Identifying the molecular signatures that shape the course of synovial pathology in inflammatory arthritis.

Cossins, Benjamin 2020. Identifying the molecular signatures that shape the course of synovial pathology in inflammatory arthritis. PhD Thesis, Cardiff University. Item availability restricted.

Preview	PDF - Accepted Post-Print Version Download (23MB) | Preview
	PDF (Cardiff University Electronic Publication Form) - Supplemental Material Restricted to Repository staff only Download (459kB)

Abstract

Advances in precision medicine offer exciting opportunities to improve healthcare provision and clinical decision-making. Here, developments in diagnostic capabilities provide greater insights into the mechanisms of disease progression and allow the stratification of patients for the selection of therapies for optimal treatment. Innovations in precision medicine, therefore, contribute to improved clinical outcomes, a patient’s quality of life, and health economics. Experiment presented in this investigated the development of bioinformatic tools that could be used to stratify patients based on transcriptomic data derived inflamed tissues. To support this approach, I used open access repository datasets from patients with rheumatoid arthritis. Rheumatoid arthritis (RA) is a chronic and systemic autoimmune disease that affects around 1% of the adult population. Here, inflammation of the joint (synovitis) drives disease progression and irreversible joint damage. The clinical presentation of synovitis is highly heterogeneous with distinct histological features that affects the response commonly used therapeutics (e.g., biological drugs against cytokines). Examination of synovial histopathology reveals three forms of the disease termed Follicular – with extensive infiltration and the presence of lymphoid aggregates; Diffuse – extensive infiltration but with relatively few B cells; and Pauci-immune – which is driven by the stromal tissue compartment. Using transcriptomic data from each of these pathologies I designed and validated a disease classifier that supports the stratification of disease and the interrogation of results from independent patient cohorts where batch effects often restrict interpretations. Thus, I now present a tool that allows discrimination of these pathologies according to synovial transcriptomic data. Results presented in this thesis identified two gene signatures that perform well as identifiers of follicular and pauci-immune synovitis. The characterisation of diffuse synovitis is, however, more challenging and the application of the disease classifier tools showed that this form of pathology comprises a spectrum of sub-pathologies that require further characterisation. In an extension of these studies I further show how these bioinformatic tools may be used to record patient responses to biological drug therapy and unearth the biological signal pathways responsible for disease progression. Whilst these studies have focussed on RA as a case study, the methodologies are disease agnostic, and offer exciting opportunities for additional applications in other disease settings.

Item Type:	Thesis (PhD)
Date Type:	Completion
Status:	Unpublished
Schools:	Medicine
Date of First Compliant Deposit:	19 February 2021
Last Modified:	19 Feb 2022 02:30
URI:	https://orca.cardiff.ac.uk/id/eprint/138674

Actions (repository staff only)

Edit Item