Inactivation of RARβ Inhibits Wnt1-induced Mammary Tumorigenesis by Suppressing Epithelial-mesenchymal Transitions
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it