The contribution of germline rearrangements to the spectrum of <i>BRCA2</i> mutations
Bibliographic record
Abstract
BACKGROUND: Few germline BRCA2 rearrangements have been described compared with the large number of germline rearrangements reported in the BRCA1 gene. However, some BRCA2 rearrangements have been reported in families that included at least one case of male breast cancer. OBJECTIVE: To estimate the contribution of large genomic rearrangements to the spectrum of BRCA2 defects. METHODS: Quantitative multiplex PCR of short fluorescent fragments (QMPSF) was used to screen the BRCA2 gene for germline rearrangements in highly selected families. QMPSF was previously used to detect heterozygous deletions/duplications in many genes including BRCA1 and BRCA2. RESULTS: We selected a subgroup of 194 high risk families with four or more breast cancers with an average age at diagnosis of < or = 50 years, who were recruited through 14 genetic counselling centres in France and one centre in Switzerland. BRCA2 mutations were detected in 18.6% (36 index cases) and BRCA1 mutations in 12.4% (24 index cases) of these families. Of the 134 BRCA1/2 negative index cases in this subgroup, 120 were screened for large rearrangements of BRCA2 using QMPSF. Novel and distinct BRCA2 deletions were detected in three families and their boundaries were determined. We found that genomic rearrangements represent 7.7% (95% confidence interval 0% to 16%) of the BRCA2 mutation spectrum. CONCLUSION: The molecular diagnosis of breast cancer predisposition should include screening for BRCA2 rearrangements, at least in families with a high probability of BRCA2 defects.
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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 | 0.001 | 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".