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Understanding immune phenotypes in human gastric disease tissues by multiplexed immunohistochemistry
Le Ying1,2, Feng Yan1,3, Qiaohong Meng3
1Faculty of Medical Laboratory Science, Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, 227 Chongqing Road South, Shanghai, 200025, China.
Insights
This study introduces a new multiplexed immunohistochemistry method to analyze immune cells in gastric disease. The findings reveal immune cell patterns that could guide PD-L1 therapy and predict patient responses to immunotherapy.
Area of Science:
- Immunology
- Oncology
- Pathology
Background:
- Understanding immune phenotypes in gastric disease requires advanced imaging techniques.
- Multiplexed immunohistochemistry (mIHC) with multispectral imaging enables precise analysis of tissue microenvironments.
Purpose of the Study:
- To develop and validate a novel 4-color mIHC assay for interrogating immune checkpoints in human gastric diseases.
- To analyze immune cell phenotypes and their spatial distribution within the gastric tumor microenvironment.
Main Methods:
- A 4-color mIHC assay using tyramide signal amplification was developed.
- The assay reliably detected programmed death-ligand 1 (PD-L1), CD8+ T cells, and Foxp3+ regulatory T cells.
- Cell phenotypes, co-localization, and density were analyzed in formalin-fixed, paraffin-embedded gastric tissues.
Main Results:
- PD-L1 expression was observed in gastric ulcers and tumor cells, but not in normal or pre-neoplastic tissues.
- Ratios of CD8+ T cells to Foxp3+ cells and PD-L1+ cells were altered in tumor tissues compared to normal tissues.
- Hierarchical analysis identified distinct immune phenotypes and patient subgroups based on immune cell ratios, aiding in risk stratification.
Conclusions:
- Multiplexed immunohistochemistry characterization of immune phenotypes can inform PD-L1-based clinical therapy for gastric cancer.
- Analysis of tissue microenvironments improves understanding of immune cell interactions and predicts immunotherapy response.
- This approach can help stratify patients into different risk groups for personalized treatment strategies.
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.