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DriverNet: uncovering the impact of somatic driver mutations on transcriptional networks in cancer

Ali Bashashati1, Gholamreza Haffari12, Jiarui Ding13, Gavin Ha14, Kenneth Lui1, Jamie Rosner1, David G Huntsman56, Carlos Caldas7, Samuel A Aparicio15 and Sohrab P Shah135*

  • * Corresponding author: Sohrab P Shah

  • † Equal contributors

Author Affiliations

1 Department of Molecular Oncology, British Columbia Cancer Agency, 675 West 10th Avenue, Vancouver, BC, V5Z 1L3, Canada

2 Faculty of Information Technology, Monash University, Wellington Road, Clayton, VIC 3800, Australia

3 Department of Computer Science, University of British Columbia, 2366 Main Mall, Vancouver, BC, V6T 1Z4, Canada

4 Bioinformatics Training Program, University of British Columbia, 570 West 7th Avenue, Vancouver, BC, V5Z 4S6, Canada

5 Department of Pathology and Laboratory Medicine, University of British Columbia, 2211 Wesbrook Mall, Vancouver, BC, V6T 2B5, Canada

6 Centre for Translational and Applied Genomics, BC Cancer Agency, 600 West 10th Avenue, Vancouver, BC, V5Z 4E6 Canada

7 Cancer Research UK, Cambridge Research Institute, Li Ka Shing Centre, Robinson Way, Cambridge, CB2 0RE, UK

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Genome Biology 2012, 13:R124  doi:10.1186/gb-2012-13-12-r124

Published: 22 December 2012


Simultaneous interrogation of tumor genomes and transcriptomes is underway in unprecedented global efforts. Yet, despite the essential need to separate driver mutations modulating gene expression networks from transcriptionally inert passenger mutations, robust computational methods to ascertain the impact of individual mutations on transcriptional networks are underdeveloped. We introduce a novel computational framework, DriverNet, to identify likely driver mutations by virtue of their effect on mRNA expression networks. Application to four cancer datasets reveals the prevalence of rare candidate driver mutations associated with disrupted transcriptional networks and a simultaneous modulation of oncogenic and metabolic networks, induced by copy number co-modification of adjacent oncogenic and metabolic drivers. DriverNet is available on Bioconductor or at webcite.

driver mutations; sequencing; cancer; transcriptional networks.