. Furthermore, in this single-cell omics era, the cluster markers from cancer single-cell transcriptomics studies remain an underutilized prognostic option. Additionally, no bioinformatics online tool evaluates the associations between the enrichment of canonical cell types and survival across cancers. Here we have developed Survival Genie, a web tool to perform survival analysis on single-cell RNA-seq (scRNA-Seq) data and a variety of other molecular inputs such as gene sets, genes ratio, tumor infiltrating immune cells proportion, gene expression profile scores, and tumor mutation burden. For a comprehensive analysis, Survival Genie contains 53 datasets of 27 distinct malignancies from 11 different cancer programs related to adult and pediatric cancers. Users can upload scRNA-Seq data or gene sets and select a gene expression partitioning method (i.e., mean, median, quartile, cutp) to determine the effect of expression levels on survival outcomes. The tool provides comprehensive results including box plots of low and high-risk groups, Kaplan-Meier plots with univariate Cox proportional hazards model, and correlation of immune cell enrichment and molecular profile.The analytical options and comprehensive collection of cancer datasets make Survival Genie a unique resource to correlate gene sets, pathways, cellular enrichment, and single-cell signatures to clinical outcomes to assist in developing next-generation prognostic and therapeutic biomarkers. Survival Genie is open-source and available online at https://bbisr.shinyapps.winship.emory.edu/SurvivalGenie/.

Relapse- and continuous complete remission (CCR)-associated pediatric acute myeloid leukemia (AML) patient bone marrows collected at the time of diagnosis (Dx), end of induction (EOI) and relapse were analyzed by single cell RNA sequencing. A novel AML blasts-associated 7-genes signature (CLEC11A, PRAME, AZU1, NREP, ARMH1, C1QBP, TRH) displayed a strong correlation with blast percentages and overall survival in the TARGET AML dataset (HR = 2.3; P-value = .007). Distinct clusters of AML-blasts at Dx were observed for relapse- and CCR-associated samples with differential expression of genes associated with survival. Relapse-associated samples demonstrated enrichment of exhausted T cells and M2 macrophages as opposed to inflammatory M1 macrophages in CCR-associated samples at Dx. EOI treatment resistant blast cells overexpressed fatty acid oxidation, tumor growth and stemness genes. Also, a relapse-associated EOI samples T cells subset showed downregulation of MHC Class I and regulatory genes. Altogether, this study deeply characterizes pediatric AML relapse-/CCR-associated tumor microenvironment transcriptome landscape.

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.

Several therapeutic options are available for metastatic RCC, but responses are almost never complete, and resistance to therapy develops in the vast majority of patients. Consequently, novel treatments are needed to combat resistance to current therapies and to improve patient outcomes. We have applied integrated transcriptome and proteome analyses to identify cathepsin B (CTSB), a cysteine proteinase of the papain family, as one of the most highly upregulated gene products in established human RCC xenograft models of resistance to vascular endothelial growth factor receptor (VEGFR) tyrosine kinase inhibitors (TKI). We used established RCC models to test the significance of CTSB in the progression of renal cancer. Our evaluation of CTSB showed that stable CTSB knockdown suppressed RCC growth in vitro and in vivo. Stable over-overexpression of wild-type CTSB (CTSBwt/hi), but not of an CTSB active site mutant (CTSBN298A), rescued cell growth in CTSB knockdown cells and abolished the efficacy of VEGFR TKI treatment. Genome-wide transcriptome profiling of CTSB knockdown cells demonstrated significant effects on multiple metabolic and stem cell-related pathways, with ALDHA1A (ALDH1) as one of the most significantly downregulated genes. Importantly, survival analysis across 16 major TCGA cancers revealed that CTSB overexpression is associated with low rates of three and five year patient survival rates (P = 2.5e–08, HR = 1.4). These data strongly support a contribution of CTSB activity to RCC cell growth and tumorigenicity. They further highlight the promise of CTSB inhibition in development of novel combination therapies designed to improve efficacy of current TKI treatments of metastatic RCC.

