Dataset Information

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

ABSTRACT: BACKGROUND: The 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. METHODS: In 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. RESULTS: We 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). CONCLUSION: We 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.

SUBMITTER: Gurevich M

PROVIDER: S-EPMC2725113 | biostudies-literature | 2009

REPOSITORIES: biostudies-literature

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

Gurevich Michael M Tuller Tamir T Rubinstein Udi U Or-Bach Rotem R Achiron Anat A

BMC medical genomics 20090722

<h4>Background</h4>The 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.<h4>Methods</h4>In 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 pat ...[more]

PMID: 19624813

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Project description:Background: The ability to predict the spatial frequency of relapses in multiple sclerosis (MS) would enable treating physicians to decide when to intervene more aggressively and to plan clinical trials more accurately. Methods: In 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. Results: We 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, resulting in a prediction with a resolution of 50 days as to the timing of the next relapse. The error rate of this 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 not only in untreated patients but were also valid for MS patients which 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). Conclusions: We 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 Keywords: Disease prediction 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. We 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. 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, resulting in a prediction with a resolution of 50 days as to the timing of the next relapse. The predictors were further evaluated and found effective not only in untreated patients but were also valid for MS patients which subsequently received immunomodulatory treatments after the initial testing.

Project description:BackgroundMultiple sclerosis (MS) is characterized by increased activation of peripheral blood mononuclear cells (PBMCs), linked to perturbations in the phosphorylation of signaling proteins.MethodsWe developed a phosphoflow cytometry protocol to assess the levels of 11 phosphorylated nuclear proteins at baseline conditions and after cell activation in distinct PBMC populations from 41 treatment-naïve relapsing-remitting (RR) MS subjects and 37 healthy controls, and in a second cohort of 9 untreated RRMS patients and 10 secondary progressive (SP) MS patients. Levels of HLA-ABC, HLA-E, and HLA-DR were also assessed. Phosphorylation levels of selected proteins were also assessed in mouse splenocytes isolated from myelin oligodendrocyte glycoprotein (MOG)35-55-induced experimental autoimmune encephalomyelitis (EAE).ResultsModest differences were observed at baseline between patients and controls, with general lower phosphorylation levels in cells from affected individuals. Conversely, a dramatic increase in phosphorylated p38MAPK and STAT proteins was observed across all cell types in MS patients compared to controls after in vitro activation. A similar phosphorylation profile was observed in mouse lymphocytes primed in vivo with MOG. Furthermore, levels of all p-STAT proteins were found directly correlated with HLA expression in monocytes. Levels of phosphorylated proteins did not differ between relapsing-remitting and secondary progressive MS patients either in baseline conditions or after stimulation. Lastly, phosphorylation levels appear to be independent of the genotype.ConclusionThe response to IFN-α through STAT proteins signaling is strongly dysregulated in MS patients irrespective of disease stage. These findings suggest that the aberrant activation of this pathway could lead to changes in the expression of HLA molecules in antigen presenting cells, which are known to play important roles in the regulation of the immune response in health and disease.

Dataset Information

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

Publications

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

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