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Measuring cancer evolution from the genome.

Trevor A GrahamAndrea Sottoriva
Published in: The Journal of pathology (2016)
The temporal dynamics of cancer evolution remain elusive, because it is impractical to longitudinally observe cancers unperturbed by treatment. Consequently, our knowledge of how cancers grow largely derives from inferences made from a single point in time - the endpoint in the cancer's evolution, when it is removed from the body and studied in the laboratory. Fortuitously however, the cancer genome, by virtue of ongoing mutations that uniquely mark clonal lineages within the tumour, provides a rich, yet surreptitious, record of cancer development. In this review, we describe how a cancer's genome can be analysed to reveal the temporal history of mutation and selection, and discuss why both selective and neutral evolution feature prominently in carcinogenesis. We argue that selection in cancer can only be properly studied once we have some understanding of what the absence of selection looks like. We review the data describing punctuated evolution in cancer, and reason that punctuated phenotype evolution is consistent with both gradual and punctuated genome evolution. We conclude that, to map and predict evolutionary trajectories during carcinogenesis, it is critical to better understand the relationship between genotype change and phenotype change. Copyright © 2016 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.
Keyphrases
  • papillary thyroid
  • squamous cell
  • genome wide
  • machine learning
  • healthcare
  • systematic review
  • gene expression
  • neural network
  • data analysis