The TAM family receptor tyrosine kinases TYRO3, AXL and MERTK are potential therapeutic targets in a variety of cancers. In previous studies, inhibition of MERTK decreased PD-1 checkpoint proteins in the leukemia microenvironment and prolonged survival in a syngeneic BCR-ABL+/Arf-/- B-cell acute leukemia model, implicating MERTK as a promising immune-oncology target in leukemia. Strikingly, Mertk-/- mice were largely protected from leukemia. In our current studies, Tyro3-/- almost completely prevented development of leukemia, comparable to Mertk-/-, while Axl-/- mice died with similar timing to wild type (WT) mice (20-40 days). These data demonstrate differential roles for TAM kinases in the anti-leukemia immune response. Depletion studies were conducted to evaluate potential roles for T cells and dendritic cells (DCs) in anti-leukemia immunity in Mertk-/- mice. Selective depletion of CD8+ T cells abrogated protection from leukemia in Mertk-/- mice, but survival was still prolonged relative to WT. Thus, while CD8+ T cells were required for complete protection from leukemia, the anti-leukemia response remained partially intact even in the absence of CD8+ T cells, implicating an innate immune mechanism. Indeed, combined depletion of CD8+ T cell and CD8α+ DC subsets completely abrogated the anti-leukemic effects in Mertk-/- mice, revealing a critical immunosuppressive role for MERTK in DCs in the leukemia microenvironment. In contrast to Mertk-/- mice, selective depletion of CD8+ T cells completely abrogated protection from leukemia in Tyro3-/- mice, indicating a mechanism less dependent on DCs. Similarly, single cell RNA sequencing revealed CD8+ DCs with a more mature and antigen-presenting phenotype in Mertk-/- mice compared to WT, while antigen-presenting DCs were not increased in Tyro3-/- mice. Single cell sequencing data also suggest induction of an anti-leukemic DC - T cell axis in WT leukemic mice treated with the MERTK-selective inhibitor MRX-2843. DCs were nearly absent in leukemic bone marrow from saline-treated mice and were dramatically increased in response to treatment with MRX-2843. Treatment with MRX-2843 also decreased the incidence of CD8+ T cells expressing high levels of Tox, which has been associated with T cell exhaustion. These changes coincided with decreased leukemic blasts, even in the context of established disease.Together, our findings support a model whereby MERTK inhibition promotes DC function and CD8+ T cell activity, leading to anti-leukemia immunity. In contrast, anti-leukemia immunity in response to TYRO3 inhibition is less dependent on DCs. Differential roles for the TAM kinases in the leukemia microenvironment provide rationale for development of MERTK and/or TYRO3 targeted immunotherapies to treat acute leukemia. Citation Format: Justus M. Huelse, Swati S. Bhasin, Beena E. Thomas, Madison L. Chimenti, Xiaodong Wang, Stephen V. Frye, H. Shelton Earp, Manoj Bhasin, Deborah DeRyckere, Douglas K. Graham. MERTK inhibition induces an anti-leukemia dendritic cell - T cell axis while TYRO3 inhibition protects through a separate mechanism [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 240.

Purpose: The BRAFV600E oncogene modulates the papillary thyroid carcinoma (PTC) microenvironment, in which pericytes are critical regulators of tyrosine-kinase (TK)-dependent signaling pathways. Although BRAFV600E and TK inhibitors are available, their efficacy as bimodal therapeutic agents in BRAFV600E-PTC is still unknown. Experimental Design: We assessed the effects of vemurafenib (BRAFV600E inhibitor) and sorafenib (TKI) as single agents or in combination in BRAFWT/V600E-PTC and BRAFWT/WT cells using cell-autonomous, pericyte coculture, and an orthotopic mouse model. We also used BRAFWT/V600E-PTC and BRAFWT/WT-PTC clinical samples to identify differentially expressed genes fundamental to tumor microenvironment. Results: Combined therapy blocks tumor cell proliferation, increases cell death, and decreases motility via BRAFV600E inhibition in thyroid tumor cells in vitro. Vemurafenib produces cytostatic effects in orthotopic tumors, whereas combined therapy (likely reflecting sorafenib activity) generates biological fluctuations with tumor inhibition alternating with tumor growth. We demonstrate that pericytes secrete TSP-1 and TGFβ1, and induce the rebound of pERK1/2, pAKT and pSMAD3 levels to overcome the inhibitory effects of the targeted therapy in PTC cells. This leads to increased BRAFV600E-PTC cell survival and cell death refractoriness. We find that BRAFWT/V600E-PTC clinical samples are enriched in pericytes, and TSP1 and TGFβ1 expression evoke gene-regulatory networks and pathways (TGFβ signaling, metastasis, tumor growth, tumor microenvironment/ECM remodeling functions, inflammation, VEGF ligand–VEGF receptor interactions, immune modulation, etc.) in the microenvironment essential for BRAFWT/V600E-PTC cell survival. Critically, antagonism of the TSP-1/TGFβ1 axis reduces tumor cell growth and overcomes drug resistance. Conclusions: Pericytes shield BRAFV600E-PTC cells from targeted therapy via TSP-1 and TGFβ1, suggesting this axis as a new therapeutic target for overcoming resistance to BRAFV600E and TK inhibitors.

