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Nat Methods. 2018 Sep;15(9):707-714. doi: 10.1038/s41592-018-0108-x. Epub 2018 Aug 31.

Detecting repeated cancer evolution from multi-region tumor sequencing data.

Author information

1
Evolutionary Genomics and Modelling Lab, Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK. giulio.caravagna@icr.ac.uk.
2
School of Informatics, University of Edinburgh, Edinburgh, UK. giulio.caravagna@icr.ac.uk.
3
School of Informatics, University of Edinburgh, Edinburgh, UK.
4
Centre for Medical Informatics, Usher Institute, University of Edinburgh, Edinburgh, UK.
5
Department of Pathology, Stanford University, Stanford, CA, USA.
6
Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, UK.
7
Centre for Tumour Biology, Barts Cancer Institute, Queen Mary University of London, London, UK.
8
School of Informatics, University of Edinburgh, Edinburgh, UK. gsanguin@inf.ed.ac.uk.
9
Evolutionary Genomics and Modelling Lab, Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK. andrea.sottoriva@icr.ac.uk.

Abstract

Recurrent successions of genomic changes, both within and between patients, reflect repeated evolutionary processes that are valuable for the anticipation of cancer progression. Multi-region sequencing allows the temporal order of some genomic changes in a tumor to be inferred, but the robust identification of repeated evolution across patients remains a challenge. We developed a machine-learning method based on transfer learning that allowed us to overcome the stochastic effects of cancer evolution and noise in data and identified hidden evolutionary patterns in cancer cohorts. When applied to multi-region sequencing datasets from lung, breast, renal, and colorectal cancer (768 samples from 178 patients), our method detected repeated evolutionary trajectories in subgroups of patients, which were reproduced in single-sample cohorts (n = 2,935). Our method provides a means of classifying patients on the basis of how their tumor evolved, with implications for the anticipation of disease progression.

PMID:
30171232
PMCID:
PMC6380470
DOI:
10.1038/s41592-018-0108-x
[Indexed for MEDLINE]
Free PMC Article

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