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
Open Access articles citing this article.
Clinical Epigenetics Open Access 11 October 2023
Characterization of tumour microenvironment reprogramming reveals invasion in epithelial ovarian carcinoma
Journal of Ovarian Research Open Access 10 October 2023
Genomic signatures of past and present chromosomal instability in Barrett’s esophagus and early esophageal adenocarcinoma
Nature Communications Open Access 04 October 2023
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 / 30 days
cancel any time
Subscribe to this journal
Receive 12 print issues and online access
$189.00 per year
only $15.75 per issue
Rent or buy this article
Prices vary by article type
Prices may be subject to local taxes which are calculated during checkout
McGranahan, N. & Swanton, C. Biological and therapeutic impact of intratumor heterogeneity in cancer evolution. Cancer Cell 27, 15–26 (2015).
Fisher, R. A. The Genetical Theory of Natural Selection (The Clarendon Press, 1930).
Lynch, M. et al. Genetic drift, selection and the evolution of the mutation rate. Nat. Rev. Genet. 17, 704–714 (2016).
Bozic, I. et al. Evolutionary dynamics of cancer in response to targeted combination therapy. eLife 2, e00747 (2013).
Bozic, I. et al. Accumulation of driver and passenger mutations during tumor progression. Proc. Natl Acad. Sci. USA 107, 18545–18550 (2010).
Durrett, R. Population genetics of neutral mutations in exponentially growing cancer cell populations. Ann. Appl. Probab. 23, 230–250 (2013).
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.
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.
Iwasa, Y., Nowak, M. A. & Michor, F. Evolution of resistance during clonal expansion. Genetics 172, 2557–2566 (2006).
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.
Altrock, P. M., Liu, L. L. & Michor, F. The mathematics of cancer: integrating quantitative models. Nat. Rev. Cancer 15, 730–745 (2015).
Martincorena, I. et al. Universal patterns of selection in cancer and somatic tissues. Cell 171, 1029–1041 (2017).
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.
Zapata, L. et al. Negative selection in tumor genome evolution acts on essential cellular functions and the immunopeptidome. Genome Biol. 19, 67 (2018).
Donnelly, P. & Tavare, S. The population genealogy of the infinitely-many neutral alleles model. J. Math. Biol. 25, 381–391 (1987).
Griffiths, R. C. The frequency spectrum of a mutation, and its age, in a general diffusion model. Theor. Popul. Biol. 64, 241–251 (2003).
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).
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).
Sansregret, L. et al. APC/C dysfunction limits excessive cancer chromosomal instability. Cancer Discov. 7, 218–233 (2017).
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).
Loeb, L. A. Mutator phenotype in cancer: origin and consequences. Semin. Cancer Biol. 20, 279–280 (2010).
Kimura, M. The Neutral Theory of Molecular Evolution (Cambridge Univ. Press, 1983). This classic textbook outlines the neutral theory of molecular evolution.
Hughes, A. L. Near neutrality: leading edge of the neutral theory of molecular evolution. Ann. NY Acad. Sci. 1133, 162–179 (2008).
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.
Lee-Six, H. et al. Population dynamics of normal human blood inferred from somatic mutations. Nature 561, 473–478 (2018).
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.
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.
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).
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).
Yates, L. R. et al. Subclonal diversification of primary breast cancer revealed by multiregion sequencing. Nat. Med. 21, 751–759 (2015).
Graham, T. A. & Sottoriva, A. Measuring cancer evolution from the genome. J. Pathol. 241, 183–191 (2017).
Gerlinger, M. et al. Cancer: evolution within a lifetime. Annu. Rev. Genet. 48, 215–236 (2014).
Markowetz, F. A saltationist theory of cancer evolution. Nat. Genet. 48, 1102–1103 (2016).
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.
Nik-Zainal, S. et al. The life history of 21 breast cancers. Cell 149, 994–1007 (2012).
Roth, A. et al. PyClone: statistical inference of clonal population structure in cancer. Nat. Methods 11, 396–398 (2014).
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).
Deshwar, A. G. et al. PhyloWGS: reconstructing subclonal composition and evolution from whole-genome sequencing of tumors. Genome Biol. 16, 35 (2015).
Smith, J. M. & Haigh, J. The hitch-hiking effect of a favourable gene. Genet. Res. 89, 391–403 (2007).
Zheng, G. X. et al. Haplotyping germline and cancer genomes with high-throughput linked-read sequencing. Nat. Biotechnol. 34, 303–311 (2016).
Kim, C. et al. Chemoresistance evolution in triple-negative breast cancer delineated by single-cell sequencing. Cell 173, 879–893 (2018).
