Prof Maya Buch
Dr D Plant
Prof A Morris
No more applications being accepted
Competition Funded PhD Project (European/UK Students Only)
Rheumatoid Arthritis (RA) is a complex and heterogeneous syndrome, yet we manage all patients similarly. Treatment cycling and failure to targeted therapies have emerged as key clinical challenges. Identifying optimal treatment at time of diagnosis is paramount. Obtaining greater depth of response is increasingly recognised to deliver better longer-term outcomes, and defining and treating to molecular remission emerging as intuitive if aiming for drug-free remission/cure. Definition of this, how closely treatments in RA can achieve this, and predictors of these definitions of remission remain unclear.
A randomised controlled trial cohort of 120 treatment-naïve early RA (‘VEDERA’) patients, randomised to either conventional disease modifying anti-rheumatic drug (DMARD) or first-line biologic DMARD (an anti-TNF) will form the basis of this project. The student will analyse existing longitudinal peripheral blood cell subset and whole blood transcriptomic data from VEDERA. Blood samples from VEDERA will be available to generate SWATH-MS proteomic datasets as part of this project. This multi-omics VEDERA dataset will be analysed alongside the trial clinical and ultrasound imaging assessments to identify transcriptomic and proteomic correlates and predictors of current definitions of remission derived from clinical and imaging data (immediate and longer-term). The student will also obtain transcriptomic and proteomic datasets from a healthy control population to define a molecular state of health. The ability of each treatment strategy to achieve this and in which patient/disease subgroup will be characterised. Resistant signatures to treatment will also be identified. The predictors from this test cohort can then be validated in a prospective, standard early RA cohort.
The student will develop extensive skills in whole blood and cell subset transcriptomic profiling and state-of-the-art proteomic testing. They will be trained in data preparation for multi-omic bioinformatics analysis and cutting edge statistical techniques to perform integrated longitudinal analysis and prediction modelling with the wider clinical datasets. A deep understanding of RA disease mechanisms, and towards diagnostics will be acquired.
This project will identify predictors of first-line therapy towards the goal of molecular remission. This would ultimately inform on the development of a clinically feasible in vitro diagnostic to inform on stratification of first-line treatment in early RA.
Host environment: UnivManc and Centre for Musculoskeletal Research deliver high impact research, addressing the key health needs. Buch (recently joined as Director, Experimental Medicine), together with Morris, Plant (and Barnes, QMUL) will provide support and access to the Centre’s wide-ranging specialist expertise including trials, bioinformatics analysts and database experts.
Applications are invited from UK/EU nationals only. Applicants must have obtained, or be about to obtain, at least an upper second class honours degree (or equivalent) in a relevant subject.
This project is to be funded under the MRC Doctoral Training Partnership. If you are interested in this project, please make direct contact with the Principal Supervisor to arrange to discuss the project further as soon as possible. You MUST also submit an online application form - full details on how to apply can be found on the MRC DTP website www.manchester.ac.uk/mrcdtpstudentships
As an equal opportunities institution we welcome applicants from all sections of the community regardless of gender, ethnicity, disability, sexual orientation and transgender status. All appointments are made on merit.
(1) Tasaki S, et al. Nat Comm, 2018; 9: 2755
(2) Dumitru RB, Horton S, et al. A prospective, single-centre, randomised study evaluating the clinical, imaging and immunological depth of remission achieved by very early versus delayed Etanercept in patients with Rheumatoid Arthritis (VEDERA). BMC MSK Dis, 2016; 17:61
(3) Emery P....Buch MH VEDERA trial in preparation.
(4) Foulkes AC, Watson DS, Carr DF, Kenny JG, Slidel T, Parslew R, Pirmohamed M; PSORT Consortium, Anders S, Reynolds NJ, Griffiths CEM, Warren RB, Barnes MR. (2018) A framework for multi-omic prediction of treatment response to biologic therapy for psoriasis, J Invest Dermatol. S0022-202X(18)32355-8 (last author)
(5) Watson DS, Krutzinna J, Bruce IN, Griffiths CE, McInnes IB, Barnes MR*, Floridi L*. (2019) Clinical applications of machine learning algorithms: beyond the black box. BMJ. ;364:l886.