AB0108 USING RNA SEQUENCING TO IDENTIFY GENE EXPRESSION SIGNATURES ASSOCIATED WITH RESPONSE TO ABATACEPT IN PATIENTS WITH RHEUMATOID ARTHRITIS
Authors: H. H. Chen, W. C. Chao, J. R. Wang, T. M. Ko
Journal: Annals of the Rheumatic Diseases (2021)

Background: Rheumatoid arthritis (RA) is a common chronic autoimmune disease. Abatacept (CTLA4-immunoglobulin) is one of the biological disease-modifying antirheumatic drug (bDMARD) for RA patients with indequate response to methotrexate. Recently, Yokoyama-Kokuryo et al. compared gene expression levels between abatacept responders and non-responders in RA patients using a microarray and found that type I IFN score and expression levels of nine genes may be used as a biomarker to predict response to abatacept. However, little study used RNA sequencing (RNA-seq) to identify whole blood gene expression signatures to predict therapeutic response to abatacept. Objectives: The aim of this study is to identify gene expression signatures to predict therapeutic responses to abatacept in RA patients using RNA-seq. Methods: This study is a single-center, prospective study. We used a PAX gene Blood RNA kit to collect whole blood at baseline and 4 weeks after abatacept treatment from RA patients. We also measured DAS28, physician global assessment, HAQ, ESR, CRP at baseline and 12 week to calculate EULAR response at 12 week. Patients with good EULAR response were defined as responders and those with moderate or no EULAR response were defined as non-responders. Results: We finally conducted RNA-seq for whole blood from 7 RA patients initiating abatacept therapy. Of the 7 RA patients, one was non-responder and 6 were responders. We first use DESeq2 to analyze the differentially expressed genes of non-responder and responder before taking the drug. We used hierarchical clustering and PCA to evaluate the overall similarity of the samples, and group the patient data, and find that the nonresponder can be distinguished from responders. Subsequently, we analyzed the differentially expressed genes of the two groups of non-responder and responder patients before taking the drug. Before treatment, we found that 72 genes had a higher expression in the non-responder, and 23 genes had a higher expression level in responders. Figure 1 showed the top 20 DEG Heatmap between the non-responder and responders. Using these two sets of genes for GO analysis, we found that most of the pathways in the non-responder are related to immune response and cytokine production, and most of the pathways in the responders are related to antigen processing and MHC class II. Figure 1. Top 20 DEG Heatmap between non-responder and responders Conclusion: The study showed that most of the pathways in RA patients with no EULAR response to abatacept are related to immune response and cytokine production; while most of the pathways in RA patients with moderate/good response to abatacept are related to antigen processing and MHC class II. References: [1]Yokoyama-Kokuyo W, Yamazaki H, Takeuchi T, et al. Identification of molecules associated with response to abatacept in patients with rheumatoid arthritis. Arthritis Research &amp; Therapy. 2020;22:46. Disclosure of Interests: Hsin-Hua Chen Grant/research support from: This is an investigator-sponsored trial with Bristol-Myers Squibb who provides funding support., Wen-Cheng Chao: None declared, Jing-Rong Wang: None declared, Tai-Ming Ko: None declared