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Combined burden and functional impact tests for cancer driver discovery using DriverPower

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dc.contributor.author Shuai, Shimin
dc.contributor.author PCAWG Drivers and Functional Interpretation Working Group
dc.contributor.author Gallinger, Steven
dc.contributor.author Stein, Lincoln D.
dc.contributor.author PCAWG Consortium
dc.contributor.author Deu-Pons, Jordi
dc.contributor.author Gut, Ivo Glynne
dc.contributor.author Muiños, Ferran
dc.contributor.author Mularoni, Loris
dc.contributor.author Rubio Pérez, Carlota, 1990-
dc.contributor.author Tamborero Noguera, David
dc.date.accessioned 2020-04-21T10:03:12Z
dc.date.available 2020-04-21T10:03:12Z
dc.date.issued 2020
dc.identifier.citation Shuai S, PCAWG Drivers and Functional Interpretation Working Group, Gallinger S, Stein L, PCAWG Consortium. Combined burden and functional impact tests for cancer driver discovery using DriverPower. Nat Commun. 2020 Feb 5; 11(1): 734. DOI: 10.1038/s41467-019-13929-1
dc.identifier.issn 2041-1723
dc.identifier.uri http://hdl.handle.net/10230/44293
dc.description.abstract 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.
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher Nature Research
dc.relation.ispartof Nature Communications. 2020 Feb 5;11(1):734
dc.rights © 2020 Shimin Shuai et al. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made
dc.rights.uri http://creativecommons.org/licenses/by/4.0/
dc.subject.other Càncer
dc.subject.other Genomes
dc.title Combined burden and functional impact tests for cancer driver discovery using DriverPower
dc.type info:eu-repo/semantics/article
dc.identifier.doi http://dx.doi.org/10.1038/s41467-019-13929-1
dc.rights.accessRights info:eu-repo/semantics/openAccess
dc.type.version info:eu-repo/semantics/publishedVersion

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