Contribution of large genomic BRCA1 alterations to early-onset breast cancer selected for family history and tumour morphology: a report from The Breast Cancer Family Registry
Bibliographic record
Abstract
INTRODUCTION: Selecting women affected with breast cancer who are most likely to carry a germline mutation in BRCA1 and applying the most appropriate test methodology remains challenging for cancer genetics services. We sought to test the value of selecting women for BRCA1 mutation testing on the basis of family history and/or breast tumour morphology criteria as well as the value of testing for large genomic alterations in BRCA1. METHODS: We studied women participating in the Breast Cancer Family Registry (BCFR), recruited via population-based sampling, who had been diagnosed with breast cancer before the age of 40 years who had a strong family history of breast or ovarian cancer (n = 187) and/or a first primary breast tumour with morphological features consistent with carrying a BRCA1 germline mutation (n = 133; 37 met both criteria). An additional 184 women diagnosed before the age of 40 years who had a strong family history of breast or ovarian cancer and who were not known to carry a germline BRCA1 mutation were selected from among women who had been recruited into the BCFR from clinical genetics services. These 467 women had been screened for BRCA1 germline mutations, and we expanded this testing to include a screen for large genomic BRCA1 alterations using Multiplex Ligation-dependent Probe Amplification. RESULTS: Twelve large genomic BRCA1 alterations were identified, including 10 (4%) of the 283 women selected from among the population-based sample. In total, 18 (12%), 18 (19%) and 16 (43%) BRCA1 mutations were identified in the population-based groups selected on the basis of family history only (n = 150), the group selected on the basis of tumour morphology only (n = 96) and meeting both criteria (n = 37), respectively. CONCLUSIONS: Large genomic alterations accounted for 19% of all BRCA1 mutations identified. This study emphasises the value of combining information about family history, age at diagnosis and tumour morphology when selecting women for germline BRCA1 mutation testing as well as including a screen for large genomic alterations.
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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 | 0.001 | 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".