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Detection of Aggregation-Prone Behavior in Mutant P53 V157F Breast Cancer Cells Using Multipoint Thioflavin T Fluorescence
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Quantitative analysis of p53 expression in human normal and cancer tissue microarray with global normalization

Halliday A Idikio1

  • 1Department of Pathology and Laboratory Medicine, University of Alberta Edmonton, ALBERTA T6G 2B7, Canada. hidikio@ualberta.ca

International Journal of Clinical and Experimental Pathology
|July 9, 2011
PubMed
Summary

Global normalization of protein expression in tissue microarrays improves cancer biomarker analysis. This method enhances the accuracy of identifying positive p53 staining, crucial for developing reliable cancer biomarkers.

Keywords:
Global normalizationcancer biomarkersimmunohistochemistryp53protein

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Area of Science:

  • Biomarker Discovery
  • Proteomics
  • Cancer Research

Background:

  • Tissue microarrays (TMAs) and immunohistochemistry (IHC) are key for cancer biomarker validation.
  • Manual scoring of IHC in TMAs has limitations; digital analysis needs further refinement, such as normalization.
  • Accurate protein expression quantification is essential for reliable biomarker development.

Purpose of the Study:

  • To evaluate the utility of a quantitative proteomics-based global normalization method for analyzing p53 protein expression in human normal and cancer tissue microarrays.
  • To assess the impact of global normalization on defining positive staining and establishing clear cut-off points for cancer biomarkers.

Main Methods:

  • Quantitative proteomics-based global normalization was applied to formalin-fixed tissue microarray sections.
  • The method involved calculating ratios of core stain intensities using beta-actin, followed by log transformation and normalization.
  • p53 protein expression was analyzed with and without global normalization, using mean and median intensity values as cut-offs.

Main Results:

  • Without global normalization, p53 positive cores were 44% (median) and 40% (mean).
  • With global normalization, positive p53 cores decreased to 20% (median) and 15.8% (mean).
  • Global normalization clearly defined positive p53 staining and confirmed all negatively stained cores.

Conclusions:

  • Quantitative proteomics-based global normalization effectively refines the analysis of protein expression in tissue microarrays.
  • This normalization method aids in establishing precise cut-off points for cancer biomarkers, enhancing their clinical utility.
  • Implementing such normalization strategies is crucial for the accurate validation of clinically relevant cancer biomarkers.