RE: GITR GENE DELETION AND GITR-FC SOLUBLE PROTEIN ADMINISTRATION INHIBIT MULTIPLE ORGAN FAILURE INDUCED BY ZYMOSAN. DOI: 10.1097/SHK.0B013E3182262C48 - Salvatore Cuzzocrea (Shock, 2024)
Background Programmed death-ligand 1 (PD-L1) expression and CD8-positive (CD8+) T-cell infiltration in tumor tissue are associated with prognosis in non-small cell lung cancer (NSCLC). However, the prognostic value of combined PD-L1/CD8 immune phenotyping in surgically treated NSCLC and the feasibility of predicting high-risk immune phenotypes using routine clinical indicators remain unclear. This study aimed to evaluate the prognostic significance of PD-L1/CD8 status and to develop machine learning models for predicting PD-L1-positive/CD8-negative (PD-L1+ CD8-) status. Methods This study included 844 patients with NSCLC who underwent surgical resection at Wuhan Union Hospital and Renmin Hospital of Wuhan University between March 2012 and November 2022. PD-L1 expression and CD8+ T-cell infiltration were assessed by immunohistochemistry, and preoperative clinical data were collected. Kaplan-Meier analysis, stratified survival analysis, univariable and multivariable Cox proportional hazards regression models were used to evaluate the association between PD-L1/CD8 status and overall survival (OS). Patients from Wuhan Union Hospital were divided into training, test, and internal validation sets, whereas patients from Renmin Hospital of Wuhan University were used as an external validation set. Multiple machine learning models were developed to predict PD-L1+ CD8- status. Feature importance and and SHapley Additive exPlanations (SHAP) analysis were used to interpret model predictions. Results Based on PD‑L1/CD8 status, tumors from the 844 patients were classified into four immune phenotypes. Kaplan-Meier analyses revealed significant differences in OS among the four PD-L1/CD8 subgroups, with the PD-L1+ CD8- subgroup displaying the poorest survival. This pattern was also observed in several stratified analyses. Multivariable Cox regression analysis further demonstrated that PD-L1+ CD8- status was independently associated with worse OS compared with PD-L1+ CD8+ status (hazard ratio = 3.261, 95% confidence interval: 1.310-8.116, P = 0.011). On this basis, machine learning models were developed to predict PD-L1+ CD8- status. Among the evaluated models, the stacking model showed the best overall performance, with area under the curve values of 0.998, 0.700, 0.853, and 0.783 in the training, test, internal validation, and external validation sets, respectively. Feature importance and SHAP analyses suggested that PD-L1+ CD8- status was associated with a composite pattern involving hematologic, biochemical, inflammatory, and coagulation-related indicators. Conclusions PD-L1/CD8-based phenotyping identified prognostically distinct subgroups in surgically treated NSCLC, with PD-L1+ CD8- status defining a high-risk phenotype associated with poor survival. A machine learning model based on clinical indicators may enable efficient, noninvasive identification of PD-L1+ CD8- status and help guide treatment decisions.