0506: Immunophenotypic Categorization of Systemic Immune-mediated Diseases

Shinji Izuka¹, Toshihiko Komai¹, Takahiro Itamiya¹, Mineto Ota², Saeko Yamada¹, Yasuo Nagafuchi², Hirofumi Shoda¹, Kosuke Matsuki³, Kazuhiko Yamamoto⁴, Tomohisa Okamura² and Keishi Fujio¹, ¹Department of Allergy and Rheumatology, Graduate School of Medicine, The University of Tokyo, Bunkyo City, Tokyo, Japan, ²Department of Allergy and Rheumatology, Graduate School of Medicine, The University of Tokyo, Department of Functional Genomics and Immunological Diseases, Graduate School of Medicine, The University of Tokyo, Bunkyo City, Tokyo, Japan, ³Research Division, Chugai Pharmaceutical Co., Ltd., Kamakura, Japan, ⁴Laboratory for Autoimmune Diseases, Center for Integrative Medical Sciences, the Institute of Physical and Chemical Research (RIKEN), Wako, Saitama, Japan

Background/Purpose: Systemic immune-mediated diseases are heterogeneous, and the pathogenesis varies among and within each disease. Some studies tried to stratify patients with immune-mediated diseases into some subgroups by the data of molecular patter or mass spectrometry [1,2]; however, to date, classification of the disease generally is based on symptoms and some specific serological features, and the treatment is relatively uniform depending on the diagnosis and affected organs. We conducted this study to reveal what kinds of immune cells are involved in each disease and reclassify them with cell proportions.

Methods: Immunophenotyping of 258 patients with systemic immune-mediated diseases, including 21 mixed connective tissue disease (MCTD), 59 idiopathic inflammatory myopathy (IIM), 20 rheumatoid arthritis (RA), 87 systemic lupus erythematosus (SLE), 52 systemic sclerosis (SSc), and 19 Takayasu arteritis (TAK) were undertaken (Table 1). We collected the clinical data and sorted a total of 32 types of immune cells from PBMCs using flow cytometry. We selected 11 out of 32 disease-specific immune cells whose variances were more than 0.10 and compared the proportion of immune cells and clinical data in each population. Categorical and continuous variables were compared using the chi-square test and analysis of variance (ANOVA). A hierarchical clustering procedure by calculating Euclidean distance and using Ward's method was performed to form clusters from the Z score of immune cell proportions.

Results: The hierarchical clustering of flow cytometry data of 11 immune cells, including CD8⁺ T, central memory CD8⁺ T, CD4⁺ T, naïve CD4⁺ T, follicular helper T (Tfh), switched memory B, unswitched memory B, classical monocytes, non-classical monocytes, myeloid dendritic cell (DC), and CD141⁺ myeloid DC identified 4 clusters (Table 2, Figure 1A). Cluster 1,2, and 4 were dominant SLE, SSc, and IIM, respectively (Figure 1B). Cluster 3 contained miscellaneous diseases but most TAK patients were classified into this group. Each cluster showed high proportions of CD8⁺ T in cluster 1, central memory CD8⁺ T, Tfh, non-classical monocytes in cluster 2, unswitched memory B, switched memory B, myeloid DC, and CD141⁺ myeloid DC in cluster 3, and CD4⁺ T, naïve CD4⁺ T, and classical monocytes in cluster 4.

Conclusion: The systemic immune-mediated diseases such as MCTD, IIM, RA, SLE, SSc, and TAK were classified into four groups by the immune cell proportions. Our data may corroborate the understanding of the pathogenesis of various systemic immune-mediated diseases and can be a clue to reclassifying them by the associated immune cells.
Table 1. Patients’ characteristics in each systemic immune-mediated disease.

Table 2. Cell populations of four clusters.

Figure 1. Heatmap of hierarchical clustering of 11 types of immune cells (A) and Proportion of disease in four clusters (B).
Disclosures: S. Izuka, None; T. Komai, Chugai Pharmaceutical.; T. Itamiya, None; M. Ota, Chugai Pharmaceutical.; S. Yamada, None; Y. Nagafuchi, Chugai Pharmaceutical.; H. Shoda, Chugai Pharmaceutical.; K. Matsuki, Chugai Pharmaceutical.; K. Yamamoto, None; T. Okamura, Chugai Pharmaceutical.; K. Fujio, Chugai Pharmaceutical..