Nomograms for stratified prognosis prediction of gastric cancer by integrating programmed death ligand 1 and tumor-infiltrating immune cells

Abstract

Abstract Purpose To develop nomograms for predicting disease-free survival (DFS) and overall survival (OS) of gastric cancer (GC) by integrating programmed death ligand 1 (PD-L1) and tumor-infiltrating immune cells. Materials and methods Immunohistochemistry for PD-L1, CD4+ and CD8+ T lymphocytes and CD68+ macrophages was performed on 126 surgically-resected GC. The expression of PD-L1 and tumor-infiltrating immune cells, in combination with multiple clinicopathologic features, was used to formulate nomograms for predicting DFS or OS based on the results of multivariate Cox regression analysis. The performance of the nomograms for DFS or OS was verified in the 10-fold cross-validation of the study cohort and measured by Harrell's concordance-index (C-index). Results High PD-L1 expression (hazard ratio [HR] = 2.17, 95% confidence interval [CI] 1.37–3.43), high CD8 + T-cells population (HR = 0.35, 95% CI 0.15–0.81), high CD68 + macrophages population (HR = 1.84, 95% CI 1.17–2.89), and microsatellite instability-high (HR = 0.41, 95% CI 0.20–0.83) were independently associated with DFS. High PD-L1 expression (HR = 2.64, 95% CI 1.61–4.34]), high CD4 + T-cells population (HR = 1.98, 95% CI 1.21–3.24), high CD8 + T-cells population (HR = 0.23 95% CI 0.07–0.73), high CD68 + macrophages population (HR = 2.31, 95% CI 1.43–3.74), microsatellite instability-high (HR = 0.26, 95% CI 0.12–0.60) and tumor–node–metastasis stage (stage III vs stage I + II, HR = 1.61, 95% CI 1.01–2.56) were independently associated with OS. These factors were then selected to establish nomograms for DFS and OS individually. The established nomogram for DFS yielded a corrected C-index of 0.679 by 10-fold cross-validation. Similarly, the established nomogram for OS yielded a corrected C-index of 0.755. Conclusions The developed prognostic nomograms for gastric cancer offer a more personalized and precise prediction of DFS and OS for patients, which can help to improve prognostic stratification.