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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.

Change history

  • 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.

References

  1. Greaves, M. & Maley, C. C. Clonal evolution in cancer. Nature 481, 306–313 (2012).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  2. Gay, L., Baker, A.-M. & Graham, T. A. Tumor cell heterogeneity. F1000Res 5, 238 (2016).

    Article  Google Scholar 

  3. McGranahan, N. & Swanton, C. Clonal heterogeneity and tumor evolution: past. Cell 168, 613–628 (2017).

    Article  PubMed  CAS  Google Scholar 

  4. Burrell, R. A. & Swanton, C. Re-evaluating clonal dominance in cancer evolution. Trends Cancer 2, 263–276 (2016).

    Article  PubMed  Google Scholar 

  5. Hartl, D. L. & Clark, A. G. Principles of Population Genetics. (Sinauer Associates, Inc.: Sunderland, MA, USA, 1997).

    Google Scholar 

  6. Marjoram, P. & Tavaré, S. Modern computational approaches for analyzing molecular-genetic-variation data. Nat. Rev. Genet. 7, 759–770 (2006).

    Article  PubMed  CAS  Google Scholar 

  7. Fu, Y. X. & Li, W. H. Estimating the age of the common ancestor of a sample of DNA sequences. Mol. Biol. Evol. 14, 195–199 (1997).

    Article  PubMed  CAS  Google Scholar 

  8. Tavaré, S., Balding, D. J., Griffiths, R. C. & Donnelly, P. Inferring coalescence times from DNA sequence data. Genetics 145, 505–518 (1997).

    PubMed  PubMed Central  Google Scholar 

  9. Tsao, J. L. et al. Colorectal adenoma and cancer divergence. Evidence of multilineage progression. Am. J. Pathol. 154, 1815–1824 (1999).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  10. Tsao, J. L. et al. Genetic reconstruction of individual colorectal tumor histories. Proc. Natl Acad. Sci. USA 97, 1236–1241 (2000).

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  11. Williams, M. J., Werner, B., Barnes, C. P., Graham, T. A. & Sottoriva, A. Identification of neutral tumor evolution across cancer types. Nat. Genet. 48, 238–244 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  12. Sun, R. et al. Between-region genetic divergence reflects the mode and tempo of tumor evolution. Nat. Genet. 49, 1015–1024 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  13. Durrett, R. Branching Process Models of Cancer. (Springer: New York, 2015).

    Book  Google Scholar 

  14. Bozic, I. et al. Accumulation of driver and passenger mutations during tumor progression. Proc. Natl Acad. Sci. USA 107, 18545–18550 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  15. Cheek, D. & Antal, T. Mutation frequencies in a birth–death branching process. Preprint at https://arxiv.org/abs/1710.09783 (2017).

  16. Kessler, D. A. & Levine, H. Scaling solution in the large population limit of the general asymmetric stochastic Luria–Delbrück evolution process. J. Stat. Phys. 158, 783–805 (2015).

    Article  PubMed  Google Scholar 

  17. Durrett, R. Population genetics of neutral mutations in exponentially growing cancer cell populations. Ann. Appl. Probab. 23, 230–250 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  18. Nik-Zainal, S. et al. The life history of 21 breast cancers. Cell 149, 994–1007 (2012).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  19. Levy, S. F. et al. Quantitative evolutionary dynamics using high-resolution lineage tracking. Nature 519, 181–186 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  20. Gillespie, J. H. Genetic drift in an infinite population. The pseudo-hitchhiking model. Genetics 155, 909–919 (2000).

    PubMed  PubMed Central  CAS  Google Scholar 

  21. Wu, C.-I., Wang, H.-Y., Ling, S. & Lu, X. The ecology and evolution of cancer: the ultra-microevolutionary process. Annu. Rev. Genet. 50, 347–369 (2016).

