Combined Consideration of Tumor-Associated Immune Cell Density and Immune Checkpoint Expression in the Peritumoral Microenvironment for Prognostic Stratification of Non-Small-Cell Lung Cancer Patients - Yang, Yong; Yang, Xiaobao; Wang, Yichao; Xu, Jingsong; Shen, Hanyu; Gou, Hongquan; Qin, Xiong; Jiang, Gening (Frontiers in Immunology, 2022)
Given the complexity and highly heterogeneous nature of the microenvironment and its effects on antitumor immunity and cancer immune evasion, the prognostic value of a single immune marker is limited. Here, we show how the integration of immune checkpoint molecule expression and tumor-associated immune cell distribution patterns can influence prognosis prediction in non-small-cell lung cancer (NSCLC) patients. We analyzed tissue microarray (TMA) data derived from multiplex immunohistochemistry results and measured the densities of tumor-infiltrating CD8+ and FOXP3+ immune cells and tumor cells (PanCK+), as well as the densities of programmed cell death 1 (PD-1)+ and programmed cell death ligand 1 (PD-L1)+ cells in the peritumor and intratumor subregions. We found a higher density of infiltrating CD8+ and FOXP3+ immune cells in the peritumoral compartment than in the intratumoral compartment. In addition, unsupervised hierarchical clustering analysis of these markers revealed that the combination of high CD8/FOXP3 expression, low PD-1 and PD-L1 immune checkpoint expression, and lack of epidermal growth factor receptor (EGFR) mutation could be a favorable predictive marker. On the other hand, based on the clustering analysis, low CD8/FOXP3 and immune checkpoint (PD-1 and PD-L1) expression might be a marker for patients who are likely to respond to strategies targeting regulatory T (Treg) cells. Furthermore, an immune risk score model was established based on multivariate Cox regression, and the risk score was determined to be an independent prognostic factor for NSCLC patients. These results indicate that the immune context is heterogeneous because of the complex interactions of different components and that using multiple factors in combination might be promising for predicting the prognosis of and stratifying NSCLC patients.