Fernandez, Nicolas

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.

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

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