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11:02
Detecting Somatic Genetic Alterations in Tumor Specimens by Exon Capture and Massively Parallel Sequencing
Published on: October 18, 2013
Functional impact bias reveals cancer drivers
Abel Gonzalez-Perez1, Nuria Lopez-Bigas
1Research Programme on Biomedical Informatics - GRIB, Universitat Pompeu Fabra - UPF, Parc de Recerca Biomèdica de Barcelona. Dr. Aiguader, 88, E-08003 Barcelona, Spain. abel.gonzalez@upf.edu
Nucleic Acids Research
|August 21, 2012
Summary
Oncodrive-fm identifies cancer driver genes by detecting functional impact bias, not just mutation recurrence. This novel approach uncovers lowly recurrent drivers missed by traditional methods.
Area of Science:
- Genomics
- Cancer Biology
- Computational Biology
Background:
- Identifying cancer driver genes is crucial for understanding tumor development.
- Recurrence-based methods have limitations in detecting all drivers, especially those with low mutation frequency.
- Estimating background mutation rates accurately is challenging for traditional approaches.
Purpose of the Study:
- To introduce a novel method, Oncodrive-fm, for identifying cancer driver genes.
- To overcome limitations of recurrence-based driver gene detection.
- To detect candidate driver genes and gene modules based on functional impact bias.
Main Methods:
- Developed a method to measure functional impact bias (FM bias) in somatic variants.
- Hypothesized that functional impact bias indicates positive selection.
- Applied the Oncodrive-fm method to three tumor somatic variant datasets.
Main Results:
- Demonstrated that most well-known cancer genes exhibit significant FM bias.
- Showcased Oncodrive-fm's ability to identify lowly recurrent candidate cancer drivers.
- Validated the hypothesis that functional impact bias is a reliable indicator of positive selection.
Conclusions:
- Oncodrive-fm offers a complementary approach to recurrence-based methods for cancer driver gene discovery.
- The method effectively identifies candidate drivers, including those with low recurrence rates.
- Functional impact bias is a valuable metric for detecting positively selected genes in cancer genomics.
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