Casasent, A. K. et al. Multiclonal invasion in breast tumors identified by topographic single cell sequencing. Cell 172, 205–217 (2018).
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).
Eirew, P. et al. Dynamics of genomic clones in breast cancer patient xenografts at single-cell resolution. Nature 518, 422–426 (2015).
Xu, X. et al. Single-cell exome sequencing reveals single-nucleotide mutation characteristics of a kidney tumor. Cell 148, 886–895 (2012).
Zhang, K. Stratifying tissue heterogeneity with scalable single-cell assays. Nat. Methods 14, 238–239 (2017).
McPherson, A. et al. Divergent modes of clonal spread and intraperitoneal mixing in high-grade serous ovarian cancer. Nat. Genet. 48, 758–767 (2016).
Leung, M. L. et al. Highly multiplexed targeted DNA sequencing from single nuclei. Nat. Protoc. 11, 214–235 (2016).
Roth, A. et al. Clonal genotype and population structure inference from single-cell tumor sequencing. Nat. Methods 13, 573–576 (2016).
Zahn, H. et al. Scalable whole-genome single-cell library preparation without preamplification. Nat. Methods 14, 167–173 (2017).
Worrall, J. T. et al. Non-random mis-segregation of human chromosomes. Cell Rep. 23, 3366–3380 (2018).
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.
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.
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).
Kessler, D. A. & Levine, H. Large population solution of the stochastic Luria-Delbruck evolution model. Proc. Natl Acad. Sci. USA 110, 11682–11687 (2013).
Bozic, I., Gerold, J. M. & Nowak, M. A. Quantifying clonal and subclonal passenger mutations in cancer evolution. PLOS Comput. Biol. 12, e1004731 (2016).
Sun, R. et al. Between-region genetic divergence reflects the mode and tempo of tumor evolution. Nat. Genet. 49, 1015–1024 (2017).
Levy, S. F. et al. Quantitative evolutionary dynamics using high-resolution lineage tracking. Nature 519, 181–186 (2015).
Abbosh, C. et al. Phylogenetic ctDNA analysis depicts early-stage lung cancer evolution. Nature 545, 446–451 (2017).
Murtaza, M. et al. Multifocal clonal evolution characterized using circulating tumour DNA in a case of metastatic breast cancer. Nat. Commun. 6, 8760 (2015).
Sottoriva, A. et al. A Big Bang model of human colorectal tumor growth. Nat. Genet. 47, 209–216 (2015).
Yang, Z. & Bielawski, J. P. Statistical methods for detecting molecular adaptation. Trends Ecol. Evol. 15, 496–503 (2000).
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.
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).
Heide, T. et al. Reply to ‘Neutral tumour evolution?’. Nat. Genet. 50, 1633–1637 (2018).
Tarabichi, M. et al. Neutral tumor evolution? Nat. Genetics 50, 1630–1633 (2018).
Rocha, E. P. et al. Comparisons of dN/dS are time dependent for closely related bacterial genomes. J. Theor. Biol. 239, 226–235 (2006).
Kryazhimskiy, S. & Plotkin, J. B. The population genetics of dN/dS. PLOS Genet. 4, e1000304 (2008).
Hartl, D. L. & Clark, A. G. Principles of Population Genetics 4th edn (Sinauer, 2006).
Lipinski, K. A. et al. Cancer evolution and the limits of predictability in precision cancer medicine. Trends Cancer 2, 49–63 (2016).
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.
Goldschmidt, R. The Material Basis of Evolution (Yale Univ. Press, 1982). This classic text postulates punctuated genetic evolution in speciation.
Burrell, R. A. et al. Replication stress links structural and numerical cancer chromosomal instability. Nature 494, 492–496 (2013).
Bakhoum, S. F. et al. The mitotic origin of chromosomal instability. Curr. Biol. 24, R148–R149 (2014).
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).
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).
Leibowitz, M. L., Zhang, C. Z. & Pellman, D. Chromothripsis: a new mechanism for rapid karyotype evolution. Annu. Rev. Genet. 49, 183–211 (2015).
Zack, T. I. et al. Pan-cancer patterns of somatic copy number alteration. Nat. Genet. 45, 1134–1140 (2013).
Davoli, T. et al. Cumulative haploinsufficiency and triplosensitivity drive aneuploidy patterns and shape the cancer genome. Cell 155, 948–962 (2013).
Solimini, N. L. et al. Recurrent hemizygous deletions in cancers may optimize proliferative potential. Science 337, 104–109 (2012).