    Article  PubMed  CAS  Google Scholar 

  22. Toni, T. & Stumpf, M. P. H. Simulation-based model selection for dynamical systems in systems and population biology. Bioinformatics 26, 104–110 (2010).

    Article  PubMed  CAS  Google Scholar 

  23. Honda, O. et al. Doubling time of lung cancer determined using three-dimensional volumetric software: comparison of squamous cell carcinoma and adenocarcinoma. Lung Cancer 66, 211–217 (2009).

    Article  PubMed  Google Scholar 

  24. Peer, P. G., van Dijck, J. A., Hendriks, J. H., Holland, R. & Verbeek, A. L. Age-dependent growth rate of primary breast cancer. Cancer 71, 3547–3551 (1993).

    Article  PubMed  CAS  Google Scholar 

  25. Tilanus-Linthorst, M. M. A. et al. BRCA1 mutation and young age predict fast breast cancer growth in the Dutch, United Kingdom and Canadian magnetic resonance imaging screening trials. Clin. Cancer Res. 13, 7357–7362 (2007).

    Article  PubMed  CAS  Google Scholar 

  26. Griffith, M. et al. Optimizing cancer genome sequencing and analysis. Cell Syst. 1, 210–223 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  27. Zhang, J. et al. Intratumor heterogeneity in localized lung adenocarcinomas delineated by multi-region sequencing. Science 346, 256–259 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  28. Alexandrov, L. B. et al. Signatures of mutational processes in human cancer. Nature 500, 415–421 (2013).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  29. The Cancer Genome Atlas Network. Comprehensive molecular characterization of human colon and rectal cancer. Nature 487, 330–337 (2012).

    Article  CAS  Google Scholar 

  30. Wang, K. et al. Whole-genome sequencing and comprehensive molecular profiling identify new driver mutations in gastric cancer. Nat. Genet. 46, 573–582 (2014).

    Article  PubMed  CAS  Google Scholar 

  31. Jamal-Hanjani, M. et al. Tracking the evolution of non-small-cell lung cancer. N. Engl. J. Med. 376, 2109–2121 (2017).

    Article  PubMed  CAS  Google Scholar 

  32. Robinson, D. R. et al. Integrative clinical genomics of metastatic cancer. Nature 548, 297–303 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  33. Lässig, M., Mustonen, V. & Walczak, A. M. Predicting evolution. Nat. Ecol. Evol. 1, 77 (2017).

    Article  PubMed  Google Scholar 

  34. Shah, S. P. et al. The clonal and mutational evolution spectrum of primary triple-negative breast cancers. Nature 486, 395–399 (2012).

    Article  PubMed  CAS  Google Scholar 

  35. Merkle, F. T. et al. Human pluripotent stem cells recurrently acquire and expand dominant-negative P53 mutations. Nature 545, 229–233 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  36. Rutledge, S. D. et al. Selective advantage of trisomic human cells cultured in nonstandard conditions. Sci. Rep. 6, 22828 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  37. Vermeulen, L. et al. Defining stem cell dynamics in models of intestinal tumor initiation. Science 342, 995–998 (2013).

    Article  PubMed  CAS  Google Scholar 

  38. Klein, A. M., Brash, D. E., Jones, P. H. & Simons, B. D. Stochastic fate of p53-mutant epidermal progenitor cells is tilted toward proliferation by UV B during pre-neoplasia. Proc. Natl Acad. Sci. USA 107, 270–275 (2010).

    Article  PubMed  CAS  Google Scholar 

  39. Lenski, R. E. & Travisano, M. Dynamics of adaptation and diversification: a 10,000-generation experiment with bacterial populations. Proc. Natl Acad. Sci. USA 91, 6808–6814 (1994).

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  40. Seshadri, R., Kutlaca, R. J., Trainor, K., Matthews, C. & Morley, A. A. Mutation rate of normal and malignant human lymphocytes. Cancer Res. 47, 407–409 (1987).

