Prediction of acute multiple sclerosis relapses by transcription levels of peripheral blood cells

Abstract

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.

Cervical Disk Pathology in Patients With Multiple Sclerosis: Two Case Reports

“…Prediction of relapse using MRI loading, or factoring the number and location of lesions, is becoming more promising to diagnose exacerbations. 10 Signs and symptoms of MS depend on the area of CNS involvement and can result in a plethora of symptoms. Patients with MS have sensorimotor and balance impairments, ataxia, tremor, seizures, cranial motor impairments, cognitive deficits, and behavioral disorders.…”

Gene Expression Changes in Multiple Sclerosis Relapse Suggest Activation of T and Non-T Cells

“…We replicated their findings that BCL2 is decreased and BNIP3 is increased. More recent work from the same group reports that expression of a small set of mRNA can be used to predict relapse (14), but the transcripts they used as predictors were not altered in our patients. Arthur et al also reported a comparison of unpaired relapse and remission patients (6), but comparison with their results is difficult since they compared relapse to controls and remission to controls rather than directly comparing relapse and remission.…”

“…Dendrogram constructed using BRB ArrayTools with centered correlation and average linkage. The branch labels give the experiment number (1)(2)(3)(4)(5), the subject number (1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14), clinical status, treatment, and gender. The samples from the first two experiments formed large clusters.…”

Paramètres cliniques, biochimiques et d’imagerie prédictifs de I’ évolution de la sclérose en plaques

“…le taux d'erreur du deuxième niveau était 2,3 fois plus faible que celui de prédictions aléatoires (taux d'erreur = 0,35, p < 0,001). 106 …”