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Published on: December 9, 2015
Validation of transcriptome signature reversion for drug repurposing in oncology
Karel K M Koudijs1, Stefan Böhringer1,2, Henk-Jan Guchelaar1
1Department of Clinical Pharmacy and Toxicology, Leiden University Medical Center (LUMC); 2333 ZA Leiden, The Netherlands.
Abstract:
Transcriptome signature reversion (TSR) has been extensively proposed and used to discover new indications for existing drugs (i.e. drug repositioning, drug repurposing) for various cancer types. TSR relies on the assumption that a drug that can revert gene expression changes induced by a disease back to original, i.e. healthy, levels is likely to be therapeutically active in treating the disease. Here, we aimed to validate the concept of TSR using the PRISM repurposing data set, which is-as of writing-the largest pharmacogenomic data set. The predictive utility of the TSR approach as it has currently been used appears to be much lower than previously reported and is completely nullified after the drug gene expression signatures are adjusted for the general anti-proliferative downstream effects of drug-induced decreased cell viability. Therefore, TSR mainly relies on generic anti-proliferative drug effects rather than on targeting cancer pathways specifically upregulated in tumor types.
Insights
Transcriptome signature reversion (TSR) for drug repurposing in cancer shows limited predictive utility. Its effectiveness is nullified when accounting for general anti-proliferative effects, suggesting it targets general cell death rather than specific cancer pathways.
Area of Science:
- Pharmacogenomics
- Cancer Therapeutics
- Drug Discovery
Background:
- Transcriptome signature reversion (TSR) is a method for drug repurposing, assuming drugs normalizing disease-induced gene expression are effective.
- This approach has been widely applied to identify new cancer treatments from existing drugs.
Purpose of the Study:
- To validate the predictive utility of the TSR approach for drug repurposing.
- To assess TSR's effectiveness using the large PRISM pharmacogenomic dataset.
Main Methods:
- Utilized the PRISM dataset, the largest available pharmacogenomic dataset.
- Analyzed drug-induced gene expression signatures against disease signatures.
- Adjusted signatures for general anti-proliferative effects related to decreased cell viability.
Main Results:
- The predictive utility of TSR was found to be significantly lower than previously reported.
- TSR's predictive power was completely nullified after adjusting for anti-proliferative effects.
- TSR appears to primarily reflect general anti-proliferative drug actions.
Conclusions:
- The current application of TSR has limited value for identifying cancer-specific drug repurposing candidates.
- TSR's reliance on generic anti-proliferative effects, rather than specific cancer pathway targeting, diminishes its utility.
- Further refinement of TSR methodology is needed to improve its specificity and predictive accuracy for targeted cancer therapies.
