RANKL/RANK control Brca1 mutation-driven mammary tumors
Bibliographic record
Abstract
Breast cancer is the most common female cancer, affecting approximately one in eight women during their life-time. Besides environmental triggers and hormones, inherited mutations in the breast cancer 1 (BRCA1) or BRCA2 genes markedly increase the risk for the development of breast cancer. Here, using two different mouse models, we show that genetic inactivation of the key osteoclast differentiation factor RANK in the mammary epithelium markedly delayed onset, reduced incidence, and attenuated progression of Brca1;p53 mutation-driven mammary cancer. Long-term pharmacological inhibition of the RANK ligand RANKL in mice abolished the occurrence of Brca1 mutation-driven pre-neoplastic lesions. Mechanistically, genetic inactivation of Rank or RANKL/RANK blockade impaired proliferation and expansion of both murine Brca1;p53 mutant mammary stem cells and mammary progenitors from human BRCA1 mutation carriers. In addition, genome variations within the RANK locus were significantly associated with risk of developing breast cancer in women with BRCA1 mutations. Thus, RANKL/RANK control progenitor cell expansion and tumorigenesis in inherited breast cancer. These results present a viable strategy for the possible prevention of breast cancer in BRCA1 mutant patients.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".