Combined burden and functional impact tests for cancer driver discovery using DriverPower.
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PCAWG Drivers and Functional Interpretation Working Group
Springer Science and Business Media LLC
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Shuai, S., PCAWG Drivers and Functional Interpretation Working Group, Gallinger, S., Stein, L., & PCAWG Consortium. (2020). Combined burden and functional impact tests for cancer driver discovery using DriverPower.. Nat Commun, 11 (1) https://doi.org/10.1038/s41467-019-13929-1
Funder: Province of Ontario
The discovery of driver mutations is one of the key motivations for cancer genome sequencing. 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 describe DriverPower, a software package that uses mutational burden and functional impact evidence to identify driver mutations in coding and non-coding sites within cancer whole genomes. Using a total of 1373 genomic features derived from public sources, DriverPower's background mutation model explains up to 93% of the regional variance in the mutation rate across multiple tumour types. By incorporating functional impact scores, we are able to further increase the accuracy of driver discovery. Testing across a collection of 2583 cancer genomes from the PCAWG project, DriverPower identifies 217 coding and 95 non-coding driver candidates. Comparing to six published methods used by the PCAWG Drivers and Functional Interpretation Working Group, DriverPower has the highest F1 score for both coding and non-coding driver discovery. This demonstrates that DriverPower is an effective framework for computational driver discovery.
Article, /631/67/395, /631/208/212/2301, /692/699/67/395, /45, /45/23, /141, article
External DOI: https://doi.org/10.1038/s41467-019-13929-1
This record's URL: https://www.repository.cam.ac.uk/handle/1810/317149
Attribution 4.0 International (CC BY 4.0)
Licence URL: https://creativecommons.org/licenses/by/4.0/