Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Resolving genetic heterogeneity in cancer

Nature Reviews Genetics volume 20, pages 404–416 (2019)Cite this article

Subjects

An Author Correction to this article was published on 28 October 2019

This article has been updated

Abstract

To a large extent, cancer conforms to evolutionary rules defined by the rates at which clones mutate, adapt and grow. Next-generation sequencing has provided a snapshot of the genetic landscape of most cancer types, and cancer genomics approaches are driving new insights into cancer evolutionary patterns in time and space. In contrast to species evolution, cancer is a particular case owing to the vast size of tumour cell populations, chromosomal instability and its potential for phenotypic plasticity. Nevertheless, an evolutionary framework is a powerful aid to understand cancer progression and therapy failure. Indeed, such a framework could be applied to predict individual tumour behaviour and support treatment strategies.

This is a preview of subscription content, access via your institution

Relevant articles

Open Access articles citing this article.

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

Learn more

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Modes of cancer evolution.
Fig. 2: Challenges in detecting selection.
Fig. 3: Clonal evolution and metastases.
Fig. 4: Clonal evolution of treatment resistance.

Change history

  • 28 October 2019

    An amendment to this paper has been published and can be accessed via a link at the top of the paper.

References

  1. McGranahan, N. & Swanton, C. Biological and therapeutic impact of intratumor heterogeneity in cancer evolution. Cancer Cell 27, 15–26 (2015).

    CAS  PubMed  Google Scholar 

  2. Fisher, R. A. The Genetical Theory of Natural Selection (The Clarendon Press, 1930).

  3. Lynch, M. et al. Genetic drift, selection and the evolution of the mutation rate. Nat. Rev. Genet. 17, 704–714 (2016).

    CAS  PubMed  Google Scholar 

  4. Bozic, I. et al. Evolutionary dynamics of cancer in response to targeted combination therapy. eLife 2, e00747 (2013).

    PubMed Central  PubMed  Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

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

    PubMed Central  PubMed  Google Scholar 

  7. 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). This study indicates that, in some cases, intratumour heterogeneity is explainable by neutral evolution rather than by selection.

    CAS  PubMed Central  PubMed  Google Scholar 

  8. Williams, M. J. et al. Quantification of subclonal selection in cancer from bulk sequencing data. Nat. Genet. 50, 895–903 (2018). This study introduces mathematical methods to extract quantitative information on the evolutionary dynamics of cancer subclones from routine sequencing data.

    CAS  PubMed Central  PubMed  Google Scholar 

  9. Iwasa, Y., Nowak, M. A. & Michor, F. Evolution of resistance during clonal expansion. Genetics 172, 2557–2566 (2006).

    PubMed Central  PubMed  Google Scholar 

  10. Tsao, J. L. et al. Genetic reconstruction of individual colorectal tumor histories. Proc. Natl Acad. Sci. USA 97, 1236–1241 (2000). This seminal paper shows how the temporal dynamics of tumour evolution could be inferred from genetic data collected at a single time point.

    CAS  PubMed  PubMed Central  Google Scholar 

  11. Altrock, P. M., Liu, L. L. & Michor, F. The mathematics of cancer: integrating quantitative models. Nat. Rev. Cancer 15, 730–745 (2015).

    CAS  PubMed  Google Scholar 

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

    CAS  PubMed Central  PubMed  Google Scholar 

  13. Marty, R., Thompson, W. K., Salem, R. M., Zanetti, M. & Carter, H. Evolutionary pressure against MHC class II binding cancer mutations. Cell 175, 416–428 (2018). This study demonstrates how immune predation is a selective force shaping the cancer genome.

    CAS  Google Scholar 

  14. Zapata, L. et al. Negative selection in tumor genome evolution acts on essential cellular functions and the immunopeptidome. Genome Biol. 19, 67 (2018).

    PubMed Central  PubMed  Google Scholar 

  15. Donnelly, P. & Tavare, S. The population genealogy of the infinitely-many neutral alleles model. J. Math. Biol. 25, 381–391 (1987).

    CAS  PubMed  Google Scholar 

  16. Griffiths, R. C. The frequency spectrum of a mutation, and its age, in a general diffusion model. Theor. Popul. Biol. 64, 241–251 (2003).

    CAS  PubMed  Google Scholar 

  17. McFarland, C. D., Mirny, L. A. & Korolev, K. S. Tug-of-war between driver and passenger mutations in cancer and other adaptive processes. Proc. Natl Acad. Sci. USA 111, 15138–15143 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  18. McFarland, C. D., Korolev, K. S., Kryukov, G. V., Sunyaev, S. R. & Mirny, L. A. Impact of deleterious passenger mutations on cancer progression. Proc. Natl Acad. Sci. USA 110, 2910–2915 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  19. Sansregret, L. et al. APC/C dysfunction limits excessive cancer chromosomal instability. Cancer Discov. 7, 218–233 (2017).

    CAS  PubMed Central  PubMed  Google Scholar 

  20. Datta, R. S., Gutteridge, A., Swanton, C., Maley, C. C. & Graham, T. A. Modelling the evolution of genetic instability during tumour progression. Evol. Appl. 6, 20–33 (2013).

