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Background/Aims The use of tumour necrosis factor inhibitors (TNFi) has advanced the clinical management of rheumatoid arthritis (RA). However, only 30-40% of patients who receive initial treatment with a TNFi experience a beneficial response. The discovery of reliable biomarkers of TNFi response is, therefore, a priority. Whilst T-cell dysfunction is documented in RA pathophysiology, T-cell involvement in TNFi treatment response is unclear. We aimed to phenotype peripheral blood-derived T-cells in RA patients over a number of time-points through the first 12-weeks of TNFi treatment; and to apply a cluster-free association testing method developed on single-cell CITE-seq data—co-varying neighbourhood analysis (CNA)—to high-dimensional single-cell CyTOF data. Methods 16 TNFi-naïve RA patients commenced treatment with a TNFi (10 on Adalimumab, 6 on Etanercept) and peripheral blood samples were collected at 6-7 time-points between pre-treatment and 12-weeks. Peripheral blood mononuclear cells were extracted, and stained with a 35-marker CyTOF panel. Treatment response was classified using the EULAR response criteria, and patients were characterised as good, moderate or non-responders. Data were cleaned in FlowJo and analysed using both i) a cluster-based approach, where data were clustered into 12 populations, and linear mixed models were fitted per cluster to test for significant differences in population abundance and ii) a cluster-free approach called CNA where association testing is purely data-driven. Both analyses controlled for age, sex, type of TNFi and TNFi concentration. Results Cluster-based analysis showed at 12-weeks, 1 of the 12 clusters (CD4+CD39+HLA-DR+) significantly increased in abundance in the non-responder cohort compared to the good and moderate responders (adjusted p = 0.037). Interestingly, HLA-DR and CD39 genetic effects have previously been associated with TNFi response. No significant differences were observed with CNA. Conclusion These results demonstrate that significant differences in T-cell abundance can be seen as early as 12-weeks after commencing treatment with a TNFi. Validation of these findings is required before the predictive value of this T-cell population can be tested. Disclosure M.A. Sutcliffe: None. S. Ling: None. A. Banyard: None. L. Rumker: None. S. Viatte: None. A. Morris: None. A. Barton: None. S. Raychaudhuri: None. D. Plant: None.

Background For patients with rheumatoid arthritis (RA), introduction of early, effective therapy has consistently been shown to improve long-term outcomes. Low-dose methotrexate (MTX) is commonly prescribed as first-line treatment for RA. However, MTX is not effective for a large minority of patients and there is currently no way to determine ahead of therapy which patients are most likely to benefit. Metabolomics and lipidomics are emerging approaches for studying patient stratification in RA and have the potential to identify disease processes that underpin treatment outcomes. Here we apply state-of-the-art machine learning algorithms to predict MTX treatment response, by testing serum lipid levels measured at two time-points (pre-treatment and following 4 weeks on drug) to predict MTX response by 6 months. Methods This study included patients from the Rheumatoid Arthritis Medication Study (RAMS), a UK multi-centre one-year prospective observational study investigating predictors of response to MTX in patients with RA. Since 2008, patients who are about to start MTX for the first time are asked to provide demographic and clinical data, as well as blood samples to permit DNA, RNA and serum-based biomarker studies. Patients about to commence MTX treatment were followed longitudinally and those categorised as good or non-responders following 6 months on-drug using EULAR response criteria were analysed. Serum lipid levels were measured at pre-treatment and following 4 weeks on drug using ultra-performance liquid chromatography tailored for complex lipid analysis, coupled to mass spectrometry. State-of-the-art supervised machine learning methods were then applied to predict EULAR response at 6 months. Models including lipid levels were compared to models including clinical covariates (including: MTX start dose, steroid use at inclusion, BMI, number of swollen joints, number of tender joints, CRP levels, patients’ assessment of their overall wellbeing, gender, age-at-inclusion, age-at-onset, disease duration, HAQ score and pre-treatment smoking habits). Results Following quality control, 3,366 features (1,060 in negatively-charged mode and 2,306 in positive mode) were available for analysis at pre-treatment and 4 weeks from 100 RA patients categorised as good (GR, n = 50) or poor (NR, n = 50) responders to MTX following 6 months on drug. The best model performance for the classifier including clinical covariates was observed using L1/L2-regularised logistic regression (ROC AUC 0.68 ± 0.02). However, the clinical covariate model outperformed the classifier including lipid levels when either pre- or on-treatment time-points were investigated (ROC AUC 0.61 ± 0.02). Conclusion These data do not support the utility of early treatment lipidomic monitoring in routine clinical practice in patients started on MTX for their RA. Disclosures M. Maciejewski: Shareholder/stock ownership; owns stock or stock options in Pfizer. C. Sands None. N. Nair None. S. Ling None. S. Verstappen None. K. Hyrich None. A. Barton None. D. Ziemek Shareholder/stock ownership; owns stock or stock options in Pfizer. M. Lewis None. D. Plant None.

Summary Immune-mediated inflammatory diseases (IMIDs) are characterized by dysregulation of the normal immune response, which leads to inflammation. Together, they account for a high disease burden in the population, given that they are usually chronic conditions with associated co-morbidities. Examples include systemic lupus erythematosus, rheumatoid arthritis, Crohn's disease and type 1 diabetes. Since the advent of genome-wide association studies, evidence of considerable genetic overlap in the loci predisposing to a wide range of IMIDs has emerged. Understanding the genetic risk and extent of genetic overlap between IMIDs may help to determine which genes control which aspects of the different diseases; it may identify potential novel therapeutic targets for a number of these conditions, and/or it may facilitate repurposing existing therapies developed originally for different conditions. The findings show that autoantibody-mediated autoimmune diseases cluster more closely with each other than autoantibody-negative diseases such as psoriasis, psoriatic arthritis, Crohn's disease and ankylosing spondylitis which, instead, form a seronegative genetic cluster. The genetic clustering largely mirrors the known response to existing biological therapies, but apparent anomalies in treatment response are discussed.