ASSOCIATION OF BRCA1 AND BRCA2 GENE MUTATIONS WITH BREAST CANCER RISK AMONG WOMEN WITH POSITIVE FAMILY HISTORY: A SYSTEMATIC REVIEW
Bibliographic record
Abstract
Background: Pathogenic variants in the BRCA1 and BRCA2 genes significantly elevate breast cancer risk, particularly among women with a positive family history. However, precise risk quantification for this specific, genetically predisposed subpopulation requires consolidation from the growing body of recent literature. Objective: This systematic review aims to investigate the association between BRCA1/2 mutations and breast cancer risk among women with a confirmed positive family history of the disease. Methods: A systematic review was conducted following PRISMA guidelines. Electronic databases (PubMed, Scopus, Web of Science, Cochrane Library) were searched for observational studies published between 2019-2024. Included studies reported breast cancer risk estimates for BRCA carriers versus non-carriers within cohorts of women with a family history. Study quality was assessed using the Newcastle-Ottawa Scale. Results: Eight studies (n=35,842 participants) were included. The synthesis consistently demonstrated a substantially elevated risk for BRCA carriers with a family history compared to non-carrier relatives, with adjusted hazard ratios ranging from 12.5 to 28.4. Cumulative risk estimates by age 70 were high, between 66% and 72%. The strength of the family history was identified as a key effect modifier, with stronger family aggregation associated with higher penetrance. Conclusion: The evidence confirms that BRCA1/2 mutations confer a profoundly high risk of breast cancer in women with a positive family history. These findings are critical for refining risk assessment, guiding genetic counseling, and personalizing clinical management strategies for this high-risk population. Future research should focus on standardizing family history reporting and integrating genetic modifiers into risk prediction models.
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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 | 0.007 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".