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Subclonal reconstruction of tumors by using machine learning and population genetics.

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Date
2020-09-02
ICR Author
Heide, Timon
Chkhaidze, Ketevan
Sottoriva, Andrea
Chesler, Louis
Author
Caravagna, G
Heide, T
Williams, MJ
Zapata, L
Nichol, D
Chkhaidze, K
Cross, W
Cresswell, GD
Werner, B
Acar, A
Chesler, L
Barnes, CP
Sanguinetti, G
Graham, TA
Sottoriva, A
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Type
Journal Article
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Abstract
Most cancer genomic data are generated from bulk samples composed of mixtures of cancer subpopulations, as well as normal cells. Subclonal reconstruction methods based on machine learning aim to separate those subpopulations in a sample and infer their evolutionary history. However, current approaches are entirely data driven and agnostic to evolutionary theory. We demonstrate that systematic errors occur in the analysis if evolution is not accounted for, and this is exacerbated with multi-sampling of the same tumor. We present a novel approach for model-based tumor subclonal reconstruction, called MOBSTER, which combines machine learning with theoretical population genetics. Using public whole-genome sequencing data from 2,606 samples from different cohorts, new data and synthetic validation, we show that this method is more robust and accurate than current techniques in single-sample, multiregion and longitudinal data. This approach minimizes the confounding factors of nonevolutionary methods, thus leading to more accurate recovery of the evolutionary history of human cancers.
URI
https://repository.icr.ac.uk/handle/internal/3966
DOI
https://doi.org/10.1038/s41588-020-0675-5
Collections
  • Cancer Therapeutics
  • Clinical Studies
  • Molecular Pathology
Subject
Humans
Neoplasms
Genetics, Population
Genomics
Clonal Evolution
Machine Learning
Whole Genome Sequencing
Research team
Evolutionary Genomics & Modelling
Paediatric Solid Tumour Biology and Therapeutics
Language
eng
Date accepted
2020-07-01
License start date
2020-09-02
Citation
Nature genetics, 2020, 52 (9), pp. 898 - 907

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