Stat1 is a suppressor of ErbB2/Neu-mediated cellular transformation and mouse mammary gland tumor formation
Bibliographic record
Abstract
The anti-tumor function of Stat1 as a regulator of innate immunity and tumor immune surveillance has been long studied and is well understood; however, less clear is its tumor-site specific role. Although Stat1 phosphorylated at tyrosine (Y) 701 and serine (S) 727 is essential for interferon (IFN) signalling, its function in signalling induced in breast cancer cells is not understood. Herein, we show that Stat1 Y701 phosphorylation is increased in human breast tumor cells with elevated levels of ErbB2/HER-2 and in cells transfected with ErbB2/Neu. However, pharmacological inhibition of ErbB2/HER-2 results in the inhibition of Stat1 Y701 phosphorylation indicating an atypical role of phosphorylated Stat1 in the inhibition of ErbB2/HER-2 signalling. Consistent with this notion, we found that Stat1 suppresses tumor development by an activated form of ErbB2/Neu in mouse embryonic fibroblasts in xenograft tumor assays; however, this anti-tumor function of Stat1 does not rely on Y701 and S727 phosphorylation. Experiments with transgenic mice demonstrated that Stat1 acts to suppress Neu-mediated breast tumorigenesis through immune regulatory and tumor-site specific mechanisms. Our data reveal a previous uncharacterized anti-tumor activity of Stat1 in ErbB2/Neu-mediated cell transformation and breast oncogenesis with possible implications in the diagnosis and treatment of ErbB2-positive breast cancers.
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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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".