Evolution of the cancer genome

Lucy R. Yates, Peter J. Campbell
2012 Nature reviews genetics  
The advent of massively parallel sequencing technologies has allowed the characterization of cancer genomes at an unprecedented resolution. Investigation of the mutational landscape of tumours is providing new insights into cancer genome evolution, laying bare the interplay of somatic mutation, adaptation of clones to their environment and natural selection. These studies have demonstrated the extent of the heterogeneity of cancer genomes, have allowed inferences to be made about the forces
more » ... act on nascent cancer clones as they evolve and have shown insight into the mutational processes that generate genetic variation. Here we review our emerging understanding of the dynamic evolution of the cancer genome and of the implications for basic cancer biology and the development of antitumour therapy. Cancer is a disease of the genome. The classic model of carcinogenesis describes multiple, successive clonal expansions driven by the accumulation of genomic changes or 'mutations' that are preferentially selected by the tumour environment 1,2,3 . This traditional picture of linear cancer genome evolution has become more nuanced over the past decade as the research scalpel allows ever-sharper prosection of the underlying biology (FIG. 1; BOX 1) . Recent advances in sequencing technologies have delivered, for the first time, the opportunity to scrutinize all expressed genes ('transcriptomes'), all exons ('exomes') and whole cancer genomes at base-pair resolution 4 . A number of different sequencing platforms now exist, including pico-titre plate pyrosequencing and ligation-based sequencing. From the viewpoint of understanding cancer genome evolution, the key aspect of this generation of sequencing technologies is that billions of independent sequencing reads are generated in parallel, with each read deriving from a single molecule of DNA. Thus, albeit with some sampling biases, the data represent a random sample of DNA molecules (and hence the genomes of individual cells) contained in the tumour sample. By contrast, the data derived from conventional genomic approaches, such as capillary sequencing or copy number arrays, are aggregate signals from many thousands of DNA molecules (BOX 2). Harnessing the attractive statistical properties of massively parallel data thus enables us to draw robust inferences about the mutational mix of a tumour sample, generating unprecedented insights into the fundamental genomic events that underlie the development of cancers and the order, rate and mechanisms by which they occur 5-7 . These approaches have been used to generate comprehensive catalogues of somatic mutations by comparing the genomic sequence of DNA taken from a patient's cancer cells to the sequence of their germline DNA 7,8 . In particular, these studies have given an
doi:10.1038/nrg3317 pmid:23044827 pmcid:PMC3666082 fatcat:ecynvhbvorb7pe6wqq52sliexi