Limits...
Novel approaches to detect serum biomarkers for clinical response to interferon-beta treatment in multiple sclerosis.

Gandhi KS, McKay FC, Diefenbach E, Crossett B, Schibeci SD, Heard RN, Stewart GJ, Booth DR, Arthur JW - PLoS ONE (2010)

Bottom Line: However, some patients fail to respond to treatment.APOA1, A2M, and FIBB were identified as putative clinical response markers.In a complementary targeted approach, Cytometric Beadarray (CBA) analysis showed no significant difference in the serum concentration of IL-6, IL-8, MIG, Eotaxin, IP-10, MCP-1, and MIP-1alpha, between clinical responders and non-responders, despite the association of these proteins with IFNbeta treatment in MS.

View Article: PubMed Central - PubMed

Affiliation: Westmead Millennium Institute, University of Sydney, Sydney, Australia.

ABSTRACT
Interferon beta (IFNbeta) is the most common immunomodulatory treatment for relapsing-remitting multiple sclerosis (RRMS). However, some patients fail to respond to treatment. In this study, we identified putative clinical response markers in the serum and plasma of people with multiple sclerosis (MS) treated with IFNbeta. In a discovery-driven approach, we use 2D-difference gel electrophoresis (DIGE) to identify putative clinical response markers and apply power calculations to identify the sample size required to further validate those markers. In the process we have optimized a DIGE protocol for plasma to obtain cost effective and high resolution gels for effective spot comparison. APOA1, A2M, and FIBB were identified as putative clinical response markers. Power calculations showed that the current DIGE experiment requires a minimum of 10 samples from each group to be confident of 1.5 fold difference at the p<0.05 significance level. In a complementary targeted approach, Cytometric Beadarray (CBA) analysis showed no significant difference in the serum concentration of IL-6, IL-8, MIG, Eotaxin, IP-10, MCP-1, and MIP-1alpha, between clinical responders and non-responders, despite the association of these proteins with IFNbeta treatment in MS.

Show MeSH

Related in: MedlinePlus

Power curve showing the minimum % effect size (fold change) detectable as a function of sample size with 80% power at two different significance levels.
© Copyright Policy
Related In: Results  -  Collection


getmorefigures.php?uid=PMC2864746&req=5

pone-0010484-g002: Power curve showing the minimum % effect size (fold change) detectable as a function of sample size with 80% power at two different significance levels.

Mentions: The primary aim of the discovery driven approach was to produce sufficient 2D-DIGE data to enable statistical power calculations to determine optimal sample size. DIGE studies use an internal standard to help eliminate gel-to-gel variation [39]. However, normal biological variation between the samples remains and this can lead to false conclusions as this variation interferes with the ability to detect variation between the groups of interest. Proteomic studies require sufficient statistical power to overcome these sources of variation. To address this issue, we conducted power calculations based on the modified protocol of Hunt et al [18]. To our knowledge this is first DIGE study which incorporates power calculations to determine optimal sample size. The power analysis tool was used to calculate the minimal detectable difference defined as the size of effect required to give a chosen statistical power at a specific significance level. The results are shown in Figure 2. The power calculation showed that a minimum of 10 biological variants from each group are required to be confident of a 1.5-fold (50% effect size) and 2 fold (100% effect size) change in abundance between CR and CNR at the p<0.05 and p<0.005 level of statistical significance respectively.


Novel approaches to detect serum biomarkers for clinical response to interferon-beta treatment in multiple sclerosis.

Gandhi KS, McKay FC, Diefenbach E, Crossett B, Schibeci SD, Heard RN, Stewart GJ, Booth DR, Arthur JW - PLoS ONE (2010)

Power curve showing the minimum % effect size (fold change) detectable as a function of sample size with 80% power at two different significance levels.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2864746&req=5

pone-0010484-g002: Power curve showing the minimum % effect size (fold change) detectable as a function of sample size with 80% power at two different significance levels.
Mentions: The primary aim of the discovery driven approach was to produce sufficient 2D-DIGE data to enable statistical power calculations to determine optimal sample size. DIGE studies use an internal standard to help eliminate gel-to-gel variation [39]. However, normal biological variation between the samples remains and this can lead to false conclusions as this variation interferes with the ability to detect variation between the groups of interest. Proteomic studies require sufficient statistical power to overcome these sources of variation. To address this issue, we conducted power calculations based on the modified protocol of Hunt et al [18]. To our knowledge this is first DIGE study which incorporates power calculations to determine optimal sample size. The power analysis tool was used to calculate the minimal detectable difference defined as the size of effect required to give a chosen statistical power at a specific significance level. The results are shown in Figure 2. The power calculation showed that a minimum of 10 biological variants from each group are required to be confident of a 1.5-fold (50% effect size) and 2 fold (100% effect size) change in abundance between CR and CNR at the p<0.05 and p<0.005 level of statistical significance respectively.

Bottom Line: However, some patients fail to respond to treatment.APOA1, A2M, and FIBB were identified as putative clinical response markers.In a complementary targeted approach, Cytometric Beadarray (CBA) analysis showed no significant difference in the serum concentration of IL-6, IL-8, MIG, Eotaxin, IP-10, MCP-1, and MIP-1alpha, between clinical responders and non-responders, despite the association of these proteins with IFNbeta treatment in MS.

View Article: PubMed Central - PubMed

Affiliation: Westmead Millennium Institute, University of Sydney, Sydney, Australia.

ABSTRACT
Interferon beta (IFNbeta) is the most common immunomodulatory treatment for relapsing-remitting multiple sclerosis (RRMS). However, some patients fail to respond to treatment. In this study, we identified putative clinical response markers in the serum and plasma of people with multiple sclerosis (MS) treated with IFNbeta. In a discovery-driven approach, we use 2D-difference gel electrophoresis (DIGE) to identify putative clinical response markers and apply power calculations to identify the sample size required to further validate those markers. In the process we have optimized a DIGE protocol for plasma to obtain cost effective and high resolution gels for effective spot comparison. APOA1, A2M, and FIBB were identified as putative clinical response markers. Power calculations showed that the current DIGE experiment requires a minimum of 10 samples from each group to be confident of 1.5 fold difference at the p<0.05 significance level. In a complementary targeted approach, Cytometric Beadarray (CBA) analysis showed no significant difference in the serum concentration of IL-6, IL-8, MIG, Eotaxin, IP-10, MCP-1, and MIP-1alpha, between clinical responders and non-responders, despite the association of these proteins with IFNbeta treatment in MS.

Show MeSH
Related in: MedlinePlus