Reconstructing evolutionary trajectories of mutation signature activities in cancer using TrackSig. Nat Commun 2020 Feb 05;11(1):731
Date
02/07/2020Pubmed ID
32024834Pubmed Central ID
PMC7002414DOI
10.1038/s41467-020-14352-7Scopus ID
2-s2.0-85079071600 (requires institutional sign-in at Scopus site) 28 CitationsAbstract
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.
Author List
Rubanova Y, Shi R, Harrigan CF, Li R, Wintersinger J, Sahin N, Deshwar AG, PCAWG Evolution and Heterogeneity Working Group, Morris QD, PCAWG ConsortiumAuthors
Akinyemi Ojesina MD, PhD Assistant Professor in the Obstetrics and Gynecology department at Medical College of WisconsinJanet Sue Rader MD Chair, Professor in the Obstetrics and Gynecology department at Medical College of Wisconsin
MESH terms used to index this publication - Major topics in bold
Computational BiologyComputer Simulation
Evolution, Molecular
Gene Frequency
Genome, Human
Humans
Mutation
Neoplasms
Polymorphism, Single Nucleotide
Whole Genome Sequencing