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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

Error probabilities of the FLP and the FTP. A. The improvement in the error probability of the best FLP as function of the number of predicting genes. Each improvement was significantly better (p-value < 0.05) than the previous one (see Methods). B. The error rate distribution of the best FLP on the test sets. C. The improvement in the error probability of the best FTP. Each improvement was significantly better (p-value < 0.05) than the previous one (see Methods). D. The error rate distribution of the best FTP on the test sets.
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Figure 2: Error probabilities of the FLP and the FTP. A. The improvement in the error probability of the best FLP as function of the number of predicting genes. Each improvement was significantly better (p-value < 0.05) than the previous one (see Methods). B. The error rate distribution of the best FLP on the test sets. C. The improvement in the error probability of the best FTP. Each improvement was significantly better (p-value < 0.05) than the previous one (see Methods). D. The error rate distribution of the best FTP on the test sets.

Mentions: Based on expression of all 10,594 genes, we performed a Leave 20% Out Cross Validation (L20OCV) procedure to evaluate FLP that simultaneously discriminated between patients that experienced acute relapse in one of the three periods mentioned above during clinical follow up (as was described in the Methods section). The cases where the FLP does not classify patients to their correct groups are defined as errors. The output L20OCV was a group of predictors that are based on the gene expression of sets of 1 to 10 genes with error rate range between 0.37 (for single gene) to 0.079 (for 10 genes), see Figure 2A. There are a few dozen predictors that gave similar results. For example, Additional file 3 includes 36 FLP with error rate < 0.1.


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)

Error probabilities of the FLP and the FTP. A. The improvement in the error probability of the best FLP as function of the number of predicting genes. Each improvement was significantly better (p-value < 0.05) than the previous one (see Methods). B. The error rate distribution of the best FLP on the test sets. C. The improvement in the error probability of the best FTP. Each improvement was significantly better (p-value < 0.05) than the previous one (see Methods). D. The error rate distribution of the best FTP on the test sets.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 2: Error probabilities of the FLP and the FTP. A. The improvement in the error probability of the best FLP as function of the number of predicting genes. Each improvement was significantly better (p-value < 0.05) than the previous one (see Methods). B. The error rate distribution of the best FLP on the test sets. C. The improvement in the error probability of the best FTP. Each improvement was significantly better (p-value < 0.05) than the previous one (see Methods). D. The error rate distribution of the best FTP on the test sets.
Mentions: Based on expression of all 10,594 genes, we performed a Leave 20% Out Cross Validation (L20OCV) procedure to evaluate FLP that simultaneously discriminated between patients that experienced acute relapse in one of the three periods mentioned above during clinical follow up (as was described in the Methods section). The cases where the FLP does not classify patients to their correct groups are defined as errors. The output L20OCV was a group of predictors that are based on the gene expression of sets of 1 to 10 genes with error rate range between 0.37 (for single gene) to 0.079 (for 10 genes), see Figure 2A. There are a few dozen predictors that gave similar results. For example, Additional file 3 includes 36 FLP with error rate < 0.1.

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