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Title: Identifying immune biomarkers to predict treatment response to biologic drugs in rheumatoid arthritis
Author: Mulhearn, Ben
ISNI:       0000 0004 7429 6940
Awarding Body: University of Manchester
Current Institution: University of Manchester
Date of Award: 2018
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Rheumatoid arthritis (RA) is a chronic, heterogeneous, autoimmune disease that causes inflammation of synovial joints leading to pain, stiffness and swelling. If left untreated, RA results in irreversible joint destruction and long term disability. Initial treatment with glucocorticoids and other immunosuppressive agents suppresses inflammation. However, many of these drugs are not well-tolerated due to extensive side effects or are simply ineffective. The discovery of tumour necrosis factor-α (TNF) as a key mediator of inflammation in RA led to the development of monoclonal anti-TNF antibody therapy. Since then, other biologic drugs targeting immune pathways have been developed for RA, including interleukin-6 (IL-6) blockade, B cell depletion, and T cell co-stimulation blockade. Not all patients will respond to their first biologic drug and currently there is no way to predict which patient will respond to each different class of drug. Generally, 3-6 months are required to determine clinical efficacy, during which time joint inflammation proceeds. Therefore, discovering biomarkers to predict treatment response is a research priority. Biologic drugs target immune pathways. As single cell technology advances and has increasing capacity to identify subtle changes in many cell subsets, I hypothesise that studying the blood immune cell landscape will define cellular biomarker profiles relevant to each individual patient's disease. In the first chapter I developed an optimal protocol that allowed the collection and processing of PBMCs from distant locations. The second chapter utilises this protocol to examine innate and adaptive immune cell populations by 17-channel flow cytometry from 100 RA patients prior to commencing a biologic drug. The final chapter develops and utilises a 37-channel mass cytometry protocol that is superior at deeply immunophenotyping immune cells. Collectively this thesis identifies a number of candidate cellular biomarkers which are associated with biologic drug treatment response. In particular, patients with a low number of CD28+ Tregs, or aberrant inflammatory cytokine production upon stimulation of T cells, had reduced response to anti-TNFs. As the complexity of single cell technologies increases it is more likely that a meaningful correlation will be revealed to inform treatment response.
Supervisor: Barton, Anne ; Viatte, Sebastien ; Hussell, Tracy Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: anti-TNF ; flow cytometry ; biologics ; cytokines ; linear regression ; precision medicine ; immunology ; rheumatoid arthritis ; stratified medicine