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POS1034 IMMUNOLOGICAL PROFILING IDENTIFIED CLINICAL CORRELATES AND ABATACEPT TREATMENT RESPONSE PREDICTOR OF RHEUMATOID ARTHRITIS
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POS1034 IMMUNOLOGICAL PROFILING IDENTIFIED CLINICAL CORRELATES AND ABATACEPT TREATMENT RESPONSE PREDICTOR OF RHEUMATOID ARTHRITIS
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Journal Article
POS1034 IMMUNOLOGICAL PROFILING IDENTIFIED CLINICAL CORRELATES AND ABATACEPT TREATMENT RESPONSE PREDICTOR OF RHEUMATOID ARTHRITIS
2023
Overview
BackgroundRheumatoid arthritis (RA) patients are heterogeneity in their clinical phenotype and in response to molecular targeted therapies. To date, only limited number of studies addressed the immunological changes that lie behind the clinical heterogeneity.ObjectivesTo identify immunological correlates of clinical phenotypes and predictors of treatment response, we performed peripheral blood multi-omics profiling of patients initiating abatacept treatment.MethodsIn the PREDICTABA study, we have recruited 104 RA patients starting abatacept treatment at six hospitals in Japan (Figure 1A). Peripheral blood mass cytometry analysis was performed in discovery and validation cohorts (n=79 and 22). In total, 10 million T and B cells were clustered by cell surface protein expression (Figure 1B, 1C). After accounting for the effects of age, sex, CDAI disease activity, anti-CCP antibody titer, and donor batch effects, immune cell clusters were tested for their association with baseline treatment and future abatacept treatment response (CDAI improvement at 6 months) using mixed-effects modeling of association of single cells [1]. Peripheral blood RNA-seq identified immune cell signatures and immune pathway signatures.ResultsAt baseline, median age was 73 years. 80% were female and 80% were seropositive. Median CDAI improved from 17 to 5 after 6 months of abatacept treatment. RNA-seq immune cell gene signatures validated mass cytometry immune cell cluster frequencies. Naive CD8 T cells were negatively associated with age (odds ratio (OR) 0.57 and 0.32, P-values<0.001). Naive B cells were negatively associated with baseline prednisolone use (OR 0.53 and 0.25, P-values<0.001). Plasmablasts were negatively associated with baseline methotrexate use (OR 0.59 and P-value<0.001, Figure 1B, 1D, 1E), and with IL6_JAK_STAT3 signaling gene signature (Rho=-0.36, P-value<0.001). Finally, CD8+CD25+ T cells showed suggestive association with CDAI improvement after abatacept treatment (OR 1.4 and 1.6, P-values 0.01 and 0.07, Figure 1C, 1F, 1G).ConclusionThrough immunophenotyping analysis of RA patients, we have revealed that B cell subsets are associated with conventional RA treatments. Abundance of CD8+CD25+ T cells may be predicative of good abatacept response.Reference[1] Fonseka CY, Rao DA, Teslovich NC, Korsunsky I, Hannes SK, Slowikowski K, Gurish MF, Donlin LT, Lederer JA, Weinblatt ME et al: Mixed-effects association of single cells identifies an expanded effector CD4(+) T cell subset in rheumatoid arthritis. Sci Transl Med 2018, 10(463).Characters from table content including title and footnotes:Figure 1.(A) Overview of the study. (B-C) UMAP visualization of B cells (B) and CD8 T cells (C) from mass cytometry analysis. (D) Mixed-effects modeling of association of single cells (MASC) between 32 mass cytometry T and B cell clusters and baseline methotrexate (MTX) dose. Red dashed line indicates Bonferroni corrected P-value threshold for P-value < 0.05. (E) Negative correlation between baseline MTX dose and B_C7 Plasmablast frequencies by the Spearman’s rank correlation coefficient test. (F) MASC analysis of CDAI improvement rate at 6 months after the start of abatacept treatment. (G) The correlation between CDAI improvement rate at 6 months after abatacept treatment and CD8_C5: CD8+CD25+ T cells cluster by the Spearman’s rank correlation coefficient test. (B-G) Discovery cohort dataAcknowledgementsWe thank all the study participants and all the members of the recruitment sites for the collection of clinical data. The supercomputing resource SHIROKANE was provided by the Human Genome Center at The University of Tokyo.Disclosure of InterestsYasuo Nagafuchi Speakers bureau: Bristol-Myers Squibb, Employee of: I belong to the Social Cooperation Program, Department of functional genomics and immunological diseases, supported by Chugai Pharmaceutical., Saeko Yamada Speakers bureau: Bristol-Myers Squibb, Mineto Ota Employee of: I belong to the Social Cooperation Program, Department of functional genomics and immunological diseases, supported by Chugai Pharmaceutical, Hiroaki Hatano: None declared, Kanae Kubo Speakers bureau: Bristol-Myers Squibb, Kenichi Shimane: None declared, Keigo Setoguchi: None declared, Takanori Azuma: None declared, Mizuko Mamura: None declared, Tomohisa Okamura Employee of: I belong to the Social Cooperation Program, Department of functional genomics and immunological diseases, supported by Chugai Pharmaceutical., Kazuyoshi Ishigaki Speakers bureau: Bristol-Myers Squibb, Keishi Fujio Speakers bureau: Bristol-Myers Squibb, Grant/research support from: Bristol-Myers Squibb, Ono pharmaceutical.
