AbstractMolecular subtyping of tumors promises a personalized stratification into different treatment regimens. In gastric adenocarcinoma, the four molecularly defined subtypes chromosomal instable (CIN), microsatellite unstable (MSI), genomically stable (GS) and EBV-positive subtype have been proposed. Following an integrative kinomics approach this computational analysis aimed to predict the best kinase inhibitor for every molecular subtype of gastric adenocarcinomas using publicly available TCGA data (n=404). Intriguingly, using the regulatory network of gastric cancer to estimate protein activity, 43% of all samples could be identified to be kinase-driven. These samples were divided into three clusters with mutually exclusive kinase activities that were independent of the established molecular subtypes. Integrating the pattern of kinase overexpression with an unsupervised target landscape of 37 approved clinical kinase drugs revealed that sunitinib had the best target spectrum across the activated kinases in all three sample clusters. Future work is warranted to validate the kinase-driven subsets of gastric cancer and sunitinib as a potential common inhibitor.