Identification of the immune checkpoint signature of multiple myeloma using mass cytometry‐based single‐cell analysis
Abstract Objectives New targets or strategies are needed to increase the success of immune checkpoint‐based immunotherapy for multiple myeloma (MM). However, immune checkpoint signals in MM microenvironment have not been fully elucidated. Here, we aimed to have a broad overview of the different immu...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Clinical & Translational Immunology |
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| Online Access: | https://doi.org/10.1002/cti2.1132 |
| _version_ | 1819116254126931968 |
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| author | Jinheng Wang Yongjiang Zheng Chenggong Tu Hui Zhang Karin Vanderkerken Eline Menu Jinbao Liu |
| author_facet | Jinheng Wang Yongjiang Zheng Chenggong Tu Hui Zhang Karin Vanderkerken Eline Menu Jinbao Liu |
| author_sort | Jinheng Wang |
| collection | DOAJ |
| description | Abstract Objectives New targets or strategies are needed to increase the success of immune checkpoint‐based immunotherapy for multiple myeloma (MM). However, immune checkpoint signals in MM microenvironment have not been fully elucidated. Here, we aimed to have a broad overview of the different immune subsets and their immune checkpoint status, within the MM microenvironment, and to provide novel immunotherapeutic targets to treat MM patients. Methods We performed immune checkpoint profiling of bone marrow (BM) samples from MM patients and healthy controls using mass cytometry. With high‐dimensional single‐cell analysis of 30 immune proteins containing 10 pairs of immune checkpoint axes in 0.55 million of BM cells, an immune landscape of MM was mapped. Results We identified an abnormality of immune cell composition by demonstrating a significant increase in activated CD4 T, CD8 T, CD8+ natural killer T‐like and NK cells in MM BM. Our data suggest a correlation between MM cells and immune checkpoint phenotypes and expand the view of MM immune signatures. Specifically, several critical immune checkpoints, such as programmed cell death 1 (PD‐1)/PD ligand 2, galectin‐9/T‐cell immunoglobulin mucin‐3, and inducible T‐cell costimulator (ICOS)/ICOS ligand, on both MM and immune effector cells and a number of activated PD‐1+ CD8 T cells lacking CD28 were distinguished in MM patients. Conclusion A clear interaction between MM cells and the surrounding immune cells was established, leading to immune checkpoint dysregulation. The analysis of the immune landscape enhances our understanding of the MM immunological milieu and proposes novel targets for improving immune checkpoint blockade‐based MM immunotherapy. |
| first_indexed | 2024-12-22T05:14:10Z |
| format | Article |
| id | doaj.art-c1e31d6901674d79b6a08f9accffbdca |
| institution | Directory Open Access Journal |
| issn | 2050-0068 |
| language | English |
| last_indexed | 2024-12-22T05:14:10Z |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Clinical & Translational Immunology |
| spelling | doaj.art-c1e31d6901674d79b6a08f9accffbdca2022-12-21T18:37:54ZengWileyClinical & Translational Immunology2050-00682020-01-0195n/an/a10.1002/cti2.1132Identification of the immune checkpoint signature of multiple myeloma using mass cytometry‐based single‐cell analysisJinheng Wang0Yongjiang Zheng1Chenggong Tu2Hui Zhang3Karin Vanderkerken4Eline Menu5Jinbao Liu6Affiliated Cancer Hospital & Institute of Guangzhou Medical University Guangzhou Municipal and Guangdong Provincial Key Laboratory of Protein Modification and Degradation State Key Laboratory of Respiratory Disease School of Basic Medical Sciences Guangzhou Medical University Guangzhou ChinaDepartment of Hematology The Third Affiliated Hospital of Sun Yat‐Sen University Guangzhou ChinaAffiliated Cancer Hospital & Institute of Guangzhou Medical University Guangzhou Municipal and Guangdong Provincial Key Laboratory of Protein Modification and Degradation State Key Laboratory of Respiratory Disease School of Basic Medical