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Cancer Res. 2017 Jun 1;77(11):3057-3069. doi: 10.1158/0008-5472.CAN-17-0096. Epub 2017 Mar 17.

Integrative Cancer Pharmacogenomics to Infer Large-Scale Drug Taxonomy.

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Integrative Computational Systems Biology, Institut de Recherches Cliniques de Montréal, Montreal, Quebec, Canada.
Department of Biomedical Sciences. Université de Montréal, Montreal, Quebec, Canada.
Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada.
Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada.
Department of Computer Science, University of Toronto, Toronto, Ontario, Canada.
School of Computer Science, McGill University, Montreal, Quebec, Canada.
The Donnelly Centre, Toronto, Ontario, Canada.
The Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, Ontario, Canada.
Hospital for Sick Children, Toronto, Ontario, Canada.
Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada.
Ontario Institute of Cancer Research, Toronto, Ontario, Canada.


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 largely relies on prior information including side effects, therapeutic indication, and chemoinformatics. 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 toward development of unbiased approaches that can elucidate drug relationships and efficiently classify new compounds with basic input data. We propose here a new integrative computational pharmacogenomic approach, referred to as Drug Network Fusion (DNF), to infer scalable drug taxonomies that rely only on basic drug characteristics toward 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. DNF taxonomy succeeded in identifying pertinent and novel drug-drug relationships, making it suitable for investigating experimental drugs with potential new targets or MoA. The scalability of DNF facilitated identification of key drug relationships across different drug categories, providing 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 compound MoA and potential insights for drug repurposing. Cancer Res; 77(11); 3057-69. ©2017 AACR.

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