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A repertoire of monoclonal antibodies was generated by immunization of mice with cancer-associated glycoprotein CA19.9, and two of them were selected as optimal capture and detecting counterparts for sandwich test system for detection of CA19.9. Fine epitope specificity of the antibodies was determined using printed glycan array, enzyme-linked immunosorbent assay, and inhibitory enzyme-linked immunosorbent assay. Unexpectedly, both immunoglobulins did not bind key epitope of CA19.9 glycoprotein, tetrasaccharide SiaLe A , as well as its defucosylated form sialyl Le C (known as CA-50 epitope). The antibodies were found to have different glycan-binding profiles; however, they recognized similar glycotopes with common motif Galβ1-3GlcNAcβ (Le C ), thus resembling specificity of human natural cancer-associated anti-Le C antibodies. We propose that cancer-specific glycopeptide epitope includes Galβ1-3GlcNAcβ fragment of a glycoprotein O-chain in combination with proximal hydrophobic amino acid(s) of the polypeptide chain.

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The paper presents approaches to automated classification of bone marrow cells in the diagnosis of acute lymphoblastic leukemia and minimal residual disease using image recognition procedures. The classification methods that show the best accuracy in the recognition of eight types of bone marrow cells were experimentally determined. Recommendations for their use are given.