SAT0033 USING CHROMOSOME CONFORMATION FOR INSIGHT INTO PATHOGENESIS OF EARLY RHEUMATOID ARTHRITIS ENDOTYPES
Authors: C. Duncan, E. Hunter, C. Koutsothanasi, M. Salter, A. Akoulitchev, I. Mcinnes, C. Goodyear
Journal: Annals of the Rheumatic Diseases (2020)

Background: Rheumatoid arthritis (RA) is a chronic autoimmune disease with substantial immunopathogenic heterogeneity. It is well established that early diagnosis and initiation of effective therapy is crucial to prevent loss of function. Previously, various RA treatment trajectories have been identified, however there are currently no clinically validated biomarkers that can identify these trajectories at the start of treatment. Evaluation of the structural epigenome has revealed that chromosome conformation signatures (CCS) offer great potential as binary, informative biomarkers, and have been previously shown to predict early RA patient response to Methotrexate with 90% sensitivity (1). These signatures can also reveal highly regulated areas of the genome, which may be underpinning disease endotypes. Objectives: The objective of this study was to evaluate the structural epigenome in early RA over longitudinal samples to determine whether it is associated with treatment trajectories. Methods: Patient data and samples were from the Scottish Early Rheumatoid Arthritis (SERA) cohort; a pan-Scotland inception cohort. CDAI, DAS28 ESR and DAS28 CRP measurements were calculated at baseline, 6 months and 12 months to determine longitudinal treatment response. From 3 principal longitudinal response trajectories, 18 patients (who had equivalent disease activity at baseline) were chosen to investigate the structural epigenome. These 18 comprised of responders (R), non-responders (NR) and initial responders (IR; low disease activity/remission at 6 months but moderate/high disease activity at 12 months) with 6 patients per group at each time point. 20 pooled healthy samples were used as a comparator population. EpiSwitch libraries were probed on 180K Agilent SureSelect custom arrays that were designed using EpiSwitch propriety information and publicly available data from Walsh et al (2). Microarray data was analysed using the Limma package within R studio. Results: EpiSwitch array analysis showed that there were &gt;10,000 statistically significant differential chromosomal loops between R, NR and IR. Evaluation of the 3 trajectory groups (R, NR and IR), taking into account the healthy chromosomal conformation, revealed an RA-associated structural epigenome that comprised of 10,445 chromosomal loops that were stable, over the three time points. Subsequent analysis of the distinct treatment trajectories demonstrated that 3683 of the stable, disease-associated chromosomal loops were shared by all 3. However, 4496 were associated with distinct response trajectories, with 1221, 2574 and 701 loops unique to R, NR and IR respectively. Conclusion: The stable chromosomal architecture unique to each treatment trajectory suggests that various underlying molecular endotypes may exist. Moreover, the stable loops common to all groups allude to a baseline level of dysregulation in RA and offers the potential to discover novel drivers of disease. This work provides the foundation to further our understanding of RA pathogenesis and the potential of finding a biomarker that would be of significant value in a clinical setting. References: [1] Carini, C., Hunter, E., Scottish Early Rheumatoid Arthritis Inception cohort Investigators, Ramadass, A. S., Green, J., Akoulitchev, A., et al. (2018). Chromosome conformation signatures define predictive markers of inadequate response to methotrexate in early rheumatoid arthritis. Journal of Translational Medicine , 16 (1), 18–11 [2] Walsh, A. M., Whitaker, J. W., Huang, C. C., Cherkas, Y., Lamberth, S. L., Brodmerkel, C., et al. (2016). Integrative genomic deconvolution of rheumatoid arthritis GWAS loci into gene and cell type associations. Genome Biology , 17 (1), 2205 Disclosure of Interests: Caitlin Duncan: None declared, Ewan Hunter: None declared, Christina Koutsothanasi: None declared, Matthew Salter: None declared, Alexandre Akoulitchev: None declared, Iain McInnes Grant/research support from: Bristol-Myers Squibb, Celgene, Eli Lilly and Company, Janssen, and UCB, Consultant of: AbbVie, Bristol-Myers Squibb, Celgene, Eli Lilly and Company, Gilead, Janssen, Novartis, Pfizer, and UCB, Carl Goodyear: None declared