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Genotyping tumor clones from single-cell data

Nature Methods volume 13, pages 555–556 (2016)Cite this article

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A new statistical approach mitigates technical errors in single-cell DNA sequencing data to advance the study of tumor evolution and diversity.

Single-cell sequencing (SCS) has had a major impact on cancer research by providing insights into clonal evolution, cell plasticity, intratumor heterogeneity, aneuploidy and metastasis1. A new generation of computational methods2,3,4, including the Single Cell Genotyper (SCG) presented by Roth et al.5 in this issue of Nature Methods, are powering sequencing approaches by handling the unique properties of single-cell sequence data.

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Figure 1: Technical errors can lead to incorrect results for clonal genotypes and lineages.

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Acknowledgements

This work was supported by the Eric & Liz Lefkofsky Family Foundation, NCI (1R01CA169244-01 to N.E.N., R01CA172652 to K.C.) and the American Cancer Society (129098-RSG-16-092-01-TBG to N.E.N.). N.E.N. and K.C. are Andrew Sabin Family Fellows. The study was supported by the MD Anderson Cancer Moonshot Knowledge Gap Award and the Center for Genetics & Genomics.

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  1. Nicholas E. Navin is at the Department of Genetics and the Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.,

    Nicholas E Navin

  2. Ken Chen is at the Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.,

    Ken Chen

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  1. Nicholas E Navin
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  2. Ken Chen
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Correspondence to Nicholas E Navin.

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Navin, N., Chen, K. Genotyping tumor clones from single-cell data. Nat Methods 13, 555–556 (2016). https://doi.org/10.1038/nmeth.3903

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