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Resolving genetic heterogeneity in cancer

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

This article has been updated


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.

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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.


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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.

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The authors contributed equally to all aspects of the article.

Corresponding authors

Correspondence to Trevor Graham or Charles Swanton.

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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.

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This article is dedicated to the memory of Martin Gore.

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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.

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Turajlic, S., Sottoriva, A., Graham, T. et al. Resolving genetic heterogeneity in cancer. Nat Rev Genet 20, 404–416 (2019).

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