The standard treatment for metastatic prostate cancer, androgen deprivation therapy (ADT), is designed to suppress androgen receptor (AR) activity. However, men invariably progress to castration-resistant prostate cancer (CRPC), and AR reactivation contributes to progression in most cases. To identify mechanisms that may drive CRPC, we examined a VCaP prostate cancer xenograft model as tumors progressed from initial androgen sensitivity prior to castration to castration resistance and then on to relapse after combined therapy with further AR-targeted drugs (abiraterone plus enzalutamide). AR activity persisted in castration-resistant and abiraterone/enzalutamide–resistant xenografts and was associated with increased expression of the AR gene and the AR-V7 splice variant. We then assessed expression of individual AR-regulated genes to identify those that persisted, thereby contributing to tumor growth, versus those that decreased and may therefore exhibit tumor suppressor activities. The most significantly decreased AR target gene was dipeptidyl peptidase 4 (DPP4), which encodes a membrane-anchored protein that cleaves dipeptides from multiple growth factors, resulting in their increased degradation. DPP4 mRNA and protein were also decreased in clinical CRPC cases, and inhibition of DPP4 with sitagliptin enhanced the growth of prostate cancer xenografts following castration. Significantly, DPP4 inhibitors are frequently used to treat type 2 diabetes as they increase insulin secretion. Together, these results implicate DPP4 as an AR-regulated tumor suppressor gene whose loss enhances growth factor activity and suggest that treatment with DPP4 inhibitors may accelerate emergence of resistance to ADT. Significance: These findings identify DPP4 as an AR-stimulated tumor suppressor gene that is downregulated during progression to castration-resistant prostate cancer, warning that treatment with DPP4 inhibitors, commonly used to treat type 2 diabetes, may accelerate prostate cancer progression following androgen deprivation therapy. Cancer Res; 78(22); 6354–62. ©2018 AACR.

To understand the diabetic wound healing microenvironment, we profiled 174,962 single cells from foot, forearm, and PBMCs using single-cell RNA sequencing (scRNASeq) approach. Our analysis shows enrichment of a unique population of fibroblasts overexpressing MMP1, MMP3, MMP11, HIF1A, CHI3L1, and TNFAIP6 genes and M1 macrophage polarization in the DFU patients with healing wounds. Further, scRNASeq of spatially separated samples from same patient and spatial transcriptomics (ST) revealed preferential localization of these healing associated fibroblasts toward deep wound/ulcer bed as compared to wound edge or non-wounded skin. ST also validated our findings of higher enrichment of M1 macrophages in healers and M2 macrophages in non-healers. Our analysis provides deep insights into the wound healing microenvironment, identifying cell types that could be critical in promoting DFU healing, and may inform novel therapeutic approaches for DFU treatment.

