Integrative cancer pharmacogenomics to infer large-scale drug taxonomy

Research output: Contribution to journalArticle

Authors

  • Nehme El-Hachem
  • Laleh Soltan Ghoraie
  • Zhaleh Safikhani
  • Petr Smirnov
  • Christina Chung
  • Kenan Deng
  • Ailsa Fang
  • Erin Birkwood
  • Chantal Ho
  • Ruth Isserlin
  • Gary D. Bader
  • Anna Goldenberg
  • Benjamin Haibe-Kains

Colleges, School and Institutes

External organisations

  • Integrative Computational Systems Biology, Institut de Recherches Cliniques de Montréal, Montreal, Quebec, Canada.
  • Princess Margaret Cancer Center, University Health Network, Toronto, M5G 2C1, Canada.
  • The Donnelly Centre, Toronto, Ontario, Canada.
  • The Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, Ontario, Canada.
  • The Hospital for Sick Children, Toronto, Canada
  • Ontario Institute Cancer Research
  • Université de Montréal
  • University of Toronto
  • McGill University
  • Princess Margaret Cancer Centre, University Health Network

Abstract

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.

Details

Original languageEnglish
Pages (from-to)3057-3069
JournalCancer Research
Volume77
Issue number11
Early online date17 Mar 2017
Publication statusPublished - Jun 2017