Narrative review proposes framework for TWAS signature-matching in drug prioritization

This narrative review and framework proposal evaluates the TWAS signature-matching approach, which utilizes varying parameters such as the TWAS method, eQTL tissue model, similarity metric, gene set size, and CMap cell line. The setting for this work is in silico, and the scope focuses on drug candidate prioritization rather than clinical outcomes in specific patient populations.

Key synthesized findings indicate that the performance of this approach is highly sensitive to parameter choice. Specifically, the selection of the cell line used for drug signatures alone can dramatically alter results. Despite these sensitivities, the authors note that TWAS signature-matching can successfully prioritize known first-line treatments, demonstrating its potential utility in a proof-of-concept context.

The authors acknowledge significant limitations, primarily the absence of a consensus on optimal methodology. Because this is a framework proposal rather than a primary trial, no specific sample sizes, p-values, or confidence intervals are reported. Safety data, including adverse events or tolerability, were not reported. The review concludes by proposing a best-practice framework for robust, genetically-informed drug prioritisation using TWAS signature-matching.