Given the implications of tumor dynamics for precision medicine, there is a need to systematically characterize the mode of evolution across diverse solid tumor types. In particular, methods to infer the role of natural selection within established human tumors are lacking. By simulating spatial tumor growth under different evolutionary modes and examining patterns of between-region subclonal genetic divergence from multiregion sequencing (MRS) data, we demonstrate that it is feasible to distinguish tumors driven by strong positive subclonal selection from those evolving neutrally or under weak selection, as the latter fail to dramatically alter subclonal composition. We developed a classifier based on measures of between-region subclonal genetic divergence and projected patient data into model space, finding different modes of evolution both within and between solid tumor types. Our findings have broad implications for how human tumors progress, how they accumulate intratumoral heterogeneity, and ultimately how they may be more effectively treated.
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This work was funded by awards from the NIH (R01CA182514), the Susan G. Komen Foundation (IIR13260750), and the Breast Cancer Research Foundation (BCRF-16-032) to C.C. and an award from the NIH (R01CA185016) to D.S. Z.H. is supported by an Innovative Genomics Initiative (IGI) Postdoctoral Fellowship. A.S. is supported by the Chris Rokos Fellowship. T.A.G. was supported by Cancer Research UK. This work was supported in part by NIH P30 CA124435 using the Genetics Bioinformatics Service Center within the Stanford Cancer Institute Shared Resource. The results are in part based upon data generated from the following studies: EGAD00001001394, EGAD00001000714, EGAD00001000900, EGAD00001000984, and EGAD00001001113. We thank members of the Curtis laboratory for helpful discussions.
The authors declare no competing financial interests.
Supplementary Figures 1–36, Supplementary Tables 1–3 and Supplementary Note. (PDF 20850 kb)
SFS-derived ITH metrics for virtual tumors. (XLSX 303 kb)
SFS-derived ITH metrics and SVM-based classification of patient samples. (XLSX 18 kb)
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Sun, R., Hu, Z., Sottoriva, A. et al. Between-region genetic divergence reflects the mode and tempo of tumor evolution. Nat Genet 49, 1015–1024 (2017). https://doi.org/10.1038/ng.3891
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