Abstract 5114: Integrative analysis of the Cancer Cell Line Encyclopedia reveals genetic and transcriptional predictors of compound sensitivity
Authors: Stransky Nicolas , Kryukov Gregory V. , Caponigro Giordano , Barretina Jordi , Venkatesan Kavitha , Margolin Adam A. , Wilson Christopher J. , Lehar Joseph , Jones Michael D. , Palescandolo Emanuele , Sougnez Carrie , Onofrio Robert C. , MacConaill Laura , Ardlie Kristin , Golub Todd R. , Morrissey Michael , Selers William R. , Schlegel Robert , Garraway Levi A. 
Published: August 2012

The Cancer Cell Line Encyclopedia (CCLE) represents a collaborative effort to assemble a comprehensive resource of human cancer models for basic and translational research. It contains a detailed genetic profiling of approximately 1,000 human cancer cell lines spanning many tumor types. Thus far, high-density SNP array data, gene expression microarray data and mutation data from hybrid capture sequencing of 1,650 cancer genes has been obtained. Additionally, we have assessed the sensitivity of these same cell lines using a series of pharmacological compounds that represent both conventional cytotoxic and targeted agents. On major goal of the CCLE effort involves systematic integration of the genomic and pharmacologic datasets in order to identify putative targets of prevalent genetic alterations as well as predictors and modifiers of pharmacologic sensitivity and resistance. The availability of high-quality data generated by uniform criteria across hundreds of cell lines markedly enhances the statistical power to discover genetic alterations involved in carcinogenesis and molecular predictors of pharmacologic vulnerability. We developed a framework based on an elastic net machine-learning regression algorithm, and combined with a bootstrapping procedure, to derive predictive models of the sensitivity to each compound, using all genetic features of the cell lines in the collection. Through this computational prediction approach, we have both rediscovered molecular features predicting response to most drugs in our set but also uncovered novel potential biomarkers of sensitivity and resistance to targeted agents and chemotherapy drugs. For instance, we have found that response to topoisomerase 1 inhibitors seems to be linked to the expression of a single gene. We have also observed that tissue lineage is a key predictor for sensitivity to certain compounds, providing rationale for clinical trials of these drugs in particular cancer types and we identified potential stratifiers for existing EGFR targeted therapies. Finally, we have found an additional target for a certain chemotype of MEK inhibitors, and shown that this interaction was responsible for growth suppression, which might be a new indication for this kind of drug. Our cell line-based platform provides a valuable tool for the development of personalized cancer medicine, revealing critical tumor dependencies and helping to stratify patients for clinical trials.

Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 5114. doi:1538-7445.AM2012-5114