Mononuclear cell transcriptome changes associated with dimethyl fumarate in MS.

ObjectiveTo identify short-term changes in gene expression in peripheral blood mononuclear cells (PBMCs) associated with treatment response to dimethyl fumarate (DMF, Tecfidera) in patients with relapsing-remitting MS (RRMS).MethodsBlood samples were collected from 24 patients with RRMS (median Expanded Disability Status Scale score, 2.0; range 1-7) at baseline, 6 weeks, and 15 months after the initiation of treatment with DMF (BG-12; Tecfidera). Seven healthy controls were also recruited, and blood samples were collected over the same time intervals. PBMCs were extracted from blood samples and sequenced using next-generation RNA sequencing. Treatment responders were defined using the composite outcome measure "no evidence of disease activity" (NEDA-4). Time-course and cross-sectional differential expression analyses were performed to identify transcriptomic markers of treatment response.ResultsTreatment responders (NEDA-4 positive, 8/24) over the 15-month period had 478 differentially expressed genes (DEGs) 6 weeks after the start of treatment. These were enriched for nuclear factor (erythroid-derived 2)-like 2 (Nrf2) and inhibition of nuclear factor κB (NFκB) pathway transcripts. For patients who showed signs of disease activity, there were no DEGs at 6 weeks relative to their (untreated) baseline. Contrasting transcriptomes expressed at 6 weeks with those at 15 months of treatment, 0 and 1,264 DEGs were found in the responder and nonresponder groups, respectively. Transcripts in the nonresponder group (NEDA-4 negative, 18/24) were enriched for T-cell signaling genes.ConclusionShort-term PBMC transcriptome changes reflecting activation of the Nrf2 and inhibition of NFκB pathways distinguish patients who subsequently show a medium-term treatment response with DMF. Relative stabilization of gene expression patterns may accompany treatment-associated suppression of disease activity

Serum Neurofilament Light and Multiple Sclerosis Progression Independent of Acute Inflammation

Introduction Efforts to explore the utility of neurofilament light (NfL) as a biomarker associated with disability progression in multiple sclerosis (MS) have accelerated in recent years in the absence of pharmacodynamic or treatment response markers for clinical trials or patient care.1 The International Progressive MS Alliance stated in 2020 that serum NfL (sNfL) measurements may serve as a useful biomarker associated with progressive MS, although further work is needed to define the relative contributions of inflammatory activity and neurodegeneration to longitudinal changes in disability and sNfL.2 Using data from a large clinical trial of patients with secondary progressive MS (a phase 3, randomized, double-blind, placebo-controlled trial exploring the effect of natalizumab on disease progression in participants with Secondary Progressive Multiple Sclerosis [ASCEND in SPMS]; NCT01416181), we investigated whether sNfL could be used as a dynamic biomarker associated with progressive MS disease course. That is, we investigated whether longitudinal changes in sNfL concentration were associated with disability progression measures in the absence of relapses and magnetic resonance imaging (MRI) evidence of inflammatory activit

Glial fibrillary acidic protein and multiple sclerosis progression independent of acute inflammation

Background: The clinical relevance of serum glial fibrillary acidic protein (sGFAP) concentration as a biomarker of MS disability progression independent of acute inflammation has yet to be quantified.// Objective: To test whether baseline values and longitudinal changes in sGFAP concentration are associated with disability progression without detectable relapse of magnetic resonance imaging (MRI) inflammatory activity in participants with secondary-progressive multiple sclerosis (SPMS).// Methods: We retrospectively analyzed longitudinal sGFAP concentration and clinical outcome data from the Phase 3 ASCEND trial of participants with SPMS, with no detectable relapse or MRI signs of inflammatory activity at baseline nor during the study (n = 264). Serum neurofilament (sNfL), sGFAP, T2 lesion volume, Expanded Disability Status Scale (EDSS), Timed 25-Foot Walk (T25FW), 9-Hole Peg Test (9HPT), and composite confirmed disability progression (CDP) were measured. Linear and logistic regressions and generalized estimating equations were used in the prognostic and dynamic analyses.// Results: We found a significant cross-sectional association between baseline sGFAP and sNfL concentrations and T2 lesion volume. No or weak correlations between sGFAP concentration and changes in EDSS, T25FW, and 9HPT, or CDP were observed.// Conclusion: Without inflammatory activity, changes in sGFAP concentration in participants with SPMS were neither associated with current nor predictive of future disability progression./

