Transforming Growth Factor–β Regulates Mammary Carcinoma Cell Survival and Interaction with the Adjacent Microenvironment
Bibliographic record
Abstract
Transforming growth factor (TGF)-beta signaling has been associated with early tumor suppression and late tumor progression; however, many of the mechanisms that mediate these processes are not known. Using Cre/LoxP technology, with the whey acidic protein promoter driving transgenic expression of Cre recombinase (WAP-Cre), we have now ablated the type II TGF-beta receptor (T beta RII) expression specifically within mouse mammary alveolar progenitors. Transgenic expression of the polyoma virus middle T antigen, under control of the mouse mammary tumor virus enhancer/promoter, was used to produce mammary tumors in the absence or presence of Cre (T beta RII((fl/fl);PY) and T beta RII((fl/fl);PY;WC), respectively). The loss of TGF-beta signaling significantly decreased tumor latency and increased the rate of pulmonary metastasis. The loss of TGF-beta signaling was significantly correlated with increased tumor size and enhanced carcinoma cell survival. In addition, we observed significant differences in stromal fibrovascular abundance and composition accompanied by increased recruitment of F4/80(+) cell populations in T beta RII((fl/fl);PY;WC) mice when compared with T beta RII((fl/fl);PY) controls. The recruitment of F4/80(+) cells correlated with increased expression of known inflammatory genes including Cxcl1, Cxcl5, and Ptgs2 (cyclooxygenase-2). Notably, we also identified an enriched K5(+) dNp63(+) cell population in primary T beta RII((fl/fl);PY;WC) tumors and corresponding pulmonary metastases, suggesting that loss of TGF-beta signaling in this subset of carcinoma cells can contribute to metastasis. Together, our current results indicate that loss of TGF-beta signaling in mammary alveolar progenitors may affect tumor initiation, progression, and metastasis through regulation of both intrinsic cell signaling and adjacent stromal-epithelial interactions in vivo.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".