Stromal retinoic acid receptor β promotes mammary gland tumorigenesis
Bibliographic record
Abstract
Retinoic acid is a potent differentiation and antiproliferative agent of breast cancer cells, and one of its receptors, retinoic acid receptor β (RARβ), has been proposed to act as a tumor suppressor. In contrast, we report herein that inactivation of Rarb in the mouse results in a protective effect against ErbB2-induced mammary gland tumorigenesis. Strikingly, tissue recombination experiments indicate that the presence of Rarb in the stromal compartment is essential for the growth of mammary carcinoma. Ablation of Rarb leads to a remodeling of the stroma during tumor progression that includes a decrease in angiogenesis, in the recruitment of inflammatory cells, and in the number myofibroblasts. In agreement with this finding, we observed that a markedly reduced expression of chemokine (C-X-C motif) ligand 12 (Cxcl12) in the stroma of Rarb-null mice is accompanied by a decrease in the CXCL12/chemokine C-X-C receptor 4 (CXCR4)/ErbB2 signaling axis in the tumors. Relevance to the human disease is underlined by the finding that gene-expression profiling of the Rarb-deficient mammary stromal compartment identified an ortholog RARβ signature in human microdissected breast tissues that differentiates tumor from normal stroma. Our study thus implicates RARβ in promoting tumorigenesis and suggests that retinoid-based approaches for the prevention and treatment of breast cancer should be redesigned.
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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 | 0.000 |
| 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".