Characterising the stratification potential of a novel epigenetic signature in Rheumatoid Arthritis.

Studentship strategic priority area: Basic and Clinical Research
Keywords: Rheumatoid Arthritis, Biomarkers, Precision Medicine, Bioinformatics, Epigenetics
Abstract:
A substantial clinical challenge in early rheumatoid arthritis (RA) is the delivery of effective therapeutics, as early intervention and response to first-line treatment is the most significant predictor of long-term outcome. Initial first-line treatment only delivers clinically meaningful response rates in approximately 65% of RA patients. The ability to stratify patients a priori into responders and non-responders would provide significant clinical benefits and would be an invaluable clinical tool.

Given the need for predictive biomarker, a considerable amount of research has been conducted in genotypic and transcriptional profiling but has unfortunately not provided clinically useful predictive biomarkers. Chromosome conformation signatures have emerged as uniquely informative biomarkers offering significant potential over other modalities (mRNA, proteomics) for examining disease states. These long-range chromatin interactions reflect highly informative and stable high-order epigenetic states, which strongly impact cellular activation and transcriptional profiles. Our recent and on-going precision medicine studies in the Scottish Early Rheumatoid Arthritis cohort have demonstrated the utility of this specific class of epigenetic state to stratify early RA patients. Importantly, we have now identified a unique chromosome conformation signature in the peripheral blood of drug naïve early RA patients that stratifies patients into those who will or will not respond to one specific first-line drug. The utility and socio-economic benefit of this predictive biomarker to pave the way towards personalized medicine in RA is incontrovertible.

This project will build on our initial observation to evaluate the usefulness of this signature across all early RA patients regardless of the type of treatment used, define the stability of this signature and finally capitalize on the pathogenetic information contained in the identification of such 'response-critical' loci in the genome. In summation, this project will provide essential information that will eventually be used clinical to determine the optimal treatment regime for newly diagnosed RA patients.

The student will receive training in a range of the key skills. In brief, this project will require the student to become proficient in the interrogation of clinical outcomes in RA, and the generation and statistical evaluation of 'omic' data (transcriptional, epigenetic) - specifically entailing high order bioinformatics skills. Basic cellular and molecular pathology techniques will be attained, namely the functional characterization of the epigenetic motif upon immune cell function that will require large scale data analysis. Finally, and most importantly, at the end of this studentship the student will have all of the necessary skills to seamlessly transition between biological, clinical, and computational elements of biomedical science.