TY - JOUR
T1 - Combined burden and functional impact tests for cancer driver discovery using DriverPower
AU - PCAWG Drivers and Functional Interpretation Working Group
AU - PCAWG Consortium
AU - Shuai, Shimin
AU - Abascal, Federico
AU - Amin, Samirkumar B.
AU - Bader, Gary D.
AU - Bandopadhayay, Pratiti
AU - Barenboim, Jonathan
AU - Beroukhim, Rameen
AU - Bertl, Johanna
AU - Boroevich, Keith A.
AU - Brunak, Søren
AU - Campbell, Peter J.
AU - Carlevaro-Fita, Joana
AU - Chakravarty, Dimple
AU - Chan, Calvin Wing Yiu
AU - Chen, Ken
AU - Choi, Jung Kyoon
AU - Deu-Pons, Jordi
AU - Dhingra, Priyanka
AU - Diamanti, Klev
AU - Feuerbach, Lars
AU - Fink, J. Lynn
AU - Fonseca, Nuno A.
AU - Frigola, Joan
AU - Gambacorti-Passerini, Carlo
AU - Park, Keunchil
AU - Akbani, Rehan
AU - Akdemir, Kadir C.
AU - Chakravarty, Dimple
AU - Chen, Yiwen
AU - Chong, Zechen
AU - Czerniak, Bogdan
AU - El-Naggar, Adel
AU - Fan, Yu
AU - Futreal, P. Andrew
AU - Gerstung, Moritz
AU - Han, Leng
AU - Huska, Matthew R.
AU - von Kalle, Christof
AU - Kube, Dieter
AU - Lazic, Ana Mijalkovic
AU - Liang, Sheng Ben
AU - Lu, Yong Jie
AU - Mills, Gordon B.
AU - Sotiriou, Christos
AU - Sungalee, Stephanie
AU - Umer, Husen M.
AU - Van den Eynden, Gert G.
AU - Wang, Wenyi
AU - Wang, Zhining
AU - Weisenberger, Daniel J.
N1 - Publisher Copyright:
© 2020, The Author(s).
PY - 2020/12/1
Y1 - 2020/12/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85079072523&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85079072523&partnerID=8YFLogxK
U2 - 10.1038/s41467-019-13929-1
DO - 10.1038/s41467-019-13929-1
M3 - Article
C2 - 32024818
AN - SCOPUS:85079072523
SN - 2041-1723
VL - 11
JO - Nature communications
JF - Nature communications
IS - 1
M1 - 734
ER -