Individualized discovery of rare cancer drivers in global network context.

ABSTRACT: Late advances in genome sequencing expanded the space of known cancer driver genes several-fold. However, most of this surge was based on computational analysis of somatic mutation frequencies and/or their impact on the protein function. On the contrary, experimental research necessarily accounted for functional context of mutations interacting with other genes and conferring cancer phenotypes. Eventually, just such results become 'hard currency' of cancer biology. The new method, NEAdriver employs knowledge accumulated thus far in the form of global interaction network and functionally annotated pathways in order to recover known and predict novel driver genes. The driver discovery was individualized by accounting for mutations' co-occurrence in each tumour genome - as an alternative to s

SUBMITTER: Petrov I 

PROVIDER: S-EPMC9159755 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature