AUTHOR=Feng Dehua , Hao Jingwen , Li Lingxu , Chen Jian , Liu Xinying , Zhang Ruijie , Han Huirui , Li Tianyi , Wang Xuefeng , Li Xia , Yu Lei , Li Bing , Li Jin , Wang Limei 

TITLE=Drug discovery for chemotherapeutic resistance based on pathway-responsive gene sets and its application in breast cancer

JOURNAL=Frontiers in Bioinformatics

VOLUME=Volume 5 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/bioinformatics/articles/10.3389/fbinf.2025.1661601

DOI=10.3389/fbinf.2025.1661601

ISSN=2673-7647

ABSTRACT=IntroductionChemotherapy response variability in cancer patients necessitates novel strategies targeting chemoresistant populations. While combinatorial regimens show promise through synergistic pharmacological interactions, traditional pathway enrichment methods relying on static gene sets fail to capture drug-induced dynamic transcriptional perturbations.MethodsTo address this challenge, we developed the Pathway-Responsive Gene Sets (PRGS) framework to systematically identify chemoresistance-associated pathways and guide therapeutic intervention. Comparative evaluation of three computational strategies (GSEA-like method, Hypergeometric test-based method, Bates test-based method) revealed that the GSEA-like methodology exhibited superior performance, enabling precise identification of drug-induced pathway dysregulation.ResultsKey experimental findings demonstrated PRGS’s superiority over conventional Pathway Member Gene Sets (PMGS), exhibiting statistical independence (p < 0.0001) and enhanced detection of chemotherapy-driven pathway dysregulation. Application of PRGS to the GDSC dataset identified 8 resistance-associated pathways. Screening of agents targeting these pathways yielded candidates with predicted anti-resistance activity. An in vitro cellular experiment demonstrated that the bortezomib-bleomycin combination exhibited synergistic cytotoxicity (IDAcomboScore = 0.014) in T47D cells, highlighting the potential of PRGS-guided therapeutic strategies.DiscussionThis study establishes a PRGS-based methodological framework that integrates genomic perturbations with precision oncology, demonstrating its capacity to decode resistance mechanisms and guide therapeutic development through dynamic pathway analysis.