Biallelic Mutations in <i>BRCA1</i> Cause a New Fanconi Anemia Subtype
Bibliographic record
Abstract
UNLABELLED: Deficiency in BRCA-dependent DNA interstrand crosslink (ICL) repair is intimately connected to breast cancer susceptibility and to the rare developmental syndrome Fanconi anemia. Bona fide Fanconi anemia proteins, BRCA2 (FANCD1), PALB2 (FANCN), and BRIP1 (FANCJ), interact with BRCA1 during ICL repair. However, the lack of detailed phenotypic and cellular characterization of a patient with biallelic BRCA1 mutations has precluded assignment of BRCA1 as a definitive Fanconi anemia susceptibility gene. Here, we report the presence of biallelic BRCA1 mutations in a woman with multiple congenital anomalies consistent with a Fanconi anemia-like disorder and breast cancer at age 23. Patient cells exhibited deficiency in BRCA1 and RAD51 localization to DNA-damage sites, combined with radial chromosome formation and hypersensitivity to ICL-inducing agents. Restoration of these functions was achieved by ectopic introduction of a BRCA1 transgene. These observations provide evidence in support of BRCA1 as a new Fanconi anemia gene (FANCS). SIGNIFICANCE: We establish that biallelic BRCA1 mutations cause a distinct FA-S, which has implications for risk counselling in families where both parents harbor BRCA1 mutations. The genetic basis of hereditary cancer susceptibility syndromes provides diagnostic information, insights into treatment strategies, and more accurate recurrence risk counseling to families.
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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 | 0.000 |
| 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) | 0.003 | 0.001 |
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".