PT  - JOURNAL ARTICLE
AU  - Ferran Muiños
AU  - Francisco Martinez-Jimenez
AU  - Oriol Pich
AU  - Abel Gonzalez-Perez
AU  - Nuria Lopez-Bigas
TI  - In silico saturation mutagenesis of cancer genes
AID  - 10.1101/2020.06.03.130211
DP  - 2020 Jan 01
TA  - bioRxiv
PG  - 2020.06.03.130211
4099  - http://biorxiv.org/content/early/2020/06/09/2020.06.03.130211.short
4100  - http://biorxiv.org/content/early/2020/06/09/2020.06.03.130211.full
AB  - Extensive bioinformatics analysis of datasets of tumor somatic mutations data have revealed the presence of some 500-600 cancer driver genes. The identification of all potential driver mutations affecting cancer genes is essential to implement precision cancer medicine and to understand the interplay of mutation probability and selection in tumor development. Here, we present an in silico saturation mutagenesis approach to identify all driver mutations in 568 cancer genes across 66 tumor types. For most cancer genes the mutation probability across tissues --underpinned by active mutational processes-- influences which driver variants have been observed, although this differs significantly between tumor suppressor and oncogenes. The role of selection is apparent in some of the latter, the observed and unobserved driver mutations of which are equally likely to occur. The number of potential driver mutations in a cancer gene roughly determines how many mutations are available for detection across newly sequenced tumors.Competing Interest StatementThe authors have declared no competing interest.