Abstract
We present a novel method for the identification of sets of mutually exclusive gene alterations in a given set of genomic profiles. We scan the groups of genes with a common downstream effect on the signaling network, using a mutual exclusivity criterion that ensures that each gene in the group significantly contributes to the mutual exclusivity pattern. We test the method on all available TCGA cancer genomics datasets, and detect multiple previously unreported alterations that show significant mutual exclusivity and are likely to be driver events.Electronic supplementary materialThe online version of this article (doi:10.1186/s13059-015-0612-6) contains supplementary material, which is available to authorized users.
Highlights
A small fraction of genomic alterations present in a tumor are selected directly because of their ability to increase cellular proliferation and to unlock barriers against growth and metastasis
Measure for mutual exclusion To measure mutual exclusion of a group of genes, we test each gene against the union of all other alterations in the group, and use the least significant P value as the initial score of the group
To control the false discovery rate (FDR) in the resulting groups, we estimate the null distribution of the final scores by running the same analysis on a set of permuted datasets, where gene alteration ratios and network connectivity are preserved, but sample distributions of alterations are shuffled
Summary
A small fraction of genomic alterations present in a tumor are selected directly because of their ability to increase cellular proliferation and to unlock barriers against growth and metastasis. Differentiating drivers from passengers in cancer can help us to identify tumorigenic mechanisms and drug targets, and to design patient-specific therapeutic interventions. Pivotal driver events, such as TP53 loss-of-function mutations, can be identified by their significantly high alteration rate in a set of tumors. Not one but several alternative driver alterations in different genes can lead to similar downstream events. In those cases, the selection bonus is divided among the alteration frequencies of these genes. For current cancer genomics studies where the number of samples is two orders of magnitude smaller than the number of profiled genes per sample, the statistical power of naive frequency-based methods is not sufficient to differentiate these substitutive drivers from passengers (Figure 1)
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
