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

Lead Research Organisation: University of Manchester
Department Name: School of Biological Sciences


Background: Tumour necrosis factor inhibitor (TNFi) therapy is ineffective for approximately 20% of rheumatoid arthritis (RA) patients and there is currently no way to predict which patients will not benefit. To address this precision medicine question, reliable biomarkers of TNFi response are needed. The role of CD4+ T-cells in the pathogenesis of RA is well established and high dimensional single cell technologies such as mass cytometry (i.e. CyTOF) have revealed a broad diversity of CD4+ T-cells that are expanded in RA and contract with successful treatment (1). However, the precise T-cell subsets and key effector functions driving and perpetuating RA remains to be completely resolved and very few studies have specifically investigated their role in treatment response to TNFi.

PBMCs and serum samples will be made available from 40 RA patients commencing treatment with TNFi. Samples are collected at pre-treatment and 1-hour, followed by trough sampling at 2-weeks, 4-weeks and 6-weeks, with follow-up collection at 3-months. Disease activity recorded using the DAS28 scoring system will also be available. The successful candidate will oversee the generation of drug level data in serum collected at each follow-up visit. Immunophenotyping of CD4+ T-cell will be performed using a CyTOF antibody panel that is already established in our laboratory (2). Laboratory data will be generated during the first year of the studentship by the laboratory team in the Centre for Musculoskeletal Research and the Flow Cytometry Core Facility within University of Manchester. The candidate has the option of being involved in the laboratory work if there is a desire to acquire some laboratory experience. This is an ongoing collection therefore additional samples will be made available for validation experiments later in the project timeline.

This studentship will apply state-of-the-art statistical techniques to characterise T-cell subset abundance, intracellular signal transduction and to identify the most important cell-types associated with trough serum drug levels and improvement in disease activity during early treatment with TNFi.

Identifying biomarkers of treatment response to TNFi in RA will enhance patient stratification and targeted therapy, better characterise the role of pathogenic T-cell subsets in determining important clinical outcomes and characterise molecular signatures that are resistant to treatment or correlated with good treatment response.


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