The Australian multiple sclerosis (MS) immunotherapy study: A prospective, multicentre study of drug utilisation using the MSBase platform

To prospectively characterise treatment persistence and predictors of treatment discontinuation in an Australian relapsing-remitting multiple sclerosis (RRMS) population. Tertiary MS treatment centres participating in the MSBase registry prospectively assessed treatment utilisation, persistence, predictors of treatment discontinuation and switch rates. Multivariable survival analyses were used to compare treatment persistence between drugs and to identify predictors of treatment discontinuation. 1113 RRMS patients were studied. Patients persisted on their first disease-modifying therapy (DMT) for a median of 2.5 years. Treatment persistence on GA was shorter than on all IFNβ products (p<0.03). Younger age at treatment initiation and higher EDSS were predictive of DMT discontinuation. Patients persisted on subsequent DMTs, for 2.3 years. Patients receiving natalizumab (NAT) as a subsequent DMT persisted longer on treatment than those on IFNβ or GA (p<0.000). The primary reason for treatment discontinuation for any drug class was poor tolerability. Annualised switch or cessation rates were 9.5–12.5% for individual IFNβ products, 11.6% for GA and 4.4% for NAT. This multicentre MS cohort study is the first to directly compare treatment persistence on IFNβ and GA to NAT. We report that treatment persistence in our Australian RRMS population is short, although patients receiving IFNβ as a first DMT persisted longer on treatment than those on GA. Additionally, patients receiving NAT as a subsequent DMT were more likely to persist on treatment than those switched to IFNβ or GA. EDSS and age at DMT initiation were predictive of DMT discontinuation. Treatment intolerance was the principal reason for treatment cessation

Multiple Sclerosis risk variants regulate gene expression in innate and adaptive immune cells

At least 200 single-nucleotide polymorphisms (SNPs) are associated with multiple sclerosis (MS) risk. A key function that could mediate SNP-encoded MS risk is their regulatory effects on gene expression. We performed microarrays using RNA extracted from purified immune cell types from 73 untreated MS cases and 97 healthy controls and then performed Cis expression quantitative trait loci mapping studies using additive linear models. We describe MS risk expression quantitative trait loci associations for 129 distinct genes. By extending these models to include an interaction term between genotype and phenotype, we identify MS risk SNPs with opposing effects on gene expression in cases compared with controls, namely, rs2256814 MYT1 in CD4 cells (q = 0.05) and rs12087340 RF00136 in monocyte cells (q = 0.04). The rs703842 SNP was also associated with a differential effect size on the expression of the METTL21B gene in CD8 cells of MS cases relative to controls (q = 0.03). Our study provides a detailed map of MS risk loci that function by regulating gene expression in cell types relevant to MS

Interferon beta treatment is a potent and targeted epigenetic modifier in multiple sclerosis

IntroductionMultiple Sclerosis (MS) has a complex pathophysiology that involves genetic and environmental factors. DNA methylation (DNAm) is one epigenetic mechanism that can reversibly modulate gene expression. Cell specific DNAm changes have been associated with MS, and some MS therapies such as dimethyl fumarate can influence DNAm. Interferon Beta (IFNβ), was one of the first disease modifying therapies in multiple sclerosis (MS). However, how IFNβ reduces disease burden in MS is not fully understood and little is known about the precise effect of IFNβ treatment on methylation.MethodsThe objective of this study was to determine the changes in DNAm associated with INFβ use, using methylation arrays and statistical deconvolutions on two separate datasets (total ntreated = 64, nuntreated = 285).ResultsWe show that IFNβ treatment in people with MS modifies the methylation profile of interferon response genes in a strong, targeted, and reproducible manner. Using these identified methylation differences, we constructed a methylation treatment score (MTS) that is an accurate discriminator between untreated and treated patients (Area under the curve = 0.83). This MTS is time-sensitive and in consistent with previously identified IFNβ treatment therapeutic lag. This suggests that methylation changes are required for treatment efficacy. Overrepresentation analysis found that IFNβ treatment recruits the endogenous anti-viral molecular machinery. Finally, statistical deconvolution revealed that dendritic cells and regulatory CD4+ T cells were most affected by IFNβ induced methylation changes.DiscussionIn conclusion, our study shows that IFNβ treatment is a potent and targeted epigenetic modifier in multiple sclerosis

Natalizumab, Fingolimod and Dimethyl Fumarate Use and Pregnancy-Related Relapse and Disability in Women With Multiple Sclerosis

