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Tanaka H, Okada Y, Nakayamada S, Miyazaki Y, Sonehara K, Namba S, Honda S, Shirai Y, Yamamoto K, Kubo S, Ikari K, Harigai M, Sonomoto K, Tanaka Y. Extracting immunological and clinical heterogeneity across autoimmune rheumatic diseases by cohort-wide immunophenotyping. Ann Rheum Dis 2024; 83:242-252. [PMID: 37903543 PMCID: PMC10850648 DOI: 10.1136/ard-2023-224537] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Accepted: 09/13/2023] [Indexed: 11/01/2023]
Abstract
OBJECTIVE Extracting immunological and clinical heterogeneity across autoimmune rheumatic diseases (AIRDs) is essential towards personalised medicine. METHODS We conducted large-scale and cohort-wide immunophenotyping of 46 peripheral immune cells using Human Immunology Protocol of comprehensive 8-colour flow cytometric analysis. Dataset consisted of >1000 Japanese patients of 11 AIRDs with deep clinical information registered at the FLOW study, including rheumatoid arthritis (RA) and systemic lupus erythematosus (SLE). In-depth clinical and immunological characterisation was conducted for the identified RA patient clusters, including associations of inborn human genetics represented by Polygenic Risk Score (PRS). RESULTS Multimodal clustering of immunophenotypes deciphered underlying disease-cell type network in immune cell, disease and patient cluster resolutions. This provided immune cell type specificity shared or distinct across AIRDs, such as close immunological network between mixed connective tissue disease and SLE. Individual patient-level clustering dissected patients with AIRD into several clusters with different immunological features. Of these, RA-like or SLE-like clusters were exclusively dominant, showing immunological differentiation between RA and SLE across AIRDs. In-depth clinical analysis of RA revealed that such patient clusters differentially defined clinical heterogeneity in disease activity and treatment responses, such as treatment resistance in patients with RA with SLE-like immunophenotypes. PRS based on RA case-control and within-case stratified genome-wide association studies were associated with clinical and immunological characteristics. This pointed immune cell type implicated in disease biology such as dendritic cells for RA-interstitial lung disease. CONCLUSION Cohort-wide and cross-disease immunophenotyping elucidate clinically heterogeneous patient subtypes existing within single disease in immune cell type-specific manner.
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Affiliation(s)
- Hiroaki Tanaka
- First Department of Internal Medicine, University of Occupational and Environmental Health, Kitakyushu, Fukuoka, Japan
- Department of Statistical Genetics, Osaka University School of Medicine Graduate School of Medicine, Suita, Osaka, Japan
| | - Yukinori Okada
- Department of Statistical Genetics, Osaka University School of Medicine Graduate School of Medicine, Suita, Osaka, Japan
- Department of Genome Informatics, Graduate School of Medicine, the University of Tokyo, Tokyo, Japan
- Laboratory for Systems Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
- Laboratory of Statistical Immunology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Suita, Japan
- Premium Research Institute for Human Metaverse Medicine (WPI-PRIMe), Osaka University, Suita, Japan
| | - Shingo Nakayamada
- First Department of Internal Medicine, University of Occupational and Environmental Health, Kitakyushu, Fukuoka, Japan
| | - Yusuke Miyazaki
- First Department of Internal Medicine, University of Occupational and Environmental Health, Kitakyushu, Fukuoka, Japan
| | - Kyuto Sonehara
- Department of Statistical Genetics, Osaka University School of Medicine Graduate School of Medicine, Suita, Osaka, Japan
- Department of Genome Informatics, Graduate School of Medicine, the University of Tokyo, Tokyo, Japan
- Laboratory for Systems Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Shinichi Namba
- Department of Statistical Genetics, Osaka University School of Medicine Graduate School of Medicine, Suita, Osaka, Japan
| | - Suguru Honda
- Department of Rheumatology, Department of Internal Medicine, Tokyo Women's Medical University, Shinjuku-ku, Tokyo, Japan
| | - Yuya Shirai
- Department of Statistical Genetics, Osaka University School of Medicine Graduate School of Medicine, Suita, Osaka, Japan
- Laboratory of Statistical Immunology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Suita, Japan
- Department of Respiratory Medicine and Clinical Immunology, Osaka University Graduate School of Medicine, Suita, Japan
| | - Kenichi Yamamoto
- Department of Statistical Genetics, Osaka University School of Medicine Graduate School of Medicine, Suita, Osaka, Japan
- Laboratory of Children's health and Genetics, Division of Health Science, Osaka University Graduate School of Medicine, Suita, Japan
- Department of Pediatrics, Osaka University Graduate School of Medicine, Suita, Japan
| | - Satoshi Kubo
- First Department of Internal Medicine, University of Occupational and Environmental Health, Kitakyushu, Fukuoka, Japan
| | - Katsunori Ikari
- Department of Orthopedics, Tokyo Women's Medical University, Tokyo, Japan
| | - Masayoshi Harigai
- Department of Rheumatology, Department of Internal Medicine, Tokyo Women's Medical University, Shinjuku-ku, Tokyo, Japan
| | - Koshiro Sonomoto
- First Department of Internal Medicine, University of Occupational and Environmental Health, Kitakyushu, Fukuoka, Japan
- Department of Clinical Nursing, School of Health Sciences, University of Occupational and Environmental Health, Kitakyushu, Japan
| | - Yoshiya Tanaka
- First Department of Internal Medicine, University of Occupational and Environmental Health, Kitakyushu, Fukuoka, Japan
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