Abstract 5561: Clinical drug response can be predicted using baseline gene expression levels and in vitro drug sensitivity in cell lines

Robust prediction of in vivo chemotherapeutic response, using baseline gene expression and drug sensitivity data gathered on cancer cell lines, has been a profoundly important, long standing and controversial problem in pharmacogenomics. Here, we present for the first time, a solution to this problem. Currently, personalizing cancer chemotherapy relies on pathology and more recently molecular biomarker-based approaches (e.g. ERBB2 amplification in breast cancer). However, as the driving biology are normally not fully understood, the majority of existing biomarkers do not capture a substantial proportion of variability in drug response. This partly explains the commonly observed lack of reproducibility of findings (e.g. from many conventional gene expression signatures) when these markers are applied to new datasets. In this study, we developed an approach to predict in vivo drug sensitivity that leverages whole-genome gene expression microarray data and allows the expression of every gene to influence the prediction by a small amount. The method works by fitting a ridge regression model of baseline genome-wide gene expression levels against in vitro drug sensitivity in a very large panel of approximately 700 cancer cell lines. Then, after a (crucial) data homogenization step, these models are applied to baseline expression levels from primary tumor biopsies. Our method successfully predicted patient response to different chemotherapeutic agents in three (of four total suitable) independent, publicly available clinical trials, each investigating different drugs and different types of cancer. In each of these cases, we predicted drug response at least as accurately as previously published models that had been derived from the clinical data itself. Interestingly, our approach could also predict clinical response in the absence of any known drug sensitivity biomarker. We effectively enriched for drug responders in breast, myeloma and lung cancers, treated with docetaxel, bortezomib and erlotinib respectively, thus identifying responders to both cytotoxic and targeted agents. Many previous clinical trials and in vitro assays have attempted to discover biomarkers of drug sensitivity, but found that the genes/aberrations which they had identified, performed poorly as predictors, once applied to out-of-batch sets of samples. Our models, on the other hand, are trained on an independent set of cancer cell lines and performed well on three completely separate and independent clinical trial datasets (all assessed using different microarray platforms). These results have far-reaching implications for personalized medicine and drug development (e.g. for the development of companion diagnostics). All datasets and bioinformatics tools to reproduce our results are publicly available. Citation Format: Paul Geeleher, Nancy Cox, R. Stephanie Huang. Clinical drug response can be predicted using baseline gene expression levels and in vitro drug sensitivity in cell lines. [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 5561. doi:10.1158/1538-7445.AM2014-5561