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Quantification of subclonal selection in cancer from bulk sequencing data

Nature Genetics volume 50, pages 895–903 (2018)Cite this article

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An Author Correction to this article was published on 18 July 2018

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Abstract

Subclonal architectures are prevalent across cancer types. However, the temporal evolutionary dynamics that produce tumor subclones remain unknown. Here we measure clone dynamics in human cancers by using computational modeling of subclonal selection and theoretical population genetics applied to high-throughput sequencing data. Our method determined the detectable subclonal architecture of tumor samples and simultaneously measured the selective advantage and time of appearance of each subclone. We demonstrate the accuracy of our approach and the extent to which evolutionary dynamics are recorded in the genome. Application of our method to high-depth sequencing data from breast, gastric, blood, colon and lung cancer samples, as well as metastatic deposits, showed that detectable subclones under selection, when present, consistently emerged early during tumor growth and had a large fitness advantage (>20%). Our quantitative framework provides new insight into the evolutionary trajectories of human cancers and facilitates predictive measurements in individual tumors from widely available sequencing data.

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Fig. 1: Modeling patterns of subclonal selection in sequencing data.
Fig. 2: Accurate recovery of evolutionary parameters from simulated data using approximate Bayesian computation.
Fig. 3: Quantifying selection from high-depth bulk sequencing of human cancers.
Fig. 4: Quantifying selection in large cohorts of primary tumors and metastatic lesions.
Fig. 5: Predicting the future evolution of subclones.

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  • 18 July 2018

    In the version of this article originally published, in the “Theoretical framework of subclonal selection” section of the main text, ref. 11 instead of ref. 19 should have been cited at the end of the phrase “Our previously presented frequentist approach to detect subclonal selection from bulk sequencing data involves an R2 test statistic.” The error has been corrected in the HTML and PDF versions of the article.

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Acknowledgements

We thank W. Huang and K. Chkhaidze for fruitful discussions. We are grateful to A. Chinnaiyan and M. Cieslik for providing us with data from the MET500 cohort and to S. Leung for providing access to the gastric cancer cohort. A.S. is supported by the Chris Rokos Fellowship in Evolution and Cancer and by Cancer Research UK (grant no. A22909). T.A.G. is supported by Cancer Research UK (grant no. A19771). C.P.B. is supported by the Wellcome Trust (grant no. 097319/Z/11/Z). B.W. is supported by the Geoffrey W. Lewis Postdoctoral Training fellowship. A.S. and T.A.G. are jointly supported by the Wellcome Trust (grant no. 202778/B/16/Z and 202778/Z/16/Z, respectively). C.C. is supported by awards from the NIH (R01CA182514), Susan G. Komen Foundation (IIR13260750) and the Breast Cancer Research Foundation (BCRF-16-032). M.J.W. is supported by a Medical Research Council student scholarship. This work was also supported by Wellcome Trust funding to the Center for Evolution and Cancer (grant no. 105104/Z/14/Z).

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Authors and Affiliations

  1. Evolution and Cancer Laboratory, Barts Cancer Institute, Queen Mary University of London, London, UK

    Marc J. Williams & Trevor A. Graham

  2. Department of Cell and Developmental Biology, University College London, London, UK

    Marc J. Williams & Chris P. Barnes

  3. Centre for Mathematics and Physics in the Life Sciences and Experimental Biology (CoMPLEX), University College London, London, UK

    Marc J. Williams

  4. Evolutionary Genomics & Modelling Lab, Centre for Evolution and Cancer, Institute of Cancer Research, London, UK

    Benjamin Werner, Timon Heide & Andrea Sottoriva

  5. Departments of Medicine and Genetics, Stanford University School of Medicine, Stanford, CA, USA

    Christina Curtis

  6. Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA

    Christina Curtis

  7. UCL Genetics Institute, University College London, London, UK

    Chris P. Barnes

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Contributions

M.J.W. wrote all of the simulation code and performed mathematical and bioinformatics analysis; B.W. performed mathematical analysis; T.H. performed bioinformatics analysis; M.J.W., B.W., T.H., C.C., C.P.B., A.S. and T.A.G. analyzed the data; M.J.W., B.W., C.P.B., A.S. and T.A.G. wrote the manuscript; C.P.B., A.S. and T.G. jointly conceived, designed, supervised and funded the study.

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Correspondence to Chris P. Barnes, Andrea Sottoriva or Trevor A. Graham.

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Williams, M.J., Werner, B., Heide, T. et al. Quantification of subclonal selection in cancer from bulk sequencing data. Nat Genet 50, 895–903 (2018). https://doi.org/10.1038/s41588-018-0128-6

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