    CAS  Google Scholar 

  21. Loeb, L. A. Mutator phenotype in cancer: origin and consequences. Semin. Cancer Biol. 20, 279–280 (2010).

    PubMed Central  PubMed  Google Scholar 

  22. Kimura, M. The Neutral Theory of Molecular Evolution (Cambridge Univ. Press, 1983). This classic textbook outlines the neutral theory of molecular evolution.

  23. Hughes, A. L. Near neutrality: leading edge of the neutral theory of molecular evolution. Ann. NY Acad. Sci. 1133, 162–179 (2008).

    PubMed  Google Scholar 

  24. Martincorena, I. et al. Tumor evolution. High burden and pervasive positive selection of somatic mutations in normal human skin. Science 348, 880–886 (2015). This study demonstrates the use of dN/dS tools to identify positive selection from sequencing data in human tissues.

    CAS  PubMed Central  PubMed  Google Scholar 

  25. Lee-Six, H. et al. Population dynamics of normal human blood inferred from somatic mutations. Nature 561, 473–478 (2018).

    CAS  PubMed Central  PubMed  Google Scholar 

  26. Turajlic, S. et al. Deterministic evolutionary trajectories influence primary tumor growth: TRACERx renal. Cell 173, 595–610 (2018). The is the first prospective study to show how distinct patterns of clonal evolution determine the clinical phenotype, reconciling the variable behaviour of renal cancer.

    CAS  PubMed Central  PubMed  Google Scholar 

  27. Jamal-Hanjani, M. et al. Tracking the evolution of non-small-cell lung cancer. N. Engl. J. Med. 376, 2109–2121 (2017). This is the first prospective study to show how chromosomal instability drives relapse of lung cancer following surgical resection with curative intent.

    CAS  PubMed  Google Scholar 

  28. Okosun, J. et al. Integrated genomic analysis identifies recurrent mutations and evolution patterns driving the initiation and progression of follicular lymphoma. Nat. Genet. 46, 176–181 (2014).

    CAS  PubMed  Google Scholar 

  29. Melchor, L. et al. Single-cell genetic analysis reveals the composition of initiating clones and phylogenetic patterns of branching and parallel evolution in myeloma. Leukemia 28, 1705–1715 (2014).

    CAS  PubMed  Google Scholar 

  30. Yates, L. R. et al. Subclonal diversification of primary breast cancer revealed by multiregion sequencing. Nat. Med. 21, 751–759 (2015).

    CAS  PubMed Central  PubMed  Google Scholar 

  31. Graham, T. A. & Sottoriva, A. Measuring cancer evolution from the genome. J. Pathol. 241, 183–191 (2017).

    PubMed  Google Scholar 

  32. Gerlinger, M. et al. Cancer: evolution within a lifetime. Annu. Rev. Genet. 48, 215–236 (2014).

    CAS  PubMed  Google Scholar 

  33. Markowetz, F. A saltationist theory of cancer evolution. Nat. Genet. 48, 1102–1103 (2016).

    CAS  PubMed  Google Scholar 

  34. Eldredge, N. & Gould, S. J. On punctuated equilibria. Science 276, 338–341 (1997). This study presents a discussion of an evolutionary theory that is proposed as an alternative to phyletic gradualism.

    CAS  PubMed  Google Scholar 

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

    CAS  PubMed Central  PubMed  Google Scholar 

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

    CAS  PubMed Central  PubMed  Google Scholar 

  37. Miller, C. A. et al. SciClone: inferring clonal architecture and tracking the spatial and temporal patterns of tumor evolution. PLOS Comput. Biol. 10, e1003665 (2014).

    PubMed Central  PubMed  Google Scholar 

  38. Deshwar, A. G. et al. PhyloWGS: reconstructing subclonal composition and evolution from whole-genome sequencing of tumors. Genome Biol. 16, 35 (2015).

    PubMed Central  PubMed  Google Scholar 

  39. Smith, J. M. & Haigh, J. The hitch-hiking effect of a favourable gene. Genet. Res. 89, 391–403 (2007).

    PubMed  Google Scholar 

  40. Zheng, G. X. et al. Haplotyping germline and cancer genomes with high-throughput linked-read sequencing. Nat. Biotechnol. 34, 303–311 (2016).

    CAS  PubMed Central  PubMed  Google Scholar 

  41. Kim, C. et al. Chemoresistance evolution in triple-negative breast cancer delineated by single-cell sequencing. Cell 173, 879–893 (2018).

    CAS  PubMed Central  PubMed  Google Scholar 

  42. Casasent, A. K. et al. Multiclonal invasion in breast tumors identified by topographic single cell sequencing. Cell 172, 205–217 (2018).

    CAS  PubMed Central  PubMed  Google Scholar 

  43. Gao, J. et al. Loss of IFN-gamma pathway genes in tumor cells as a mechanism of resistance to anti-CTLA-4 therapy. Cell 167, 397–404 (2016).

    CAS  PubMed Central  PubMed  Google Scholar 

  44. Eirew, P. et al. Dynamics of genomic clones in breast cancer patient xenografts at single-cell resolution. Nature 518, 422–426 (2015).

