Preclinical Evaluation of a Fluorescent Probe Targeting Receptor CDCP1 for Identification of Ovarian Cancer
Bibliographic record
Abstract
Optimal cytoreduction for ovarian cancer is often challenging because of aggressive tumor biology and advanced stage. It is a critical issue since the extent of residual disease after surgery is the key predictor of ovarian cancer patient survival. For a limited number of cancers, fluorescence-guided surgery has emerged as an effective aid for tumor delineation and effective cytoreduction. The intravenously administered fluorescent agent, most commonly indocyanine green (ICG), accumulates preferentially in tumors, which are visualized under a fluorescent light source to aid surgery. Insufficient tumor specificity has limited the broad application of these agents in surgical oncology including for ovarian cancer. In this study, we developed a novel tumor-selective fluorescent agent by chemically linking ICG to mouse monoclonal antibody 10D7 that specifically recognizes an ovarian cancer-enriched cell surface receptor, CUB-domain-containing protein 1 (CDCP1). 10D7 ICG has high affinity for purified recombinant CDCP1 and CDCP1 that is located on the surface of ovarian cancer cells in vitro and in vivo. Our results show that intravenously administered 10D7 ICG accumulates preferentially in ovarian cancer, permitting visualization of xenograft tumors in mice. The data suggest CDCP1 as a rational target for tumor-specific fluorescence-guided surgery for ovarian cancer.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".