    PubMed  CAS  Google Scholar 

  41. Lugli, N. et al. Enhanced rate of acquisition of point mutations in mouse intestinal adenomas compared to normal tissue. Cell Rep 19, 2185–2192 (2017).

    Article  PubMed  CAS  Google Scholar 

  42. Sottoriva, A. et al. A Big Bang model of human colorectal tumor growth. Nat. Genet. 47, 209–216 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  43. Castro-Giner, F., Ratcliffe, P. & Tomlinson, I. The mini-driver model of polygenic cancer evolution. Nat. Rev. Cancer 15, 680–685 (2015).

    Article  PubMed  CAS  Google Scholar 

  44. Martincorena, I. et al. Universal patterns of selection in cancer and somatic tissues. Cell 171, 1029–1041 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  45. Enriquez-Navas, P. M. et al. Exploiting evolutionary principles to prolong tumor control in preclinical models of breast cancer. Science Transl. Med. 8, 327ra24 (2016).

    Article  CAS  Google Scholar 

  46. Zhang, J., Cunningham, J. J., Brown, J. S. & Gatenby, R. A. Integrating evolutionary dynamics into treatment of metastatic castrate-resistant prostate cancer. Nat. Commun. 8, 1816 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  47. Fusco, D., Gralka, M., Kayser, J., Anderson, A. & Hallatschek, O. Excess of mutational jackpot events in expanding populations revealed by spatial Luria–Delbrück experiments. Nat. Commun. 7, 12760 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  48. Waclaw, B. et al. A spatial model predicts that dispersal and cell turnover limit intratumor heterogeneity. Nature 525, 261–264 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  49. Stead, L. F., Sutton, K. M., Taylor, G. R., Quirke, P. & Rabbitts, P. Accurately identifying low–allelic fraction variants in single samples with next-generation sequencing: applications in tumor subclone resolution. Hum. Mutat. 34, 1432–1438 (2013).

    Article  PubMed  CAS  Google Scholar 

  50. Roth, A. et al. PyClone: statistical inference of clonal population structure in cancer. Nat. Methods 11, 396–398 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  51. Gerstung, M. et al. Reliable detection of subclonal single-nucleotide variants in tumor cell populations. Nat. Commun. 3, 811 (2012).

    Article  PubMed  CAS  Google Scholar 

  52. Pritchard, J. K., Seielstad, M. T., Perez-Lezaun, A. & Feldman, M. W. Population growth of human Y chromosomes: a study of Y chromosome microsatellites. Mol. Biol. Evol. 16, 1791–1798 (1999).

    Article  PubMed  CAS  Google Scholar 

  53. Del Moral, P., Doucet, A. & Jasra, A. Sequential Monte Carlo samplers. J. R. Stat. Soc. Series B Stat. Methodol. 68, 411–436 (2006).

    Article  Google Scholar 

  54. Robert, C. P., Cornuet, J.-M., Marin, J.-M. & Pillai, N. S. Lack of confidence in approximate Bayesian computation model choice. Proc. Natl Acad. Sci. USA 108, 15112–15117 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  55. Barnes, C. P., Filippi, S., Stumpf, M. P. H. & Thorne, T. Considerate approaches to constructing summary statistics for ABC model selection. Stat. Comput. 22, 1181–1197 (2012).

    Article  Google Scholar 

  56. Cibulskis, K. et al. Sensitive detection of somatic point mutations in impure and heterogeneous cancer samples. Nat. Biotechnol. 31, 213–219 (2013).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  57. Favero, F. et al. Sequenza: allele-specific copy-number and mutation profiles from tumor-sequencing data. Ann. Oncol. 26, 64–70 (2015).

    Article  PubMed  CAS  Google Scholar 

  58. Rosenthal, R., McGranahan, N., Herrero, J., Taylor, B. S. & Swanton, C. DeconstructSigs: delineating mutational processes in single tumors distinguishes DNA repair deficiencies and patterns of carcinoma evolution. Genome Biol. 17, 31 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

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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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