Multi-omics discovery analysis of response to advanced therapies in psoriatic arthritis

About the Project

Background: Psoriatic arthritis (PsA) is an inflammatory arthritis which causes joint inflammation, pain and resultant disability. PsA affects up to 30% of those with psoriasis (PSc). It is a genetically complex disease, characterised by environmental and genetic risk factors. 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. Targeting TNFi medication to those who are most likely to respond would be a major shift in treatment leading to a stratified/precision medicine approach. Genomic, transcriptomic and proteomic studies have discovered putative signals of association with response. It is unlikely that a single omics platform will be predictive for translation into the clinic, however.

This project incorporates advanced methodologies such as machine learning to uncover molecular mechanisms underlying TNFi response, setting the foundation for stratified, precision medicine approaches in PsA and PSc.

Aim: This project will measure and integrate multi-omics to develop a model of response to advanced therapeutics in psoriatic arthritis and psoriasis.

Methods: Patient data and blood samples from the Outcomes of Treatment in PsA Study Syndicate (OUTPASS) will be available. Corresponding data from psoriasis will be from The Biomarkers and Stratification To Optimise outcomes in Psoriasis study (B-STOP) established in 2011 led from King’s College London.

The successful candidate will:

1. conduct a literature review to establish previously identified multi-omics predictors of response to TNFi.
2. Proteomic Techniques: Proteome-Wide Hypothesis-Free Screening will identify novel biomarkers.
3. Mendelian Randomization will evaluate causal relationships between specific proteins and TNFi response
4. Genetic and transcriptomic data has been generated for a sub-set of these patients. Machine learning models will predict TNFi response, integrating multi-omics data.

Eligibility

Applicants must have obtained or be about to obtain a minimum Upper Second class UK honours degree, or the equivalent qualifications gained outside the UK, in a relevant discipline.

Before you Apply

Applicants must make direct contact with preferred supervisors before applying. It is your responsibility to make arrangements to meet with potential supervisors, prior to submitting a formal online application.

How to Apply

To be considered for this project you MUST submit a formal online application form – on the application form select PhD Bioinformatics Programme. Full details on how to apply can be found on the Website: How to apply for postgraduate research at The University of Manchester

If you have any queries regarding making an application please contact our admissions team FBMH.doctoralacademy.admissions@manchester.ac.uk

Equality, Diversity and Inclusion

Equality, diversity and inclusion is fundamental to the success of The University of Manchester, and is at the heart of all of our activities. The full Equality, diversity and inclusion statement can be found on the website: Equality, diversity and inclusion (EDI | Postgraduate Research | Biology, Medicine and Health | University of Manchester)