Novel mouse model for carcinoembryonic antigen-based therapy
Bibliographic record
Abstract
Many novel cancer therapies, including immunotherapy and gene therapy, are specifically targeted to tumor-associated molecules, among which carcinoembryonic antigen (CEA) represents a popular example. Discrepancies between preclinical experimental data in animal models and clinical outcome in terms of therapeutic response and toxicity, however, often arise. Preclinical testing can be compromised by the lack of CEA and other closely related human CEA family members in rodents, which lack analogous genes for most human CEA family members. Here, we report the construction of a transgenic mouse with a 187-kb human bacterial artificial chromosome (CEABAC) that contains part of the human CEA family gene cluster including complete human CEA (CEACAM5), CEACAM3, CEACAM6, and CEACAM7 genes. The spatiotemporal expression pattern of these genes in the CEABAC mice was found to be remarkably similar to that of humans. This novel mouse will ensure better assessment than previously utilized models for the preclinical testing of CEA-targeted therapies and perhaps allow the testing of CEACAM6, which is overexpressed in many solid tumors and leukemias, as a therapeutic target. Moreover, expression of CEA family genes in gastrointestinal, breast, hematopoietic, urogenital, and respiratory systems could facilitate other clinical applications, such as the development of therapeutic agents against Neisseria gonorrhoeae infections, which use CEA family members as major receptors.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".