ADGRE5-centered Tsurv model in T cells recognizes responders to neoadjuvant cancer immunotherapy
BackgroundNeoadjuvant 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...
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Frontiers Media S.A.
2024-01-01
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| Series: | Frontiers in Immunology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fimmu.2024.1304183/full |
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| author | Jian Li Zhouwenli Meng Zhengqi Cao Wenqing Lu Yi Yang Ziming Li Shun Lu |
| author_facet | Jian Li Zhouwenli Meng Zhengqi Cao Wenqing Lu Yi Yang Ziming Li Shun Lu |
| author_sort | Jian Li |
| collection | DOAJ |
| description | BackgroundNeoadjuvant 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.MethodsWe 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.ResultsThrough 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.ConclusionOur research provides insights into mechanisms underlying MPR in neo-antiPD1 and a well-performed model for the identification of non-MPR. |
| first_indexed | 2024-03-08T11:28:39Z |
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| id | doaj.art-191a451e7cfc4708a8b8d6e8e1ead9b9 |
| institution | Directory Open Access Journal |
| issn | 1664-3224 |
| language | English |
| last_indexed | 2024-03-08T11:28:39Z |
| publishDate | 2024-01-01 |
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| series | Frontiers in Immunology |
| spelling | doaj.art-191a451e7cfc4708a8b8d6e8e1ead9b92024-01-26T04:23:21ZengFrontiers Media S.A.Frontiers in Immunology1664-32242024-01-011510.3389/fimmu.2024.13041831304183ADGRE5-centered Tsurv model in T cells recognizes responders to neoadjuvant cancer immunotherapyJian LiZhouwenli MengZhengqi CaoWenqing LuYi YangZiming LiShun LuBackgroundNeoadjuvant 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.MethodsWe 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.ResultsThrough 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.ConclusionOur research provides insights into mechanisms underlying MPR in neo-antiPD1 and a well-performed model for the identification of non-MPR.https://www.frontiersin.org/articles/10.3389/fimmu.2024.1304183/fullnon-small cell lung cancerneo-adjuvant immunotherapy therapytumor microenvironmentScRNA-seqmultimodal |
| spellingShingle | Jian Li Zhouwenli Meng Zhengqi Cao Wenqing Lu Yi Yang Ziming Li Shun Lu ADGRE5-centered Tsurv model in T cells recognizes responders to neoadjuvant cancer immunotherapy Frontiers in Immunology non-small cell lung cancer neo-adjuvant immunotherapy therapy tumor microenvironment ScRNA-seq multimodal |
| title | ADGRE5-centered Tsurv model in T cells recognizes responders to neoadjuvant cancer immunotherapy |
| title_full | ADGRE5-centered Tsurv model in T cells recognizes responders to neoadjuvant cancer immunotherapy |
| title_fullStr | ADGRE5-centered Tsurv model in T cells recognizes responders to neoadjuvant cancer immunotherapy |
| title_full_unstemmed | ADGRE5-centered Tsurv model in T cells recognizes responders to neoadjuvant cancer immunotherapy |
| title_short | ADGRE5-centered Tsurv model in T cells recognizes responders to neoadjuvant cancer immunotherapy |
| title_sort | adgre5 centered tsurv model in t cells recognizes responders to neoadjuvant cancer immunotherapy |
| topic | non-small cell lung cancer neo-adjuvant immunotherapy therapy tumor microenvironment ScRNA-seq multimodal |
| url | https://www.frontiersin.org/articles/10.3389/fimmu.2024.1304183/full |
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