Bibliographic record
Abstract
Introduction: HER2+ breast cancer (BC) makes up 15-20% of BC cases, where HER2 overexpression drives BC progression via proliferative signals. Targeted therapies inhibit HER2's activity but face challenges like resistance and metastasis. Thus, HER2+ BC is a developing research area where many preclinical studies are being conducted, and mice are often used due to their genetic and physiological similarities to humans. This study aims to review and compare existing mouse models to determine the best model of HER2+ BC. Methods: A literature search on PubMed in February 2024 using search terms including HER2, neu, neuNT, MMTV, and mammary tumors identified 16 primary research articles that used Neu-overexpressing mice. The papers were categorized under five models: MMTV-Neu, MMTV-NeuNT, FloxNeo-NeuNT, MMTV-NIC, and MMTV-HER2. Results: Among examined studies, Andrechek et al.'s FloxNeo-NeuNT model had the longest average tumor onset at 447 days, while Kuang et al.'s MMTV-NIC had the shortest at 126 days. Ursini-Segal et al.'s MMTV-NIC mice showed 100% mammary tumor incidence. Most models resulted in mammary adenocarcinomas, with lung metastases commonly observed, especially in papers that used MMTV-NIC. Discussion: Human HER2+ BC is commonly adenocarcinoma and often metastasizes to lungs, bones, liver, and brain. The mouse models were also found to be adenocarcinomas, but they primarily showed lung metastasis, possibly due to insufficient tumor detection methods for brain and bone metastasis. Neu copy numbers in Andrechek et al.’s FloxNeo-NeuNT model also align with findings on HER2 copy number amplification in human HER2+ BC. Conclusion: We have found that MMTV-NIC is the optimal mouse model for HER2+ BC research due to its short latency, 100% tumor incidence, and high rate of lung metastasis.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".