Abstract B13: OncoPredictor: An integrative genomics platform to systematically predict responsive tumor populations from in vitro drug response data

A key challenge in drug development is identifying clinically relevant genomic biomarkers from preclinical data. Here, we developed OncoPredictor, a cellular screening and bioinformatics platform to (1) evaluate multiple types of genomic biomarkers for association with in vitro drug response and (2) analyze identified biomarkers in clinical tumor populations, thereby suggesting potential drug development strategies. We examined data for 18 targeted, anticancer agents tested across more than 200 cancer cell lines for which detailed genomic data were collected. To support association analysis of drug response and genomic biomarkers, a cell line biomarker catalog was constructed including gene and pathway mutations, copy number aberrations and gene expression changes, in addition to tumor-derived gene expression molecular subtypes. Similarly, to support the rapid translation of in vitro biomarkers to clinical tumor populations, a tumor biomarker catalog was developed, characterizing the frequency of biomarkers across the Oncomine database, which includes more than 40,000 genomic profiles from tumor specimens. Notably, our analysis uncovered several known and novel drug- biomarker-tumor population associations providing a series of cancer treatment hypotheses. For example, within Ras/Raf mutant cell lines, overexpression of a novel biomarker associated with sensitivity to CI-1040, a MEK inhibitor, and identified a sub-population of Ras/Raf mutant colorectal cancer and melanoma that may be more likely to benefit from MEK inhibitors. Similarly, we showed that a growth factor activation network associated with a dramatic response to sunitinib and identified a fraction of glioblastoma and particular types of sarcoma with activation, suggesting that these indications may benefit from a tumor intrinsic response to sunitinib. Finally, for everolimus, an mTOR inhibitor, we found that a gene expression-based molecular subtype of breast cancer were particularly sensitive and demonstrated that the while subtype-positive breast cancers have a good prognosis, 10% of breast cancers that metastasize are subtype-positive, suggesting a defined clinical population that could benefit from mTOR inhibitors. In summary, our platform provides a comprehensive and clinically relevant framework to discover and apply genomic biomarkers of drug response. Citation Information: Clin Cancer Res 2010;16(14 Suppl):B13.