Application of <i>BRCA1</i> and <i>BRCA2</i> mutation carrier prediction models in breast and/or ovarian cancer families of French Canadian descent
Bibliographic record
Abstract
The BRCAPRO, Couch, Myriad I and II, Ontario Family History Assessment Tool (FHAT), and Manchester models have been used to predict BRCA1 or BRCA2 mutation carrier status of women at high risk for developing the heritable form of breast and ovarian cancers. We have evaluated these models for their accuracy in classifying 224 French Canadian families with at least three cases of breast cancer (diagnosed before the age of 65 years), ovarian cancer, or male breast cancer where mutation status was known for an index affected case used to assess the model. This series includes 44 BRCA1 and 52 BRCA2 mutation-positive families. Using receiver operator characteristics analyses, the C-statistics were found to be 0.81, 0.80, 0.79, and 0.74 for the BRCAPRO, FHAT, Manchester, and Myriad II models, respectively, when incorporating both BRCA1 and BRCA2 mutation carrier predictions. For the BRCAPRO model, 75% scored greater than a 0.43 probability in the mutation-positive group and 75% scored less than 0.50 in the mutation-negative group. Only 38 of 128 (30%) mutation-negative group had a probability greater than 0.43 with the BRCAPRO model. While all models were highly predictive of carrier status, the BRCAPRO model was the most accurate where a cut-off of 10% would have eliminated 60 of 128 (47%) mutation-negative families for genetic testing and only miss 10 of 96 (10%) mutation-positive families. A review of the cancer phenotypes with high BRCAPRO probabilities showed that significantly more metachronous bilateral breast cancer cases occurred in BRCA1/2 mutation carrier families in comparison to mutation-negative families, a feature which is not discriminated in the BRCAPRO model.
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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 | 0.000 | 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) | 0.000 | 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".