AB0746 Diagnostic autoantibody signatures of rheumatoid arthritis patients identified with a bead-based assay approach

Background Rheumatoid arthritis (RA) is an autoimmune disease typically characterized by chronic inflammation, accumulation of self-reactive B-cells and production of autoantibodies of which anti–cyclic citrullinated peptide (anti-CCP) antibodies and rheumatoid factor (RF) have diagnostic utility. Despite these two well established markers up to 30% of RA patients remain sero-negative making an early diagnosis of RA more difficult. Although progress has been made to characterize risk factors for anti-CCP negative RA, studies on autoantibody profiles in CCP-negative RA are so far lacking. Objectives To improve the current diagnosis of RA, we aimed to determine whether the group of anti-CCP negative RA patients can be identified based on specific autoantibody profiles. Our major goal is to develop a novel autoantibody-based diagnostic test that allows to correctly diagnosing CCP- and RF-negative early RA patients. Methods In order to identify novel autoantigens characteristic for CCP-negative RA patients we performed a large-scale screen of 5800 proteins in a total of 150 serum samples of patients with stable RA and healthy volunteers. The major technology platform employed in this study is an automated bead-based Luminex xMAP technology which enables to measure the reactivity of autoantibodies to up to 500 different antigens in one single serum sample. All proteins are produced from E. coli , are highly purified by affinity capturing, sequenced by mass spectrometry, and each protein is coupled in optimised concentration to individual colour coded Luminex beads. Results Using both univariate and multivariate statistical algorithms we identified 144 novel antigens in RA patients. Furthermore, our data indicated heterogeneity in the autoantibody profile of RA patients and some overlap between the group of CCP-positive and CCP-negative patients. To address this complexity and heterogeneity we developed biomarker panels comprising five to ten antigens to identify CCP-negative RA patients. Notably, one panel with six antigens showed high specificity comparable to CCP. Another panel was able to detect about 60% of CCP-negative patients for whom currently no diagnostic marker is available. Conclusions CCP sero-negative RA patients can be identified based on specific set of autoantibodies. Further studies are currently conducted to validate the biomarker panels in early RA patients. Disclosure of Interest None Declared