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

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

Authors:	Shuai, S.; PCAWG Drivers and Functional Interpretation Working Group; Gallinger, S.; Stein, L.; & PCAWG Consortium
Contributors:	Kahles, A.; Lehmann, K. V.; Liu, E. M.; Sander, C.; Abeshouse, A.; Al-Ahmadie, H.; Armenia, J.; Chen, H. W.; Davidson, N. R.; Gao, J.; Ghossein, R.; Giri, D. D.; Gundem, G.; Heins, Z.; Huse, J.; Iacobuzio-Donahue, C. A.; King, T. A.; Kundra, R.; Levine, D. A.; Ochoa, A.; Pastore, A.; Rätsch, G.; Reis-Filho, J.; Reuter, V.; Roehrl, M. H. A.; Sanchez-Vega, F.; Schultz, N.; Senbabaoglu, Y.; Singer, S.; Socci, N. D.; Stark, S. G.; Yellapantula, V. D.; Zhang, H.
Article Title:	Combined burden and functional impact tests for cancer driver discovery using DriverPower
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. © 2020, The Author(s).
Keywords:	mutation; genome; tumor; software; testing method; cancer
Journal Title:	Nature Communications
Volume:	11
ISSN:	2041-1723
Publisher:	Nature Publishing Group
Date Published:	2020-02-05
Start Page:	734
Language:	English
DOI:	10.1038/s41467-019-13929-1
PUBMED:	32024818
PROVIDER:	scopus
PMCID:	PMC7002750
DOI/URL:	https://www.scopus.com/inward/record.uri?eid=2-s2.0-85079072523&doi=10.1038%2fs41467-019-13929-1&partnerID=40&md5=fe2984ce5bcc4cc22e5e066362d572d1
Notes:	Article -- Erratum issued, see DOI: 10.1038/s41467-022-32343-8 -- Export Date: 2 March 2020 -- Source: Scopus