Abstract 6290: sc2DAT: workflow for targeting tumor subpopulations of single cells

Abstract Single-cell RNA sequencing (scRNA-seq) provides enhanced insights into the cellular composition of human tissues. In cancer, normal proportions of single cells are aberrantly dysregulated, and thus targeting subpopulations of cells within a tumor might be more effective than targeting all cells of a tumor. Here we present Single Cells to Drugs And Targets (sc2DAT), a unique workflow for the analysis of bulk and single cell RNA-seq datasets collected from tumors of the Clinical Proteomic Tumor Analysis Consortium (CPTAC). sc2DAT can take in as input single cell or bulk RNA profiles from one or two conditions. When processing scRNA-seq samples, sc2DAT first clusters the single cell vectors of expression to identify cell type subpopulations. It then bulkifies the vectors of expression in each cluster of single cells. For bulk RNA-seq, sc2DAT applies deconvolution with BayesPrism to infer cell-type-specific expression profiles for each identified subpopulation within a tumor. The sc2DAT platform currently has over 20 precomputed single-cell reference datasets derived from human and mouse studies. The expression vectors in each cluster of single cells are analyzed to discover genes that give rise to cell-surface proteins that are highly expressed in the tumor’s subpopulation, but lowly expressed across all human healthy cell types and tissues. These identified cell-surface proteins can become immunotherapy targets for selectively elimination of subpopulation of single cells within a tumor. In addition, sc2DAT integrates the LINCS L1000 perturbational gene expression dataset to predict drugs and preclinical compounds that could induce a healthy phenotype in subpopulations of single cells within a tumor. Once datasets are uploaded to sc2DAT, the results are presented in a comprehensive report with an abstract, introduction, methods, results, conclusions, and references sections. The reports have many figures and tables, and it can be exported and shared in several formats including a PDF file. We applied sc2DAT to several CPTAC3 and CPTAC4 datasets to identify cell-type-specific subpopulation targets and reverser compounds, uncovering many potential personalized therapeutic strategies for immunotherapy and small-molecule interventions. sc2DAT is available from: https://sc2dat.maayanlab.cloud. Citation Format: Giacomo B. Marino, Nasheath Ahmed, Daniel J. Clarke, Avi Ma'ayan. sc2DAT: workflow for targeting tumor subpopulations of single cells [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 6290.