ArticleBiosensors2025
Characterization of M11-like and OC125-like Monoclonal Antibody Binding to CA125 Tandem Repeats.
Article in Biosensors, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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Who cites it
2 citing papers in PubMed.
- Polyclonal antibodies targeting defined epitopes on cancer biomarker MUC16.Analytical and bioanalytical chemistry · 2026Article
- A Rational Optimization Approach for the Development of a Multiplexed Lateral Flow Immunoassay: Detection of Nonepithelial Ovarian Cancer Markers in Human Serum.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
The CA125 epitope within the MUC16 tandem repeat region is detected via the CA125 II test for ovarian cancer surveillance. This test utilizes the M11 and OC125 antibodies. A revised model of MUC16 with 19 tandem repeats has recently been identified, including splice variants that exclude entire repeats. Additionally, OC125 has exhibited gaps in coverage of the tandem repeat region. To identify antibodies that bind more repeats and are suitable for spliceoform detection, more antibodies must be characterized using the revised model. This study characterized the binding of two M11-like and two OC125-like antibodies against the updated tandem repeat numbering system. 16 individual tandem repeats were expressed and purified. Binding interactions between each of the antibodies and recombinant repeats were examined by indirect enzyme-linked immunosorbent assay (ELISA) and surface plasmon resonance (SPR). The M11-like antibodies displayed different binding patterns when compared to each other, while the two OC125-like antibodies exhibited similar binding patterns. M11-like clone M77161 bound to all 16 repeats tested, indicating that it may be suitable for accurate detection of CA125. These findings demonstrate how different antibodies vary in their binding to CA125, contributing to ongoing development of improved clinical and research tools for ovarian cancer.
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