Abstract 105: Integrative analysis of genomic and pharmacologic data from the Cancer Cell Line Encyclopedia
Authors: Stransky Nicolas , Venkhatesan Kavitha , Morrissey Mike , Barretina Jordi , Raman Pichai , Getz Gad , Berger Mike , Johannessen Cory , Callahan Adam , Morais Paula , Mahan Scott , Gupta Supriya , Onofrio Robert , Sougnez Carrie , Winckler Wendy , Niu Lili , Kehoe Sarah , Hatton Charlie , Liefeld Ted , Ding Yan , Sonkin Dmitriy , Jones Mike , Caponigro Giordiano , Wilson Chris , Kim Sungjoon , Che John , Su Andrew , MacConaill Laura , Gabriel Stacey , Ardlie Kristin , Reich Michael , Warmuth Markus , Meyerson Matthew , Golub Todd , Harris Jennifer , Sellers William , Schlegel Bob , Garraway Levi 
Published: February 2011

Abstract
The Cancer Cell Line Encyclopedia (CCLE) represents a collaborative effort to assemble a comprehensive resource of human cancer models for basic and translational research. Thus far, the CCLE contains high-density SNP array data, gene expression microarray data and selected cancer gene mutation data for approximately 1,000 human cancer cell lines spanning many tumor types. Additionally, we are assessing the sensitivity of these same cell lines using a series of pharmacological compounds that represent both conventional cytotoxic and targeted agents. Another goal of the CCLE collaboration 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.
As proof of principle, we have carried out systematic nomination of putative targets of genetic alterations using integrative analyses. Here, significant regions of genomic gains and losses have been linked to expression and mutation data to find significant correlations at both single-gene and pathway levels. We have also begun to assemble systematic algorithms that identify genetic predictors of sensitivity or resistance to particular pharmacological compounds, taking advantage of the fact that the CCLE is a comprehensive resource with extensive genomic characterization. Toward this end, we integrated a preliminary sensitivity dataset for 28 compounds accurately profiled against more than 400 cell lines with all genomic data available in the CCLE. To enhance the robustness of our method, we reduced the number of significant genomic features for each cell line to a number that allows properly determined prediction of sensitivity. Expression data was converted to cell line-specific readouts of gene set expression; and DNA gains and losses are reduced to statistically significant regions using the GISTIC algorithm. These values were combined with critical oncogene mutations as inputs to a multifaceted prediction model for pharmacological sensitivity, the accuracy of which was assessed using cross-validation. Our results suggest that this integrative approach applied to a robust cancer cell line collection has considerable power to discover novel associations that augment ongoing basic research into cancer biology and drug discovery.
Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 105.