Most of the cancer driver mutations were discovered due to their frequent occurrence in sequenced genomes. Still in many tumors none of these drivers is present – except maybe p53. The remaining “dark matter” of driver space likely consists of individually rare events, which escape the frequency-based detection and – consequently – existing targeted therapies. Our new method, NEAdriver individualized the discovery by accounting for mutations’ co-occurrence and potential of interacting with cancer pathways. Its ability to spot previously unnoticed drivers emerged from combining the global network and individual genomic contexts. This revealed driver genes with individually sparse mutation profiles that would not be detectable by other computational methods and related to cancer biology domains poorly covered by previous analyses. In particular, recurrent alterations in the collagen, laminin, and integrin families were observed in adenocarcinomas and glioblastomas. Considering such driver constellations opens a novel avenue for personalized cancer medicine: https://elifesciences.org/articles/74010
