Reconstructing evolutionary trajectories of mutation signature activities in cancer using TrackSig

Yulia Rubanova, Ruian Shi, Caitlin F. Harrigan, Roujia Li, Jeff Wintersinger, Nil Sahin, Amit G. Deshwar and Quaid D. Morris ()
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Yulia Rubanova: University of Toronto
Ruian Shi: University of Toronto
Caitlin F. Harrigan: University of Toronto
Roujia Li: University of Toronto
Jeff Wintersinger: University of Toronto
Nil Sahin: Vector Institute
Amit G. Deshwar: University of Toronto
Quaid D. Morris: Vector Institute

Nature Communications, 2020, vol. 11, issue 1, 1-12

Abstract: Abstract The type and genomic context of cancer mutations depend on their causes. These causes have been characterized using signatures that represent mutation types that co-occur in the same tumours. However, it remains unclear how mutation processes change during cancer evolution due to the lack of reliable methods to reconstruct evolutionary trajectories of mutational signature activity. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole-genome sequencing data from 2658 cancers across 38 tumour types, we present TrackSig, a new method that reconstructs these trajectories using optimal, joint segmentation and deconvolution of mutation type and allele frequencies from a single tumour sample. In simulations, we find TrackSig has a 3–5% activity reconstruction error, and 12% false detection rate. It outperforms an aggressive baseline in situations with branching evolution, CNA gain, and neutral mutations. Applied to data from 2658 tumours and 38 cancer types, TrackSig permits pan-cancer insight into evolutionary changes in mutational processes.

Date: 2020
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DOI: 10.1038/s41467-020-14352-7

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