Germline Mutations in 12 Genes and Risk of Ovarian Cancer in Three Population-Based Cohorts
Bibliographic record
Abstract
BACKGROUND: With the widespread use of multigene panel genetic testing, population-based studies are necessary to accurately assess penetrance in unselected individuals. We evaluated the prevalence of germline pathogenic or likely pathogenic variants (mutations) in 12 cancer-predisposition genes and associations with ovarian cancer risk in three population-based prospective studies [Nurses' Health Study (NHS), NHSII, Cancer Prevention Study II]. METHODS: We included women with epithelial ovarian or peritoneal cancer (n = 776) and controls who were alive and had at least one intact ovary at the time of the matched case diagnosis (n = 1,509). Germline DNA was sequenced for mutations in 12 genes. Conditional logistic regression was used to estimate odds ratios (OR) and 95% confidence intervals (CI) for ovarian cancer risk by mutation status. RESULTS: The mutation frequency across all 12 genes was 11.2% in cases and 3.3% in controls (P < 0.0001). BRCA1 and BRCA2 were the most frequently mutated (3.5% and 3.8% of cases and 0.3% and 0.5% of controls, respectively) and were associated with increased ovarian cancer risk [OR, BRCA1 = 12.38; 95% confidence interval (CI) = 4.72-32.45; OR, BRCA2 = 9.18; 95% CI = 3.98-21.15]. Mutation frequencies for the other genes were ≤1.0% and only PALB2 was significantly associated with risk (OR = 5.79; 95% CI = 1.09-30.83). There was no difference in survival for women with a BRCA germline mutation versus no mutation. CONCLUSIONS: Further research is needed to better understand the role of other mutations in ovarian cancer among unselected populations. IMPACT: Our data support guidelines for germline genetic testing for BRCA1 and BRCA2 among women diagnosed with epithelial ovarian cancer; testing for PALB2 may be warranted.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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 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".