Defining the Role of Prolactin as an Invasion Suppressor Hormone in Breast Cancer Cells
Bibliographic record
Abstract
Prolactin hormone (PRL) is well characterized as a terminal differentiation factor for mammary epithelial cells and as an autocrine growth/survival factor in breast cancer cells. However, this function of PRL may not fully signify its role in breast tumorigenesis. Cancer is a complex multistep progressive disease resulting not only from defects in cell growth but also in cell differentiation. Indeed, dedifferentiation of tumor cells is now recognized as a crucial event in invasion and metastasis. PRL plays a critical role in inducing/maintaining differentiation of mammary epithelial cells, suggesting that PRL signaling could serve to inhibit tumor progression. We show here that in breast cancer cells, PRL and Janus-activated kinase 2, a major kinase involved in PRL signaling, play a critical role in regulating epithelial-mesenchymal transformation (EMT), an essential process associated with tumor metastasis. Activation of the PRL receptor (PRLR), achieved by restoring PRL/JAK2 signaling in mesenchymal-like breast cancer cells, MDA-MB-231, suppressed their mesenchymal properties and reduced their invasive behavior. While blocking PRL autocrine function in epithelial-like breast cancer cells, T47D, using pharmacologic and genetic approaches induced mesenchymal-like phenotypic changes and enhanced their invasive propensity. Moreover, our results indicate that blocking PRL signaling led to activation of mitogen-activated protein kinase (extracellular signal-regulated kinase 1/2) and transforming growth factor-beta/Smad signaling pathways, two major prometastatic pathways. Furthermore, our results indicate that following PRL/JAK2 inhibition, ERK1/2 activation precedes and is required for Smad2 activation and EMT induction in breast cancer cells. Together, these results highlight PRL as a critical regulator of epithelial plasticity and implicate PRL as an invasion suppressor hormone in breast cancer.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| 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".