    CAS  PubMed  Google Scholar 

  45. Xu, X. et al. Single-cell exome sequencing reveals single-nucleotide mutation characteristics of a kidney tumor. Cell 148, 886–895 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  46. Zhang, K. Stratifying tissue heterogeneity with scalable single-cell assays. Nat. Methods 14, 238–239 (2017).

    CAS  PubMed  Google Scholar 

  47. McPherson, A. et al. Divergent modes of clonal spread and intraperitoneal mixing in high-grade serous ovarian cancer. Nat. Genet. 48, 758–767 (2016).

    CAS  PubMed  Google Scholar 

  48. Leung, M. L. et al. Highly multiplexed targeted DNA sequencing from single nuclei. Nat. Protoc. 11, 214–235 (2016).

    CAS  PubMed Central  PubMed  Google Scholar 

  49. Roth, A. et al. Clonal genotype and population structure inference from single-cell tumor sequencing. Nat. Methods 13, 573–576 (2016).

    CAS  PubMed  Google Scholar 

  50. Zahn, H. et al. Scalable whole-genome single-cell library preparation without preamplification. Nat. Methods 14, 167–173 (2017).

    CAS  PubMed  Google Scholar 

  51. Worrall, J. T. et al. Non-random mis-segregation of human chromosomes. Cell Rep. 23, 3366–3380 (2018).

    CAS  PubMed Central  PubMed  Google Scholar 

  52. Laks, E. et al. Resource: scalable whole genome sequencing of 40,000 single cells identifies stochastic aneuploidies, genome replication states and clonal repertoires. Preprint at bioRxiv https://www.biorxiv.org/content/10.1101/411058v2 (2018). This is the first report of single-cell DNA sequencing at scale.

  53. Luria, S. E. & Delbruck, M. Mutations of bacteria from virus sensitivity to virus resistance. Genetics 28, 491–511 (1943). This classic paper provides evidence of pre-existing resistance in bacterial populations and develops the mathematical theory of neutral evolution in growing populations.

    CAS  PubMed Central  PubMed  Google Scholar 

  54. Maruvka, Y. E., Kessler, D. A. & Shnerb, N. M. The birth-death-mutation process: a new paradigm for fat tailed distributions. PLOS ONE 6, e26480 (2011).

    CAS  PubMed Central  PubMed  Google Scholar 

  55. Kessler, D. A. & Levine, H. Large population solution of the stochastic Luria-Delbruck evolution model. Proc. Natl Acad. Sci. USA 110, 11682–11687 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  56. Bozic, I., Gerold, J. M. & Nowak, M. A. Quantifying clonal and subclonal passenger mutations in cancer evolution. PLOS Comput. Biol. 12, e1004731 (2016).

    PubMed Central  PubMed  Google Scholar 

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

    CAS  PubMed Central  PubMed  Google Scholar 

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

    CAS  PubMed Central  PubMed  Google Scholar 

  59. Abbosh, C. et al. Phylogenetic ctDNA analysis depicts early-stage lung cancer evolution. Nature 545, 446–451 (2017).

    CAS  PubMed Central  PubMed  Google Scholar 

  60. Murtaza, M. et al. Multifocal clonal evolution characterized using circulating tumour DNA in a case of metastatic breast cancer. Nat. Commun. 6, 8760 (2015).

    PubMed  Google Scholar 

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

    CAS  PubMed Central  PubMed  Google Scholar 

  62. Yang, Z. & Bielawski, J. P. Statistical methods for detecting molecular adaptation. Trends Ecol. Evol. 15, 496–503 (2000).

    CAS  PubMed  PubMed Central  Google Scholar 

  63. Lawrence, M. S. et al. Mutational heterogeneity in cancer and the search for new cancer-associated genes. Nature 499, 214–218 (2013). This large-scale study uses pan-cancer exome sequencing data and mutation recurrence methods to find cancer driver genes.

    CAS  PubMed Central  PubMed  Google Scholar 

  64. 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).

    CAS  PubMed  Google Scholar 

  65. Heide, T. et al. Reply to ‘Neutral tumour evolution?’. Nat. Genet. 50, 1633–1637 (2018).

    CAS  PubMed  Google Scholar 

  66. Tarabichi, M. et al. Neutral tumor evolution? Nat. Genetics 50, 1630–1633 (2018).

    CAS  PubMed Central  PubMed  Google Scholar 

  67. Rocha, E. P. et al. Comparisons of dN/dS are time dependent for closely related bacterial genomes. J. Theor. Biol. 239, 226–235 (2006).

    CAS  PubMed  Google Scholar 

  68. Kryazhimskiy, S. & Plotkin, J. B. The population genetics of dN/dS. PLOS Genet. 4, e1000304 (2008).

    PubMed Central  PubMed  Google Scholar 

  69. Hartl, D. L. & Clark, A. G. Principles of Population Genetics 4th edn (Sinauer, 2006).

  70. Lipinski, K. A. et al. Cancer evolution and the limits of predictability in precision cancer medicine. Trends Cancer 2, 49–63 (2016).

    PubMed Central  PubMed  Google Scholar 

  71. Beroukhim, R. et al. The landscape of somatic copy-number alteration across human cancers. Nature 463, 899–905 (2010). This is the first large-scale pan-cancer report of somatic CNAs across cancers.

