ANALYSIS OF PROTEIN QUANTITATIVE TRAIL LOCI TO IDENTIFY GENETIC BIOMARKERS OF TREATMENT RESPONSE TO ETANERCEPT IN PATIENTS WITH RHEUMATOID ARTHRITIS

S. Ling, C. F. Yap, N. Nair, J. Bluett, A. Morgan, J. Isaacs, A. G. Wilson, K. Hyrich, A. Barton, D. PlantThe University of Manchester, Centre for Genetics and Genomics Versus Arthritis, Manchester, United Kingdom NIHR Manchester Biomedical Research Centre, Manchester, United Kingdom University of Leeds, School of Medicine, Leeds, United Kingdom NIHR Leeds Biomedical Research Centre, Leeds, United Kingdom NIHR In Vitro Diagnostic Co-Operative, Leeds, United Kingdom Newcastle University, Translational and Clinical Research Institute, Newcastle, United Kingdom Newcastle-upon-Tyne Hospitals NHS Foundation Trust, Musculoskeletal Unit, Newcastle-upon-Tyne, United Kingdom University College Dublin, Conway Institute, Dublin, Ireland The University of Manchester, Centre for Epidemiology Versus Arthritis, Manchester, United Kingdom  Background Treatment response to etanercept in patients with rheumatoid arthritis (RA) is heterogeneous, with up to 40% switching due to failure/ineffectiveness. There are no validated pre-treatment biomarkers of response. Due to their diverse biological roles, proteins can reflect ongoing RA disease processes and may provide biomarkers of response; however, studying proteins in isolation can lead to challenges in separating the cause from the consequence of inflammation. If protein levels are under genetic control, then potentially, stable pre-treatment genetic biomarkers of etanercept response may be identified. Objectives To determine whether there is a genetic basis underlying protein expression in patients with RA treated with etanercept. Methods Participants were recruited from a UK-based prospective multi-centre study of patients fulfilling either the 1987 ACR or 2010 ACR/EULAR classification criteria for RA, starting etanercept as a first biologic. Quantitative proteomics were performed using Sequential Window Acquisition of all THeoretical fragment ion spectra mass spectrometry (SWATH-MS). Genotyping was carried out using the Illumina Infinium HumanCoreExome 12 BeadChip kit and genotype calling was carried out using GenomeStudio software (both Illumina, San Diego, CA, USA). Following standard genetic QC and imputation, a protein quantitative trait loci (pQTL) analysis was performed using a linear model adjusted for potential confounding covariates (age, biological sex, disease duration, concurrent DMARD use, seropositive status). A suggestive significance level of p<1E-05 was set for cis pQTLs; trans pQTLs were not considered due to modest sample size. Significance thresholds were adjusted for false discovery rate and subsequently, any adjusted result with p < 0.05 was considered to be significant. pQTLs were sought at baseline and after 3 months of treatment. Results 147 participants were recruited, with a median age of 56.39 years [IQR 49.34-64.73], median disease duration of 6 years [IQR 2-13] and of whom 108 (75.52%) were female. 482 unique proteins were available for analysis following proteomics and genetics data QC. At baseline (pre-treatment), 2,184 cis pQTLs were identified for 60 proteins (this may reflect many pQTLs in strong linkage disequilibrium with one another). After 3 months of treatment, 1,432 cis pQTLs were identified for 68 proteins. 2 proteins measured at pre-treatment had pQTLs (rs150571376, padj=3E-03 and rs188695391, padj=1.07E-02) where the protein expression was also associated with DAS28 at 3 months (TCPH, padj=1.40E-02 and EHD1, padj=6.54E-03, respectively). Conclusion pQTL analysis carried out in RA patients starting etanercept identified numerous loci that are significantly associated with paired protein expression data. 2 baseline proteins, TCPH and EHD1, were found to be associated with post-treatment DAS28 and also had a genetic basis underlying their expression, and these could prove to be valuable candidates for future study, as both proteins are involved in processes during ATP hydrolysis. Reference [1]Finckh et al. Ann Rheum Dis 2006;65(6):746-52. Acknowledgements: NIL. Disclosure of Interests Stephanie Ling: None declared, Chuan Fu Yap: None declared, Nisha Nair: None declared, James Bluett Grant/research support from: Research grant from Pfizer, Ann Morgan: None declared, John Isaacs: None declared, Anthony G Wilson: None declared, Kimme Hyrich: None declared, Anne Barton: None declared, Darren Plant: None declared. Keywords: Genetics/Epigenetics, -omics, Rheumatoid arthritis DOI: 10.1136/annrheumdis-2023-eular.6091Citation: , volume 82, supplement 1, year 2023, page 69Session: Genetics and EpiGenetics of RMDs (Oral Presentations)