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Exploring the influence of proteogenomics in patients receiving biologic drugs for treatment of psoriatic arthritis


Project Description

Background: Psoriatic arthritis (PsA) is an inflammatory arthritis, associated with the chronic skin condition psoriasis, which causes joint inflammation. PsA is estimated to affect up to 1% of the population and affects up to 30% of those with psoriasis. It is a genetically complex disease, characterised by environmental and genetic risk factors (1). Biologic drugs, such as TNF inhibitor (TNFi) drugs are prescribed to treat the disease. Not everyone responds to TNFi medication however, up to 30-40% of patients will experience non-response. Response is likely to be due to a number of different factors including demographic, clinical, drug levels and pharmacogenetics (2). Targeting anti-TNF medication to those who are most likely to respond would be a major shift in treatment leading to a stratified/precision medicine approach. A number of genetic variants have been associated with PsA response but the function of these genetic variants is less well understood. Proteins are under the proximal influence of genetic variants. Proteogenomic analysis (protein quantitative trait loci; pQTL) may therefore provide insights into the understanding of the mechanism of treatment response and offer predictive biomarkers of response (3).

Aim: The aim of this project is to identify biomarkers (genetic and proteomic) of response to TNFi medication in PsA.

Methods: Patient data and blood samples will be available from the Outcomes of Treatment in PsA Study Syndicate (OUTPASS). OUTPASS is a multi-centre national UK prospective observational study established in 2013. The successful candidate will conduct a literature review to establish previously identified proteomic predictors of response to TNFi. Genotyping of genetic variants associated with response and measurement of protein levels using high-throughput mass spectrometry techniques will be performed.

This studentship will apply state-of-the-art statistical techniques, including machine learning, to produce a model of pQTL that are associated with disease activity at baseline and following TNFi treatment.

Training will be provided in:

• Proteogenomic analysis
• Genome wide association studies
• Machine learning and statistical modelling
• Conducting a literature review

Specifically, in-house training courses in statistical analysis are held annually. The successful applicant will join other students and research staff investigating genetic and proteomic aspects of treatment response.

Entry Requirements:
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.

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 http://www.internationalphd.manchester.ac.uk

Funding Notes

Applications are invited from self-funded students. 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).

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.

References

1. Chandran V, Schentag CT, Brockbank JE, Pellett FJ, Shanmugarajah S, Toloza SM, et al. Familial aggregation of psoriatic arthritis. Ann Rheum Dis. 2009;68(5):664-7.
2. Jani M, Barton A, Ho P. Pharmacogenetics of treatment response in psoriatic arthritis. Curr Rheumatol Rep. 2015;17(7):44.
3. Mahendran SM, Chandran V. Exploring the Psoriatic Arthritis Proteome in Search of Novel Biomarkers. Proteomes. 2018;6(1).

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