Preprint: Model-based tumor subclonal reconstruction

Giulio Caravagna, Timon Heide, Marc Williams, Luis Zapata, Daniel Nichol, Ketevan Chkhaidze, William Cross, George D. Cresswell, Benjamin Werner, Ahmet Acar, Chris P. Barnes, Guido Sanguinetti, Trevor A. Graham and Andrea Sottoriva 2019. Preprint: Model-based tumor subclonal reconstruction. biorxiv.org. https://doi.org/10.1101/586560

TitlePreprint: Model-based tumor subclonal reconstruction
AuthorsGiulio Caravagna, Timon Heide, Marc Williams, Luis Zapata, Daniel Nichol, Ketevan Chkhaidze, William Cross, George D. Cresswell, Benjamin Werner, Ahmet Acar, Chris P. Barnes, Guido Sanguinetti, Trevor A. Graham and Andrea Sottoriva
Description

The vast majority of cancer next-generation sequencing data consist of bulk samples composed of mixtures of cancer and normal cells. To study tumor evolution, subclonal reconstruction approaches based on machine learning are used to separate subpopulation of cancer cells and reconstruct their ancestral relationships. However, current approaches are entirely data-driven and agnostic to evolutionary theory. We demonstrate that systematic errors occur in subclonal reconstruction if tumor evolution is not accounted for, and that those errors increase when multiple samples are taken from the same tumor. To address this issue, we present a novel approach for model-based subclonal reconstruction that combines data-driven machine learning with evolutionary theory. Using public, synthetic and newly generated data, we show the method is more robust and accurate than current techniques in both single-sample and multi-region sequencing data. With careful data curation and interpretation, we show how the method allows minimizing the confounding factors that affect non-evolutionary methods, leading to a more accurate recovery of the evolutionary history of human tumors.

Year2019
Output mediabioRxiv preprint
Publisherbiorxiv.org
Publication dates
Published26 Mar 2019
Digital Object Identifier (DOI)https://doi.org/10.1101/586560
Web address (URL)https://doi.org/10.1101/586560

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