Immunomarker Support Vector Machine Classifier for Prediction of Gastric Cancer Survival and Adjuvant Chemotherapeutic Benefit

Purpose: Current TNM staging system can't provide adequate information for prediction of prognosis and chemotherapeutic benefits. To construct a classifier to predict prognosis and identify a subset of patients who can benefit from adjuvant chemotherapy. Experimental Design: We detected expression of 15 immunohistochemistry (IHC) features in tumors from 251 GC patients and evaluated the association of their expression level with overall survival (OS) and disease-free survival (DFS). Then, integrating multiple clinicopathologic features and IHC features, we used support vector machine (SVM) -based methods to develop a prognostic classifier (GC-SVM classifier) with eleven features. Further validation of the GC-SVM classifier was performed in two validation cohort of 535 patients. Results: The GC-SVM classifier integrated patient sex, CEA, lymph node metastasis and the protein expression level of eight features, including CD3_{invasive margin (IM)}, CD3_{center of tumor (CT)}, CD45RO_CT, CD57_IM, CD66b_IM, CD68_CT and CD34. Significant differences were found between the high- and low- GC-SVM patients in 5-year OS and DFS in training and validation cohorts. Multivariate analysis revealed that the GC-SVM classifier was an independent prognostic factor. The classifier had higher predictive accuracy for OS and DFS than TNM stage and can complement the prognostic value of the TNM staging system. Further analysis revealed that stage II and III GC patients with high-GC-SVM were pone to benefit from adjuvant chemotherapy. Conclusion: The newly developed GC-SVM classifier was a powerful predictor of OS and DFS. Moreover, the GC-SVM classifier could predict which patients with stage II and III GC benefit from adjuvant chemotherapy.

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