The glycoprotein recognized by the monoclonal antibody (mAb) 17-1A is present on most carcinomas, which makes it an attractive target for immunotherapy. Indeed, adjuvant treatment with mAb 17-1A did successfully reduce the 5 years mortality among colorectal cancer patients with minimal residual disease. Currently the antibody is approved for clinical use in Germany, and is on its way to approval in a number of other countries. New immunotherapeutic strategies targeting the 17-1A antigen are in development or even in early-phase clinical trials. Therefore, a better understanding of the biology of the 17-1A antigen may result in improved strategies for the treatment and diagnosis of human carcinomas. In this review the properties of the 17-1A antigen are discussed concerning tumor biology and the function of the molecule. This 40-kDa glycoprotein functions as an Epithelial Cell Adhesion Molecule, therefore the name Ep-CAM was suggested. Ep-CAM mediates Ca2+-independent homotypic cell-cell adhesions. Formation of Ep-CAM-mediated adhesions has a negative regulatory effect on adhesions mediated by classic cadherins, which may have strong effects on the differentiation and growth of epithelial cells. Indeed, in vivo expression of Ep-CAM is related to increased epithelial proliferation and negatively correlates with cell differentiation. A regulatory function of Ep-CAM in the morphogenesis of epithelial tissue has been demonstrated for a number of tissues, in particular pancreas and mammary gland. The function of Ep-CAM should be taken into consideration when developing new therapeutic approaches targeting this molecule.
"The most highly conserved regions are the thyroglobulin repeat domain and the transmembrane region. Unlike, Trop2, which is intronless, Epcam consists of nine coding exons; Exons 1–6 encode the extracellular domain, exon 7 the transmembrane region, and exons 8–9 the intracellular tail (Balzar et al., 1999). The cysteine positions and distributions of hydrophilic and hydrophobic residues in the thyroglobulin repeat domain are conserved between TROP2 and EpCAM. "
"EpCAM is a human gallbladder epithelial cell marker EpCAM is a cell surface marker that was first described in colorectal cancer (Koprowski et al., 1979). Its expression has since been found on a wide variety of epithelial cells such as keratinocytes, thymic epithelial cells and IHBD cells (Balzar et al., 1999; de Boer et al., 1999). Previously, we have determined that mouse gallbladder epithelial cells were EpCAM +, and subsequently used EpCAM to label these cells by flow cytometry (Manohar et al., 2011). "
"Data for the study was collected with a functionalized and structured medical wire (FSMW)  that is a CE-certified medical device for the isolation of CTCs. Human carcinoma cells expresses the epithelial cell adhesion molecule (EpCAM) on their surface while this molecule is absent from the surface of haematological cells   . The FSMW is functionalized with anti-EpCAM antibodies and was inserted into the cubital vein of a patient through a standard 20 G intravenous cannula, where it was left for 30 minutes collecting CTCs from the blood that flows past . "
[Show abstract][Hide abstract] ABSTRACT: Application of personalized medicine requires integration of different data to determine each patient’s unique clinical constitution. The automated analysis of medical data is a growing field where different machine learning techniques are used to minimize the time-consuming task of manual analysis. The evaluation, and often training, of automated classifiers requires manually labelled data as ground truth. In many cases such labelling is not perfect, either because of the data being ambiguous even for a trained expert or because of mistakes. Here we investigated the interobserver variability of image data comprising fluorescently stained circulating tumor cells and its effect on the performance of two automated classifiers, a random forest and a support vector machine. We found that uncertainty in annotation between observers limited the performance of the automated classifiers, especially when it was included in the test set on which classifier performance was measured. The random forest classifier turned out to be resilient to uncertainty in the training data while the support vector machine’s performance is highly dependent on the amount of uncertainty in the training data. We finally introduced the consensus data set as a possible solution for evaluation of automated classifiers that minimizes the penalty of interobserver variability.
Journal of Immunology Research 01/2015; 2015:1-9. DOI:10.1155/2015/573165 · 2.93 Impact Factor
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