Meta-analysis of preclinical pharmacogenomic studies to discover robust and translatable biomarkers of drug response

Abstract

Preclinical pharmacogenomic studies provide an opportunity to discover novel biomarkers for drug response. However, pharamcogenomic studies linking gene expression profiles to drug response do not always agree on the significance or strength of biomarkers. We apply a statistical meta-analysis approach to 7 large independent pharmacogenomic studies, testing for tissue-specific gene expression markers predictive of response among cancer cell lines. We found 4,338 statistically-significant biomarkers across 8 tissue types and 34 drugs. Significant biomarkers were found to be closer than random to drug targets in a gene network built on pathway co-membership (average distance of 2 vs 2.9). However, functional relationships with the drug target did not predict reproducibility across studies. To validate these biomarkers, we utilized 10 clinical datasets, allowing 42/4338 biomarkers to be assessed for clinical translation. Of the 42 candidate biomarkers, the expression of ODC1 was found to be significantly predictive of Paclitaxel response as a neoadjuvant treatment of breast carcinoma across 2 independent clinical studies of >200 patients each. We expect that as more clinical transcriptomics data matched with response are available, our results can be used to prioritize which genes to evaluate as clinical biomarkers of drug response.