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BackgroundThe ability to predict the spatial frequency of relapses in multiple sclerosis (MS) would enable physicians to decide when to intervene more aggressively and to plan clinical trials more accurately.MethodsIn the current study our objective was to determine if subsets of genes can predict the time to the next acute relapse in patients with MS. Data-mining and predictive modeling tools were utilized to analyze a gene-expression dataset of 94 non-treated patients; 62 patients with definite MS and 32 patients with clinically isolated syndrome (CIS). The dataset included the expression levels of 10,594 genes and annotated sequences corresponding to 22,215 gene-transcripts that appear in the microarray.ResultsWe designed a two stage predictor. The first stage predictor was based on the expression level of 10 genes, and predicted the time to next relapse with a resolution of 500 days (error rate 0.079, p < 0.001). If the predicted relapse was to occur in less than 500 days, a second stage predictor based on an additional different set of 9 genes was used to give a more accurate estimation of the time till the next relapse (in resolution of 50 days). The error rate of the second stage predictor was 2.3 fold lower than the error rate of random predictions (error rate = 0.35, p < 0.001). The predictors were further evaluated and found effective both for untreated MS patients and for MS patients that subsequently received immunomodulatory treatments after the initial testing (the error rate of the first level predictor was < 0.18 with p < 0.001 for all the patient groups).ConclusionWe conclude that gene expression analysis is a valuable tool that can be used in clinical practice to predict future MS disease activity. Similar approach can be also useful for dealing with other autoimmune diseases that characterized by relapsing-remitting nature.

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BackgroundRetrospective single center natural history studies have shown that times to reach disability milestones and ages at which they are reached are similar in primary (PPMS) and secondary (SPMS) progressive multiple sclerosis suggesting that they may be phenotypic variations of the same disease.ObjectiveHere we compared longitudinal disease activity in SPMS and PPMS in the context of international multicenter clinical trials.MethodsWe analyzed all objective outcome measures that were systematically collected over 2 years for all subjects randomized to placebo arms in one SPMS and one PPMS clinical trial over the last decade. Conventional and exploratory definitions of clinical disease activity were used. Disease activity was analyzed in 3 different categories intermittent activity, progression, and improvement. Conventional MRI measures and one patient reported outcome measure of quality of life were included when available for comparison. Heat maps were drawn for all results followed by hierarchical clustering.ResultsThere were 101 outcome variables from 206 SPMS subjects and 79 outcome variables from 135 PPMS subjects. The comparison revealed that SPMS and PPMS subjects exhibited similar disease activity over 2 years in all but two of the variables in common worsening in the EDSS sensory system was more common in PPMS while worsening on the 9 hole PEG was more common in SPMS. Intermittent activity was the most common pattern of disease activity in SPMS and PPMS. Clinical worsening and improvement occurred at similar frequency in both.ConclusionLongitudinal disease activity was nearly identical in SPMS and PPMS subjects in the context of the two multicenter international clinical trials we examined.