Breast and Ovarian Cancer Risk and Risk Reduction in Jewish <i>BRCA1/2</i> Mutation Carriers
Bibliographic record
Abstract
PURPOSE: Mutations in BRCA1/2 dramatically increase the risk of both breast and ovarian cancers. Three mutations in these genes (185delAG, 5382insC, and 6174delT) occur at high frequency in Ashkenazi Jews. We evaluated how these common Jewish mutations (CJMs) affect cancer risks and risk reduction. METHODS: Our cohort comprised 4,649 women with disease-associated BRCA1/2 mutations from 22 centers in the Prevention and Observation of Surgical End Points Consortium. Of these women, 969 were self-identified Jewish women. Cox proportional hazards models were used to estimate breast and ovarian cancer risks, as well as risk reduction from risk-reducing salpingo-oophorectomy (RRSO), by CJM and self-identified Jewish status. RESULTS: Ninety-one percent of Jewish BRCA1/2-positive women carried a CJM. Jewish women were significantly more likely to undergo RRSO than non-Jewish women (54% v 41%, respectively; odds ratio, 1.87; 95% CI, 1.44 to 2.42). Relative risks of cancer varied by CJM, with the relative risk of breast cancer being significantly lower in 6174delT mutation carriers than in non-CJM BRCA2 carriers (hazard ratio, 0.35; 95% CI, 0.18 to 0.69). No significant difference was seen in cancer risk reduction after RRSO among subgroups. CONCLUSION: Consistent with previous results, risks for breast and ovarian cancer varied by CJM in BRCA1/2 carriers. In particular, 6174delT carriers had a lower risk of breast cancer. This finding requires additional confirmation in larger prospective and population-based cohort studies before being integrated into clinical care.
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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 | 0.001 | 0.003 |
| 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".