    CAS  PubMed Central  PubMed  Google Scholar 

  72. Goldschmidt, R. The Material Basis of Evolution (Yale Univ. Press, 1982). This classic text postulates punctuated genetic evolution in speciation.

  73. Burrell, R. A. et al. Replication stress links structural and numerical cancer chromosomal instability. Nature 494, 492–496 (2013).

    CAS  PubMed Central  PubMed  Google Scholar 

  74. Bakhoum, S. F. et al. The mitotic origin of chromosomal instability. Curr. Biol. 24, R148–R149 (2014).

    CAS  PubMed Central  PubMed  Google Scholar 

  75. Heng, H. H. et al. Chromosomal instability (CIN): what it is and why it is crucial to cancer evolution. Cancer Metastasis Rev. 32, 325–340 (2013).

    PubMed  Google Scholar 

  76. Heng, H. H., Regan, S. M., Liu, G. & Ye, C. J. Why it is crucial to analyze non clonal chromosome aberrations or NCCAs? Mol. Cytogenet. 9, 15 (2016).

    PubMed Central  PubMed  Google Scholar 

  77. Leibowitz, M. L., Zhang, C. Z. & Pellman, D. Chromothripsis: a new mechanism for rapid karyotype evolution. Annu. Rev. Genet. 49, 183–211 (2015).

    CAS  PubMed  Google Scholar 

  78. Zack, T. I. et al. Pan-cancer patterns of somatic copy number alteration. Nat. Genet. 45, 1134–1140 (2013).

    CAS  PubMed Central  PubMed  Google Scholar 

  79. Davoli, T. et al. Cumulative haploinsufficiency and triplosensitivity drive aneuploidy patterns and shape the cancer genome. Cell 155, 948–962 (2013).

    CAS  PubMed Central  PubMed  Google Scholar 

  80. Solimini, N. L. et al. Recurrent hemizygous deletions in cancers may optimize proliferative potential. Science 337, 104–109 (2012).

    CAS  PubMed Central  PubMed  Google Scholar 

  81. Foijer, F. et al. Deletion of the MAD2L1 spindle assembly checkpoint gene is tolerated in mouse models of acute T cell lymphoma and hepatocellular carcinoma. eLife 6, e20873 (2017).

    PubMed Central  PubMed  Google Scholar 

  82. Sotillo, R., Schvartzman, J. M., Socci, N. D. & Benezra, R. Mad2-induced chromosome instability leads to lung tumour relapse after oncogene withdrawal. Nature 464, 436–440 (2010). This study presents a functional demonstration of the importance of CIN in driving cancer progression.

    CAS  PubMed Central  PubMed  Google Scholar 

  83. Hochhaus, A. et al. Molecular and chromosomal mechanisms of resistance to imatinib (STI571) therapy. Leukemia 16, 2190–2196 (2002).

    CAS  PubMed  Google Scholar 

  84. Targa, A. & Rancati, G. Cancer: a CINful evolution. Curr. Opin. Cell Biol. 52, 136–144 (2018).

    CAS  PubMed  Google Scholar 

  85. Tang, Y. C. & Amon, A. Gene copy-number alterations: a cost-benefit analysis. Cell 152, 394–405 (2013).

    CAS  PubMed Central  PubMed  Google Scholar 

  86. Yona, A. H. et al. Chromosomal duplication is a transient evolutionary solution to stress. Proc. Natl Acad. Sci. USA 109, 21010–21015 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  87. Sheltzer, J. M. et al. Single-chromosome gains commonly function as tumor suppressors. Cancer Cell 31, 240–255 (2017).

    CAS  PubMed Central  PubMed  Google Scholar 

  88. Rutledge, S. D. et al. Selective advantage of trisomic human cells cultured in non-standard conditions. Sci. Rep. 6, 22828 (2016).

    CAS  PubMed Central  PubMed  Google Scholar 

  89. Turajlic, S. & Swanton, C. Metastasis as an evolutionary process. Science 352, 169–175 (2016).

    CAS  PubMed  Google Scholar 

  90. Endesfelder, D. et al. Chromosomal instability selects gene copy-number variants encoding core regulators of proliferation in ER+breast cancer. Cancer Res. 74, 4853–4863 (2014).

    CAS  PubMed Central  PubMed  Google Scholar 

  91. Turajlic, S. et al. Tracking cancer evolution reveals constrained routes to metastases: TRACERx renal. Cell 173, 581–594 (2018). This is the first study to contrast metastasizing and nonmetastasizing clones on patient-specific bases; it shows selection of chromosomal risk events in metastasis.

    CAS  PubMed Central  PubMed  Google Scholar 

  92. Gao, C. et al. Chromosome instability drives phenotypic switching to metastasis. Proc. Natl Acad. Sci. USA 113, 14793–14798 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  93. Bakhoum, S. F. et al. Chromosomal instability drives metastasis through a cytosolic DNA response. Nature 553, 467–472 (2018).

    CAS  PubMed Central  PubMed  Google Scholar 

  94. Mackenzie, K. J. et al. cGAS surveillance of micronuclei links genome instability to innate immunity. Nature 548, 461–465 (2017).

