Use this URL to cite or link to this record in EThOS: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.733236
Title: Predicting response to disease modifying treatment in multiple sclerosis
Author: Gafson, Arie R.
ISNI:       0000 0004 6496 9443
Awarding Body: Imperial College London
Current Institution: Imperial College London
Date of Award: 2018
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Abstract:
Multiple sclerosis (MS) is an autoimmune disease affecting the central nervous system (CNS) that most commonly begins with a relapsing-remitting course (RRMS). Many disease modifying treatments now are available, but none have efficacy in all patients, all are expensive and all are associated with possible adverse events. Stratifying patients to the best tolerated and most efficacious treatment either prior to or soon after commencing treatment would enhance relative benefits and reduce harm. Effective stratification depends on an understanding of relevant aspects of a drug’s mechanism of action, characterisation of key pharmacodynamic effects and being able to monitor disease activity over time. In this study, I set out to determine whether multi-omics profiling (transcriptome, cytokines, lipoproteins and metabolome) can fulfil these three requirements for one of the newer, oral treatments for RRMS, dimethyl fumarate (DMF). Chapter 1 provides an introduction to MS and explores the need for a stratified approach to treatment. Chapter 2 outlines the materials and methods used in this study including a discussion of modelling approaches that are used for data reduction. In Chapter 3, I aimed to discriminate MS patients from healthy controls using multi-omics profiling. The RRMS patients showed greater expression of immune pathway genes, as well as raised concentrations of lipids within lipoprotein sub-fractions, relative to healthy controls. The lipid measures were predictive of disability as measured using the Expanded Disability Status Scale (EDSS) when combined in a multivariate regression model. In Chapter 4, I tested whether multi-omics profiling could further elucidate the pharmacodynamic actions of dimethyl fumarate (DMF), a disease modifying treatment for RRMS. Comparisons of patient samples pre- and 6 weeks post- initiation of DMF revealed transcriptome changes enriched for activation of nuclear factor (erythroid-derived 2)-like 2 (Nrf2) and inhibition of nuclear factor κB (NFκB). Metabolomics profiling defined elevated levels of tricarboxylic acid metabolites, fumarate, succinate, succinyl-carnitine and methyl-succinylcarnitine. In Chapter 5, I used my prospective longitudinal data to test whether gene expression and metabolite changes associated with drug action in the blood mononuclear cell fraction at 6 weeks are associated with clinical and radiological responses at 15 months. Patients responding to treatment (measured using the composite outcome measure ‘no evidence of disease activity’) showed robust transcriptome changes between baseline and 6-weeks that were not present in non-responders. They also showed a relative stabilisation of gene expression over the remaining study period. My study thus provides evidence that multi-omics profiling could be a useful tool for stratified medicine in MS. It promises to elucidate differences that exist between disease and healthy states, further understanding of the pharmacodynamics of treatments and can provide longitudinal measures of response for monitoring the impact of a medicine. The latter could be used to optimise treatment choice for individual patients. If these methods were reduced to practice they could increase the chances of better clinical outcomes whilst avoiding otherwise unnecessary adverse events.
Supervisor: Matthews, Paul ; Giovannoni, Gavin Sponsor: Wellcome Trust
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.733236  DOI:
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