Utilizing Cancer - Functional Gene Set - Compound Networks to Identify Putative Drugs for Breast Cancer

Area of Science:

• Systems biology
• Computational biology
• Drug discovery

Background:

• Limited availability and low response rates of current anticancer drugs necessitate novel therapeutic strategies.
• Developing new anticancer drugs is time-consuming and expensive.
• Drug repositioning offers a promising avenue to accelerate cancer therapy development and enhance treatment efficacy.

Purpose of the Study:

• To propose and validate a systems biology method for identifying novel compound candidates for cancer treatment through drug repositioning.
• To establish a framework for connecting functional gene sets associated with cancer survival to potential therapeutic compounds.

Main Methods:

• Construction of a gene set-compound network using gene set enrichment analysis on compound response expression profiles.
• Application of survival analyses to breast cancer patient cohorts to identify cancer-survival-associated gene sets.
• Development of a cancer-functional gene set-compound network to identify candidate anticancer compounds.

Main Results:

• Identification of 162 breast cancer survival-associated gene sets and 172 putative compounds using the developed method.
• Demonstration of linking clinical relevance of gene sets from prior studies to candidate compounds via gene expression data.
• Identification of six novel compounds that can modulate specific gene sets associated with breast cancer prognosis.

Conclusions:

• The proposed systems biology method effectively identifies compounds for repositioning as anticancer agents.
• The method links drug mechanisms of action to patient survival data, enabling the discovery of compounds with potential clinical relevance.
• This approach facilitates the identification of new therapeutic strategies by leveraging existing drug knowledge and patient-specific data.