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Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization
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Published on: October 3, 2025

Predicting drug response based on gene expression

Jacques Robert1, Antoine Vekris, Philippe Pourquier

  • 1Institut Bergonié and Université Victor Segalen Bordeaux 2, 229 cours de l'Argonne, 33076 Bordeaux, France. robert@bergionie.org

Critical Reviews in Oncology/Hematology
|August 28, 2004
PubMed

Insights

Predicting cancer drug response remains difficult. Research identifies drug resistance mechanisms and uses gene expression profiling to correlate gene activity with drug sensitivity, aiming for personalized cancer treatments.

Area of Science:

  • Oncology
  • Molecular Biology
  • Pharmacogenomics

Background:

  • Predicting individual patient response to chemotherapy is a significant challenge in cancer treatment.
  • Historically, cellular assays for predicting in vitro drug response faced limitations in routine clinical adoption.
  • Numerous mechanisms of anticancer drug resistance have been identified in cell lines, including issues with drug transport, metabolism, target alteration, DNA repair, and cell death pathways.

Purpose of the Study:

  • To review the mechanisms of anticancer drug resistance.
  • To explore the role of molecular biology techniques, particularly gene expression profiling, in understanding drug sensitivity.
  • To discuss the potential of identified genes and proteins as predictive markers for personalized chemotherapy selection.

Main Methods:

  • Identification of drug resistance mechanisms in cultured cancer cell lines.
  • Application of molecular biology techniques, including differential gene expression analysis.
  • Utilizing gene expression profiling to correlate gene activity with drug sensitivity in vitro and in human cancers.

Main Results:

  • Established correlations between gene expression patterns and the drug sensitivity of tumor cells.
  • Identified specific genes and proteins implicated in various drug resistance mechanisms.
  • Demonstrated the feasibility of using gene expression data to predict drug response.

Conclusions:

  • Gene expression profiling offers a powerful approach to understand and predict anticancer drug response.
  • Identifying predictive markers holds promise for developing more rational and individualized cancer chemotherapy strategies.
  • The integration of molecular data into clinical practice could significantly improve patient outcomes.

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