Exploring the influence of treatment on CD4+ T-cell sub-populations in patients receiving biologic drugs for their inflammatory arthritis

Abstract

Background: Rheumatoid arthritis (RA) is a complex autoimmune disease that targets synovial joints, causing pain, swelling and bone erosion if left untreated. Typically, patients are prescribed the cheapest conventional disease modifying anti-rheumatic drug (e.g. methotrexate), and when this no longer works, they escalate onto more expensive biologic drugs such as tumour necrosis factor inhibitor (TNFi) agents (e.g. adalimumab and etanercept). Currently, a large minority of patients do not experience a beneficial response to their first TNFi, and in the instance of non-response, the onset of irreversible joint damage is common, which significantly limits patient quality of life. Cycling patients though a number of treatment options is also expensive, and creates a socioeconomic strain on the National Health Service (NHS). The identification of predictive biomarkers of treatment response, before or very early in treatment would improve patient outcome and limit the socioeconomic burden of RA. Aims: To identify peripheral blood predictive biomarkers of treatment response in RA patients beginning TNFi therapy. Methods: First, an epigenome-wide association study (EWAS) was conducted using pre-treatment DNA methylation data acquired from 72 patients who had commenced treatment with adalimumab. Treatment response was assessed after 3-months. Differentially methylated positions were assessed between good and non-responders. Meta-analysis was also conducted to determine if similar methylation patterns were present in a published dataset of RA patients treated with etanercept. Second, published whole blood transcriptomics datasets were analysed for change in transcript expression between pre-treatment and post-treatment in 70 RA patients treated with adalimumab, 85 patients treated with methotrexate and 10 disease free controls using previously defined gene co-expression modules. Linear mixed models were used to test if module expression transitioned toward a disease-free state. Finally, T-cells from 15 RA patients were phenotyped through the first 12-weeks of treatment using a 35 marker CyTOF panel. Generalised linear mixed models were used to assess changes in T-cell abundance and marker expression in good, moderate and non-responders to TNFi treatment and to determine the earliest time point these changes were observable. For all studies, treatment response was characterised using the European League Against Rheumatism (EULAR) response criteria and data analysis was performed using the R programming language. Results: A total of 27 differentially methylated positions (DMP) were identified in patients treated with adalimumab (p

Date of Award	31 Dec 2023
Original language	English
Awarding Institution	The University of Manchester
Supervisor	Andrew Morris (Supervisor), Anne Barton (Supervisor), Darren Plant (Supervisor) & Sebastien Viatte (Supervisor)