Abstract 2987: Next generation sequencing analysis of genetically engineered mouse models of human cancers

Abstract

Abstract Genetically engineered mouse models (GEMM) of cancer are invaluable tools for oncology research. They recapitulate critical aspects of tumor etiology and allow for unique interrogation of the tumor stroma interactions during tumorigenesis. In addition, these models are poised for assessing therapeutic drug efficacy and resistance mechanisms. Although these models are widely used, the mutational landscape and transcriptional profile of GEMMs have yet to be thoroughly characterized. In this study we sought to investigate the precise mutational landscape of four well-validated GEMMs representing three types of cancers, non-small cell lung cancer (NSCLC), pancreatic ductal adenocarcinoma (PDAC) and melanoma. Using next generation sequencing techniques, we profiled one adenovirally-induced Kras mutant NSCLC model (KrasLSL.G12D; p53frt/frt), two Kras mutant models of spontaneous PDAC (KrasLSL.G12D; p16/p19fl/fl; Pdx1.Cre and KrasLSL.G12D; p16/p19fl/wt; p53LSL.R270H/wt; Pdx1.Cre) and one Braf mutant melanoma model (BrafV600E/wt; PTENfl/fl; TyrCreER). All four GEMMs incorporate mutations that are well documented to occur in the human patient population. Additionally these models have been documented to approximate histological and phenotypic aspects of their human correlate. We carried out whole exome sequencing for each model (NSCLC n = 120; PDAC n = 73 and n = 40, respectively; melanoma n = 17) to comprehensively characterize the baseline genomes. We also looked for additional recurrent driver mutations beyond those engineered within each model. Interestingly, both intra and intertumoral heterogeneity was evident. Furthermore, we performed RNA sequencing and identified significantly affected genes and gene sets. Finally, we compared our GEMMs to human tumors from TCGA (LUAD, LUSC, PAAD and SKCM) at the genomic and transcriptomic level. Taken together, the results we will present provide a clearer understanding of the genomic and transcriptional landscape of these GEMMs and their relationship with human patients. More importantly, these analyses will allow us to better interpret results from previous and future experiments using these GEMMs of cancer. Citation Format: Wei-Jen Chung, Jason Long, Jason Cheng, Chris Tran, Anwesha Dey, Anneleen Daemen, Melissa Junttila. Next generation sequencing analysis of genetically engineered mouse models of human cancers. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 2987. doi:10.1158/1538-7445.AM2015-2987