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Published on: October 3, 2025
Mining Drug-Target Associations in Cancer: Analysis of Gene Expression and Drug Activity Correlations
Monica M Arroyo1,2, Alberto Berral-González1, Santiago Bueno-Fortes1
1Bioinformatics and Functional Genomics Group, Cancer Research Center (CiC-IMBCC, CSIC/USAL/IBSAL), Consejo Superior de Investigaciones Científicas (CSIC) and University of Salamanca (USAL), 37007 Salamanca, Spain.
Abstract:
Cancer is a complex disease affecting millions of people worldwide, with over a hundred clinically approved drugs available. In order to improve therapy, treatment, and response, it is essential to draw better maps of the targets of cancer drugs and possible side interactors. This study presents a large-scale screening method to find associations of cancer drugs with human genes. The analysis is focused on the current collection of Food and Drug Administration (FDA)-approved drugs (which includes about one hundred chemicals). The approach integrates global gene-expression transcriptomic profiles with drug-activity profiles of a set of 60 human cell lines obtained for a collection of chemical compounds (small bioactive molecules). Using a standardized expression for each gene versus standardized activity for each drug, Pearson and Spearman correlations were calculated for all possible pairwise gene-drug combinations. These correlations were used to build a global bipartite network that includes 1007 gene-drug significant associations. The data are integrated into an open web-tool called GEDA (Gene Expression and Drug Activity) which includes a relational view of cancer drugs and genes, disclosing the putative indirect interactions found for FDA-approved drugs as well as the known targets of these drugs. The results also provide insight into the complex action of pharmaceuticals, presenting an alternative view to address predicted pleiotropic effects of the drugs.
Insights
This study developed a screening method to map associations between Food and Drug Administration (FDA)-approved cancer drugs and human genes. The findings reveal 1007 significant gene-drug interactions, aiding in understanding drug mechanisms and improving cancer therapies.
Area of Science:
- Genomics
- Pharmacology
- Bioinformatics
Background:
- Cancer impacts millions globally, necessitating improved therapeutic strategies.
- Understanding cancer drug targets and their interactions is crucial for enhancing treatment efficacy.
- Existing knowledge of drug-gene associations is limited, particularly for FDA-approved compounds.
Purpose of the Study:
- To develop a large-scale screening method for identifying associations between cancer drugs and human genes.
- To analyze the targets and potential side interactors of FDA-approved cancer drugs.
- To create a comprehensive resource for exploring gene-drug relationships in cancer therapy.
Main Methods:
- Integrated global gene-expression transcriptomic profiles with drug-activity profiles from 60 human cell lines.
- Calculated Pearson and Spearman correlations for all pairwise gene-drug combinations.
- Constructed a global bipartite network of significant gene-drug associations.
Main Results:
- Identified 1007 significant gene-drug associations.
- Developed the Gene Expression and Drug Activity (GEDA) web-tool for data integration and visualization.
- Disclosed putative indirect interactions and known targets for FDA-approved drugs.
Conclusions:
- The study provides a novel method for mapping cancer drug-gene associations.
- The GEDA tool offers a valuable resource for researchers investigating drug mechanisms and potential pleiotropic effects.
- Findings contribute to a deeper understanding of complex drug actions in cancer treatment.
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