Transgenic overexpression of the <scp>miR</scp>‐200b/200a/429 cluster prevents mammary tumor initiation in <i>Neu/Erbb2</i> transgenic mice
Bibliographic record
Abstract
Abstract Although significant progress in the treatment of breast cancer has been achieved, toxic therapies would not be required if breast cancer could be prevented from developing in the first place. While breast cancer prevention is difficult to study in humans due to long disease latency and stochastic cancer development, transgenic mouse models with 100% incidence and defined mammary tumor onset, provide excellent models for tumor prevention studies. In this study, we used Neu/Erbb2 transgenic mice (MTB‐TAN) as a model of human HER2 + breast cancer to investigate whether a family of microRNAs, known as the miR‐200 family, can prevent mammary tumor development. Overexpression of Neu induced palpable mammary tumors in 100% of the mice within 38 days of Neu overexpression. When the miR‐200b/200a/429 cluster was co‐overexpressed with Neu in the same mammary epithelial cells (MTB‐TANba429 mice), the miR‐200b/200a/429 cluster prevented Neu from inducing mammary epithelial hyperplasia and mammary tumor development. RNA sequencing revealed alterations in the extracellular matrix of the mammary gland and a decrease in stromal cells including myoepithelial cells in Neu transgenic mice. Immunohistochemistry for smooth muscle actin confirmed that mammary epithelial cells in control and MTB‐TANba429 mice were surrounded by a layer of myoepithelial cells and these myoepithelial cells were lost in MTB‐TAN mice with hyperplasia. Thus, we have shown for the first time that elevated expression of miR‐200 family members in mammary epithelial cells can completely prevent mammary tumor development in Neu transgenic mice possibly through regulating myoepithelial cells.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".