Identifying critical pathways regulating autoimmunity in immuno-oncology and arthritis patients

Details

Background: Immune checkpoint inhibitors (CPIs) such as BLOCKING antibodies targeting CTLA4 (e.g. ipilimumab) have revolutionised the treatment of metastatic cancers by offering long-term remission, by boosting the immune response. Unfortunately, these agents are associated with serious autoimmune adverse events. Conversely, patients with rheumatoid arthritis (RA), an autoimmune condition, can be treated using abatacept, an ACTIVATING CTLA4 fusion protein, which therapeutically induces tolerance.

For CPIs, downstream cellular and molecular pathways will be responsible for anti-tumour efficacy whilst others pathways will be responsible for driving autoimmunity.

However, given the pleiotropic effects of immune checkpoints, it is likely that most downstream pathways are of little relevance. The same can be said of the downstream pathways induced by abatacept treatment. Combined analysis of samples from patients treated with CPIs and patients treated by abatacept provides the optimal study design to identify pathways mediating the autoimmune process.

Aim: To identify critical pathways regulating autoimmunity, we will study Peripheral Blood Mononuclear Cells (PBMCs) from patients on CPIs who either develop or do not develop autoimmune adverse events and patients treated with abatacept who either respond to therapy or not.

Methods: PBMCs from melanoma patients on ipilimumab and RA patients on abatacept (n=35) have been deeply immunophenotyped with a 40-marker mass cytometry panel. Bulk RNA-seq on B-cells and T-cells sorted by flow cytometry is also available. Mathematical modelling of cell-cell and protein-protein interaction networks and statistical association testing will be performed to identify the critical pathways. By incorporating directionality of shared pathway responses in relation to clinical outcome, we can further increase the specificity of our in silico prediction.

This project is a data analysis / bioinformatics project only, and does not include any wet lab component. A strong interest in command line programming in Linux is required (R, Python).

Eligibility 

Candidates are expected to hold (or be about to obtain) a minimum upper second class honours degree (or equivalent) in bioinformatics / data analysis/ systems biology / genomics / information technology or related area / subject. Candidates with experience and a strong interest in command line programming in Linux (R, Python) are encouraged to apply. 

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 Immunology 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

Funding Notes

Applications are invited from self-funded students. This project has a Band 2 (med) fee. Details of our different fee bands can be found on our website https://www.bmh.manchester.ac.uk/study/research/fees/