Development and validation of a prognostic and predictive 32-gene signature for gastric cancer

The ability to predict the survival and response to treatment of cancer patients may improve patient care. Here, the authors generate a 32 gene signature that can predict the survival and response to treatment in gastric cancer patients. Genomic profiling can provide prognostic and predictive information to guide clinical care. Biomarkers that reliably predict patient response to chemotherapy and immune checkpoint inhibition in gastric cancer are lacking. In this retrospective analysis, we use our machine learning algorithm NTriPath to identify a gastric-cancer specific 32-gene signature. Using unsupervised clustering on expression levels of these 32 genes in tumors from 567 patients, we identify four molecular subtypes that are prognostic for survival. We then built a support vector machine with linear kernel to generate a risk score that is prognostic for five-year overall survival and validate the risk score using three independent datasets. We also find that the molecular subtypes predict response to adjuvant 5-fluorouracil and platinum therapy after gastrectomy and to immune checkpoint inhibitors in patients with metastatic or recurrent disease. In sum, we show that the 32-gene signature is a promising prognostic and predictive biomarker to guide the clinical care of gastric cancer patients and should be validated using large patient cohorts in a prospective manner.

Automated summary

The ability to predict the survival and response to treatment of gastric cancer patients can be improved by generating a 32 gene signature. This study identifies four prognostic molecular subtypes through genomic profiling and shows that the 32-gene signature is a promising prognostic and predictive biomarker for guiding the clinical care of gastric cancer patients.