Bayes analysis provides evidence of pathogenicity for the BRCA1 c.135-1G>T (IVS3-1) and BRCA2 c.7977-1G>C (IVS17-1) variants displaying in vitro splicing results of equivocal clinical significance
Bibliographic record
Abstract
Although in vitro splicing assays can provide useful information about the clinical interpretation of sequence variants in high-risk cancer genes such as BRCA1 and BRCA2, results can sometimes be difficult to interpret. The BRCA1 c.135-1G>T (IVS3-1G>T) variant has been shown to give rise to an in-frame deletion of exon 5 (BRCA1 c.135_212del) that is predicted to encode 26 amino acids. BRCA2 c.7977-1G>C (IVS17-1G>C) was shown to increase the expression of two naturally occurring transcripts that contain frameshifts (BRCA2, c.7977_8311del (exon 18 deletion); BRCA2, c.7806_8331del (exon 17&18 deletion)). In this study we conducted multifactorial likelihood analysis to evaluate the clinical significance of these two variants, including assessing variant segregation in families by Bayes analysis, and breast tumor pathology features suggestive of positive mutation status. Multifactorial analysis provided strong evidence for causality for both of these variants. The Bayes scores from a single family with BRCA1 c.135-1G>T was 9528:1, and incorporation of pathology features gave an overall likelihood of causality of 28108:1. The Bayes scores from five informative families with BRCA2 c.7977-1G>C was 47401:1, and the combined Bayes-pathology odds of causality was 29389:1. Multifactorial likelihood analysis indicates that the BRCA1 c.135-1G>T and BRCA2 c.7977-1G>C variants are disease-associated mutations which should be managed clinically in the same fashion as classical truncating mutations.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".