Leveraging synthetic lethality to uncover potential therapeutic target in gastric cancer.

Abstract

Since trastuzumab was approved in 2012 for the first-line treatment of gastric cancer (GC), no significant advancement in GC targeted therapies has occurred. Synthetic lethality refers to the concept that simultaneous dysfunction of a pair of genes results in a lethal effect on cells, while the loss of an individual gene does not cause this effect. Through exploiting synthetic lethality, novel targeted therapies can be developed for the individualized treatment of GC. In this study, we proposed a computational strategy named Gastric cancer Specific Synthetic Lethality inference (GSSL) to identify synthetic lethal interactions in GC. GSSL analysis was used to infer probable synthetic lethality in GC using four accessible clinical datasets. In addition, prediction results were confirmed by experiments. GSSL analysis identified a total of 34 candidate synthetic lethal pairs, which included 33 unique targets. Among the synthetic lethal gene pairs, TP53-CHEK1 was selected for further experimental validation. Both computational and experimental results indicated that inhibiting CHEK1 could be a potential therapeutic strategy for GC patients with TP53 mutation. Meanwhile, in vitro experimental validation of two novel synthetic lethal pairs TP53-AURKB and ARID1A-EP300 further proved the universality and reliability of GSSL. Collectively, GSSL has been shown to be a reliable and feasible method for comprehensive analysis of inferring synthetic lethal interactions of GC, which may offer novel insight into the precision medicine and individualized treatment of GC.