Abstract 4174: An integrative genomic analysis for clinically relevant cancer genetic aberration and targeted therapeutic prediction

Abstract

Abstract While the genomic and genetic basis of many cancers has been partly elucidated, less is known about the identity of specific cancer genes that are associated with clinical parameters such as clinical stage, metastasis, and survival. To address this question, we compiled multiple genomic data types (mutations, copy number alterations, gene expression and methylation status) as well as clinical meta-data for twelve different cancers in the Cancer Genome Atlas Project. We used an elastic-net regularized regression method on the combined genomic data to identify genetic aberrations and their associated cancer genes that are indicators of clinical parameters in each cancer type. To facilitate the identification of clinically actionable genetic aberrations from this cancer genomic data, we utilized genomic and drug response data from the Cancer Cell Line Encyclopedia (CCLE). These expansive data sets of hundreds of cancer cell lines include three genomic data types (mutations, copy number alterations, gene expression) and drug response data. To demonstrate the performance and validity of this approach, we demonstrated that our bioinformatics pipeline successfully identified i) synthetic genes that have their CNVs perfectly associated with stages from a simulated data ii) mutations of TGFBR2, methylation of MLH1 and other driver genes that have genetic changes associated with colorectal cancer (CRC) that demonstrate microsatellite instability CRC from TCGA data, and iii) IDH1 that obtained mutations associated with survival according to glioblastoma (GBM) data. We identify the top ranked genes that delineate key clinical features such as survival analysis or clinical stages for 13 cancers. A fit of the elastic-net regularized regression to more than 200 samples/cancer type and integrative analysis of integrative genomic platforms identified the set of top gene predictors of clinical parameters (TCGA data) and drug response (CCLE data). There are several genes ranked top in multiple cancer types while most of them are unique to a cancer type. These results provide us with new insights into the underlying genetic similarities and differences among 13 cancers; namely, common genetic characteristics that have implications for clinical phenotype. Finally, we are identifying the core set of gene aberrations that are both clinically relevant and drug responsible. Citation Format: HoJoon Lee, Hanlee P. Ji. An integrative genomic analysis for clinically relevant cancer genetic aberration and targeted therapeutic prediction. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 4174. doi:10.1158/1538-7445.AM2014-4174