Inactivation of RARβ Inhibits Wnt1-induced Mammary Tumorigenesis by Suppressing Epithelial-mesenchymal Transitions
Bibliographic record
Abstract
Retinoic acid receptor β (RARβ) has been proposed to act as a tumor suppressor in breast cancer. In contrast, recent data have shown that RARβ promotes ERBB2-induced mammary gland tumorigenesis through remodeling of the stromal compartment and activation of cancer-associated fibroblasts. However, it is currently unknown whether RARβ oncogenic activity is specific to ERBB2-induced tumors, or whether it influences the initiation and progression of other breast cancer subtypes. Accordingly, we set out to investigate the involvement of RARβ in basal-like breast cancer using mouse mammary tumor virus (MMTV)-wingless-related integration site 1 (Wnt1)-induced mammary gland tumorigenesis as a model system. We found that compared with wild type mice, inactivation of Rarb resulted in a lengthy delay in Wnt1-induced mammary gland tumorigenesis and in a significantly slower tumor growth rate. Ablation of Rarb altered the composition of the stroma, repressed the activation of cancer-associated fibroblasts, and reduced the recruitment of inflammatory cells and angiogenesis. Reduced expression of IGF-1 and activity of its downstream signaling pathway contribute to attenuate EMT in the Rarb-null tumors. Our results show that, in the absence of retinoid signaling via RARβ, reduced IGF-1 signaling results in suppression of epithelial-mesenchymal transition and delays tumorigenesis induced by the Wnt1 oncogene. Accordingly, our work reinforces the concept that antagonizing RARβ-dependent retinoid signaling could provide a therapeutic avenue to treat poor outcome 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) | 0.000 | 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) | 0.001 | 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".