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The advent of next-generation sequencing technologies enabled the characterization of cancer genomes at unprecedented resolution (Milestone 7). Billions of sequencing reads—representing genomes from a random subset of individual cells contained in a tumour sample—were used to build detailed catalogues of somatic mutations by comparing patients’ cancer DNA to their germline DNA.

Insights into the genomic landscapes of some cancers promised to move cancer treatment towards genotype-guided approaches. However, most cancers, even highly drug-sensitive tumours with pronounced initial responses, develop resistance to targeted molecular therapies (Milestone 1). In 2012, Gerlinger et al. confirmed what had long been suspected: cancers are highly dynamic evolutionary entities presenting major challenges to personalized medicine. The cancer genome is characterized by extreme heterogeneity not only across tumour types, primary and secondary tumours or individuals with the same histological tumour type, but also within individual tumours.

The notion of cancer as an evolutionary process was introduced in 1976 by Peter Nowell, who hypothesized that natural selection acting on tumours in the form of clonal selection continuously drives evolutionary adaption. Nowell’s original evolutionary model reflected an essentially linear path with sequentially dominant clones underlying disease progression. The idea that tumour genetic heterogeneity fuels therapeutic resistance was subsequently taken up by James Goldie and Andrew Coldman, who posited that clonal heterogeneity of tumours is the predominant factor underlying evolutionary changes resulting from cancer treatment.

In the 2000s, several studies emerged characterizing the genetic basis for disease progression, metastasis, resistance and relapse in both haematological and solid cancers. These studies demonstrated the complexity of clonal evolution and supported a model in which clonal diversity underlies disease progression and resistance to therapy.

In 2011, Anderson et al. and Notta et al. tracked the evolutionary paths taken by different subclones during progression of acute lymphoblastic leukaemia. Both studies showed that the degree of genetic heterogeneity in the leukaemia-initiating cell subpopulation was similar to that in the population of leukaemia cells in the sample. Moreover, branching evolutionary trajectories did not fit the linear clonal succession model of cancer evolution, whereby cancers progress through single-cell clone bottlenecks.

Gerlinger et al. then provided a direct demonstration of multiple genetically related subclones within a solid tumour and their phylogenetic relationships. The team applied exome sequencing, chromosome aberration analysis and ploidy profiling to primary renal carcinoma and associated metastasis samples obtained from four patients before and after treatment. Strikingly, no two samples from the same patient were genetically identical, and phylogenetic analyses revealed a branching pattern rather than linear tumour evolution. This study drove the shift in thinking of tumours in Darwinian terms—from linear to tree-like cancer evolution. The evolutionary forces of mutation, genetic drift and selection act on billions of cancer cells and their microenvironment, thus giving rise to tumours’ emergent behaviours. New mutations and selection acting on mutations beneficial to the tumour drive the expansion of subclones; subsequently, cell division and continuing acquisition of mutations generate the genetic heterogeneity needed for evolutionary adaptation.

Genomic data have enabled inferences regarding the evolutionary forces that act on cancer clones as they evolve, yielding insights into the generation of genetic variation and the order, rate and mechanisms through which this evolution occurs. Since the work by Gerlinger et al., new approaches to target cancer evolution have emerged, and focus has shifted to dissecting how subclonal populations might interact (antagonizing or synergizing) to evolve and affect clinical outcomes. Interactions between clonal driver mutations (those within the phylogenetic tree’s ‘trunk’) and subclonal driver mutations (those within a phylogenetic branch) and competition among subclones define the clonal architecture of individual cancers. This dynamic is shaped by the pressures exerted by the environment, such as drug treatment.

Genetic heterogeneity fuels therapeutic resistance. A comprehensive understanding of the dynamics of cancer evolution and assessment of tumour heterogeneity will therefore be essential for prognostication, drug development and therapy.

Further reading

Nowell, P. C. The clonal evolution of tumor cell populations. Science 194, 23–28 (1976).

Goldie, J. H. & Coldman, A. J. The genetic origin of drug resistance in neoplasms: implications for systemic therapy. Cancer Res. 44, 3643–3653 (1984).

Shah, N. P. et al. Multiple BCR-ABL kinase domain mutations confer polyclonal resistance to the tyrosine kinase inhibitor imatinib (STI571) in chronic phase and blast crisis chronic myeloid leukemia. Cancer Cell 2, 117–125 (2002).

Maley, C. C. et al. Genetic clonal diversity predicts progression to esophageal adenocarcinoma. Nat. Genet. 38, 468–473 (2006).

Campbell, P. J. et al. Subclonal phylogenetic structures in cancer revealed by ultra-deep sequencing. Proc. Natl Acad. Sci. USA 105, 13081–13086 (2008).

Mullighan C. G. et al. Genomic analysis of the clonal origins of relapsed acute lymphoblastic leukemia. Science 322, 1377–1380 (2008).

Ding, L. et al. Genome remodelling in a basal-like breast cancer metastasis and xenograft. Nature 464, 999–1005 (2010).

Anderson, K. et al. Genetic variegation of clonal architecture and propagating cells in leukaemia. Nature 469, 356–361 (2011).

Navin, N. et al. Tumour evolution inferred by single-cell sequencing. Nature 472, 90–94 (2011).

Notta, F. et al. Evolution of human BCR–ABL1 lymphoblastic leukaemia-initiating cells. Nature 469, 362–367 (2011).