Determining whether recurrent but rare cancer mutations are bona fide driver mutations
remains a bottleneck in cancer research. Here we present the most comprehensive
analysis of retrovirus driven lymphomagenesis produced to date, sequencing 700,000
mutations from >500 malignancies collected at time points throughout tumor
development. This scale of data allows novel, novel statistical approaches for identifying
driver mutations and yields a high-resolution, genome wide map of the selective forces
surrounding cancer gene loci. We also demonstrate negative selection of mutations that
may be deleterious to tumor development indicating novel avenues for therapy.
Screening two BCL2 transgenic models confirms known drivers of human B-cell non-
Hodgkin lymphoma, and implicates novel candidates including modifiers of
immunosurveillance and MHC loci. Correlating mutations with genotypic and phenotypic
features also gives robust identification of known cancer genes independently of local
variance in mutation density. An online resource http://mulv.lms.mrc.ac.uk allows
customized queries of the entire dataset.