Understanding immune phenotypes in human gastric disease tissues by multiplexed immunohistochemistry

Abstract

BACKGROUND: Understanding immune phenotypes and human gastric disease in situ requires an approach that leverages multiplexed immunohistochemistry (mIHC) with multispectral imaging to facilitate precise image analyses. METHODS: We developed a novel 4-color mIHC assay based on tyramide signal amplification that allowed us to reliably interrogate immunologic checkpoints, including programmed death-ligand 1 (PD-L1), cytotoxic T cells (CD8+T) and regulatory T cells (Foxp3), in formalin-fixed, paraffin-embedded tissues of various human gastric diseases. By observing cell phenotypes within the disease tissue microenvironment, we were able to determine specific co-localized staining combinations and various measures of cell density. RESULTS: We found that PD-L1 was expressed in gastric ulcer and in tumor cells (TCs), as well as in tumor-infiltrating immune cells (TIICs), but not in normal gastric mucosa or other gastric intraepithelial neoplastic tissues. Furthermore, we found no significant reduction in CD8+T cells, whereas the ratio of CD8+T:Foxp3 cells and CD8+T:PD-L1 cells was suppressed in tumor tissues and elevated in adjacent normal tissues. An unsupervised hierarchical analysis also identified correlations between CD8+T and Foxp3+ tumor-infiltrating lymphocyte (TIL) densities and average PD-L1 levels. Three main groups were identified based on the results of CD8+T:PD-L1 ratios in gastric tumor tissues. Furthermore, integrating CD8+T:Foxp3 ratios, which increased the complexity for immune phenotype status, revealed 6-7 clusters that enabled the separation of gastric cancer patients at the same clinical stage into different risk-group subsets. CONCLUSIONS: Characterizing immune phenotypes in human gastric disease tissues via multiplexed immunohistochemistry may help guide PD-L1 clinical therapy. Observing unique disease tissue microenvironments can improve our understanding of immune phenotypes and cell interactions within these microenvironments, providing the ability to predict safe responses to immunotherapies.