Subclonal reconstruction of tumors by using machine learning and population genetics

Giulio Caravagna, Timon Heide, Marc Williams, Luis Zapata, Daniel Nichol, Ketevan Chkhaidze, William Cross, George David Cresswell, Benjamin Werner, Ahmet Acar, Louis Chesler, Chris Barnes, Guido Sanguinetti, Trevor Graham and Andrea Sottoriva 2020. Subclonal reconstruction of tumors by using machine learning and population genetics. Nature Genetics. 52, pp. 898-907. https://doi.org/10.1038/s41588-020-0675-5

TitleSubclonal reconstruction of tumors by using machine learning and population genetics
TypeJournal article
AuthorsGiulio Caravagna, Timon Heide, Marc Williams, Luis Zapata, Daniel Nichol, Ketevan Chkhaidze, William Cross, George David Cresswell, Benjamin Werner, Ahmet Acar, Louis Chesler, Chris Barnes, Guido Sanguinetti, Trevor Graham and Andrea Sottoriva
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.

JournalNature Genetics
Journal citation52, pp. 898-907
ISSN1061-4036
1546-1718
Year2020
PublisherNature Publishing Group
Digital Object Identifier (DOI)https://doi.org/10.1038/s41588-020-0675-5
Web address (URL)http://dx.doi.org/10.1038/s41588-020-0675-5
Publication dates
Published02 Sep 2020

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