Delineating copy number and clonal substructure in human tumors from single-cell transcriptomes

• Published in: Nature biotechnology Vol. 39; no. 5; pp. 599 - 608
• Main Authors: Gao, Ruli, Bai, Shanshan, Henderson, Ying C., Lin, Yiyun, Schalck, Aislyn, Yan, Yun, Kumar, Tapsi, Hu, Min, Sei, Emi, Davis, Alexander, Wang, Fang, Shaitelman, Simona F., Wang, Jennifer Rui, Chen, Ken, Moulder, Stacy, Lai, Stephen Y., Navin, Nicholas E.
• Format: Journal Article
• Language: English
• Published: New York Nature Publishing Group US 01.05.2021; Nature Publishing Group
• Subjects: 631/114; 631/208; 631/67; Adenocarcinoma; Agriculture; Apoptosis; Bayesian analysis; Bioinformatics; Biomedical and Life Sciences; Biomedical Engineering/Biotechnology; Biomedicine; Biotechnology; Breast cancer; Breast Neoplasms - genetics; Breast Neoplasms - pathology; Cancer; Carcinoma, Pancreatic Ductal - genetics; Carcinoma, Pancreatic Ductal - pathology; Clonal Evolution; Copy number; Copy number variations; Deoxyribonucleic acid; DNA; DNA Copy Number Variations - genetics; DNA repair; Gene expression; Gene Expression Regulation, Neoplastic; Gene sequencing; Genetic aspects; Genomics; Genomics - trends; Glioblastoma; Health aspects; High-Throughput Nucleotide Sequencing; Humans; Hypoxia; Invasiveness; Life Sciences; Mesenchyme; Mutation - genetics; Pancreas; Pancreatic cancer; Ribonucleic acid; RNA; RNA sequencing; Segmentation; Single-Cell Analysis; Subpopulations; Thyroid; Thyroid cancer; Transcriptome - genetics; Transcriptomics; Tumor microenvironment; Tumor Microenvironment - genetics; Tumors; United States
• ISSN: 1087-0156; 1546-1696; 1546-1696
• DOI: 10.1038/s41587-020-00795-2

Single-cell transcriptomic analysis is widely used to study human tumors. However, it remains challenging to distinguish normal cell types in the tumor microenvironment from malignant cells and to resolve clonal substructure within the tumor. To address these challenges, we developed an integrative Bayesian segmentation approach called copy number karyotyping of aneuploid tumors (CopyKAT) to estimate genomic copy number profiles at an average genomic resolution of 5 Mb from read depth in high-throughput single-cell RNA sequencing (scRNA-seq) data. We applied CopyKAT to analyze 46,501 single cells from 21 tumors, including triple-negative breast cancer, pancreatic ductal adenocarcinoma, anaplastic thyroid cancer, invasive ductal carcinoma and glioblastoma, to accurately (98%) distinguish cancer cells from normal cell types. In three breast tumors, CopyKAT resolved clonal subpopulations that differed in the expression of cancer genes, such as KRAS , and signatures, including epithelial-to-mesenchymal transition, DNA repair, apoptosis and hypoxia. These data show that CopyKAT can aid in the analysis of scRNA-seq data in a variety of solid human tumors. Clonal subpopulations in human tumors are identified from single-cell RNA-seq data.