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Integrative cancer pharmacogenomics to infer large-scale drug taxonomy

Banff International Research Station for Mathematical Innovation and Discovery

Identification of drug targets and mechanism of action (MoA) for new and uncharacterized anticancer drugs is important for optimization of treatment efficacy. Current MoA prediction approaches largely rely on prior information including side effects, therapeutic indication and/or chemo-informatics. Such information is not transferable or applicable for newly identified, previously uncharacterized small molecules. Therefore, a shift in the paradigm of MoA predictions is necessary towards development of unbiased approaches that can elucidate drug relationships and efficiently classify new compounds with basic input data. I will describe a new integrative computational pharmacogenomic approach, referred to as Drug Network Fusion (DNF), to infer scalable drug taxonomies that relies only on basic drug characteristics towards elucidating drug-drug relationships. DNF is the first framework to integrate drug structural information, high-throughput drug perturbation and drug sensitivity profiles, enabling drug classification of new experimental compounds with minimal prior information. I will show that the DNF taxonomy succeeds in identifying pertinent and novel drug-drug relationships, making it suitable for investigating experimental drugs with potential new targets or MoA. I will highlight how the scalability of DNF facilitates identification of key drug relationships across different drug categories, and poses as a flexible tool for potential clinical applications in precision medicine. Our results support DNF as a valuable resource to the cancer research community by providing new hypotheses on the compound MoA and potential insights for drug repurposing.

Mathematics; Statistics; Biology and other natural sciences

video/mp4

Author affiliation: University Health Network

2017-09-27

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/63124

Unreviewed

http://creativecommons.org/licenses/by-nc-nd/4.0/