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Reliable quantification of protein expression and cellular localization in histological sections. | LitMetric

Reliable quantification of protein expression and cellular localization in histological sections.

PLoS One

Ludwig Boltzmann Institute for Cancer Research (LBI-CR), Vienna, Austria; Institute of Clinical Pathology, Medical University of Vienna, Vienna, Austria; Unit of Pathology of Laboratory Animals, University of Veterinary Medicine Vienna (Vetmeduni Vienna), Vienna, Austria.

Published: March 2015

AI Article Synopsis

  • Targeted therapy relies on analyzing tumors for abnormal cancer pathway activations and using biomarkers to guide treatment, but traditional methods like immunohistochemistry often suffer from subjective interpretation issues.
  • The study investigated whether automated image analysis could reliably quantify biomarker expression in tumors, particularly focusing on differences in protein levels linked to gene dosage effects in conditional mouse models with specific gene deletions.
  • Results showed that image analysis accurately detected varying levels of protein expression and improved the ability to assess nuclear localization, thereby reducing variability among pathologists when evaluating human cancer samples.*

Article Abstract

In targeted therapy, patient tumors are analyzed for aberrant activations of core cancer pathways, monitored based on biomarker expression, to ensure efficient treatment. Thus, diagnosis and therapeutic decisions are often based on the status of biomarkers determined by immunohistochemistry in combination with other clinical parameters. Standard evaluation of cancer specimen by immunohistochemistry is frequently impeded by its dependence on subjective interpretation, showing considerable intra- and inter-observer variability. To make treatment decisions more reliable, automated image analysis is an attractive possibility to reproducibly quantify biomarker expression in patient tissue samples. We tested whether image analysis could detect subtle differences in protein expression levels. Gene dosage effects generate well-graded expression patterns for most gene-products, which vary by a factor of two between wildtype and haploinsufficient cells lacking one allele. We used conditional mouse models with deletion of the transcription factors Stat5ab in the liver as well Junb deletion in a T-cell lymphoma model. We quantified the expression of total or activated STAT5AB or JUNB protein in normal (Stat5ab+/+ or JunB+/+), hemizygous (Stat5ab+/Δ or JunB+/Δ) or knockout (Stat5abΔ/Δ or JunBΔ/Δ) settings. Image analysis was able to accurately detect hemizygosity at the protein level. Moreover, nuclear signals were distinguished from cytoplasmic expression and translocation of the transcription factors from the cytoplasm to the nucleus was reliably detected and quantified using image analysis. We demonstrate that image analysis supported pathologists to score nuclear STAT5AB expression levels in immunohistologically stained human hepatocellular patient samples and decreased inter-observer variability.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4094387PMC
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0100822PLOS

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