Sciences Guangzhou Medical University Guangzhou ChinaAffiliated Cancer Hospital & Institute of Guangzhou Medical University Guangzhou Municipal and Guangdong Provincial Key Laboratory of Protein Modification and Degradation State Key Laboratory of Respiratory Disease School of Basic Medical Sciences Guangzhou Medical University Guangzhou ChinaDepartment of Hematology and Immunology Myeloma Center Brussels Vrije Universiteit Brussel Brussels BelgiumDepartment of Hematology and Immunology Myeloma Center Brussels Vrije Universiteit Brussel Brussels BelgiumAffiliated Cancer Hospital & Institute of Guangzhou Medical University Guangzhou Municipal and Guangdong Provincial Key Laboratory of Protein Modification and Degradation State Key Laboratory of Respiratory Disease School of Basic Medical Sciences Guangzhou Medical University Guangzhou ChinaAbstract Objectives New targets or strategies are needed to increase the success of immune checkpoint‐based immunotherapy for multiple myeloma (MM). However, immune checkpoint signals in MM microenvironment have not been fully elucidated. Here, we aimed to have a broad overview of the different immune subsets and their immune checkpoint status, within the MM microenvironment, and to provide novel immunotherapeutic targets to treat MM patients. Methods We performed immune checkpoint profiling of bone marrow (BM) samples from MM patients and healthy controls using mass cytometry. With high‐dimensional single‐cell analysis of 30 immune proteins containing 10 pairs of immune checkpoint axes in 0.55 million of BM cells, an immune landscape of MM was mapped. Results We identified an abnormality of immune cell composition by demonstrating a significant increase in activated CD4 T, CD8 T, CD8+ natural killer T‐like and NK cells in MM BM. Our data suggest a correlation between MM cells and immune checkpoint phenotypes and expand the view of MM immune signatures. Specifically, several critical immune checkpoints, such as programmed cell death 1 (PD‐1)/PD ligand 2, galectin‐9/T‐cell immunoglobulin mucin‐3, and inducible T‐cell costimulator (ICOS)/ICOS ligand, on both MM and immune effector cells and a number of activated PD‐1+ CD8 T cells lacking CD28 were distinguished in MM patients. Conclusion A clear interaction between MM cells and the surrounding immune cells was established, leading to immune checkpoint dysregulation. The analysis of the immune landscape enhances our understanding of the MM immunological milieu and proposes novel targets for improving immune checkpoint blockade‐based MM immunotherapy.https://doi.org/10.1002/cti2.1132immune checkpointimmunotherapymass cytometrymultiple myelomasingle‐cell analysis |
| spellingShingle | Jinheng Wang Yongjiang Zheng Chenggong Tu Hui Zhang Karin Vanderkerken Eline Menu Jinbao Liu Identification of the immune checkpoint signature of multiple myeloma using mass cytometry‐based single‐cell analysis Clinical & Translational Immunology immune checkpoint immunotherapy mass cytometry multiple myeloma single‐cell analysis |
| title | Identification of the immune checkpoint signature of multiple myeloma using mass cytometry‐based single‐cell analysis |
| title_full | Identification of the immune checkpoint signature of multiple myeloma using mass cytometry‐based single‐cell analysis |
| title_fullStr | Identification of the immune checkpoint signature of multiple myeloma using mass cytometry‐based single‐cell analysis |
| title_full_unstemmed | Identification of the immune checkpoint signature of multiple myeloma using mass cytometry‐based single‐cell analysis |
| title_short | Identification of the immune checkpoint signature of multiple myeloma using mass cytometry‐based single‐cell analysis |
| title_sort | identification of the immune checkpoint signature of multiple myeloma using mass cytometry based single cell analysis |
| topic | immune checkpoint immunotherapy mass cytometry multiple myeloma single‐cell analysis |
| url | https://doi.org/10.1002/cti2.1132 |
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