Genome-wide prediction of cancer driver genes based on SNP and cancer SNV data.

Identifying cancer driver genes and exploring their functions are essential and the most urgent need in basic cancer research. Developing efficient methods to differentiate between driver and passenger somatic mutations revealed from large-scale cancer genome sequencing data is critical to cancer driver gene discovery. Here, we compared distinct features of SNP with SNV data in detail and found that the weighted ratio of SNV to SNP (termed as WVPR) is an excellent indicator for cancer driver genes. The power of WVPR was validated by accurate predictions of known drivers. We ranked most of human genes by WVPR and did functional analyses on the list. The results demonstrate that driver genes are usually highly enriched in chromatin organization related genes/pathways. And some protein complexes, such as histone acetyltransferase, histone methyltransferase, telomerase, centrosome, sin3 and U12-type spliceosomal complexes, are hot spots of driver mutations. Furthermore, this study identified many new potential driver genes (e.g. NTRK3 and ZIC4) and pathways including oxidative phosphorylation pathway, which were not deemed by previous methods. Taken together, our study not only developed a method to identify cancer driver genes/pathways but also provided new insights into molecular mechanisms of cancer development.