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Updated: Jul 23, 2026

Evaluating the Effectiveness of Cancer Drug Sensitization In Vitro and In Vivo
Published on: February 6, 2015
Molecular features that predict the response to antimetabolite chemotherapies
Mahya Mehrmohamadi1,2,3,4, Seong Ho Jeong4, Jason W Locasale1,2,3
1Duke Cancer Institute, Duke University School of Medicine, Durham, NC 27710 USA.
Background:
Antimetabolite chemotherapeutic agents that target cellular metabolism are widely used in the clinic and are thought to exert their anti-cancer effects mainly through non-specific cytotoxic effects. However, patients vary dramatically with respect to treatment outcome, and the sources of heterogeneity remain largely unknown.
Methods:
Here, we introduce a computational method for identifying gene expression signatures of response to chemotherapies and apply it to human tumors and cancer cell lines. Furthermore, we characterize a set of 17 antimetabolite agents in various contexts to investigate determinants of sensitivity to these agents.
Results:
We identify distinct favorable and unfavorable metabolic expression signatures for 5-FU and Gemcitabine. Importantly, we find that metabolic pathways targeted by each of these antimetabolites are specifically enriched in its expression signatures. We provide evidence against the common notion about non-specific cytotoxic functions of antimetabolite drugs.
Conclusions:
This study demonstrates through unbiased analyses that the activities of metabolic pathways likely contribute to therapeutic response.
Insights
This study reveals that metabolic pathways targeted by chemotherapy drugs like 5-FU and Gemcitabine are specifically linked to patient treatment response, challenging the idea of non-specific cytotoxic effects.
Area of Science:
- Oncology
- Molecular Biology
- Computational Biology
Background:
- Antimetabolite chemotherapies targeting cellular metabolism are standard cancer treatments.
- Patient responses to these therapies vary significantly, with underlying reasons largely unknown.
Purpose of the Study:
- To develop a computational method for identifying gene expression signatures related to chemotherapy response.
- To investigate the determinants of sensitivity to 17 antimetabolite agents.
Main Methods:
- A novel computational approach was used to identify gene expression signatures.
- Analysis was performed on human tumors and cancer cell lines.
- Seventeen antimetabolite agents were characterized across various contexts.
Main Results:
- Distinct gene expression signatures associated with favorable and unfavorable responses to 5-fluorouracil (5-FU) and Gemcitabine were identified.
- Metabolic pathways directly targeted by these antimetabolites showed specific enrichment in their respective expression signatures.
- Evidence suggests that antimetabolite drugs do not solely rely on non-specific cytotoxic mechanisms.
Conclusions:
- Unbiased analyses indicate that the activity of specific metabolic pathways plays a significant role in therapeutic response to antimetabolite chemotherapy.
- This finding supports a more targeted understanding of chemotherapy efficacy and patient heterogeneity.
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