Individualized discovery of rare cancer drivers in global network context

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 gene interaction networks 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 tumour genomes. For each somatic genome change, probabilistic estimates from two lanes of network analysis were combined into joint likelihoods of being a driver. Thus, ability to detect previously unnoticed candidate driver events emerged from combining individual genomic context with network perspective. The procedure was applied to ten largest cancer cohorts followed by evaluating error rates against previous cancer gene sets. The discovered driver combinations were shown to be informative on cancer outcome. We demonstrate that the individualized discovery revealed driver events which were individually rare, not detectable by other computational approaches, and related to cancer biology domains poorly covered by previous analyses. Considering the novel driver candidates and their constellations in individual tumor genomes opens a novel avenue for personalized cancer medicine.