    CAS  PubMed Central  PubMed  Google Scholar 

  95. Umbreit, N. T. & Pellman, D. Cancer biology: genome jail-break triggers lockdown. Nature 550, 340–341 (2017).

    CAS  PubMed  Google Scholar 

  96. Davoli, T., Uno, H., Wooten, E. C. & Elledge, S. J. Tumor aneuploidy correlates with markers of immune evasion and with reduced response to immunotherapy. Science 355, eaaf8399 (2017).

    PubMed Central  PubMed  Google Scholar 

  97. Carter, S. L., Eklund, A. C., Kohane, I. S., Harris, L. N. & Szallasi, Z. A signature of chromosomal instability inferred from gene expression profiles predicts clinical outcome in multiple human cancers. Nat. Genet. 38, 1043–1048 (2006).

    CAS  PubMed  Google Scholar 

  98. Walther, A., Houlston, R. & Tomlinson, I. Association between chromosomal instability and prognosis in colorectal cancer: a meta-analysis. Gut 57, 941–950 (2008).

    CAS  PubMed  Google Scholar 

  99. Roylance, R. et al. Relationship of extreme chromosomal instability with long-term survival in a retrospective analysis of primary breast cancer. Cancer Epidemiol. Biomarkers Prev. 20, 2183–2194 (2011).

    PubMed Central  PubMed  Google Scholar 

  100. Birkbak, N. J. et al. Paradoxical relationship between chromosomal instability and survival outcome in cancer. Cancer Res. 71, 3447–3452 (2011).

    CAS  PubMed Central  PubMed  Google Scholar 

  101. Jamal-Hanjani, M. et al. Extreme chromosomal instability forecasts improved outcome in ER-negative breast cancer: a prospective validation cohort study from the TACT trial. Ann. Oncol. 26, 1340–1346 (2015).

    CAS  PubMed  Google Scholar 

  102. Swanton, C. et al. Chromosomal instability determines taxane response. Proc. Natl Acad. Sci. USA 106, 8671–8676 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  103. Duesberg, P., Stindl, R. & Hehlmann, R. Explaining the high mutation rates of cancer cells to drug and multidrug resistance by chromosome reassortments that are catalyzed by aneuploidy. Proc. Natl Acad. Sci. USA 97, 14295–14300 (2000).

    CAS  PubMed  PubMed Central  Google Scholar 

  104. Roh, W. et al. Integrated molecular analysis of tumor biopsies on sequential CTLA-4 and PD-1 blockade reveals markers of response and resistance. Sci. Transl Med. 9, eaah3560 (2017).

    PubMed Central  PubMed  Google Scholar 

  105. McGranahan, N. et al. Allele-specific HLA loss and immune escape in lung cancer evolution. Cell 171, 1259–1271 (2017).

    CAS  PubMed Central  PubMed  Google Scholar 

  106. Maley, C. C. et al. Genetic clonal diversity predicts progression to esophageal adenocarcinoma. Nat. Genet. 38, 468–473 (2006). This early report describes an evolutionary measure — in this case clonal diversity — that predicts prognosis in a human neoplasia.

    CAS  PubMed  Google Scholar 

  107. Landau, D. A. et al. Evolution and impact of subclonal mutations in chronic lymphocytic leukemia. Cell 152, 714–726 (2013).

    CAS  PubMed Central  PubMed  Google Scholar 

  108. Nadeu, F. et al. Clinical impact of the subclonal architecture and mutational complexity in chronic lymphocytic leukemia. Leukemia 32, 645–653 (2018).

    CAS  PubMed  Google Scholar 

  109. Mroz, E. A. et al. High intratumor genetic heterogeneity is related to worse outcome in patients with head and neck squamous cell carcinoma. Cancer 119, 3034–3042 (2013).

    PubMed  Google Scholar 

  110. Schwarz, R. F. et al. Spatial and temporal heterogeneity in high-grade serous ovarian cancer: a phylogenetic analysis. PLOS Med. 12, e1001789 (2015).

    PubMed Central  PubMed  Google Scholar 

  111. Andor, N. et al. Pan-cancer analysis of the extent and consequences of intratumor heterogeneity. Nat. Med. 22, 105–113 (2016).

    CAS  PubMed  Google Scholar 

  112. Rye, I. H. et al. Intra-tumor heterogeneity defines treatment-resistant HER2 + breast tumors. Mol. Oncol. 12, 1838–1855 (2018).

    CAS  PubMed Central  PubMed  Google Scholar 

  113. Johnson, D. C. et al. Neutral tumor evolution in myeloma is associated with poor prognosis. Blood 130, 1639–1643 (2017).

    CAS  PubMed Central  PubMed  Google Scholar 

  114. Field, M. G. et al. Punctuated evolution of canonical genomic aberrations in uveal melanoma. Nat. Commun. 9, 116 (2018).

    PubMed Central  PubMed  Google Scholar 

  115. Baca, S. C. et al. Punctuated evolution of prostate cancer genomes. Cell 153, 666–677 (2013).

    CAS  PubMed Central  PubMed  Google Scholar 

  116. Gao, R. et al. Punctuated copy number evolution and clonal stasis in triple-negative breast cancer. Nat. Genet. 48, 1119–1130 (2016).

    CAS  PubMed Central  PubMed  Google Scholar 

  117. Reiter, J. G. et al. Minimal functional driver gene heterogeneity among untreated metastases. Science 361, 1033–1037 (2018).

