Cysteine-Poor Region-Specific EpCAM Monoclonal Antibody Recognizing Native Tumor Cells with High Sensitivity
Bibliographic record
Abstract
EpCAM is a ∼40 kDa transmembrane glycoprotein. EpCAM overexpression is a popular trait of almost all carcinomas and is considered as a targeted cancer immunotherapy as well as a practical marker for circulating tumor cells (CTC). Its extracellular part (EpEx) consists of an N-terminal EGF-like (EGF) domain, a TY-like (TY) domain, and an uncharacterized cysteine-poor (CP) region. Most commercially available murine monoclonal antibodies (MAbs) to EpCAM, such as HEA 125 and VU-1D9, bind to the small EGF domain. In a previous study, we introduced iCeap (intact CTC enumeration and analysis procedure), keeping cellular integrity during the whole process. Unlike the CellSearch(®) CTC Test, iCeap enables downstream molecular analysis from detected CTC. Use of two EpCAM MAbs, one for immunomagnetic enrichment of rare CTC from blood samples and the other for labeling, is a concept of iCeap while an ideal MAb pair has not been found. In order to obtain a better MAb that recognizes a part of EpEx as different from EGF domain, we established a mouse hybridoma clone producing a new EpCAM MAb, KIJY2. Fluorophore-conjugated KIJY2 and HEA 125-FITC can concomitantly stain the tumor cell line LNCaP within indistinguishable cellular compartments (i.e., the cell surface). Epitope mapping reveals that KIJY2 binds to the CP region. The epitope for KIJY2 is sensitive to paraformaldehyde fixation, but native cells including MCF-7 (EpCAM high-expressing cell line) and PC-3 (EpCAM low and heterogeneously expressing cell line) are detected by KIJY2. In particular, KIJY2 detects all PC-3 cells regardless of their EpCAM expression levels. Therefore, KIJY2 and an EGF domain-directed MAb are a promising pair to form the EpCAM sandwich in iCeap. We demonstrate that KIJY2 incorporated into iCeap yielded favorable results in spike-in experiments of MCF-7 and PC-3.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".