Loss of STAT1 from Mouse Mammary Epithelium Results in an Increased Neu-Induced Tumor Burden
Bibliographic record
Abstract
Type I and type II classes of interferons (IFNs) signal through the JAK/STAT1 pathway and are known to be important in adaptive and innate immune responses and in protection against tumors. Although STAT1 is widely considered a tumor suppressor, it remains unclear, however, if this function occurs in tumor cells (cell autonomous) or if STAT1 acts primarily through immune cells. Here, the question of whether STAT1 has a cell autonomous role in mammary tumor formation was addressed in a mouse model of ERBB2/neu-induced breast cancer in the absence and presence of STAT1. For this purpose, mice that carry floxed Stat1 alleles, which permit cell-specific removal of STAT1, were generated. To induce tumors only in mammary cells lacking STAT1, Stat1 floxed mice were crossed with transgenic mice that express cre recombinase and the neu oncogene under the mouse mammary tumor virus LTR (Stat1fl/fl NIC). Stat1 was effectively deleted in mammary epithelium of virgin Stat1fl/fl NIC females. Time-to-tumor onset was significantly shorter in Stat1fl/fl NIC females than in WT NIC (Wilcoxon rank sum test, P = .02). The median time-to-tumor onset in the Stat1fl/fl NIC mice was 49.4 weeks, whereas it was 62.4 weeks in the WT NIC mice. These results suggest that STAT1 in mammary epithelial cells may play a role in suppressing tumorigenesis. The Stat1 floxed allele described in this study is also a unique resource to determine the cellular targets of IFNs and STAT1 action, which should aid our understanding and appreciation of these pathways.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.003 | 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".