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

Nature Biotechnology volume 39, pages 599–608 (2021)Cite this article

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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.

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Fig. 1: Overview of the CopyKAT analysis workflow.
Fig. 2: Comparison of bulk DNA and single-cell RNA copy number profiles.
Fig. 3: Classification of cancer cells and normal cells in human tumors.
Fig. 4: Classification of tumor and normal cells sequenced by different scRNA-seq technologies.
Fig. 5: Clonal substructure of three triple-negative breast tumors.

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Data availability

scRNA-seq data from this study were deposited in the Gene Expression Omnibus (GEO; GSE148673).

Code availability

Software is available at GitHub (https://github.com/navinlabcode/copykat).

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Acknowledgements

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.

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Authors and Affiliations

  1. The Center for Bioinformatics and Computational Biology, Department of Cardiovascular Sciences, Houston Methodist Research Institute, Houston, TX, USA

    Ruli Gao

  2. Department of Genetics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA

    Ruli Gao, Shanshan Bai, Yiyun Lin, Aislyn Schalck, Yun Yan, Tapsi Kumar, Min Hu, Emi Sei, Alexander Davis & Nicholas E. Navin

  3. Department of Genitourinary Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA

    Shanshan Bai

  4. Department of Head and Neck Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX, USA

    Ying C. Henderson, Jennifer Rui Wang & Stephen Y. Lai

  5. UTHealth Graduate School of Biomedical Sciences, The University of Texas MD Anderson Cancer Center, Houston, TX, USA

    Yiyun Lin, Aislyn Schalck, Yun Yan, Tapsi Kumar, Alexander Davis, Stephen Y. Lai & Nicholas E. Navin

  6. Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA

    Fang Wang, Ken Chen & Nicholas E. Navin

  7. Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA

    Simona F. Shaitelman & Stephen Y. Lai

  8. Department of Breast Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA

    Stacy Moulder

  9. Department of Molecular and Cellular Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA

    Stephen Y. Lai

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Contributions

R.G. and N.E.N. designed the research project. R.G. developed and implemented the computational methods with contributions from N.E.N., Y.Y., A.D., F.W. and K.C. M.H. preprocessed the data. S.F.S. and S.M. provided clinical samples. J.R.W. and S.Y.L. collected thyroid tumor samples. S.B., Y.C.H., Y.L., A.S., T.K. and E.S. performed single-cell sequencing experiments. R.G. and N.E.N. wrote the manuscript with input from all authors.

Corresponding author

Correspondence to Nicholas E. Navin.

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The authors declare no competing interests.

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Peer review information Nature Biotechnology thanks Elana Fertig, Jan Korbel and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Gao, R., Bai, S., Henderson, Y.C. et al. Delineating copy number and clonal substructure in human tumors from single-cell transcriptomes. Nat Biotechnol 39, 599–608 (2021). https://doi.org/10.1038/s41587-020-00795-2

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