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Reply to ‘Currently available bulk sequencing data do not necessarily support a model of neutral tumor evolution’

Nature Genetics volume 50, pages 1624–1626 (2018)Cite this article

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Werner et al. reply — In their correspondence, McDonald et al.1 question our assertion that the distribution of mutations in tumor bulk sequencing data suggests an underlying neutral evolutionary process in a proportion of cancers2 and instead propose alternative explanations that incorporate subclonal selection. We agree with the authors’ demonstration that it is possible, in principle, to construct models of selection that produce patterns similar to the neutral model. However, the key issue is whether the proposed models of selection are realistic, meaningful and, most importantly, more appropriate than the null neutral model. Before examining this issue, we first note that we extensively stressed in the original manuscript2 that the majority of cases we examined were not consistent with neutral evolution (~70% appeared non-neutral), and we did specifically cite Gerlinger et al.3 as an example of data dominated by selection2. Our finding that the majority of cancers do show evidence of subclonal selection is consistent with previous literature, including the cases highlighted by McDonald et al.3,4.

Arguably, clonal evolution results from the interplay of three fundamental processes: random alterations (genetic, epigenetic, etc.), random drift and non-random selection, the third of which is the most complex to define and model. In the established field of population genetics, extensive effort has been dedicated to modeling the first two processes without selection, the so-called neutral dynamics5,6,7. This includes the development of entire statistical frameworks based on neutrality, such as coalescent theory8. On the contrary, models that include selection, especially in growing populations, have been much harder to derive analytically owing to the large number of assumptions in the definition of selection, including whether selection is clone intrinsic or clone extrinsic (microenvironmentally defined) and whether the magnitude of selection is constant or fluctuates in response to population dynamics. Importantly, most models of selection describe cancer dynamics in terms of time9,10 (for example, time to fixation of a selected mutant) and therefore, although insightful, are hard to apply to cancer genomic data where temporal dynamics are often unobservable.

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Fig. 1: Sensitivity of the 1/f test to subclone cancer cell fraction.

References

  1. McDonald, T. O., Chakrabarti, S. & Michor, F. Nat. Genet. https://doi.org/10.1038/s41588-018-0217-6 (2018).

  2. Williams, M. J., Werner, B., Barnes, C. P., Graham, T. A. & Sottoriva, A. Nat. Genet. 48, 238–244 (2016).

    Article  CAS  Google Scholar 

  3. Gerlinger, M. et al. N. Engl. J. Med. 366, 883–892 (2012).

    Article  CAS  Google Scholar 

  4. Ding, L. et al. Nature 481, 506–510 (2012).

    Article  CAS  Google Scholar 

  5. Donnelly, P. & Tavaré, S. Annu. Rev. Genet. 29, 401–421 (1995).

    Article  CAS  Google Scholar 

  6. Griffiths, R. C. & Tavaré, S. Stoch. Models 14, 273–295 (1998).

    Article  Google Scholar 

  7. Durrett, R. Ann. Appl. Probab. 23, 230–250 (2013).

    Article  Google Scholar 

  8. Donnelly, P. & Tavaré, S. Annu. Rev. Genet. 29, 401–421 (1995).

    Article  CAS  Google Scholar 

  9. Beerenwinkel, N. et al. PLoS Comput. Biol. 3, e225 (2007).

    Article  Google Scholar 

  10. Bozic, I. et al. Proc. Natl. Acad. Sci. USA 107, 18545–18550 (2010).

    Article  CAS  Google Scholar 

  11. Kessler, D. A. & Levine, H. Proc. Natl. Acad. Sci. USA 110, 11682–11687 (2013).

    Article  CAS  Google Scholar 

  12. Kessler, D. A. & Levine, H. J. Stat. Phys. 158, 783–805 (2015).

    Article  Google Scholar 

  13. Wu, C.-I., Wang, H.-Y., Ling, S. & Lu, X. Annu. Rev. Genet. 50, 347–369 (2016).

    Article  CAS  Google Scholar 

  14. Niida, A., Iwasaki, W. M., Innan, H. & Kumar, S. Mol. Biol. Evol. 76, 5605 (2018).

    Google Scholar 

  15. Cannataro, V. L. & Townsend, J. P. Mol. Biol. Evol. 47, 1402 (2018).

    Google Scholar 

  16. Sun, R. et al. Nat. Genet. 49, 1015–1024 (2017).

    Article  CAS  Google Scholar 

  17. Williams, M. J. et al. Nat. Genet. 50, 895–903 (2018).

    Article  CAS  Google Scholar 

  18. Vogelstein, B. et al. Science 339, 1546–1558 (2013).

    Article  CAS  Google Scholar 

  19. Stratton, M. R., Campbell, P. J. & Futreal, P. A. Nature 458, 719–724 (2009).

    Article  CAS  Google Scholar 

  20. Nik-Zainal, S. et al. Cell 149, 994–1007 (2012).

    Article  CAS  Google Scholar 

  21. Griffith, M. et al. Cell Syst. 1, 210–223 (2015).

    Article  CAS  Google Scholar 

  22. Orr, H. A. Nat. Rev. Genet. 10, 531–539 (2009).

    Article  CAS  Google Scholar 

  23. Lipinski, K. A. et al. Trends Cancer 2, 49–63 (2016).

    Article  Google Scholar 

  24. Gao, R. et al. Nat. Genet. 48, 1119–1130 (2016).

    Article  CAS  Google Scholar 

Download references

Acknowledgements

A.S. is supported by the Chris Rokos Fellowship in Evolution and Cancer and by Cancer Research UK (A22909). T.A.G. is supported by Cancer Research UK (A19771). C.P.B. is supported by the Wellcome Trust (097319/Z/11/Z). B.W. is supported by the Geoffrey W. Lewis Post-Doctoral Training fellowship. A.S. and T.A.G. are jointly supported by the Wellcome Trust (202778/B/16/Z and 202778/Z/16/Z, respectively). This work was also supported by Wellcome Trust funding to the Centre for Evolution and Cancer (105104/Z/14/Z).

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Authors and Affiliations

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

    Benjamin Werner & Andrea Sottoriva

  2. Evolution and Cancer Laboratory, Barts Cancer Institute, Queen Marry University of London, London, UK

    Marc J. Williams & Trevor A. Graham

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

    Marc J. Williams & Chris P. Barnes

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

    Marc J. Williams

  5. Department of Genetics, Evolution and Environment, University College London, London, UK

    Chris P. Barnes

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  1. Benjamin Werner
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Contributions

B.W. performed mathematical analysis. M.J.W. performed simulation analysis. All authors participated in the discussion, conceived and designed the response, and wrote the manuscript.

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Correspondence to Andrea Sottoriva.

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Werner, B., Williams, M.J., Barnes, C.P. et al. Reply to ‘Currently available bulk sequencing data do not necessarily support a model of neutral tumor evolution’. Nat Genet 50, 1624–1626 (2018). https://doi.org/10.1038/s41588-018-0235-4

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