Identification and validation of a novel survival prediction model based on the T-cell phenotype in the tumor immune microenvironment and peripheral blood for gastric cancer prognosis

• Published in: Journal of translational medicine 2023-02, Vol.21 (1), p.73-73, Article 73
• Main Authors: Ma, Jing, Li, Jianhui, He, Nan, Qian, Meirui, Lu, Yuanyuan, Wang, Xin, Wu, Kaichun
• Format: Article
• Language: English
• Subjects: Antibodies; Antigens; B7-H1 Antigen - metabolism; Biological markers; Cancer therapies; CD223 antigen; CD4 antigen; CD8 antigen; CD8-Positive T-Lymphocytes; Cells; Development and progression; Flow cytometry; Gastric cancer; Health aspects; Hepatitis A Virus Cellular Receptor 2 - metabolism; Humans; Identification and classification; Immunofluorescence; Inhibitory molecule; Lymphocytes; Lymphocytes T; Lymphocytes, Tumor-Infiltrating; Medical prognosis; Microenvironments; Multivariate analysis; PD-1 protein; PD-L1 protein; Peripheral blood; Peripheral blood lymphocytes; Phenotypes; Prediction models; Prognosis; Programmed Cell Death 1 Receptor - metabolism; Regression analysis; Risk factors; Software; Stomach cancer; Stomach Neoplasms - pathology; Survival prediction model; T cells; Tumor immune microenvironment; Tumor Microenvironment; Tumor-infiltrating lymphocytes
• ISSN: 1479-5876; 1479-5876
• DOI: 10.1186/s12967-023-03922-0

The correlation and difference in T-cell phenotypes between peripheral blood lymphocytes (PBLs) and the tumor immune microenvironment (TIME) in patients with gastric cancer (GC) is not clear. We aimed to characterize the phenotypes of CD8 T cells in tumor infiltrating lymphocytes (TILs) and PBLs in patients with different outcomes and to establish a useful survival prediction model. Multiplex immunofluorescence staining and flow cytometry were used to detect the expression of inhibitory molecules (IMs) and active markers (AMs) in CD8 TILs and PBLs, respectively. The role of these parameters in the 3-year prognosis was assessed by receiver operating characteristic analysis. Then, we divided patients into two TIME clusters (TIME-A/B) and two PBL clusters (PBL-A/B) by unsupervised hierarchical clustering based on the results of multivariate analysis, and used the Kaplan-Meier method to analyze the difference in prognosis between each group. Finally, we constructed and compared three survival prediction models based on Cox regression analysis, and further validated the efficiency and accuracy in the internal and external cohorts. The percentage of PD-1 CD8 TILs, TIM-3 CD8 TILs, PD-L1 CD8 TILs, and PD-L1 CD8 PBLs and the density of PD-L1 CD8 TILs were independent risk factors, while the percentage of TIM-3 CD8 PBLs was an independent protective factor. The patients in the TIME-B group showed a worse 3-year overall survival (OS) (HR: 3.256, 95% CI 1.318-8.043, P = 0.006), with a higher density of PD-L1 CD8 TILs (P