Prolactin modulates TNBC aggressive phenotype limiting tumorigenesis
Bibliographic record
Abstract
Triple-negative breast cancer (TNBC) accounts for ~20% of all breast cancer cases. The management of TNBC represents a challenge due to its aggressive phenotype, heterogeneity and lack of targeted therapy. Loss of cell differentiation and enrichment with breast cancer stem-like cells (BCSC) are features of TNBC contributing to its aggressive nature. Here, we found that treatment of TNBC cells with PRL significantly depletes the highly tumorigenic BCSC subpopulations CD44+/CD24- and ALDH+ and differentiates them to the least tumorigenic CD44-/CD24- and ALDH- phenotype with limited tumorsphere formation and self-renewal capacities. Importantly, we found PRL to induce a heterochromatin phenotype marked by histone H3 lysine 9 trimethylation (H3K9me3) and accompanied by ultra-structural cellular architecture associated with differentiation and senescence rendering the cells refractory to growth signals. Crucially, we found PRL to mediate these effects in vivo in a pre-clinical animal xenograft of TNBC controlling tumor growth. These results reveal that the lactogenic hormone PRL may exert its anti-tumorigenic effects on TNBC through cellular reprogramming indicative of differentiation resulting in the depletion of BCSCs and restricting tumorigenesis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".