Abstract 5567: A scalable and integrated computational and experimental workflow to identify new driver genes in cancer genome data

High throughput sequencing has revolutionized the study of the cancer genome, enabling numerous discoveries in basic and clinical research. However, considerable sample sizes are required to find cancer driver genes with intermediate and low mutation frequencies, and for a large proportion of patients the molecular cause (e.g. driver gene(s)) of disease is unknown. Here, we describe an integrated computational and experimental workflow that combines cancer genome data, molecular network information, multiplexed in vivo tumorigenesis assays, and reanalysis of driver-gene-negative cancer patients to predict and validate new driver genes. We develop a statistic, network mutation burden, that combines molecular network information with data from 4,742 cancer genomes to accurately classify known driver genes across 21 tumor types and predict 62 driver gene candidates.Of these, 35 gene candidates were tested in multiplexed in vivo tumorigenesis cell assays using sensitized immortalized human embryonic kidney (HA1E-M) and immortalized human lung epithelial (SALE-Y) cell lines.Tumor formation in vivo was observed for 11 genes (2 in HA1E-M, 3 in SALE-Y, 6 in both). By reanalyzing 242 lung adenocarcinoma patients with an unknown molecular cause of disease we show that two of these candidates, TFDP2 and AKT2, are significantly amplified in multiple samples.Overall, we describe a scalable combined computational and experimental framework to predict and validate driver genes across many tumor types. Our proof-of-concept approach should become increasingly useful as the number of cancer genomes continues to grow. Citation Format: Heiko Horn, Michael S. Lawrence, Candace R. Chouinard, Yashaswi Shrestha, Jessica Xin Hu, Elizabeth Worstell, Emily Shea, Nina Ilic, Eejung Kim, Atanas Kamburov, Alireza Kashani, William C. Hahn, Joshua D. Campbell, Jesse S. Boehm, Gad Getz, Kasper Lage. A scalable and integrated computational and experimental workflow to identify new driver genes in cancer genome data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 5567. doi:10.1158/1538-7445.AM2017-5567