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

Research output: Contribution to journalJournal articleResearchpeer-review

  • Shimin Shuai
  • Federico Abascal
  • Samirkumar B. Amin
  • Gary D. Bader
  • Pratiti Bandopadhayay
  • Jonathan Barenboim
  • Rameen Beroukhim
  • Johanna Bertl
  • Keith A. Boroevich
  • Brunak, Søren
  • Peter J. Campbell
  • Joana Carlevaro-Fita
  • Dimple Chakravarty
  • Calvin Wing Yiu Chan
  • Ken Chen
  • Jung Kyoon Choi
  • Jordi Deu-Pons
  • Priyanka Dhingra
  • Klev Diamanti
  • Lars Feuerbach
  • J. Lynn Fink
  • Nuno A. Fonseca
  • Joan Frigola
  • Carlo Gambacorti-Passerini
  • Dale W. Garsed
  • Mark Gerstein
  • Gad Getz
  • Qianyun Guo
  • Ivo G. Gut
  • David Haan
  • Mark P. Hamilton
  • Nicholas J. Haradhvala
  • Arif O. Harmanci
  • Mohamed Helmy
  • Carl Herrmann
  • Julian M. Hess
  • Asger Hobolth
  • Ermin Hodzic
  • Chen Hong
  • Henrik Hornshøj
  • Keren Isaev
  • Jose M.G. Izarzugaza
  • Rory Johnson
  • Todd A. Johnson
  • Malene Juul
  • Randi Istrup Juul
  • Andre Kahles
  • Jakob Skou Pedersen
  • Nikos Sidiropoulos
  • Weischenfeldt, Joachim Lütken
  • PCAWG Drivers and Functional Interpretation Working Group
  • PCAWG Consortium

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.

Original languageEnglish
Article number734
JournalNature Communications
Issue number1
Number of pages12
Publication statusPublished - 2020

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