To investigate pregnancy-related disease activity in a contemporary multiple sclerosis (MS) cohort

Country, Sex, EDSS Change and Therapy Choice Independently Predict Treatment Discontinuation in Multiple Sclerosis and Clinically Isolated Syndrome

We conducted a prospective study, MSBASIS, to assess factors leading to first treatment discontinuation in patients with a clinically isolated syndrome (CIS) and early relapsing-remitting multiple sclerosis (RRMS). The MSBASIS Study, conducted by MSBase Study Group members, enrols patients seen from CIS onset, reporting baseline demographics, cerebral magnetic resonance imaging (MRI) features and Expanded Disability Status Scale (EDSS) scores. Follow-up visits report relapses, EDSS scores, and the start and end dates of MS-specific therapies. We performed a multivariable survival analysis to determine factors within this dataset that predict first treatment discontinuation. A total of 2314 CIS patients from 44 centres were followed for a median of 2.7 years, during which time 1247 commenced immunomodulatory drug (IMD) treatment. Ninety percent initiated IMD after a diagnosis of MS was confirmed, and 10% while still in CIS status. Over 40% of these patients stopped their first IMD during the observation period. Females were more likely to cease medication than males (HR 1.36, p = 0.003). Patients treated in Australia were twice as likely to cease their first IMD than patients treated in Spain (HR 1.98, p = 0.001). Increasing EDSS was associated with higher rate of IMD cessation (HR 1.21 per EDSS unit, p<0.001), and intramuscular interferon-β-1a (HR 1.38, p = 0.028) and subcutaneous interferon-β-1a (HR 1.45, p = 0.012) had higher rates of discontinuation than glatiramer acetate, although this varied widely in different countries. Onset cerebral MRI features, age, time to treatment initiation or relapse on treatment were not associated with IMD cessation. In this multivariable survival analysis, female sex, country of residence, EDSS change and IMD choice independently predicted time to first IMD cessation

Genotype and Phenotype in Multiple Sclerosis—Potential for Disease Course Prediction?

Purpose of review This review will examine the current evidence that genetic and/or epigenetic variation may influence the multiple sclerosis (MS) clinical course, phenotype, and measures of MS severity including disability progression and relapse rate. Recent findings There is little evidence that MS clinical phenotype is under significant genetic control. There is increasing evidence that there may be genetic determinants of the rate of disability progression. However, studies that can analyse disability progression and take into account all the confounding variables such as treatment, clinical characteristics, and environmental factors are by necessity longitudinal, relatively small, and generally of short duration, and thus do not lend themselves to the assessment of hundreds of thousands of genetic variables obtained from GWAS. Despite this, there is recent evidence to support the association of genetic loci with relapse rate. Summary Recent progress suggests that genetic variations could be associated with disease severity, but not MS clinical phenotype, but these findings are not definitive and await replication. Pooling of study results, application of other genomic techniques including epigenomics, and analysis of biomarkers of progression could functionally validate putative severity markers

Epigenome-wide association studies: current knowledge, strategies and recommendations

The aetiology and pathophysiology of complex diseases are driven by the interaction between genetic and environmental factors. The variability in risk and outcomes in these diseases are incompletely explained by genetics or environmental risk factors individually. Therefore, researchers are now exploring the epigenome, a biological interface at which genetics and the environment can interact. There is a growing body of evidence supporting the role of epigenetic mechanisms in complex disease pathophysiology. Epigenome-wide association studies (EWASes) investigate the association between a phenotype and epigenetic variants, most commonly DNA methylation. The decreasing cost of measuring epigenome-wide methylation and the increasing accessibility of bioinformatic pipelines have contributed to the rise in EWASes published in recent years. Here, we review the current literature on these EWASes and provide further recommendations and strategies for successfully conducting them. We have constrained our review to studies using methylation data as this is the most studied epigenetic mechanism; microarray-based data as whole-genome bisulphite sequencing remains prohibitively expensive for most laboratories; and blood-based studies due to the non-invasiveness of peripheral blood collection and availability of archived DNA, as well as the accessibility of publicly available blood-cell-based methylation data. Further, we address multiple novel areas of EWAS analysis that have not been covered in previous reviews: (1) longitudinal study designs, (2) the chip analysis methylation pipeline (ChAMP), (3) differentially methylated region (DMR) identification paradigms, (4) methylation quantitative trait loci (methQTL) analysis, (5) methylation age analysis and (6) identifying cell-specific differential methylation from mixed cell data using statistical deconvolution.</p