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

Dehua Feng1, Jingwen Hao2, Lingxu Li1

  • 1School of Intelligent Medicine and Technology, Kidney Disease Research Institute, Hainan Engineering Research Center for Health Big Data, Hainan Medical University, Haikou, Hainan, China.

Frontiers in Bioinformatics
|October 2, 2025
PubMed
Abstract

Insights

We developed Pathway-Responsive Gene Sets (PRGS) to identify cancer drug resistance pathways. PRGS accurately detects dynamic gene changes, guiding development of novel combination therapies like bortezomib-bleomycin for improved patient outcomes.

Area of Science:

  • Oncology
  • Genomics
  • Computational Biology

Background:

  • Chemotherapy response varies significantly among cancer patients, necessitating strategies to overcome chemoresistance.
  • Traditional pathway enrichment methods using static gene sets cannot capture dynamic, drug-induced transcriptional changes crucial for understanding resistance.

Purpose of the Study:

  • To develop and validate the Pathway-Responsive Gene Sets (PRGS) framework for identifying chemoresistance-associated pathways.
  • To enable precise identification of drug-induced pathway dysregulation and guide the development of targeted anti-resistance therapies.

Main Methods:

  • Developed the PRGS framework, comparing its performance against traditional Pathway Member Gene Sets (PMGS).
  • Utilized a GSEA-like methodology within PRGS, validated through comparative analysis with Hypergeometric and Bates test-based methods.
  • Applied PRGS to the GDSC dataset to identify resistance pathways and screened for targeted agents.

Main Results:

  • PRGS demonstrated statistical independence (p < 0.0001) and superior detection of chemotherapy-driven pathway dysregulation compared to PMGS.
  • Identified 8 chemoresistance-associated pathways using PRGS on the GDSC dataset.
  • An in vitro study confirmed synergistic cytotoxicity (IDAcomboScore = 0.014) for a bortezomib-bleomycin combination in T47D cells, validating PRGS-guided strategy.

Conclusions:

  • The PRGS framework provides a novel methodological approach integrating genomic perturbations with precision oncology.
  • PRGS effectively decodes cancer drug resistance mechanisms by analyzing dynamic pathway alterations.
  • This approach holds significant potential for guiding the development of more effective therapeutic strategies against chemoresistant cancers.
Keywords:
chemotherapeutic resistancedrug combination therapydrug discoverypathway-responsive gene setsprecision oncology

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