Delineating the role of osteoprotegerin as a marker of breast cancer risk among women with a BRCA1 mutation
Bibliographic record
Abstract
Women with a pathogenic germline mutation in the BRCA1 gene face a very high lifetime risk of developing breast cancer, estimated at 72% by age 80. Prophylactic bilateral mastectomy is the only effective way to lower their risk; however, most women with a mutation opt for intensive screening with annual MRI and mammography. Given that the BRCA1 gene was identified over 20 years ago, there is a need to identify a novel non-surgical approach to hereditary breast cancer prevention. Here, we provide a review of the emerging preclinical and epidemiologic evidence implicating the dysregulation of progesterone-mediated receptor activator of nuclear factor κB (RANK) signaling in the pathogenesis of BRCA1-associated breast cancer. Experimental studies have demonstrated that RANK inhibition suppresses Brca1-mammary tumorigenesis, suggesting a potential target for prevention. Data from studies conducted among women with a BRCA1 mutation further support this pathway in BRCA1-associated breast cancer development. Progesterone-containing (but not estrogen-alone) hormone replacement therapy is associated with an increased risk of breast cancer in women with a BRCA1 mutation. Furthermore, BRCA1 mutation carriers have significantly lower levels of circulating osteoprotegerin (OPG), the decoy receptor for RANK-ligand (RANKL) and thus endogenous inhibitor of RANK signaling. OPG levels may be associated with the risk of disease, suggesting a role of this protein as a potential biomarker of breast cancer risk. This may improve upon current risk prediction models, stratifying women at the highest risk of developing the disease, and further identify those who may be targets for anti-RANKL chemoprevention. Collectively, the evidence supports therapeutic inhibition of the RANK pathway for the primary prevention of BRCA1-associated breast cancer, which may generate unique prevention strategies (without prophylactic surgery) and enhance quality of life.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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 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".