Frequency of mutations in mismatch repair genes in a population-based study of women with ovarian cancer
Bibliographic record
Abstract
BACKGROUND: Mutations in genes for hereditary non-polyposis colorectal cancer (HNPCC) in ovarian cancer patients remains poorly defined. We sought to estimate the frequency and characteristics of HNPCC gene mutations in a population-based sample of women with epithelial ovarian cancer. METHODS: The analysis included 1893 women with epithelial ovarian cancer ascertained from three population-based studies. Full-germline DNA sequencing of the coding regions was performed on three HNPCC genes, MLH1, MSH2 and MSH6. Collection of demographic, clinical and family history information was attempted in all women. RESULTS: Nine clearly pathogenic mutations were identified, including five in MSH6, two each in MLH1 and MSH2. In addition, 28 unique predicted pathogenic missense variants were identified in 55 patients. Pathogenic mutation carriers had an earlier mean age at diagnosis of ovarian cancer, overrepresentation of cancers with non-serous histologies and a higher number of relatives with HNPCC-related cancers. CONCLUSIONS: Our findings suggest that fewer than 1% of women with ovarian cancer harbour a germline mutation in the HNPCC genes, with overrepresentation of MSH6 mutations. This represents a lower-range estimate due to the large number of predicted pathogenic variants in which pathogenicity could not definitively be determined. Identification of mismatch repair gene mutations has the potential to impact screening and treatment decisions in these women.
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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 | 0.000 | 0.000 |
| 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) | 0.000 | 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".