The contribution of large genomic rearrangements in BRCA1 and BRCA2 to South African familial breast cancer
Bibliographic record
Abstract
BACKGROUND: Pathogenic variants that occur in the familial breast cancer genes (BRCA1/2) lead to truncated ineffective proteins in the majority of cases. These variants are mostly represented by small deletions/insertions, nonsense- and splice-site variants, although some larger pathogenic rearrangements occur. Currently, their contribution to familial breast cancer (BC) and ovarian cancer (OVC) in South Africa (SA) is unknown. METHODS: Seven hundred and forty-four patients affected with BC or OVC were screened for larger genomic rearrangements (LGRs) by means of multiplex ligation-dependent probe amplification or Next Generation Sequencing using the Oncomine™ BRCA research assay. RESULTS: The patients represented mostly medium to high-risk families, but also included lower risk patients without a family history of the disease, diagnosed at an early age of onset (< 40 years). Eight LGRs were detected (1.1%); seven in BRCA1 with a single whole gene deletion (WGD) detected for BRCA2. These eight LGRs accounted for 8.7% of the 92 BRCA1/2 pathogenic variants identified in the 744 cases. The pathogenic LGRs ranged from WGDs to the duplication of a single exon. CONCLUSIONS: Larger rearrangements in BRCA1/2 contributed to the overall mutational burden of familial BC and OVC in SA. Almost a quarter of all pathogenic variants in BRCA1 were LGRs (7/30, 23%). The spectrum observed included two WGDs, one each for BRCA1 and BRCA2.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".