    CAS  PubMed Central  PubMed  Google Scholar 

  118. Hellman, S. & Weichselbaum, R. R. Oligometastases. J. Clin. Oncol. 13, 8–10 (1995).

    CAS  PubMed  Google Scholar 

  119. Weichselbaum, R. R. & Hellman, S. Oligometastases revisited. Nat. Rev. Clin. Oncol. 8, 378–382 (2011).

    CAS  PubMed  Google Scholar 

  120. Notta, F. et al. A renewed model of pancreatic cancer evolution based on genomic rearrangement patterns. Nature 538, 378–382 (2016). This study challenges the gradual progression model of pancreatic cancer, showing that it progresses rapidly through punctuated evolution.

    CAS  PubMed Central  PubMed  Google Scholar 

  121. Makohon-Moore, A. P. et al. Limited heterogeneity of known driver gene mutations among the metastases of individual patients with pancreatic cancer. Nat. Genet. 49, 358–366 (2017).

    CAS  PubMed Central  PubMed  Google Scholar 

  122. Stephens, P. J. et al. Massive genomic rearrangement acquired in a single catastrophic event during cancer development. Cell 144, 27–40 (2011).

    CAS  PubMed Central  PubMed  Google Scholar 

  123. Ortmann, C. A. et al. Effect of mutation order on myeloproliferative neoplasms. N. Engl. J. Med. 372, 601–612 (2015).

    PubMed Central  PubMed  Google Scholar 

  124. Caravagna, G. et al. Detecting repeated cancer evolution from multi-region tumor sequencing data. Nat. Methods 15, 707–714 (2018).

    CAS  PubMed Central  PubMed  Google Scholar 

  125. Rhim, A. D. et al. EMT and dissemination precede pancreatic tumor formation. Cell 148, 349–361 (2012).

    CAS  PubMed Central  PubMed  Google Scholar 

  126. Baker, A. M. et al. Evolutionary history of human colitis-associated colorectal cancer. Gut. https://doi.org/10.1136/gutjnl-2018-316191 (2018).

    Article  PubMed  Google Scholar 

  127. Hochhaus, A. et al. Long-term outcomes of imatinib treatment for chronic myeloid leukemia. N. Engl. J. Med. 376, 917–927 (2017).

    CAS  PubMed Central  PubMed  Google Scholar 

  128. Offin, M. et al. Tumor mutation burden and efficacy of EGFR-tyrosine kinase inhibitors in patients with EGFR-mutant lung cancers. Clin. Cancer Res. 25, 1063–1069 (2018).

    PubMed  PubMed Central  Google Scholar 

  129. Hata, A. N. et al. Tumor cells can follow distinct evolutionary paths to become resistant to epidermal growth factor receptor inhibition. Nat. Med. 22, 262–269 (2016).

    CAS  PubMed Central  PubMed  Google Scholar 

  130. Misale, S. et al. Emergence of KRAS mutations and acquired resistance to anti-EGFR therapy in colorectal cancer. Nature 486, 532–536 (2012).

    CAS  PubMed Central  PubMed  Google Scholar 

  131. Diaz, L. A. Jr. et al. The molecular evolution of acquired resistance to targeted EGFR blockade in colorectal cancers. Nature 486, 537–540 (2012).

    CAS  PubMed Central  PubMed  Google Scholar 

  132. Bozic, I. & Nowak, M. A. Timing and heterogeneity of mutations associated with drug resistance in metastatic cancers. Proc. Natl Acad. Sci. USA 111, 15964–15968 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  133. Pogrebniak, K. L. & Curtis, C. Harnessing tumor evolution to circumvent resistance. Trends Genet. 34, 639–651 (2018).

    CAS  PubMed Central  PubMed  Google Scholar 

  134. Ahn, I. E. et al. Clonal evolution leading to ibrutinib resistance in chronic lymphocytic leukemia. Blood 129, 1469–1479 (2017).

    CAS  PubMed Central  PubMed  Google Scholar 

  135. Bettegowda, C. et al. Detection of circulating tumor DNA in early- and late-stage human malignancies. Sci. Transl Med. 6, 224ra24 (2014).

    PubMed Central  PubMed  Google Scholar 

  136. Juric, D. et al. Convergent loss of PTEN leads to clinical resistance to a PI(3)Kalpha inhibitor. Nature 518, 240–244 (2015).

    CAS  PubMed  Google Scholar 

  137. Shi, H. et al. Acquired resistance and clonal evolution in melanoma during BRAF inhibitor therapy. Cancer Discov. 4, 80–93 (2014).

    CAS  PubMed  Google Scholar 

  138. Khan, K. H. et al. Longitudinal liquid biopsy and mathematical modeling of clonal evolution forecast time to treatment failure in the PROSPECT-C phase II colorectal cancer clinical trial. Cancer Discov. 8, 1270–1285 (2018).

    CAS  PubMed Central  PubMed  Google Scholar 

  139. Siravegna, G. et al. Clonal evolution and resistance to EGFR blockade in the blood of colorectal cancer patients. Nat. Med. 21, 827 (2015).

