Retesting of women who are negative for a <i>BRCA1</i> and <i>BRCA2</i> mutation using a 20-gene panel
Bibliographic record
Abstract
Background The value of retesting women who previously tested negative for a pathogenic variant (mutation) in BRCA1 and BRCA2 using an expanded panel of breast and ovarian cancer genes is unclear. Methods We studied 110 BRCA1/2-negative women who were retested using a panel of 20 breast and/or ovarian cancer susceptibility genes at the Advanced Molecular Diagnostics Laboratory (AMDL) at Mount Sinai Hospital in Toronto between March 2017 and March 2019. All patients had previously tested negative for BRCA pathogenic variants at the AMDL between January 2012 and March 2018 and were subsequently referred for retesting by their physician. Results Overall, six pathogenic variants in genes other than BRCA1 and BRCA2 were found (prevalence 5.5%). There were two pathogenic variants found in RAD51C, and one found in each of BRIP1, PALB2, PMS2 and PTEN. The prevalence of pathogenic variants was 6.5% for women affected with cancer (6 of 93), including 4.9% for women with breast cancer (4 of 82) and 22.2% for women with ovarian cancer (2 of 9). None of the 17 unaffected women had a clinically significant or pathogenic variant. There were 44 women (40%) for whom the result of the panel test was inconclusive due to the detection of a variant of uncertain significance. Conclusions Our findings indicate that the retesting of BRCA1/2-negative individuals with an expanded panel of 20 breast and ovarian cancer genes can produce clinically relevant results, with a yield of 5.5% for pathogenic variants in genes other than 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 | 0.005 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".