Spatial analysis of CD8+ T cell neighbors using multiplexed immunofluorescence (MxIF) in esophageal squamous cell carcinoma (ESCC).

Abstract

e16100 Background: Little is known of how the spatial orientation of the tumor immune microenvironment (TIME) contributes to ESCC progression. Higher CD8+ T cell number correlates with improved response to immunotherapy and better survival. However, which cell types are close to CD8+ T cells at different stages or differentiation status of ESCC is unknown. We used MxIF to identify the location of CD8+ T cell neighbors (fibroblasts, epithelial cells, macrophages, eosinophils, CD4+ T cells, Tregs) and cell states (proliferation, apoptosis, DNA damage) to determine whether there are specific cell types neighboring CD8+ T cells associated with stage or differentiation. Methods: We created a tissue microarray of 27 ESCC resection specimens from Japanese patients who had no prior therapy and performed MxIF for the aforementioned cell types/states. We generated probability maps of positive markers in a substructure specific manner using machine learning to segment cells. Each cell was summed for marker combinations related to cell type/state for accurate distances between cells. Demographic characteristics were summarized using descriptive statistics. We calculated effective neighbor count by multiplying neighbor proportion of a cell type by cell type intensity to define the expected number of neighboring cells within 50 µm of each CD8+ T cell. Proportion is the number of CD8+ T cells with that cell type as a neighbor divided by the total number of CD8+ T cells (e.g. 55% of CD8+ T cells neighbor a macrophage). Intensity is the average number of neighbors per CD8+ T cell with 1 neighbor of that cell type (e.g. each CD8+ T cell with 1 macrophage neighbor had 20.5 macrophage neighbors, for an effective macrophage neighbor count of 11.3). Effective neighbor counts were compared across differentiation status, T- and N-stage using the Kruskal–Wallis or Wilcoxon rank-sum test, as appropriate. Results: Demographics are shown in the Table. The most effective neighbor cell types are macrophages (11.3), CD4+ T cells (9.3), and fibroblasts (6.0). In well versus poorly differentiated tumors, significantly more fibroblasts neighbor CD8+ T cells (p = 0.012), but fewer CD4+ T cells neighbor CD8+ T cells (p < 0.01). Stage T2/3 specimens exhibit more neighboring fibroblasts (p = 0.017) and fewer Treg neighbors (p = 0.064) to CD8+ T cells. There were no significant differences when comparing N0 vs N+. Conclusions: In these 27 patients with ESCC, CD8+ T cells more commonly neighbor fibroblasts in poorly differentiated and higher T-stage tumors. Future studies should focus on understanding the interaction of CD8+ T cell/fibroblast crosstalk during ESCC progression. Characteristic N = 27 Age (mean) 67 years Male Gender 67% Stage T1: 59%T2: 11%T3: 30% N-stage N0: 52%N+: 48% Differentiation Well: 22%Moderate: 59%Poor: 19% Tobacco use (mean) 33.7 pack years Alcohol use Social: 7%Habitual: 56%Heavy: 37%