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Scientists develop new technology for the diagnosis of cancer cells.

The type of therapy a cancer patient receives, largely depends on the trained eye of a pathologist. Investigating diseased organs and tissues under the microscope is one of their tasks. However, human judgment is, by its very nature, subject to a certain degree of variation. To enhance the quality of diagnosis, scientists at Vetmeduni Vienna and the Ludwig Boltzmann Institute for Cancer Research have developed a software that specifically identifies cell structures and proteins in order to provide reliable diagnoses.  

The team have developed a software that is able to identify cancer cells in tissue sections and demonstrate the presence of specific biomarkers on cells. The overall information provides a precise picture of the disease and leads to the most suitable treatment. 

According to the results of the study, two independent pathologists concur with each other only in regard of every third diagnosis.  The recently developed software offers, for the first time, the option of eliminating the so-called inter-observer-variability, which is the systematic variability of judgement among different observers.

The scientists investigated and analyzed 30 liver cell carcinomas, and clearly assigned these to the categories ‘negative’ or ‘highly positive’ with the help of the software. For this purpose the scientists analyzed the expression of specific proteins like Stat5 and JunB, which play an important role in the emergence of cancer. The software utilizes specific algorithms and highly sensitive digital photography, and is able to more clearly depict the matrix of cells and the cell nucleus than the view achieved by the human eye using a microscope.

The group have been using the software in research for several years. The technology will obviously not replace pathologists, but is a supplementary technology that markedly enhances the reliability of the diagnosis. The researchers also believe that the new technology will help to specify the categories in which in cancer cells are classified with greater accuracy in the future.

Cancer therapies are expensive. The new software will help to make a better assessment as to when expensive therapy is justified and also identify those cases in which it is not necessary and the patient can be spared the burden of such treatment.

The so-called ‘precision medicine’, an advancement and evolution of personalized medicine, focusses on the health of individuals. With the aid of molecular biology-based methods, the ideal treatment is found for the individual patient. 

This type of medicine is especially promising for the treatment of cancer. Tumours differ from person to person. Pathologists investigate tumour tissue on the molecular level and thus establish the most suitable type of therapy.  For instance, cancer cells bear different surface molecules. A suitable drug must target the correct molecule in order to counteract the growth of the tumour.  Every patient should receive the most suitable therapy. Only such an approach is ethically justifiable and sensible in economic terms.

Source:  University of Veterinary Medicine Vienna (Vetmeduni Vienna)

Reliable Quantification of Protein Expression and Cellular Localization in Histological Sections.  STAT5AB protein expression in mouse Stat5ab+/+, Stat5ab+/Δ and Stat5abΔ/Δ livers.  IHC staining of STAT5AB in histological sections of the livers (n = 3) used in A. Red, AEC; blue, hematoxylin. Insets are higher magnifications. White stars indicate STAT5AB expression in Kupffer cells and endothelial cells, whereas the hepatocytes are negative for STAT5AB expression. Size bar: 100 µm.  Kenner et al 2014.
Reliable Quantification of Protein Expression and Cellular Localization in Histological Sections. STAT5AB protein expression in mouse Stat5ab+/+, Stat5ab+/Δ and Stat5abΔ/Δ livers. IHC staining of STAT5AB in histological sections of the livers (n = 3) used in A. Red, AEC; blue, hematoxylin. Insets are higher magnifications. White stars indicate STAT5AB expression in Kupffer cells and endothelial cells, whereas the hepatocytes are negative for STAT5AB expression. Size bar: 100 µm. Kenner et al 2014.

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