ASTER: A Method to Predict Clinically Actionable Synthetic Lethal Interactions

Abstract

A Synthetic Lethal (SL) interaction is a functional relationship between two genes or functional entities where the loss of either entity is viable but the loss of both is lethal. Such pairs can be used to develop targeted anticancer therapies with fewer side effects and reduced overtreatment. However, finding clinically actionable SL interactions remains challenging. Leveraging unified gene expression data of both disease-free and cancerous samples, we design a new technique based on statistical hypothesis testing, called ASTER, to identify SL pairs. We empirically find that the patterns of mutually exclusivity ASTER finds using genomic and transcriptomic data provides a strong signal of SL. For large-scale multiple hypothesis testing, we develop an extension called ASTER++ that can utilize additional input gene features within the hypothesis testing framework. Our extensive experiments demonstrate the efficacy of ASTER in identifying SL pairs with potential therapeutic benefits. CCS CONCEPTS* Applied computing [->] Computational genomics; Health informatics; * Mathematics of computing [->] Hypothesis testing and confidence interval computation. ACM Reference FormatHerty Liany, Anand Jeyasekharan, and Vaibhav Rajan. 2021. ASTER: A Method to Predict Clinically Actionable Synthetic Lethal Genetic Interactions. In Proceedings of ACM Conference. ACM, New York, NY, USA, 10 pages. https://doi.org/10.1145/nnnnnnn.nnnnnnn