RT Journal Article
SR Electronic
T1 Integrative pharmacogenomics to infer large-scale drug taxonomy
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 046219
DO 10.1101/046219
A1 Nehme El-Hachem
A1 Deena M.A. Gendoo
A1 Laleh Soltan Ghoraie
A1 Zhaleh Safikhani
A1 Petr Smirnov
A1 Christina Chung
A1 Kenan Deng
A1 Ailsa Fang
A1 Erin Birkwood
A1 Chantal Ho
A1 Ruth Isserlin
A1 Gary D. Bader
A1 Anna Goldenberg
A1 Benjamin Haibe-Kains
YR 2017
UL http://biorxiv.org/content/early/2017/01/05/046219.abstract
AB Identification of drug targets and mechanism of action (MoA) for new and uncharacterlzed drugs is important for optimization of drug 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 uncharacterlzed 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. We propose a new integrative computational pharmacogenomlc 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. We demonstrate 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. We 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 repurposlng.