Abstract PO-020: Inferring copy number substructure from single-cell transcriptomics in human tumors with CopyKat

High-throughput single cell transcriptomics analysis is widely used to study human tumors, however a major challenge is distinguishing the stromal cells from the malignant cancer cells, as well as clonal substructure within tumors. To address this challenge, we developed an integrative Bayesian segmentation approach, COPYKAT to estimate genomic copy numbers at 5MB resolution from high-throughput single cell RNA-seq data. We applied COPYKAT to 39,709 single cells from 16 tumors across 4 cancer types, including premalignant and triple-negative breast cancers, pancreatic ductal adenocarcinomas, and anaplastic thyroid cancer. From these data we could accurately (98%) classify tumor cells from stromal cells. In three TNBC tumors COPYKAT resolved multiple clonal subpopulations of genotypes that differed in expression of breast cancer genes and enrichment of cancer hallmarks including EMT and hypoxia. These data show that COPYKAT can accurately resolve clonal copy number substructure in tumors and classify tumor and normal cells in a variety of human cancers. Citation Format: Ruli Gao, Shanshan Bai, Henderson Ying, Yiyun Lin, Tapsi Seth, Min Hu, Emi Sei, Alexander Davis, Fang Wang, Jennifer Rui Wang, Ken Chen, Stacey Moulder, Stephen Lai, Nicholas Navin. Inferring copy number substructure from single-cell transcriptomics in human tumors with CopyKat [abstract]. In: Proceedings of the AACR Virtual Special Conference on Tumor Heterogeneity: From Single Cells to Clinical Impact; 2020 Sep 17-18. Philadelphia (PA): AACR; Cancer Res 2020;80(21 Suppl):Abstract nr PO-020.