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).
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.
Hochhaus, A. et al. Molecular and chromosomal mechanisms of resistance to imatinib (STI571) therapy. Leukemia 16, 2190–2196 (2002).
Targa, A. & Rancati, G. Cancer: a CINful evolution. Curr. Opin. Cell Biol. 52, 136–144 (2018).
Tang, Y. C. & Amon, A. Gene copy-number alterations: a cost-benefit analysis. Cell 152, 394–405 (2013).
Yona, A. H. et al. Chromosomal duplication is a transient evolutionary solution to stress. Proc. Natl Acad. Sci. USA 109, 21010–21015 (2012).
Sheltzer, J. M. et al. Single-chromosome gains commonly function as tumor suppressors. Cancer Cell 31, 240–255 (2017).
Rutledge, S. D. et al. Selective advantage of trisomic human cells cultured in non-standard conditions. Sci. Rep. 6, 22828 (2016).
Turajlic, S. & Swanton, C. Metastasis as an evolutionary process. Science 352, 169–175 (2016).
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).
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.
Gao, C. et al. Chromosome instability drives phenotypic switching to metastasis. Proc. Natl Acad. Sci. USA 113, 14793–14798 (2016).
Bakhoum, S. F. et al. Chromosomal instability drives metastasis through a cytosolic DNA response. Nature 553, 467–472 (2018).
Mackenzie, K. J. et al. cGAS surveillance of micronuclei links genome instability to innate immunity. Nature 548, 461–465 (2017).
Umbreit, N. T. & Pellman, D. Cancer biology: genome jail-break triggers lockdown. Nature 550, 340–341 (2017).
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).
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).
Walther, A., Houlston, R. & Tomlinson, I. Association between chromosomal instability and prognosis in colorectal cancer: a meta-analysis. Gut 57, 941–950 (2008).
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).
Birkbak, N. J. et al. Paradoxical relationship between chromosomal instability and survival outcome in cancer. Cancer Res. 71, 3447–3452 (2011).
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).
Swanton, C. et al. Chromosomal instability determines taxane response. Proc. Natl Acad. Sci. USA 106, 8671–8676 (2009).
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).
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).
McGranahan, N. et al. Allele-specific HLA loss and immune escape in lung cancer evolution. Cell 171, 1259–1271 (2017).
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.
Landau, D. A. et al. Evolution and impact of subclonal mutations in chronic lymphocytic leukemia. Cell 152, 714–726 (2013).
Nadeu, F. et al. Clinical impact of the subclonal architecture and mutational complexity in chronic lymphocytic leukemia. Leukemia 32, 645–653 (2018).
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).
Schwarz, R. F. et al. Spatial and temporal heterogeneity in high-grade serous ovarian cancer: a phylogenetic analysis. PLOS Med. 12, e1001789 (2015).
Andor, N. et al. Pan-cancer analysis of the extent and consequences of intratumor heterogeneity. Nat. Med. 22, 105–113 (2016).
Rye, I. H. et al. Intra-tumor heterogeneity defines treatment-resistant HER2 + breast tumors. Mol. Oncol. 12, 1838–1855 (2018).
Johnson, D. C. et al. Neutral tumor evolution in myeloma is associated with poor prognosis. Blood 130, 1639–1643 (2017).
Field, M. G. et al. Punctuated evolution of canonical genomic aberrations in uveal melanoma. Nat. Commun. 9, 116 (2018).
Baca, S. C. et al. Punctuated evolution of prostate cancer genomes. Cell 153, 666–677 (2013).
Gao, R. et al. Punctuated copy number evolution and clonal stasis in triple-negative breast cancer. Nat. Genet. 48, 1119–1130 (2016).
Reiter, J. G. et al. Minimal functional driver gene heterogeneity among untreated metastases. Science 361, 1033–1037 (2018).
Hellman, S. & Weichselbaum, R. R. Oligometastases. J. Clin. Oncol. 13, 8–10 (1995).
Weichselbaum, R. R. & Hellman, S. Oligometastases revisited. Nat. Rev. Clin. Oncol. 8, 378–382 (2011).
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.
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).
Stephens, P. J. et al. Massive genomic rearrangement acquired in a single catastrophic event during cancer development. Cell 144, 27–40 (2011).
Ortmann, C. A. et al. Effect of mutation order on myeloproliferative neoplasms. N. Engl. J. Med. 372, 601–612 (2015).
Caravagna, G. et al. Detecting repeated cancer evolution from multi-region tumor sequencing data. Nat. Methods 15, 707–714 (2018).
