The Interleukin (IL)-1R1 pathway is a critical negative regulator of PyMT-mediated mammary tumorigenesis and pulmonary metastasis
Bibliographic record
Abstract
Breast cancer is the most common cancer in women and the second leading cause of female cancer-related deaths worldwide. Inflammation is an established hallmark of tumorigenesis and an important determinant of tumor outcome and response to therapy. With advances in cancer immunotherapy, there is an urgent need to dissect the contribution of specific immune effectors in cancer development. Here, we genetically investigated the role of the Interleukin-1 (IL-1) receptor 1 (IL-1R1) pathway in breast cancer tumorigenesis and metastasis using the MMTV-PyMT mouse model. Our results indicate that IL-1R1 signaling suppresses mammary tumor cell proliferation early in tumorigenesis and curbs breast cancer outgrowth and pulmonary metastasis. We show that PyMT/Il1r1−/− mice had a higher primary tumor burden and increased mortality rate compared with IL-1R1-sufficient PyMT control mice. This phenotype was independent of the inflammatory caspases-1/-11 but driven by IL-1α, as PyMT/Il1a−/− mice phenocopied PyMT/Il1r1−/− mice. Collectively, our results suggest that IL-1α-mediated IL-1R1 signaling is tumor-suppressive in PyMT-driven breast cancer.
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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 | 0.000 | 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 | 0.000 |
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 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".