Predicting response to disease modifying treatment in multiple sclerosis

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.Open Acces

Remote Monitoring in the Home Validates Clinical Gait Measures for Multiple Sclerosis

Background: The timed 25-foot walk (T25FW) is widely used as a clinic performance measure, but has yet to be directly validated against gait speed in the home environment. Objectives: To develop an accurate method for remote assessment of walking speed and to test how predictive the clinic T25FW is for real-life walking. Methods: An AX3-Axivity tri-axial accelerometer was positioned on 32 MS patients (Expanded Disability Status Scale [EDSS] 0–6) in the clinic, who subsequently wore it at home for up to 7 days. Gait speed was calculated from these data using both a model developed with healthy volunteers and individually personalized models generated from a machine learning algorithm. Results: The healthy volunteer model predicted gait speed poorly for more disabled people with MS. However, the accuracy of individually personalized models was high regardless of disability (R-value = 0.98, p-value = 1.85 × 10−22). With the latter, we confirmed that the clinic T25FW is strongly predictive of the maximum sustained gait speed in the home environment (R-value = 0.89, p-value = 4.34 × 10−8). Conclusion: Remote gait monitoring with individually personalized models is accurate for patients with MS. Using these models, we have directly validated the clinical meaningfulness (i.e., predictiveness) of the clinic T25FW for the first time

Mononuclear cell transcriptome changes associated with dimethyl fumarate in MS.

ObjectiveTo identify short-term changes in gene expression in peripheral blood mononuclear cells (PBMCs) associated with treatment response to dimethyl fumarate (DMF, Tecfidera) in patients with relapsing-remitting MS (RRMS).MethodsBlood samples were collected from 24 patients with RRMS (median Expanded Disability Status Scale score, 2.0; range 1-7) at baseline, 6 weeks, and 15 months after the initiation of treatment with DMF (BG-12; Tecfidera). Seven healthy controls were also recruited, and blood samples were collected over the same time intervals. PBMCs were extracted from blood samples and sequenced using next-generation RNA sequencing. Treatment responders were defined using the composite outcome measure "no evidence of disease activity" (NEDA-4). Time-course and cross-sectional differential expression analyses were performed to identify transcriptomic markers of treatment response.ResultsTreatment responders (NEDA-4 positive, 8/24) over the 15-month period had 478 differentially expressed genes (DEGs) 6 weeks after the start of treatment. These were enriched for nuclear factor (erythroid-derived 2)-like 2 (Nrf2) and inhibition of nuclear factor κB (NFκB) pathway transcripts. For patients who showed signs of disease activity, there were no DEGs at 6 weeks relative to their (untreated) baseline. Contrasting transcriptomes expressed at 6 weeks with those at 15 months of treatment, 0 and 1,264 DEGs were found in the responder and nonresponder groups, respectively. Transcripts in the nonresponder group (NEDA-4 negative, 18/24) were enriched for T-cell signaling genes.ConclusionShort-term PBMC transcriptome changes reflecting activation of the Nrf2 and inhibition of NFκB pathways distinguish patients who subsequently show a medium-term treatment response with DMF. Relative stabilization of gene expression patterns may accompany treatment-associated suppression of disease activity

Data_Sheet_1_Remote Monitoring in the Home Validates Clinical Gait Measures for Multiple Sclerosis.docx

<p>Background: The timed 25-foot walk (T25FW) is widely used as a clinic performance measure, but has yet to be directly validated against gait speed in the home environment.</p><p>Objectives: To develop an accurate method for remote assessment of walking speed and to test how predictive the clinic T25FW is for real-life walking.</p><p>Methods: An AX3-Axivity tri-axial accelerometer was positioned on 32 MS patients (Expanded Disability Status Scale [EDSS] 0–6) in the clinic, who subsequently wore it at home for up to 7 days. Gait speed was calculated from these data using both a model developed with healthy volunteers and individually personalized models generated from a machine learning algorithm.</p><p>Results: The healthy volunteer model predicted gait speed poorly for more disabled people with MS. However, the accuracy of individually personalized models was high regardless of disability (R-value = 0.98, p-value = 1.85 × 10⁻²²). With the latter, we confirmed that the clinic T25FW is strongly predictive of the maximum sustained gait speed in the home environment (R-value = 0.89, p-value = 4.34 × 10⁻⁸).</p><p>Conclusion: Remote gait monitoring with individually personalized models is accurate for patients with MS. Using these models, we have directly validated the clinical meaningfulness (i.e., predictiveness) of the clinic T25FW for the first time.</p