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.
Highlights
Cancer genome studies revealed insights into molecular pathways, which are driving events for cancer development and progression
We show that the inter-observer variability judging nuclear STAT5AB of human hepatocellular carcinoma (HCC) patient samples significantly decreased with the support of quantitative image analysis information
In order to evaluate the reliability of automated image analysis and quantification of tissue or cell samples stained with IHC we first used a mouse model, which displays different expression levels of STAT5AB in the liver by conditional deletion of signal transducer and activator of transcription 5ab (Stat5ab) [14,22,23,24]
Summary
Cancer genome studies revealed insights into molecular pathways, which are driving events for cancer development and progression. Targeting key signaling molecules, which define core cancer pathways, are a central theme in the modern cancer treatment. Some of these drugs are of great success; for example the kinase inhibitor imatinib, which blocks BCR-ABL tyrosine kinase activity in chronic myelogenous leukemia (CML) [1], or the human epidermal growth factor receptor 2 (HER-2) inhibitors used to treat cancer patients, who aberrantly overexpress epidermal growth factor receptor (EGFR) in the tumor cells [2]. Manual evaluation of immunohistochemically stained cancer specimen is a subjective and highly individual task, which naturally depends on intra- and inter-observer variability. Beyond that it is of great importance to know which cell type (stromal or tumor cell) shows an enhanced/reduced expression of a certain biomarker
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