Ezh2 promotes mammary tumor initiation through epigenetic regulation of the Wnt and mTORC1 signaling pathways
Bibliographic record
Abstract
The regulation of gene expression through histone posttranslational modifications plays a crucial role in breast cancer progression. However, the molecular mechanisms underlying the contribution of histone modification to tumor initiation remain unclear. To gain a deeper understanding of the role of the histone modifier Enhancer of Zeste homology 2 (Ezh2) in the early stages of mammary tumor progression, we employed an inducible mammary organoid system bearing conditional Ezh2 alleles that faithfully recapitulates key events of luminal B breast cancer initiation. We showed that the loss of Ezh2 severely impairs oncogene-induced organoid growth, with Ezh2-deficient organoids maintaining a polarized epithelial phenotype. Transcriptomic profiling showed that Ezh2-deficient mammary epithelial cells up-regulated the expression of negative regulators of Wnt signaling and down-regulated genes involved in mTORC1 (mechanistic target of rapamycin complex 1) signaling. We identified Sfrp1 , a Wnt signaling suppressor, as an Ezh2 target gene that is derepressed and expressed in Ezh2-deficient epithelium. Furthermore, an analysis of breast cancer data revealed that Sfrp1 expression was associated with favorable clinical outcomes in luminal B breast cancer patients. Finally, we confirmed that targeting Ezh2 impairs mTORC1 activity through an indirect mechanism that up-regulates the expression of the tumor suppressor Pten. These findings indicate that Ezh2 integrates the mTORC1 and Wnt signaling pathways during early mammary tumor progression, arguing that inhibiting Ezh2 or therapeutically targeting Ezh2-dependent programs could be beneficial for the treatment of early-stage luminal B 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) | 0.000 | 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) | 0.001 | 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".