TY - JOUR
T1 - Delineating copy number and clonal substructure in human tumors from single-cell transcriptomes
AU - Gao, Ruli
AU - Bai, Shanshan
AU - Henderson, Ying C.
AU - Lin, Yiyun
AU - Schalck, Aislyn
AU - Yan, Yun
AU - Kumar, Tapsi
AU - Hu, Min
AU - Sei, Emi
AU - Davis, Alexander
AU - Wang, Fang
AU - Shaitelman, Simona F.
AU - Wang, Jennifer Rui
AU - Chen, Ken
AU - Moulder, Stacy
AU - Lai, Stephen Y.
AU - Navin, Nicholas E.
N1 - Funding Information:
This work was supported by grants to N.E.N. from the American Cancer Society (129098-RSG-16-092-01-TBG), the National Cancer Institute (RO1CA240526, RO1CA236864), the Emerson Collective Cancer Research Fund (20200619153514) and the CPRIT Single Cell Genomics Center (RP180684). N.E.N. is an AAAS Wachtel Scholar, AAAS Fellow, Andrew Sabin Family Fellow and Jack & Beverly Randall Innovator. This study was supported by the MD Anderson Breast Cancer Moonshot Program. This study was supported by the MD Anderson Sequencing Core Facility Grant (CA016672). This project was also supported by a Susan Komen Postdoctoral Fellowship to R.G. (PDF17487910). Other grant support includes the Anaplastic Thyroid Cancer Research Fund (S.Y.L. and J.R.W.) and an institutional multi-investigator research program grant to S.Y.L.
Publisher Copyright:
© 2021, The Author(s), under exclusive licence to Springer Nature America, Inc.
PY - 2021/5
Y1 - 2021/5
N2 - 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.
AB - 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.
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U2 - 10.1038/s41587-020-00795-2
DO - 10.1038/s41587-020-00795-2
M3 - Article
C2 - 33462507
AN - SCOPUS:85100149659
SN - 1087-0156
VL - 39
SP - 599
EP - 608
JO - Nature biotechnology
JF - Nature biotechnology
IS - 5
ER -