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T-bet+CD8+ T cells govern anti-PD-1 responses in microsatellite-stable gastric cancers
Shiying Tang1,2,3,4, Xiaofang Che1,2,3,4, Jinyan Wang5
1Department of Medical Oncology, The First Hospital of China Medical University, No. 155, Nanjing Street, Shenyang, Liaoning, China.
Abstract:
More than 90% of advanced gastric cancers (GC) are microsatellite-stable (MSS). Compared to the high response rate of immune checkpoint inhibitors (ICI) in microsatellite-instability-high (MSI-H) GCs, only 10% of unstratified MSS GCs respond to ICIs. In this study, we apply semi-supervised learning to stratify potential ICI responders in MSS GCs, achieving high accuracy, quantified by an area under the curve of 0.924. Spatial analysis of the tumor microenvironment of ICI-sensitive GCs reveals a high level of T-bet+ CD8 + T cell infiltration in their tumor compartments. T-bet+ CD8 + T cells exhibit superior anti-tumor activity due to their increased ability to infiltrate tumors and secrete cytotoxic molecules. Adoptive transfer of T-bet+ CD8 + T cells boosts anti-tumor immunity and confers susceptibility to ICIs in immune-ignorant MSS GCs in a humanized mouse model. Spatial RNA sequencing suggests a positive-feedback loop between T-bet+ T cells and PD-L1+ tumor cells, which eventually drives T cell exhaustion and can therefore be leveraged for ICI therapy. In summary, our research provides insights into the underlying mechanism of anti-tumor immunity and deepens our understanding of varied ICI responses in MSS GCs.
Insights
Researchers developed a method to identify microsatellite-stable gastric cancer patients likely to respond to immune checkpoint inhibitors (ICI). This approach targets T-bet+ CD8+ T cells, enhancing anti-tumor immunity for better ICI therapy outcomes.
Area of Science:
- Oncology
- Immunology
- Computational Biology
Background:
- Advanced gastric cancer (GC) is predominantly microsatellite-stable (MSS), with limited response to immune checkpoint inhibitors (ICIs).
- Identifying responders among MSS GC patients is crucial for effective immunotherapy.
- Current biomarkers do not adequately stratify MSS GC patients for ICI treatment.
Purpose of the Study:
- To develop a computational method for stratifying MSS GC patients who may respond to ICIs.
- To investigate the role of T-bet+ CD8+ T cells in ICI sensitivity within MSS GC.
- To explore therapeutic strategies for enhancing ICI efficacy in MSS GC.
Main Methods:
- Application of semi-supervised learning to stratify potential ICI responders in MSS GC.
- Spatial analysis of the tumor microenvironment in ICI-sensitive and resistant MSS GC.
- Adoptive transfer of T-bet+ CD8+ T cells in a humanized mouse model.
- Spatial RNA sequencing to elucidate T cell-tumor cell interactions.
Main Results:
- Semi-supervised learning model achieved high accuracy (AUC 0.924) in predicting ICI response in MSS GC.
- ICI-sensitive MSS GC tumors exhibit significant infiltration of T-bet+ CD8+ T cells.
- T-bet+ CD8+ T cells demonstrate potent anti-tumor activity and enhance ICI susceptibility.
- A positive feedback loop between T-bet+ T cells and PD-L1+ tumor cells was identified, contributing to T cell exhaustion.
Conclusions:
- T-bet+ CD8+ T cells are key players in anti-tumor immunity and ICI response in MSS GC.
- Targeting T-bet+ T cells and their interactions can overcome immune resistance in MSS GC.
- This study provides a novel strategy for patient stratification and therapeutic development for MSS GC immunotherapy.
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