RT Journal Article SR Electronic T1 A comparative analysis of network mutation burdens across 21 tumor types augments discovery from cancer genomes JF bioRxiv FD Cold Spring Harbor Laboratory SP 025445 DO 10.1101/025445 A1 Heiko Horn A1 Michael S. Lawrence A1 Jessica Xin Hu A1 Elizabeth Worstell A1 Nina Ilic A1 Yashaswi Shrestha A1 Eejung Kim A1 Atanas Kamburov A1 Alireza Kashani A1 William C. Hahn A1 Jesse S. Boehm A1 Gad Getz A1 Kasper Lage YR 2015 UL http://biorxiv.org/content/early/2015/08/25/025445.abstract AB Heterogeneity across cancer makes it difficult to find driver genes with intermediate (2-20%) and low frequency (<2%) mutations1, and we are potentially missing entire classes of networks (or pathways) of biological and therapeutic value. Here, we quantify the extent to which cancer genes across 21 tumor types have an increased burden of mutations in their immediate gene network derived from functional genomics data. We formalize a classifier that accurately calculates the significance level of a gene’s network mutation burden (NMB) and show it can accurately predict known cancer genes and recently proposed driver genes in the majority of tested tumours. Our approach predicts 62 putative cancer genes, including 35 with clear connection to cancer and 27 genes, which point to new cancer biology. NMB identifies proportionally more (4x) low-frequency mutated genes as putative cancer genes than gene-based tests, and provides molecular clues in patients without established driver mutations. Our quantitative and comparative analysis of pan-cancer networks across 21 tumour types gives new insights into the biological and genetic architecture of cancers and enables additional discovery from existing cancer genomes. The framework we present here should become increasingly useful with more sequencing data in the future.