Functional impact bias reveals cancer drivers
Top Cited Papers
Open Access
- 13 August 2012
- journal article
- research article
- Published by Oxford University Press (OUP) in Nucleic Acids Research
- Vol. 40 (21), e169
- https://doi.org/10.1093/nar/gks743
Abstract
Identifying cancer driver genes and pathways among all somatic mutations detected in a cohort of tumors is a key challenge in cancer genomics. Traditionally, this is done by prioritizing genes according to the recurrence of alterations that they bear. However, this approach has some known limitations, such as the difficulty to correctly estimate the background mutation rate, and the fact that it cannot identify lowly recurrently mutated driver genes. Here we present a novel approach, Oncodrive-fm, to detect candidate cancer drivers which does not rely on recurrence. First, we hypothesized that any bias toward the accumulation of variants with high functional impact observed in a gene or group of genes may be an indication of positive selection and can thus be used to detect candidate driver genes or gene modules. Next, we developed a method to measure this bias (FM bias) and applied it to three datasets of tumor somatic variants. As a proof of concept of our hypothesis we show that most of the highly recurrent and well-known cancer genes exhibit a clear FM bias. Moreover, this novel approach avoids some known limitations of recurrence-based approaches, and can successfully identify lowly recurrent candidate cancer drivers.Keywords
This publication has 47 references indexed in Scilit:
- NOTCH1 mutations in CLL associated with trisomy 12Blood, 2012
- Roles of Fibroblast Growth Factor Receptors in CarcinogenesisMolecular Cancer Research, 2010
- A map of human genome variation from population-scale sequencingNature, 2010
- The UCSC Genome Browser database: update 2011Nucleic Acids Research, 2010
- Diverse somatic mutation patterns and pathway alterations in human cancersNature, 2010
- Deriving the consequences of genomic variants with the Ensembl API and SNP Effect PredictorBioinformatics, 2010
- International network of cancer genome projectsNature, 2010
- A method and server for predicting damaging missense mutationsNature Methods, 2010
- New Insights Into Susceptibility to GliomaArchives of Neurology, 2010
- IntOGen: integration and data mining of multidimensional oncogenomic dataNature Methods, 2010