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

Ruli Gao; Shanshan Bai; Ying C. Henderson; Yiyun Lin; Aislyn Schalck; Yun Yan; Tapsi Kumar; Min Hu; Emi Sei; Alexander Davis; Fang Wang; Simona F. Shaitelman; Jennifer Rui Wang; Ken Chen; Stacy Moulder; Stephen Y. Lai; Nicholas E. Navin

doi:10.1038/s41587-020-00795-2

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

Ruli Gao, Shanshan Bai, Ying C. Henderson, Yiyun Lin, Aislyn Schalck, Yun Yan, Tapsi Kumar, Min Hu, Emi Sei, Alexander Davis, Fang Wang, Simona F. Shaitelman, Jennifer Rui Wang, Ken Chen, Stacy Moulder, Stephen Y. Lai, Nicholas E. Navin^*

^*Corresponding author for this work

Biochemistry and Molecular Genetics

Research output: Contribution to journal › Article › peer-review

365 Scopus citations

Abstract

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.

Original language	English (US)
Pages (from-to)	599-608
Number of pages	10
Journal	Nature biotechnology
Volume	39
Issue number	5
DOIs	https://doi.org/10.1038/s41587-020-00795-2
State	Published - May 2021

Funding

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.

ASJC Scopus subject areas

Applied Microbiology and Biotechnology
Bioengineering
Molecular Medicine
Biotechnology
Biomedical Engineering

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10.1038/s41587-020-00795-2

Cite this

Gao, R., Bai, S., Henderson, Y. C., Lin, Y., Schalck, A., Yan, Y., Kumar, T., Hu, M., Sei, E., Davis, A., Wang, F., Shaitelman, S. F., Wang, J. R., Chen, K., Moulder, S., Lai, S. Y., & Navin, N. E. (2021). Delineating copy number and clonal substructure in human tumors from single-cell transcriptomes. Nature biotechnology, 39(5), 599-608. https://doi.org/10.1038/s41587-020-00795-2

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title = "Delineating copy number and clonal substructure in human tumors from single-cell transcriptomes",

abstract = "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.",

author = "Ruli Gao and Shanshan Bai and Henderson, {Ying C.} and Yiyun Lin and Aislyn Schalck and Yun Yan and Tapsi Kumar and Min Hu and Emi Sei and Alexander Davis and Fang Wang and Shaitelman, {Simona F.} and Wang, {Jennifer Rui} and Ken Chen and Stacy Moulder and Lai, {Stephen Y.} and Navin, {Nicholas E.}",

note = "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: {\textcopyright} 2021, The Author(s), under exclusive licence to Springer Nature America, Inc.",

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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.

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

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