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A rational approach to personalized anticancer therapy: chemoinformatic analysis reveals mechanistic gene-drug
Kerby Shedden1, Leroy B Townsend, John C Drach
1Department of Statistics, The University of Michigan, 428 Church Street, Ann Arbor, Michigan 48109, USA.
Purpose:
To predict the response of cells to chemotherapeutic agents based on gene expression profiles, we performed a chemoinformatic study of a set of standard anticancer agents assayed for activity against a panel of 60 human tumor-derived cell lines from the Developmental Therapeutics Program (DTP) at the National Cancer Institute (NCI).
Methods:
Mechanistically-relevant gene:drug activity associations were identified in the scientific literature. The correlations between expression levels of drug target genes and the activity of the drugs against the NCI's 60 cell line panel were calculated across and within each tumor tissue type, using published drug activity and gene expression data.
Results:
Compared to other mechanistically-relevant gene-drug associations, that of triciribine phosphate (TCN-P) and adenosine kinase (ADK) was exceptionally strong--overall and within tumor tissue types-across the 60 cell lines profiled for chemosensitivity (1) and gene expression (2).
Conclusion:
The results suggest ADK expression may be useful for stratifying TCN-P-responsive vs. non-responsive tumors. Based on TCN-P's mechanism of action and the observed TCN-P:ADK association, we contend that catalytic drug activation provides a rational, mechanistic basis for personalizing cancer treatment based on tumor-specific differences in the expression of drug-activating enzymes.
Insights
Gene expression profiles can predict cancer drug response. Adenosine kinase (ADK) expression strongly correlates with triciribine phosphate (TCN-P) sensitivity, suggesting personalized cancer therapy based on enzyme levels.
Area of Science:
- Chemoinformatics
- Genomics
- Pharmacology
Background:
- Predicting patient response to chemotherapy is crucial for effective cancer treatment.
- Gene expression profiling offers a potential method for predicting drug efficacy.
- The National Cancer Institute's (NCI) 60 cell line panel is a valuable resource for such studies.
Purpose of the Study:
- To predict cellular response to chemotherapeutic agents using gene expression profiles.
- To identify mechanistically-relevant gene-drug associations within a panel of 60 human tumor cell lines.
- To investigate the correlation between gene expression and drug activity for standard anticancer agents.
Main Methods:
- Literature review to identify mechanistically-relevant gene-drug associations.
- Calculation of correlations between drug target gene expression and drug activity across the NCI 60 cell line panel.
- Analysis of gene expression and chemosensitivity data within and across tumor tissue types.
Main Results:
- An exceptionally strong association was found between triciribine phosphate (TCN-P) and adenosine kinase (ADK).
- This TCN-P:ADK association was significant overall and within specific tumor tissue types.
- The correlation held true across the 60 cell lines profiled for both chemosensitivity and gene expression.
Conclusions:
- Adenosine kinase (ADK) expression may serve as a biomarker for stratifying tumors responsive to TCN-P.
- The observed TCN-P:ADK association supports a mechanistic basis for personalized cancer therapy.
- Tumor-specific differences in drug-activating enzyme expression can guide personalized treatment strategies.