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

Project Description

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.

Hypothesis: Pre- and early-treatment CD4+ T-cell signatures are correlated with response to TNFi and can be used to better target biologic drugs to the patients with RA most likely to benefit from them.

Methods: PBMCs will be harvested from RA patient (n = 30) upon commencement of TNFi therapy. Samples will be 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. Drug levels will be measured in patient serum at each follow-up visit and disease activity will be recorded using the DAS28.
Immunophenotyping of CD4+ T-cell will be performed using an established CyTOF antibody panel, and visualised using viSNE maps to characterise cell abundance and intracellular signal transduction (2). Supervised machine learning techniques will be used to identify the most important cell-types associated with improvement in disease activity during early treatment. Similar regression techniques will be used to correlate cell-types with trough drug levels.

The outcome of this work has the potential to inform clinical practice and guide the development of new interventions to improve treatment pathways for individuals with RA.

Training/techniques to be provided:
AB will supervise training in RA epidemiology and literature reviewing and interpreting results. DP will provide training in statistical modelling and interpreting results. SV will provide training in immunology and interpreting results.
Training will be provided in the statistical approaches required to analyse the data available and the interpretation of the results. Specifically, in-house training courses in Epidemiology and statistical analysis are held annually. The successful applicant will join other students and research staff investigating the immunological aspects of treatment response.

For international students we also offer a unique 4 year PhD programme that gives you the opportunity to undertake an accredited Teaching Certificate whilst carrying out an independent research project across a range of biological, medical and health sciences. For more information please visit

Funding Notes

Candidates are expected to hold (or be about to obtain) a minimum upper second class honours degree (or equivalent) in a related area / subject. Candidates with experience in immunology or with an interest in biostatistics are encouraged to apply.

This project has a Band 1 fee. Details of our different fee bands can be found on our website (View Website). For information on how to apply for this project, please visit the Faculty of Biology, Medicine and Health Doctoral Academy website (View Website).

Informal enquiries may be made directly to the primary supervisor.


(1) Nitya Nair, Henrik E Mei, Shih-Yu Chen, Matthew Hale, Garry P Nolan, Holden T Maecker, et al. Mass cytometry as a platform for the discovery of cellular biomarkers to guide effective rheumatic disease therapy. Arthritis Res Ther. 2015; 17(1): 127.

(2) Rao DA, Gurish MF, Marshall JL, Slowikowski K, Fonseka CY, Liu Y, et al. Pathologically expanded peripheral T helper cell subset drives B cells in rheumatoid arthritis. Nature. 2017 Feb 1;542(7639):110–4.

(3) Viatte S, Barton A. Genetics of rheumatoid arthritis susceptibility, severity, and treatment response. Semin Immunopathol. 2017 Jun;39(4):395-408.

(4) Plant D, Wilson AG, Barton A. Genetic and epigenetic predictors of responsiveness to treatment in RA. Nat Rev Rheumatol. 2014 Jun;10(6):329-37

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