OCT 10, 2018 12:00 PM PDT
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Discovery of Biomarkers Predictive of Anticancer Drug Response in Preclinical Settings

  • Assistant Professor, Princess Margaret Cancer Center, University of Toronto
      Benjamin Haibe-Kains is a Scientist at the Princess Margaret Cancer Centre (PM), University Health Network, and Assistant Professor in the Medical Biophysics and Computer Science departments of the University of Toronto. Dr. Haibe-Kains earned his PhD in Bioinformatics at the Université Libre de Bruxelles (Belgium), for which he was awarded a Solvay Award (Belgium). Supported by a Fulbright Award, Dr. Haibe-Kains did his postdoctoral fellowship at the Dana-farber Cancer Institute and Harvard School of Public Health (USA). Dr. Haibe-Kains started his own laboratory at the Institut de Recherches Cliniques de Montréal (Canada) and moved to PM in November 2013. Dr. Haibe-Kains' research focuses on the integration of high-throughput data from various sources to simultaneously analyze multiple facets of carcinogenesis. Dr. Haibe-Kains and his team are analyzing high-throughput (pharmaco)genomic datasets to develop new prognostic and predictive models and to discover new therapeutic regimens in order to significantly improve disease management. Dr. Haibe-Kains' main scientific contributions include several prognostic gene signatures in breast cancer, subtype classification models for ovarian and breast cancers, as well as genomic predictors of drug response in cancer cell lines.


    One of the main challenges in precision medicine consists of developing predictors of drug response to select the most beneficial therapy for each individual patient. In this context, preclinical models are crucial to study the association between molecular features of tumor cells and response to chemical perturbations. However, only few predictors have been successfully translated to clinical settings. Such a low success rate is due not only to the complexity of the mechanisms underlying anticancer drug response, but also to the lack of robustness of the predictors developed in preclinical settings. To address this issue we developed PharmacoGx, a computational platform enabling meta-analysis of large-scale drug screenings of in vitro and in vivo model systems, and PharmacoDB (pharmacodb.pmgenomics.ca), a web-application enabling quick access to a large compendium of pharmacogenomics datasets. In this presentation I will show how we used our new platforms to (i) identify pathways whose genomic and transcriptomic patterns are preserved between model systems and patient tumors, and (ii) develop univariate and multivariate predictors of drug response that can be validated in clinical trial data. 

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