PR+ progenitors contribute to all mammary epithelial lineages
Bibliographic record
Abstract
ABSTRACT The central role of progesterone in breast biology and cancer is undeniable. Progesterone is a potent mitogen for mammary stem cell expansion and essential for mammopoiesis, yet only a fraction of the luminal epithelium is described to express the progesterone receptor (PR). Progestins in contraceptives and HRT regimens increases breast cancer risk, whilst progesterone inhibition reduces mammary tumorigenesis. Understanding PR within mammary epithelial dynamics is imperative, especially given mammary stem/progenitors cells are the cells-of-origin in breast cancer. Here, we show PR-primed progenitors contribute to both mammary lineages and expose a novel PR+ basal population, challenging current dogma in the field. PR lineage-tracing yields an unprecedented contribution to the luminal and basal compartments. We uncover an asymmetrically dividing PR-primed subpopulation, which has unique bipotent clonogenic capacity and forms TEB-like outgrowths upon transplantation. We enumerate PR+ basal cells and dissect their proteomic landscape, establishing PR+ basal cells as a discrete basal subset, disparate from luminal PR+ cells. Finally, we identify PR+ basal cells in multiple proteomes, scRNAseq datasets and localize PR in clinical breast specimens. This forms a new foundation for comprehending hormone receptor patterning in the breast, not only based on lineage-identity but also the progenitor-progeny hierarchy. Our study shifts the current paradigm of breast biology with implications for breast cancer treatment given the growing interest in anti-progestin based primary prevention strategies.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".