Prevalence of BRCA1 and BRCA2 mutations in unselected breast cancer patients from Greece
Bibliographic record
Abstract
BACKGROUND: Inheritance of a mutation in either BRCA1 or BRCA2 accounts for approximately 5% of all breast cancer cases, but varies by country. Investigations into the contribution of BRCA mutations to breast cancer incidence in Greece have been, for the most part, limited by small sample sizes and by the use of cases selected for their family history of cancer. The aim of the current study was to estimate BRCA mutation frequencies in breast cancer patients unselected for family history. METHODS: To do so, we enrolled 127 unselected women with breast cancer from the Alexandra Hospital in Athens, Greece, a large public hospital in the city. Mutations in BRCA1 and BRCA2 were detected using a combination of techniques and were confirmed by direct sequencing. Two large genomic deletions were sought using mutation-specific assays. A detailed family history of cancer was obtained from each patient. RESULTS: We were able to successfully complete testing on samples from 127 women. Among these, six mutations were identified (four in BRCA1 and two in BRCA2) representing 4.7% of the total or 9.5% of cases diagnosed before age forty. None of the mutation carriers had a family history of breast or ovarian cancer. Three of the four BRCA1 mutations were in exon 20: two were a G5331A mutation and the third was a 3.2 kb deletion. The fourth BRCA1 mutation was the 3819delGTAAA in exon 11. The two BRCA2 mutations were in exon 11 (3782del10 and 4512insT). CONCLUSIONS: The G5331A mutation in BRCA1 appears to be a founder mutation in the Greek population.
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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 | 0.001 |
| 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".