We have recently developed a novel method, named OncodriveCLUST, aimed to analyse the mutations observed in sets of tumor samples and identify genes involved in the disease. It is based on the feature that driver mutations in cancer genes, especially oncogenes, often cluster in particular positions of the protein. We consider this as a signal that mutations in these regions change the function of the protein in a manner that provides an adaptive advantage to cancer cells and consequently are positively selected during clonal evolution of tumors, and this property can thus be used to nominate driver genes.
The possibility to rapidly and inexpensively sequence tumor genomes is opening important new avenues for cancer research. One of the main objectives when sequencing tumor genomes is to identify the somatic alterations that have a relevant role in developing and maintaining the cancer phenotype. However the analysis of this data is hindered by the large number of mutations detected in tumors (often in the order of thousands) and the large molecular heterogeneity observed between tumor samples.
As part of the International Cancer Genome Consortium (ICGC), during the last 2-3 years I have been co-leading (together with Lincoln Stein) a working group focused on discussing how to analyze this data. The group is formed by 48 Members from 10 different countries, and we have held one teleconference nearly every month. We have now written the results of these discussions in a perspective manuscript that has been published in the current issue of Nature Methods.