An integrated classifier improves prognostic accuracy in non-metastatic gastric cancer

Abstract

The American Joint Committee on Cancer (AJCC) staging system is insufficiently prognostic for gastric cancer (GC) patients and complementary factors are in urgent need. Here we aimed to develop a comprehensive model, consisting of both immune signatures and cancer signaling molecules, which was expected to accurately improve survival prediction in non-metastatic gastric cancer (GC). We first validated the prognostic value of a combination of 18 immune features and 52 cancer-signaling molecules in The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database. Then, their expression and distribution were analyzed in consecutive 1180 GC patients using immunohistochemistry. We developed and validated a novel protein-based prognostic classifier using CDH1, an epithelial–mesenchymal transition (EMT) marker, and five immune features (CD3, CD4, CD274, GZMB, and PAX5) by Cox regression model with group LASSO penalty. We observed significant differences in the overall survival of the high- and low-prognostic risk groups (66.8% VS 27.0%, P < .001). A combination of this classifier with age and pTNM stage had better prognostic value than pTNM alone. The model was further validated in both treatment-naive patients and those treated with neoadjuvant chemotherapy. Moreover, GC patients with high-risk score exhibited a favorable prognosis to adjuvant chemotherapy. This integrated classifier could be automatically analyzed and effectively predict survival of GC patients and may provide a new clinically applicable strategy to identify patients who are more likely to benefit from adjuvant chemotherapy.

Keywords

• gastric cancer
• prognostic classifier
• immunoscore
• cdh1
• chemotherapy