Prolactin hormone exerts anti-tumorigenic effects in HER-2 overexpressing breast cancer cells through regulation of stemness
Bibliographic record
Abstract
Background Breast cancers characterized by HER2 overexpression, belong to HER-2 enriched or luminal B subtypes, are frequently associated with higher incidence of tumor recurrence and therapeutic failure. These aggressive features have been attributed to the presence of cancer stem-like cell subpopulations known to have high tumor initiation, self -renewal capacities and high metastatic potential. Depleting these stem-like cells in these tumors therefore might help in improving therapeutic response and patient outcome. Methods Here we used human breast cancer cells representative of HER2- enriched and luminal B subtypes as well as purified ALDH-positive stem-like cell subpopulation for in vitro cell viability, proliferation, tumorshpere formation analyses and gene expression studies. In addition, we used a pre-clinical xenograft HER2 mouse model (NOD/SCID mice) for in vivo tumorigenesis assessment. Furthermore, patient survival outcomes were evaluated using in silico bioinformatics analyses of publicly available datasets. Results Our results indicate that prolactin (PRL) exerts anti-tumorigenic effects in HER-2 positive breast cancer cells. Importantly, PRL caused a significant reduction in ALDH hi stem-like subpopulation, as well as their viability and tumorsphere formation capacity. Molecularly we found PRL to suppress gene expression of markers involved in stemness, tumor initiation, drug resistance and poor patient outcome found to be enriched in the ALDH hi stem-like subpopulation. Furthermore, we show PRL to impede tumor growth of HER-2 xenografts and to suppress expression of Ki67 proliferative marker. Finally, we found PRL pathway gene signature to correlate with favorable patient outcomes in HER-2 and luminal B breast cancer patients. Conclusion Together these results emphasize an anti-tumorigenic role with a potential therapeutic value for PRL in HER-2 and luminal B breast cancer subtypes targeting the cancer stem-like cells.
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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".