Identification and validation of an 11-kinase signature that predicts chemo- and radiosensitivity in gastric cancer

BACKGROUND: Most gastric cancer (GC) patients receive uniform treatment due to the lack of predictive biomarkers for chemotherapy or radiotherapy. We previously identified epithelial-mesenchymal transition (EMT) and metabolism subgroups in GC cell lines based on kinomic profiles, but their clinical relevance was unknown. METHODS: We developed an ensemble model using 37 kinases that differed between cell line subgroups to classify stage II-IV GC into EMT and metabolism subgroups. Survival differences between those who received chemotherapy or radiotherapy and those who did not were compared within each subgroup to validate the model effectiveness in predicting therapeutic response. An iteration approach was further applied to optimise and validate the feature set via multiple publicly-available datasets. Significant interaction between kinomic subgroups and treatments were noted. The chemotherapy benefits between subgroups were greater with 5-fluorouracil-based regimens than cisplatin-based ones. This kinomic taxonomy was distinct from Lauren classification and previous transcriptomic subtypes and also suggested differential therapeutic vulnerabilities between subgroups. INTERPRETATION: This model holds promise for optimising chemotherapy and radiotherapy decisions for GC. FUNDING: Biomedicine Discovery Scholarship and Graduate Research Completion Award, Monash University; National Natural Science Foundation of China (81602165) (C.H.). Australian Research Council Centre of Excellence for the Mathematical Analysis of Cellular Systems (CE230100001) (L.K.N.).