Rhim, A. D. et al. EMT and dissemination precede pancreatic tumor formation. Cell 148, 349–361 (2012).
Baker, A. M. et al. Evolutionary history of human colitis-associated colorectal cancer. Gut. https://doi.org/10.1136/gutjnl-2018-316191 (2018).
Hochhaus, A. et al. Long-term outcomes of imatinib treatment for chronic myeloid leukemia. N. Engl. J. Med. 376, 917–927 (2017).
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).
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).
Misale, S. et al. Emergence of KRAS mutations and acquired resistance to anti-EGFR therapy in colorectal cancer. Nature 486, 532–536 (2012).
Diaz, L. A. Jr. et al. The molecular evolution of acquired resistance to targeted EGFR blockade in colorectal cancers. Nature 486, 537–540 (2012).
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).
Pogrebniak, K. L. & Curtis, C. Harnessing tumor evolution to circumvent resistance. Trends Genet. 34, 639–651 (2018).
Ahn, I. E. et al. Clonal evolution leading to ibrutinib resistance in chronic lymphocytic leukemia. Blood 129, 1469–1479 (2017).
Bettegowda, C. et al. Detection of circulating tumor DNA in early- and late-stage human malignancies. Sci. Transl Med. 6, 224ra24 (2014).
Juric, D. et al. Convergent loss of PTEN leads to clinical resistance to a PI(3)Kalpha inhibitor. Nature 518, 240–244 (2015).
Shi, H. et al. Acquired resistance and clonal evolution in melanoma during BRAF inhibitor therapy. Cancer Discov. 4, 80–93 (2014).
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).
Siravegna, G. et al. Clonal evolution and resistance to EGFR blockade in the blood of colorectal cancer patients. Nat. Med. 21, 827 (2015).
Xue, Y. et al. An approach to suppress the evolution of resistance in BRAF(V600E)-mutant cancer. Nat. Med. 23, 929–937 (2017).
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).
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).
Wei, S. C., Duffy, C. R. & Allison, J. P. Fundamental mechanisms of immune checkpoint blockade therapy. Cancer Discov. 8, 1069–1086 (2018).
McGranahan, N. et al. Clonal neoantigens elicit T cell immunoreactivity and sensitivity to immune checkpoint blockade. Science 351, 1463–1469 (2016).
Miao, D. et al. Genomic correlates of response to immune checkpoint blockade in microsatellite-stable solid tumors. Nat. Genet. 50, 1271–1281 (2018).
Anagnostou, V. et al. Evolution of neoantigen landscape during immune checkpoint blockade in non-small cell lung cancer. Cancer Discov. 7, 264–276 (2017).
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.
Verdegaal, E. M. et al. Neoantigen landscape dynamics during human melanoma-T cell interactions. Nature 536, 91–95 (2016).
Zaretsky, J. M. et al. Mutations associated with acquired resistance to PD-1 blockade in melanoma. N. Engl. J. Med. 375, 819–829 (2016).
Sahin, U. et al. Personalized RNA mutanome vaccines mobilize poly-specific therapeutic immunity against cancer. Nature 547, 222–226 (2017).
Tran, E. et al. T-cell transfer therapy targeting mutant KRAS in cancer. N. Engl. J. Med. 375, 2255–2262 (2016).
Macaulay, I. C. et al. G&T-seq: parallel sequencing of single-cell genomes and transcriptomes. Nat. Methods 12, 519–522 (2015).
TracerX. TRAcking Cancer Evolution through therapy (Rx). TracerX http://tracerx.co.uk/ (2019).
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).
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).
Spina, V. et al. Circulating tumor DNA reveals genetics, clonal evolution, and residual disease in classical Hodgkin lymphoma. Blood 131, 2413–2425 (2018).
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).
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.
Nature Reviews Genetics thanks M. Nowak, J. Reiter and other anonymous reviewer(s) for their contribution to the peer review of this work.
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.
This article is dedicated to the memory of Martin Gore.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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.
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.
A complex rearrangement of the cancer genome that involves a number of chromosomes.
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.
About this article
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
This article is cited by
Immune inactivation by VISTA predicts clinical outcome and therapeutic benefit in muscle-invasive bladder cancer
BMC Cancer (2023)
Clinical Epigenetics (2023)
BMC Ecology and Evolution (2023)
Characterization of tumour microenvironment reprogramming reveals invasion in epithelial ovarian carcinoma
Journal of Ovarian Research (2023)
Lineage tracing of mutant granulosa cells reveals in vivo protective mechanisms that prevent granulosa cell tumorigenesis
Cell Death & Differentiation (2023)