Prolactin receptor-driven combined luminal and epithelial differentiation in breast cancer restricts plasticity, stemness, tumorigenesis and metastasis
Bibliographic record
Abstract
Dedifferentiation increased cellular plasticity and stemness are established derivers of tumor heterogeneity, metastasis and therapeutic failure resulting in incurable cancers. Therefore, it is essential to decipher pro/forward-differentiation mechanisms in cancer that may serve as therapeutic targets. We found that interfering with expression of the receptor for the lactogenic hormone prolactin (PRLR) in breast cancer cells representative of the luminal and epithelial breast cancer subtypes (hormone receptor positive (HR+) and HER2-enriched (HER2-E) resulted in loss of their differentiation state, enriched for stem-like cell subpopulations, and increased their tumorigenic capacity in a subtype-specific manner. Loss of PRLR expression in HR+ breast cancer cells caused their dedifferentiation generating a mesenchymal-basal-like phenotype enriched in CD44+ breast cancer stem-like cells (BCSCs) showing high tumorigenic and metastatic capacities and resistance to anti-hormonal therapy. Whereas loss of PRLR expression in HER2-E breast cancer cells resulted in loss of their luminal differentiation yet enriched for epithelial ALDH+ BCSC population showing elevated HER2-driven tumorigenic, multi-organ metastatic spread, and resistance to anti-HER2 therapy. Collectively, this study defines PRLR as a driver of precise luminal and epithelial differentiation limiting cellular plasticity, stemness, and tumorigenesis and emphasizing the function of pro/forward-differentiation pathways as a foundation for the discovery of anti-cancer therapeutic targets.
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How this classification was reachedexpand
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".