Prof Costantino Pitzalis, Dr M Lewis
No more applications being accepted
Competition Funded PhD Project (European/UK Students Only)
About the Project
Introduction and Background:
Rheumatoid arthritis (RA) affects 1% of the UK population (1), approximately 660,000 people. This disease can cause severe pain, joint destruction and ultimately lifelong disability, which is devastating for individual patients and imposes a huge social-economic cost for society. Early intervention and successful disease control particularly through biologic (b)-Disease Modifying Anti-Rheumatic Drugs (b-DMARDs) can mitigate disease progression (2). TNFa inhibitors (TNFi) are the most commonly used b-DMARDs and have transformed the treatment of RA, providing successful disease control in patients previously thought to be untreatable (3). However, TNFi have a high failure rate with approximately 40 % of patients not responding at all to treatment (4). Moreover, as there are no tests able to predict therapeutic response, currently biologics are initiated on a trial and error basis. Therefore, further investigations in the mechanisms of response and how best predict TNFi treatment response are urgently needed for precision based therapy.
Significance and Impact:
Being able to identify which patients respond to TNFi could revolutionize the treatment of rheumatoid arthritis. Our proposed algorithm will eliminate the need for trial and error in TNFi therapy and offer alternative b-DMARDs prescribed according to target expression levels in the disease tissue (synovium), akin to drug allocation in Oncology. Precision therapy will enable earlier disease and symptom control, save the NHS significant resources: money, clinician time and appointments, and reduce the cost to society associated with disability.
Novelty:
To our knowledge no large-scale studies in randomised clinical trials of synovial mRNA expression, DNA methylation data or a combination of the two have been done previously.
Hypotheses:
• Differential activation of cellular and molecular inflammatory pathways in the disease tissue (synovium) influences TNFi response.
• Epigenetic factors are responsible for dysregulated gene expression in RA synovium leading to disease resistance to TNFi in specific subsets of patients.
• Machine learning derived predictive model of gene expression in a large cohort of patients will enable the identification of specific signatures predictive of TNFi response.
Aims:
• To analyse mRNA expression and DNA methylation in the synovium of a large cohort of RA patients with known responses to TNFi from a randomised controlled trial: STRAP (EudraCT number: 2014-003529-16).
• To elucidate the links between mRNA expression and epigenetic control of mRNA expression as measured by DNA methylation in disease tissue from STRAP patients, compared with osteoarthritis controls samples, by comparative QTL analysis
• To optimize an algorithm using bioinformatics tools for easy identification of TNFi responders and non-responders in clinical settings to personalise treatment.
• To replicate / validate the established gene expression in a new cohort of 400 RA patients recruited as part of the 3TR IMI EU consortium led by QMUL/Barts Health NHS Trust
Training:
• Bioinformatics and statistics: the student will be extensively trained in coding and analyzing data using the computer programming language R by the expert bioinformatics team led by Dr Lewis. The student will become proficient in analyzing high-dimensional data and machine learning.
• Laboratory techniques: although this project does not envision a significant lab component, the student will be instructed in specific lab techniques relevant to their project including preparation of synovial biopsy samples, extraction of DNA from synovial samples, quantifying DNA methylation using Illumina EPIC array.
• Clinical training: the student will be trained in performing ultrasound guided synovial biopsies by expert clinicians in the department. They will be expected to become independent in diagnostic US scans, and US guided synovial biopsies, so they can collect their own synovial biopsy samples from the 3TR cohort.
How to apply
To apply, please click the 'institution website' button.
The successful candidate must be a registered clinician in the UK.
Funding Notes
These studentships will fund a student with a clinical qualification and GMC / GDC registration at any career stage below consultant. They will be funded for three years at current MRC rates. Studentships will include PhD fees (at home/EU levels) and up to £6000 pa for consumables. Further consumables / funding for travel may be available on application.
References
1. Symmons D, Turner G, Webb R, Asten P, Barrett E, Lunt M, et al. The prevalence of rheumatoid arthritis in the United Kingdom: new estimates for a new century. Rheumatology. 2002;41(7):793-800.
2. Goekoop-Ruiterman YP, de Vries-Bouwstra JK, Allaart CF, van Zeben D, Kerstens PJ, Hazes JMW, et al. Comparison of treatment strategies in early rheumatoid arthritis: a randomized trial. Annals of internal medicine. 2007;146(6):406-15.
3. Johnson KJ, Sanchez HN, Schoenbrunner N. Defining response to TNF-inhibitors in rheumatoid arthritis: the negative impact of anti-TNF cycling and the need for a personalized medicine approach to identify primary non-responders. Clinical Rheumatology. 2019;38(11):2967-76.
4. Scott D. Biologics-based therapy for the treatment of rheumatoid arthritis. Clinical Pharmacology & Therapeutics. 2012;91(1):30-43.
Continue with Facebook
