Abstract 3149: OncoPredictor: A systematic approach for predicting responsive cancer populations from large scale cell line screening

Investigational new drugs for cancer must demonstrate convincing preclinical efficacy and a compelling strategy to translate preclinical observations to the clinical setting. Personalized medicine approaches are gaining wider acceptance, and large scale cell line databases have demonstrated utility in identifying biomarkers of drug response that can inform clinical development strategies. However, the lack of an integrated platform to translate preclinical biomarker profiles to clinical populations limits the power of this approach. To solve this problem we developed, first, a cell line screening and genomic analysis pipeline that associates drug response across 200+ cell lines with mutation, DNA copy number, and gene expression biomarkers; and second, a parallel database of biomarker frequencies in clinical tumor samples, compiled from all available published genomic data. In the present study, we tested 8 targeted anti-cancer agents and identified cell line biomarkers representing each of the genomic data types – mutation, DNA amplification, and gene over-expression – and then assessed the distribution of these biomarkers across tumor samples. In each case the tumor populations predicted to be responsive by this unsupervised approach were validated by results from clinical trials. We also present an example of biomarker results leading to potential new indications for an approved drug. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 3149. doi:10.1158/1538-7445.AM2011-3149