    CAS  PubMed  Google Scholar 

  140. Xue, Y. et al. An approach to suppress the evolution of resistance in BRAF(V600E)-mutant cancer. Nat. Med. 23, 929–937 (2017).

    CAS  PubMed Central  PubMed  Google Scholar 

  141. Pearson, A. et al. High-level clonal FGFR amplification and response to FGFR inhibition in a translational clinical trial. Cancer Discov. 6, 838–851 (2016).

    CAS  PubMed Central  PubMed  Google Scholar 

  142. Topalian, S. L., Drake, C. G. & Pardoll, D. M. Immune checkpoint blockade: a common denominator approach to cancer therapy. Cancer Cell 27, 450–461 (2015).

    CAS  PubMed Central  PubMed  Google Scholar 

  143. Wei, S. C., Duffy, C. R. & Allison, J. P. Fundamental mechanisms of immune checkpoint blockade therapy. Cancer Discov. 8, 1069–1086 (2018).

    PubMed  Google Scholar 

  144. McGranahan, N. et al. Clonal neoantigens elicit T cell immunoreactivity and sensitivity to immune checkpoint blockade. Science 351, 1463–1469 (2016).

    CAS  PubMed Central  PubMed  Google Scholar 

  145. Miao, D. et al. Genomic correlates of response to immune checkpoint blockade in microsatellite-stable solid tumors. Nat. Genet. 50, 1271–1281 (2018).

    CAS  PubMed Central  PubMed  Google Scholar 

  146. Anagnostou, V. et al. Evolution of neoantigen landscape during immune checkpoint blockade in non-small cell lung cancer. Cancer Discov. 7, 264–276 (2017).

    CAS  PubMed  Google Scholar 

  147. Zacharakis, N. et al. Immune recognition of somatic mutations leading to complete durable regression in metastatic breast cancer. Nat. Med. 24, 724–730 (2018). This paper shows that cancer mutation neo-antigens are the target of an antitumour immune response.

    CAS  PubMed Central  PubMed  Google Scholar 

  148. Verdegaal, E. M. et al. Neoantigen landscape dynamics during human melanoma-T cell interactions. Nature 536, 91–95 (2016).

    CAS  PubMed  Google Scholar 

  149. Zaretsky, J. M. et al. Mutations associated with acquired resistance to PD-1 blockade in melanoma. N. Engl. J. Med. 375, 819–829 (2016).

    CAS  PubMed Central  PubMed  Google Scholar 

  150. Sahin, U. et al. Personalized RNA mutanome vaccines mobilize poly-specific therapeutic immunity against cancer. Nature 547, 222–226 (2017).

    CAS  PubMed  Google Scholar 

  151. Tran, E. et al. T-cell transfer therapy targeting mutant KRAS in cancer. N. Engl. J. Med. 375, 2255–2262 (2016).

    CAS  PubMed Central  PubMed  Google Scholar 

  152. Macaulay, I. C. et al. G&T-seq: parallel sequencing of single-cell genomes and transcriptomes. Nat. Methods 12, 519–522 (2015).

    CAS  PubMed  Google Scholar 

  153. TracerX. TRAcking Cancer Evolution through therapy (Rx). TracerX http://tracerx.co.uk/ (2019).

  154. Cancer Research UK. A study looking at blood and tissue samples to learn more about advanced cancer (PEACE). CRUK https://www.cancerresearchuk.org/about-cancer/find-a-clinical-trial/a-study-looking-at-blood-and-tissue-samples-to-learn-more-about-advanced-cancer-peace (updated 24 Sep 2018).

  155. Gray, E. S. et al. Circulating tumor DNA to monitor treatment response and detect acquired resistance in patients with metastatic melanoma. Oncotarget 6, 42008–42018 (2015).

    PubMed Central  PubMed  Google Scholar 

  156. Spina, V. et al. Circulating tumor DNA reveals genetics, clonal evolution, and residual disease in classical Hodgkin lymphoma. Blood 131, 2413–2425 (2018).

    CAS  PubMed  Google Scholar 

  157. O’Leary, B. et al. Early circulating tumor DNA dynamics and clonal selection with palbociclib and fulvestrant for breast cancer. Nat. Commun. 9, 896 (2018).

    PubMed Central  PubMed  Google Scholar 

Download references

Acknowledgements

S.T. is funded by Cancer Research UK (C50947/A18176), the Francis Crick Institute (FC001169), the Medical Research Council (FC001169), the Wellcome Trust (FC001169), the National Institute for Health Research (NIHR) Biomedical Research Centre at the Royal Marsden Hospital and Institute of Cancer Research (A109), the Kidney and Melanoma Cancer Fund of The Royal Marsden Cancer Charity, the Rosetrees Trust (A2204) and Ventana Medical Systems (10467 and 10530). A.S. is supported by the Wellcome Trust (202778/B/16/Z) and by Cancer Research UK (A22909). T.G. is supported by the Wellcome Trust (202778/Z/16/Z) and Cancer Research UK (A19771). The authors acknowledge funding from the US National Institutes of Health (NCI U54 CA217376) to A.S. and T.G. This work was also supported by a Wellcome Trust award to the Centre for Evolution and Cancer (105104/Z/14/Z). C.S. is Royal Society Napier Research Professor and is supported by the Francis Crick Institute (FC001169), the Medical Research Council (FC001169), the Wellcome Trust (FC001169) and the UK Medical Research Council (grant reference MR/FC001169 /1). C.S. is funded by Cancer Research UK (TRACERx and CRUK Cancer Immunotherapy Catalyst Network), the CRUK Lung Cancer Centre of Excellence, Stand Up 2 Cancer (SU2C), the Rosetrees Trust, the Butterfield Trust, the Stoneygate Trust, NovoNordisk Foundation (ID 16584), the Breast Cancer Research Foundation (BCRF), the European Research Council Consolidator Grant (FP7-THESEUS-617844), European Commission ITN (FP7-PloidyNet-607722), Chromavision and the NIHR, the University College London Hospitals Biomedical Research Centre and the Cancer Research UK University College London Experimental Cancer Medicine Centre.

