PT - JOURNAL ARTICLE AU - David G. McFadden AU - Katerina Politi AU - Arjun Bhutkar AU - Frances K. Chen AU - Xiaoling Song AU - Mono Pirun AU - Philip M. Santiago AU - Caroline Kim AU - James T. Platt AU - Emily Lee AU - Emily Hodges AU - Adam P. Rosebrock AU - Roderick Bronson AU - Nicholas D. Socci AU - Gregory Hannon AU - Tyler Jacks AU - Harold Varmus TI - The mutational landscape of <em>EGFR-</em>, <em>MYC-</em>, and <em>Kras-</em> driven genetically-engineered mouse models of lung adenocarcinoma AID - 10.1101/048058 DP - 2016 Jan 01 TA - bioRxiv PG - 048058 4099 - http://biorxiv.org/content/early/2016/04/11/048058.short 4100 - http://biorxiv.org/content/early/2016/04/11/048058.full AB - Genetically-engineered mouse models (GEMMs) of cancer are increasingly being utilized to assess putative driver mutations identified by large scale sequencing of human cancer genomes. In order to accurately interpret experiments that introduce additional mutations, an understanding of the somatic genetic profile and evolution of GEMM tumors is necessary. Here, we performed whole exome sequencing of tumors from three GEMMs of lung adenocarcinoma driven by mutant EGFR, mutant Kras or by overexpression of MYC. Tumors from EGFR- and Kras- driven models exhibited respectively 0.02 and 0.07 non-synonymous mutations/megabase, a dramatically lower average mutational frequency than observed in human lung adenocarcinomas. Tumors from models driven by strong cancer drivers (mutant EGFR and Kras) harbored few mutations in known cancer genes, whereas tumors driven by MYC, a weaker initiating oncogene in the murine lung, acquired recurrent clonal oncogenic Kras mutations. In addition, although EGFR- and Kras- driven models both exhibited recurrent whole chromosome DNA copy number alterations, the specific chromosomes altered by gain or loss were different in each model. These data demonstrate that GEMM tumors exhibit relatively simple somatic genotypes compared to human cancers of a similar type, making these autochthonous model systems useful for additive engineering approaches to assess the potential of novel mutations on tumorigenesis, cancer progression, and drug sensitivity.