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.
Bibliographical noteFunding Information:
We thank Pan-cancer Analysis of Whole Genomes (PCAWG) network, and in particular the PCAWG Evolution and Heterogeneity working group, for providing data, analysis and valuable input on this project. We would in particular like to highlight Peter Van Loo, Clemency Jolly, Stefan Dentro, David Wedge, Paul Boutros, Lydia Liu, and Moritz Gerstung who provided valuable feedback during the development of the TrackSig methodology. We acknowledge the contributions of the many clinical networks across ICGC and TCGA who provided samples and data to the PCAWG Consortium, and the contributions of the Technical Working Group and the Germline Working Group of the PCAWG Consortium for collation, realignment and harmonised variant calling of the cancer genomes used in this study. We thank the patients and their families for their participation in the individual ICGC and TCGA projects. We would like to acknowledge SciNet as part of Compute Canada for providing computational resources. This research was partially supported by an Natural Science and Engineering Research Council operating grant; an Associate Investigator award from the Ontario Institute of Cancer Research; and a subgrant from the Canadian Centre for Computational Genomics genomics technology platform funded by Genome Canada, all to QDM. It also received funding from the University of Toronto’s Medicine by Design initiative, which in part of the Canada First Research Excellence Fund (CFREF) and the Compute the Cure gift from the NVIDIA foundation. QDM is a Canada CIFAR AI chair at the Vector Institute.
© 2020, The Author(s).