Intraductal Adaptation of the 4T1 Mouse Model of Breast Cancer Reveals Effects of the Epithelial Microenvironment on Tumor Progression and Metastasis
Bibliographic record
Abstract
BACKGROUND: Low success rates in oncology drug development are prompting re-evaluation of preclinical models, including orthotopic tumor engraftment. In breast cancer models, tumor cells are typically injected into mouse mammary fat pads (MFP). However, this approach bypasses the epithelial microenvironment, potentially altering tumor properties in ways that affect translational application. MATERIALS AND METHODS: Tumors were generated by mammary intraductal (MIND) engraftment of 4T1 carcinoma cells. Growth, histopathology, and molecular features were quantified. RESULTS: Despite growth similar to that of 4T1 MFP tumors, 4T1 MIND tumors exhibit distinct histopathology and increased metastasis. Furthermore, >6,000 transcripts were found to be uniquely up-regulated in 4T1 MIND tumor cells, including genes that drive several cancer hallmarks, in addition to two known therapeutic targets that were not up-regulated in 4T1 MFP tumor cells. CONCLUSION: Engraftment into the epithelial microenvironment generates tumors that more closely recapitulate the complexity of malignancy, suggesting that intraductal adaptation of orthotopic mammary models may be an important step towards improving outcomes in preclinical drug screening and development.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".