Because injured mitochondria can accelerate cell death through the elaboration of oxidative free radicals and other mediators, it is striking that proliferator γ coactivator 1-α (PGC1α), a stimulator of increased mitochondrial abundance, protects stressed renal cells instead of potentiating injury. Here, we report that PGC1α’s induction of lysosomes via transcription factor EB (TFEB) may be pivotal for kidney protection. CRISPR and stable gene transfer showed that PGC1α-KO tubular cells were sensitized to the genotoxic stressor cisplatin, whereas Tg cells were protected. The biosensor mitochondrial-targeted Keima (mtKeima) unexpectedly revealed that cisplatin blunts mitophagy both in cells and mice. PGC1α and its downstream mediator NAD+ counteracted this effect. PGC1α did not consistently affect known autophagy pathways modulated by cisplatin. Instead RNA sequencing identified coordinated regulation of lysosomal biogenesis via TFEB. This effector pathway was sufficiently important that inhibition of TFEB or lysosomes unveiled a striking harmful effect of excess PGC1α in cells and conditional mice. These results uncover an unexpected effect of cisplatin on mitophagy and PGC1α’s reliance on lysosomes for kidney protection. Finally, the data illuminate TFEB as a potentially novel target for renal tubular stress resistance., PGC1α is renoprotective in the setting of platinum-based chemotherapy through the induction of mitophagy and lysosomal biogenesis via transcription factor EB.

Nonhealing diabetic foot ulcers (DFUs) are characterized by low-grade chronic inflammation, both locally and systemically. We prospectively followed a group of patients who either healed or developed nonhealing chronic DFUs. Serum and forearm skin analysis, both at the protein expression and the transcriptomic level, indicated that increased expression of factors such as interferon-γ (IFN-γ), vascular endothelial growth factor, and soluble vascular cell adhesion molecule-1 were associated with DFU healing. Furthermore, foot skin single-cell RNA sequencing analysis showed multiple fibroblast cell clusters and increased inflammation in the dorsal skin of patients with diabetes mellitus (DM) and DFU specimens compared with control subjects. In addition, in myeloid cell DM and DFU upstream regulator analysis, we observed inhibition of interleukin-13 and IFN-γ and dysregulation of biological processes that included cell movement of monocytes, migration of dendritic cells, and chemotaxis of antigen-presenting cells pointing to an impaired migratory profile of immune cells in DM skin. The SLCO2A1 and CYP1A1 genes, which were upregulated at the forearm of nonhealers, were mainly expressed by the vascular endothelial cell cluster almost exclusively in DFU, indicating a potential important role in wound healing. These results from integrated protein and transcriptome analyses identified individual genes and pathways that can potentially be targeted for enhancing DFU healing.

Heterocyclic aryl hydrocarbon receptor agonists modulate lymphocyte-mediated tissue repair in a murine model of colitis., Aryl hydrocarbon receptor (AHR) is an essential regulator of gut immunity and a promising therapeutic target for inflammatory bowel disease (IBD). Current AHR agonists are inadequate for clinical translation due to low activity, inadequate pharmacokinetics, or toxicity. We synthesized a structurally diverse library and used integrated computational and experimental studies to discover mechanisms governing ligand-receptor interaction and to design potent drug leads PY109 and PY108, which display physiochemical drug-likeness properties, desirable pharmacokinetic profiles, and low toxicity. In a murine model of dextran sulfate sodium–induced colitis, orally administered compounds increase interleukin-22 (IL-22) production and accelerate mucosal healing by modulating mucosal adaptive and innate lymphoid cells. AHR and IL-22 pathway induction was confirmed using RNA sequencing and characterization of the lymphocyte protein-protein interaction network. Significant induction of IL-22 was also observed using human T cells from patients with IBD. Our findings support rationally designed AHR agonists for IBD therapy.

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.

Cancer therapy is a double-edged sword, as surgery and chemotherapy can induce an inflammatory/immunosuppressive injury response that promotes dormancy escape and tumor recurrence. We hypothesized that these events could be altered by early blockade of the inflammatory cascade and/or by accelerating the resolution of inflammation. Preoperative, but not postoperative, administration of the nonsteroidal antiinflammatory drug ketorolac and/or resolvins, a family of specialized proresolving autacoid mediators, eliminated micrometastases in multiple tumor-resection models, resulting in long-term survival. Ketorolac unleashed anticancer T cell immunity that was augmented by immune checkpoint blockade, negated by adjuvant chemotherapy, and dependent on inhibition of the COX-1/thromboxane A(2) (TXA(2)) pathway. Preoperative stimulation of inflammation resolution via resolvins (RvD2, RvD3, and RvD4) inhibited metastases and induced T cell responses. Ketorolac and resolvins exhibited synergistic antitumor activity and prevented surgery- or chemotherapy-induced dormancy escape. Thus, simultaneously blocking the ensuing proinflammatory response and activating endogenous resolution programs before surgery may eliminate micrometastases and reduce tumor recurrence.

