Diffusion mapping of drug targets on disease signaling network elements reveals drug combination strategies

Jielin Xu, Kelly Regan-Fendt, Siyuan Deng, William E. Carson, Philip R.O. Payne, Fuhai Li

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

Abstract

The emergence of drug resistance to traditional chemotherapy and newer targeted therapies in cancer patients is a major clinical challenge. Reactivation of the same or compensatory signaling pathways is a common class of drug resistance mechanisms. Employing drug combinations that inhibit multiple modules of reactivated signaling pathways is a promising strategy to overcome and prevent the onset of drug resistance. However, with thousands of available FDA-approved and investigational compounds, it is infeasible to experimentally screen millions of possible drug combinations with limited resources. Therefore, computational approaches are needed to constrain the search space and prioritize synergistic drug combinations for preclinical studies. In this study, we propose a novel approach for predicting drug combinations through investigating potential effects of drug targets on disease signaling network. We first construct a disease signaling network by integrating gene expression data with disease-associated driver genes. Individual drugs that can partially perturb the disease signaling network are then selected based on a drug-disease network “impact matrix”, which is calculated using network diffusion distance from drug targets to signaling network elements. The selected drugs are subsequently clustered into communities (subgroups), which are proposed to share similar mechanisms of action. Finally, drug combinations are ranked according to maximal impact on signaling sub-networks from distinct mechanism-based communities. Our method is advantageous compared to other approaches in that it does not require large amounts drug dose response data, drug-induced “omics” profiles or clinical efficacy data, which are not often readily available. We validate our approach using a BRAF-mutant melanoma signaling network and combinatorial in vitro drug screening data, and report drug combinations with diverse mechanisms of action and opportunities for drug repositioning.

Original languageEnglish (US)
Title of host publicationPACIFIC SYMPOSIUM ON BIOCOMPUTING 2018
PublisherWorld Scientific Publishing Co. Pte Ltd
Pages92-103
Number of pages12
Edition212669
ISBN (Print)9789813235533
DOIs
StatePublished - 2018
Event23rd Pacific Symposium on Biocomputing, PSB 2018 - Kohala Coast, United States
Duration: Jan 3 2018Jan 7 2018

Other

Other23rd Pacific Symposium on Biocomputing, PSB 2018
CountryUnited States
CityKohala Coast
Period1/3/181/7/18

Keywords

  • Drug combination
  • Drug repositioning
  • Network diffusion
  • Signaling network

ASJC Scopus subject areas

  • Biomedical Engineering
  • Computational Theory and Mathematics

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