Abstract B1-01: Whole genome expression based drug repurposing

Abstract

Abstract Currently, personalizing cancer chemotherapy relies on pathology and more recently molecular biomarker-based approaches. However, as the driving biology is 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. 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. Our approach builds statistical models from gene expression and drug sensitivity data in a very large panel of cell lines, then applies these models to gene expression data from primary tumor biopsies. In this study, we applied this approach to tumor samples collected in The Cancer Genome Atlas (TCGA). We derived predicted sensitivity for over one hundred drugs in each of the tumor samples. As a proof-of-concept, we demonstrated that a targeting agent (lapatinib) designed against a specific tumor marker (HER2 positive) is indeed predicted to be more sensitive in HER2 positive breast cancers when compared to the other type of cancers. Meanwhile, we identified other agents that exhibit similar or superior sensitivity when compared to commonly prescribed agents in different disease settings. These findings warrant further evaluation of these agents to be repurposed for possible new indications. Interestingly, some of our derived drug sensitivity predictive estimates are correlated with observed survival outcomes in certain cancer patients. When screening all tumor types based on their molecular profiles, we defined several classes of drugs that maybe differentially effective based on tumor molecular profiles. In conclusion, a genome-wide expression drug sensitivity model built in cell lines can be a powerful approach in repurposing drug in cancer treatment. Citation Format: Paul Geeleher, Steven Bhutra, Jacqueline Wang, R. Stephanie Huang. Whole genome expression based drug repurposing. [abstract]. In: Proceedings of the AACR Special Conference on Computational and Systems Biology of Cancer; Feb 8-11 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 2):Abstract nr B1-01.