Reviewer information

Nature Reviews Genetics thanks M. Nowak, J. Reiter and other anonymous reviewer(s) for their contribution to the peer review of this work.

Author information

Author notes

  1. These authors contributed equally: Samra Turajlic, Andrea Sottoriva

Authors and Affiliations

  1. Cancer Evolution and Genome Instability Laboratory, Francis Crick Institute, London, UK

    Samra Turajlic & Charles Swanton

  2. Skin and Renal Units, The Royal Marsden NHS Foundation Trust, London, UK

    Samra Turajlic

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

    Andrea Sottoriva

  4. Tumour Biology, Evolution and Cancer Laboratory, Barts Cancer Institute, Queen Mary University of London, London, UK

    Trevor Graham

  5. Cancer Research UK Lung Cancer Centre of Excellence London, University College London Cancer Institute, London, UK

    Charles Swanton

  6. Department of Medical Oncology, University College London Hospitals, London, UK

    Charles Swanton

Authors
  1. Samra Turajlic
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Andrea Sottoriva
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Trevor Graham
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Charles Swanton
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

The authors contributed equally to all aspects of the article.

Corresponding authors

Correspondence to Trevor Graham or Charles Swanton.

Ethics declarations

Competing interests

C.S. reports grant support from Cancer Research UK, UCLH Biomedical Research Council, the Rosetrees Trust and AstraZeneca. C.S. has received personal fees from Boehringer Ingelheim, Novartis, Eli Lilly, Roche Ventana, GlaxoSmithKline, Pfizer, Genentech and Celgene. C.S. also reports stock options in GRAIL, APOGEN Biotechnologies and EPIC Bioscience and has stock options and is co-founder of Achilles Therapeutics. S.T. reports grant support from Cancer Research UK, RMH/ICR Biomedical Research Council and Ventana. S.T. also reports speaking fees from Ventana, outside the submitted work, and has a patent on indel burden and checkpoint inhibitor response filed and a patent on targeting of frameshift neo-antigens for personalized immunotherapy filed. A.S. and T.G. declare no competing interests.

Additional information

This article is dedicated to the memory of Martin Gore.

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Glossary

Subclones

In a tumour, subclones refer to populations of cells that harbour the same set of genomic alterations.

Clonal evolution

A process by which genetic and epigenetic alterations create diversity that acts as a substrate for natural selection.

Genetic drift

A stochastic process that changes subclone frequency.

Selection

A non-random process shaped by environmental and tumour properties that changes subclone frequency.

Chromosome instability

(CIN). A type of genomic instability that involves parts of or entire chromosomes.

Phylogenetic tree

A branching diagram showing the hierarchy of clones within the tumour.

Mutator phenotypes

Phenotypes that result in increases in mutation rates in cancer.

Neutral evolution

Clonal diversity not caused by selection.

Driver mutations

Mutations that increases clone fitness.

Clonal sweep

A reduction in diversity due to the fixation of a variant owing to strong positive selection.

Hopeful monster

An individual cell with a grossly altered genome compared with its ancestor, which may be adaptive. A hopeful monster is the result of punctuated change in the genome.

Punctuated equilibrium

Refers to rapid speciation events with long periods of intervening stasis.

Passenger mutations

Mutations that have no effect on clone fitness.

Variant allele frequency

(VAF).The relative frequency of a variant in a tumour sample, expressed as a percentage.

Chromoplexy

A complex rearrangement of the cancer genome that involves a number of chromosomes.

Chromothripsis

A complex rearrangement of the cancer genome that involves a single chromosome.

Patient-derived xenografts

Tumour models in which the tissue from a patient’s tumour is implanted in an immunodeficient mouse.

Immune checkpoint blockade

Refers to therapies that target immune checkpoints such as CTLA4 and PD1 that tumours can use to escape antitumour immune responses.

Rights and permissions

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Turajlic, S., Sottoriva, A., Graham, T. et al. Resolving genetic heterogeneity in cancer. Nat Rev Genet 20, 404–416 (2019). https://doi.org/10.1038/s41576-019-0114-6

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41576-019-0114-6

This article is cited by

Search

Advanced search

Quick links

Nature Briefing: Cancer

Sign up for the Nature Briefing: Cancer newsletter — what matters in cancer research, free to your inbox weekly.

Get what matters in cancer research, free to your inbox weekly. Sign up for Nature Briefing: Cancer