Phenocopies in BRCA1 and BRCA2 families: evidence for modifier genes and implications for screening
Bibliographic record
Abstract
BACKGROUND: The identification of BRCA1 and BRCA2 mutations in familial breast cancer kindreds allows genetic testing of at-risk relatives. Those who test negative are usually reassured and additional breast cancer surveillance is discontinued. However, we postulated that in high-risk families, such as those seen in clinical genetics centres, the risk of breast cancer might be influenced not only by the BRCA1/BRCA2 mutation but also by modifier genes. One manifestation of this would be the presence of phenocopies in BRCA1/BRCA2 kindreds. METHODS: 277 families with pathogenic BRCA1/BRCA2 mutations were reviewed and 28 breast cancer phenocopies identified. The relative risk of breast cancer in those testing negative was assessed using incidence rates from our cancer registry based on local population. RESULTS: Phenocopies constituted up to 24% of tests on women with breast cancer after the identification of the mutation in the proband. The standardised incidence ratio for women who tested negative for the BRCA1/BRCA2 family mutation was 5.3 for all relatives, 5.0 for all first-degree relatives (FDRs) and 3.2 (95% confidence interval 2.0 to 4.9) for FDRs in whose family all other cases of breast and ovarian cancer could be explained by the identified mutation. 13 of 107 (12.1%) FDRs with breast cancer and no unexplained family history tested negative. CONCLUSION: In high-risk families, women who test negative for the familial BRCA1/BRCA2 mutation have an increased risk of breast cancer consistent with genetic modifiers. In light of this, such women should still be considered for continued surveillance.
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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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".