Terminal differentiation and anti-tumorigenic effects of prolactin in breast cancer
Bibliographic record
Abstract
Breast cancer is a major disease affecting women worldwide. A woman has 1 in 8 lifetime risk of developing breast cancer, and morbidity and mortality due to this disease are expected to continue to rise globally. Breast cancer remains a challenging disease due to its heterogeneity, propensity for recurrence and metastasis to distant vital organs including bones, lungs, liver and brain ultimately leading to patient death. Despite the development of various therapeutic strategies to treat breast cancer, still there are no effective treatments once metastasis has occurred. Loss of differentiation and increased cellular plasticity and stemness are being recognized molecularly and clinically as major derivers of heterogeneity, tumor evolution, relapse, metastasis, and therapeutic failure. In solid tumors, breast cancer is one of the leading cancer types in which tumor differentiation state has long been known to influence cancer behavior. Reprograming and/or restoring differentiation of cancer cells has been proposed to provide a viable approach to reverse the cancer through differentiation and terminal maturation. The hormone prolactin (PRL) is known to play a critical role in mammary gland lobuloalveolar development/remodeling and the terminal differentiation of the mammary epithelial cells promoting milk proteins gene expression and lactation. Here, we will highlight recent discoveries supporting an anti-tumorigenic role for PRL in breast cancer as a "pro/forward-differentiation" pathway restricting plasticity, stemness and tumorigenesis.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".