Molecular based patient stratification for the personalization of giant cell arteritis management

This project seeks to transforming the care of a prevalent but commonly ignored medical condition called Giant Cell Arteritis (GCA).

GCA is an inflammatory disease which is considered a medical emergency, as delays in treatment can lead to irreversible blindness. Early initiation of steroids can prevent such outcome, however, approximately half of patients fail to fully respond and require longer term steroids. There are now biological drugs that can supplement and minimise steroid use, but they are very expensive (~10k/year) and so can only be selectively prescribed in the NHS.

Unfortunately, no tools are currently available to support clinicians in this selection process. Ideally clinicians want to identify the patients destined not to respond as early as possible in order to initiate biological therapies.

This PhD will build on preliminary work which identified cellular and molecular markers of treatment response using an advanced spatial transcriptomic platform. Bioinformatic skills will be evolved to further scrutinise existing and new data in order to optimize and validate a tissue based cellular and molecular signature of treatment response. The signature will then subserve the exploration of more simple surrogate markers in the tissue and blood which may be more easily implemented in the NHS.

Finally, the candidate markers will be integrated with clinical data in order to develop artificial intelligence generated algorithms, which can directly inform clinical decision making.