Conditional Overexpression of Active Transforming Growth Factor β1 <b> <i>In vivo</i> </b> Accelerates Metastases of Transgenic Mammary Tumors
Bibliographic record
Abstract
To address the role of transforming growth factor (TGF) beta in the progression of established tumors while avoiding the confounding inhibitory effects of TGF-beta on early transformation, we generated doxycycline (DOX)-inducible triple transgenic mice in which active TGF-beta1 expression could be conditionally regulated in mouse mammary tumor cells transformed by the polyomavirus middle T antigen. DOX-mediated induction of TGF-beta1 for as little as 2 weeks increased lung metastases >10-fold without a detectable effect on primary tumor cell proliferation or tumor size. DOX-induced active TGF-beta1 protein and nuclear Smad2 were restricted to cancer cells, suggesting a causal association between autocrine TGF-beta and increased metastases. Antisense-mediated inhibition of TGF-beta1 in polyomavirus middle T antigen-expressing tumor cells also reduced basal cell motility, survival, anchorage-independent growth, tumorigenicity, and metastases. Therefore, induction and/or activation of TGF-beta in hosts with established TGF-beta-responsive cancers can rapidly accelerate metastatic progression.
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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) | 0.000 | 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 | 0.001 |
| 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".