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Tumor Transplantation for Assessing the Dynamics of Tumor-Infiltrating CD8+ T Cells in Mice
Published on: June 12, 2021
ADGRE5-centered Tsurv model in T cells recognizes responders to neoadjuvant cancer immunotherapy
Jian Li1, Zhouwenli Meng1, Zhengqi Cao1
1Shanghai Lung Cancer Center, Shanghai Chest Hospital, Shanghai Jiaotong University, School of Medicine, Shanghai, China.
Background:
Neoadjuvant immunotherapy with anti-programmed death-1 (neo-antiPD1) has revolutionized perioperative methods for improvement of overall survival (OS), while approaches for major pathologic response patients' (MPR) recognition along with methods for overcoming non-MPR resistance are still in urgent need.
Methods:
We utilized and integrated publicly-available immune checkpoint inhibitors regimens (ICIs) single-cell (sc) data as the discovery datasets, and innovatively developed a cell-communication analysis pipeline, along with a VIPER-based-SCENIC process, to thoroughly dissect MPR-responding subsets. Besides, we further employed our own non-small cell lung cancer (NSCLC) ICIs cohort's sc data for validation in-silico. Afterward, we resorted to ICIs-resistant murine models developed by us with multimodal investigation, including bulk-RNA-sequencing, Chip-sequencing and high-dimensional cytometry by time of flight (CYTOF) to consolidate our findings in-vivo. To comprehensively explore mechanisms, we adopted 3D ex-vivo hydrogel models for analysis. Furthermore, we constructed an ADGRE5-centered Tsurv model from our discovery dataset by machine learning (ML) algorithms for a wide range of tumor types (NSCLC, melanoma, urothelial cancer, etc.) and verified it in peripheral blood mononuclear cells (PBMCs) sc datasets.
Results:
Through a meta-analysis of multimodal sequential sc sequencing data from pre-ICIs and post-ICIs, we identified an MPR-expanding T cells meta-cluster (MPR-E) in the tumor microenvironment (TME), characterized by a stem-like CD8+ T cluster (survT) with STAT5-ADGRE5 axis enhancement compared to non-MPR or pre-ICIs TME. Through multi-omics analysis of murine TME, we further confirmed the existence of survT with silenced function and immune checkpoints (ICs) in MPR-E. After verification of the STAT5-ADGRE5 axis of survT in independent ICIs cohorts, an ADGRE5-centered Tsurv model was then developed through ML for identification of MPR patients pre-ICIs and post-ICIs, both in TME and PBMCs, which was further verified in pan-cancer immunotherapy cohorts. Mechanistically, we unveiled ICIs stimulated ADGRE5 upregulation in a STAT5-IL32 dependent manner in a 3D ex-vivo system (3D-HYGTIC) developed by us previously, which marked Tsurv with better survival flexibility, enhanced stemness and potential cytotoxicity within TME.
Conclusion:
Our research provides insights into mechanisms underlying MPR in neo-antiPD1 and a well-performed model for the identification of non-MPR.
Insights
Researchers identified a stem-like T cell cluster (survT) associated with major pathologic response (MPR) after neoadjuvant anti-PD1 immunotherapy. A predictive model based on ADGRE5 was developed to identify patients likely to achieve MPR, aiding treatment strategies.
Area of Science:
- Immunology
- Oncology
- Computational Biology
Background:
- Neoadjuvant anti-PD1 immunotherapy has improved survival but lacks methods to identify patients achieving major pathologic response (MPR) or overcome resistance.
- Accurate identification of MPR is crucial for optimizing perioperative strategies and patient outcomes in cancer treatment.
Purpose of the Study:
- To dissect the cellular mechanisms underlying MPR in response to neoadjuvant anti-PD1 immunotherapy.
- To develop a predictive model for identifying patients who will achieve MPR.
- To understand resistance mechanisms in non-MPR patients.
Main Methods:
- Integrated publicly available immune checkpoint inhibitor (ICI) single-cell (sc) data for discovery.
- Developed cell-communication analysis and VIPER-based-SCENIC pipelines.
- Validated findings in non-small cell lung cancer (NSCLC) ICI cohort sc data and murine models (bulk-RNA-seq, Chip-seq, CYTOF).
- Utilized 3D ex-vivo hydrogel models and machine learning (ML) to construct an ADGRE5-centered Tsurv model.
Main Results:
- Identified an MPR-expanding T cell meta-cluster (MPR-E) characterized by stem-like CD8+ T cells (survT) with enhanced STAT5-ADGRE5 axis signaling.
- Confirmed survT cells with silenced function and immune checkpoints in MPR-E using multi-omics analysis in murine models.
- Developed an ADGRE5-centered Tsurv model using ML, validated in TME and peripheral blood mononuclear cells (PBMCs) across various cancer types, predicting MPR pre- and post-ICI treatment.
- Unveiled ICIs-stimulated ADGRE5 upregulation via STAT5-IL32, enhancing survT cell stemness and cytotoxicity.
Conclusions:
- The study provides insights into the mechanisms of MPR in neoadjuvant anti-PD1 therapy.
- An effective ADGRE5-centered Tsurv model was developed for identifying non-MPR patients.
- Findings can guide the development of novel therapeutic strategies to improve immunotherapy response rates.
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