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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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).
The authors declare no competing interests.
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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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