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Between-region genetic divergence reflects the mode and tempo of tumor evolution

Nature Genetics volume 49, pages 1015–1024 (2017)Cite this article

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Abstract

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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Figure 1: Overview of the simulation framework and the genomic data analysis pipeline.
Figure 2: Characteristics of virtual tumors simulated under different modes of evolution.
Figure 3: Colorectal tumors exhibit patterns of between-region genetic divergence consistent with effectively neutral growth or selection.
Figure 4: Single-gland WES shows spatial constraints among subclonal mutations.
Figure 5: The SFS reflects differential modes of evolution within and between tumor types.
Figure 6: Projection of patient samples onto distinct evolutionary modes.

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Acknowledgements

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.

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Author notes

  1. Ruping Sun and Zheng Hu: These authors contributed equally to this work.

Authors and Affiliations

  1. Department of Medicine, Stanford University School of Medicine, Stanford, California, USA

    Ruping Sun, Zheng Hu, Zhicheng Ma & Christina Curtis

  2. Department of Genetics, Stanford University School of Medicine, Stanford, California, USA

    Ruping Sun, Zheng Hu, Zhicheng Ma & Christina Curtis

  3. Stanford Cancer Institute, Stanford University School of Medicine, Stanford, California, USA

    Ruping Sun, Zheng Hu, Zhicheng Ma & Christina Curtis

  4. Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK

    Andrea Sottoriva

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

    Trevor A Graham

  6. Department of Biology, Stanford University, Stanford, California, USA

    Arbel Harpak

  7. Department of Molecular and Medical Genetics, Oregon Health and Science University, Portland, Oregon, USA

    Jared M Fischer

  8. Department of Pathology, Keck School of Medicine of the University of Southern California, Los Angeles, California, USA

    Darryl Shibata

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Contributions

R.S., Z.H., and C.C. designed the study. R.S. analyzed and visualized the data and performed statistical analyses. Z.H. performed simulation studies. Z.M. and D.S. generated data. R.S., Z.H., and C.C. interpreted the data. A.S. and T.A.G. contributed to earlier analysis of the COAD data set. A.H. provided statistical advice. J.M.F. performed xenograft experiments. D.S. and C.C. provided reagents and data. C.C. supervised the study and wrote the manuscript with input from R.S. and Z.H. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Christina Curtis.

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The authors declare no competing financial interests.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–36, Supplementary Tables 1–3 and Supplementary Note. (PDF 20850 kb)

Supplementary Table 4

SFS-derived ITH metrics for virtual tumors. (XLSX 303 kb)

Supplementary Table 5

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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