Impaired wound healing in the diabetic foot is a major problem often leading to amputation. Mast cells have been shown to regulate wound healing in diabetes. We developed an indole-carboxamide type mast cell stabilizer, MCS-01, which proved to be an effective mast cell degranulation inhibitor in vitro and can be delivered topically for prolonged periods through controlled release by specifically designed alginate bandages. In diabetic mice, both pre- and post-wounding, topical MCS-01 application accelerated wound healing comparable to that achieved with systemic mast cell stabilization. Moreover, MCS-01 altered the macrophage phenotype, promoting classically activated polarization. Bulk transcriptome analysis from wounds treated with MCS-01 or placebo showed that MCS-01 significantly modulated the mRNA and microRNA profile of diabetic wounds, stimulated upregulation of pathways linked to acute inflammation and immune cell migration, and activated the NF-κB complex along with other master regulators of inflammation. Single-cell RNA sequencing analysis of 6,154 cells from wounded and unwounded mouse skin revealed that MCS-01 primarily altered the gene expression of mast cells, monocytes, and keratinocytes. Taken together, these findings offer insights into the process of diabetic wound healing and suggest topical mast cell stabilization as a potentially successful treatment for diabetic foot ulceration.

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.

Millions of patients suffer from major depressive disorder (MDD), but many do not respond to selective serotonin reuptake inhibitor (SSRI) therapy. We used a pharmacometabolomics-informed pharmacogenomics research strategy to identify genes associated with metabolites that were related to SSRI response. Specifically, 306 MDD patients were treated with citalopram or escitalopram and blood was drawn at baseline, 4 and 8 weeks for blood drug levels, genome-wide single nucleotide polymorphism (SNP) genotyping and metabolomic analyses. SSRI treatment decreased plasma serotonin concentrations (P

Fifteen percent of diabetes mellitus (DM) patients are expected to develop a diabetic foot ulcer (DFU) within their lifetime. DFUs represent an ever increasing burden for the healthcare system. Chronic wounds, lack the linear progression from one classical phase of healing to the next and are mainly characterized by the persistence of the inflammatory phase. To define the transcriptome landscape of healthy uninjured skin, DM and DFU skin, we performed transcriptome profiling on skin specimens from foot surgeries obtained from 4 controls, 4 DM patients without DFU and on samples from 2 DM patients after DFU debridement . We generated the single cell transcriptome profiles of 3,766 and 3,469 cells from non-DM and DM patients and of 660 cells from patients with active DFUs. Further, bioinformatics analysis identified clusters of cells with similar transcriptome states. The most abundant cell type was fibroblast. Other clusters included adipocytes and endothelial cells. T-cells, monocytes, and macrophages were the main immune cell types identified. As macrophages are critical for healing of DFUs, we estimated the abundance of total macrophages and their polarization. Approximately 10% of cells were macrophages in each group while DFU group had significantly more M1 polarized as compared to M2 (M1/M2=2.4 times). Supervised analysis identified a set of 66 and 1genes that are significantly differentially expressed in macrophages of DFU and DM patients respectively as compared to controls. A master regulator (MR) analysis in DFU patients identified activation of inflammatory and angiogenesis related MRs (e.g., STAT3, IL15). while in DM patients it identified activation of distinct inflammatory MRs (e.g., SMAD3, CD44). In summary, we present for the first time sc-RNAseq data on lower extremity skin samples that could help delineate the molecular mechanisms of dysregulated wound healing and identify novel therapeutic targets. Disclosure G. Theocharidis: None. S.S. Bhasin: None. K. Kounas: None. M.K. Bhasin: None. A. Veves: Advisory Panel; Self; HyperMed Imaging, Inc., AOBiome.