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

Gurevich M, Tuller T, Rubinstein U, Or-Bach R, Achiron A - BMC Med Genomics (2009)

Bottom Line: 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).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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Multiple Sclerosis Center, Sheba Medical Center, Tel-Hashomer, Sackler School of Medicine, Tel-Aviv University, Tel Aviv, Israel. Michael.Gurevich@sheba.health.gov.il

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.

No MeSH data available.


Related in: MedlinePlus

The predicted time to next relapse versus observed time of the next relapse (in days) for the best FTP. The graph demonstrates a very high correlation between these two values (Spearman correlation 0.82, p-value = 10-10).
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Figure 5: The predicted time to next relapse versus observed time of the next relapse (in days) for the best FTP. The graph demonstrates a very high correlation between these two values (Spearman correlation 0.82, p-value = 10-10).

Mentions: The plot of best FTP performances vs. observed time to next relapse during 500 days of follow up appears in Figure 5. As can be seen, the two values are very correlative (Spearman correlation 0.82, p-value = 10-10). The analysis of error rate distribution of the best FTP appears in Figure 2D. In this case, the error rate has normal distribution with mean error rate of 0.35; for example, 20% of the patients have error rate < 0.2 (Figure 2D).


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

Gurevich M, Tuller T, Rubinstein U, Or-Bach R, Achiron A - BMC Med Genomics (2009)

The predicted time to next relapse versus observed time of the next relapse (in days) for the best FTP. The graph demonstrates a very high correlation between these two values (Spearman correlation 0.82, p-value = 10-10).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2725113&req=5

Figure 5: The predicted time to next relapse versus observed time of the next relapse (in days) for the best FTP. The graph demonstrates a very high correlation between these two values (Spearman correlation 0.82, p-value = 10-10).
Mentions: The plot of best FTP performances vs. observed time to next relapse during 500 days of follow up appears in Figure 5. As can be seen, the two values are very correlative (Spearman correlation 0.82, p-value = 10-10). The analysis of error rate distribution of the best FTP appears in Figure 2D. In this case, the error rate has normal distribution with mean error rate of 0.35; for example, 20% of the patients have error rate < 0.2 (Figure 2D).

Bottom Line: 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).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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Multiple Sclerosis Center, Sheba Medical Center, Tel-Hashomer, Sackler School of Medicine, Tel-Aviv University, Tel Aviv, Israel. Michael.Gurevich@sheba.health.gov.il

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.

No MeSH data available.


Related in: MedlinePlus