TY - JOUR T1 - Expanding discovery from cancer genomes by integrating protein network analyses with <em>in vivo</em> tumorigenesis assays JF - bioRxiv DO - 10.1101/151977 SP - 151977 AU - Heiko Horn AU - Michael S. Lawrence AU - Candace R. Chouinard AU - Yashaswi Shrestha AU - Jessica Xin Hu AU - Elizabeth Worstell AU - Emily Shea AU - Nina Ilic AU - Eejung Kim AU - Atanas Kamburov AU - Alireza Kashani AU - William C. Hahn AU - Joshua D. Campbell AU - Jesse S. Boehm AU - Gad Getz AU - Kasper Lage Y1 - 2017/01/01 UR - http://biorxiv.org/content/early/2017/06/19/151977.abstract N2 - Combining molecular network information with cancer genome data can complement gene-based statistical tests to identify likely new cancer genes. However, it is challenging to experimentally validate network-based approaches at scale and thus to determine their real predictive value. Here, we developed a robust network-based statistic (NetSig) to predict cancer genes and designed and implemented a large-scale and quantitative experimental framework to compare the in vivo tumorigenic potential of 23 NetSig candidates to 25 known oncogenes and 79 random genes. Our analysis shows that genes with a significantly mutated network induce tumors at rates comparable to known oncogenes and at an order of magnitude higher than random genes. Informed by our network-based statistical approach and tumorigenesis experiments we made a targeted reanalysis of nine candidate genes in 242 oncogene-negative lung adenocarcinomas and identified two new driver genes (AKT2 and TFDP2). Together, our combined computational and experimental analyses strongly support that network-based approaches can complement gene-based statistical tests in cancer gene discovery. We illustrate a general and scalable computational and experimental workflow that can contribute to explaining cancers with previously unknown driver events. ER -