A genome-wide positioning systems network algorithm for in silico drug repurposing

TLDR

Recent advances in DNA/RNA sequencing enable rapid target identification and precise drug repurposing for heterogeneous diseases by targeting individualized disease modules. The study develops GPSnet, an algorithm that targets disease modules derived from individual patient sequencing profiles mapped to the protein–protein interactome for drug repurposing. GPSnet analyzes whole‑exome and transcriptome data from ~5,000 TCGA cancer patients to map disease modules onto the protein–protein interactome. GPSnet accurately predicts drug responses for 140 approved drugs, experimentally validates ouabain’s antitumor activity in lung adenocarcinoma via a HIF1α/LEO1 pathway, and provides a network‑based framework for more efficacious drug repurposing.

Abstract

Abstract Recent advances in DNA/RNA sequencing have made it possible to identify new targets rapidly and to repurpose approved drugs for treating heterogeneous diseases by the ‘precise’ targeting of individualized disease modules. In this study, we develop a Genome-wide Positioning Systems network (GPSnet) algorithm for drug repurposing by specifically targeting disease modules derived from individual patient’s DNA and RNA sequencing profiles mapped to the human protein-protein interactome network. We investigate whole-exome sequencing and transcriptome profiles from ~5,000 patients across 15 cancer types from The Cancer Genome Atlas. We show that GPSnet-predicted disease modules can predict drug responses and prioritize new indications for 140 approved drugs. Importantly, we experimentally validate that an approved cardiac arrhythmia and heart failure drug, ouabain, shows potential antitumor activities in lung adenocarcinoma by uniquely targeting a HIF1α/LEO1-mediated cell metabolism pathway. In summary, GPSnet offers a network-based, in silico drug repurposing framework for more efficacious therapeutic selections.