Biallelic Deleterious <i>BRCA1</i> Mutations in a Woman with Early-Onset Ovarian Cancer
Bibliographic record
Abstract
UNLABELLED: BRCA1 and BRCA2 are the most important breast and ovarian cancer susceptibility genes. Biallelic mutations in BRCA2 can lead to Fanconi anemia and predisposition to cancers, whereas biallelic BRCA1 mutations have not been confirmed, presumably because one wild-type BRCA1 allele is required during embryogenesis. This study describes an individual who was diagnosed with ovarian carcinoma at age 28 and found to have one allele with a deleterious mutation in BRCA1, c.2457delC (p.Asp821Ilefs*25), and a second allele with a variant of unknown significance in BRCA1, c.5207T>C (p.Val1736Ala). Medical records revealed short stature, microcephaly, developmental delay, and significant toxicity from chemotherapy. BRCA1 p.Val1736Ala cosegregated with cancer in multiple families, associated tumors showed loss of wild-type BRCA1, and BRCA1 p.Val1736Ala showed reduced DNA damage localization. These findings represent the first validated example of biallelic deleterious human BRCA1 mutations and have implications for the interpretation of genetic test results. SIGNIFICANCE: Accurate assessment of genetic testing data for BRCA1 mutations is essential for clinical monitoring and treatment strategies. Here, we report the fi rst validated example of an individual with biallelic BRCA1 mutations, early-onset ovarian cancer, and clinically significant hypersensitivity to chemotherapy.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".