Combined genetic and splicing analysis of BRCA1 c.[594-2A>C; 641A>G] highlights the relevance of naturally occurring in-frame transcripts for developing disease gene variant classification algorithms
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Bibliographic record
Abstract
A recent analysis using family history weighting and co-observation classification modeling indicated that BRCA1 c.594-2A > C (IVS9-2A > C), previously described to cause exon 10 skipping (a truncating alteration), displays characteristics inconsistent with those of a high risk pathogenic BRCA1 variant. We used large-scale genetic and clinical resources from the ENIGMA, CIMBA and BCAC consortia to assess pathogenicity of c.594-2A > C. The combined odds for causality considering case-control, segregation and breast tumor pathology information was 3.23 10 8 . Our data indicate that c.594-2A > C is always in cis with c.641A > G. The spliceogenic effect of c.[594-2A > C;641A > G] was characterized using RNA analysis of human samples and splicing minigenes. As expected, c.[594-2A > C; 641A > G] caused exon 10 skipping, albeit not due to c.594-2A > C impairing the acceptor site but rather by c.641A > G modifying exon 10 splicing regulatory element(s). Multiple blood-based RNA assays indicated that the variant allele did not produce detectable levels of full-length transcripts, with a per allele BRCA1 expression profile composed of %70-80% truncating transcripts, and %20-30% of in-frame D9,10 transcripts predicted to encode a BRCA1 protein with tumor suppression function. We confirm that BRCA1c.[594-2A > C;641A > G] should not be considered a high-risk pathogenic variant. Importantly, results from our detailed mRNA analysis suggest that BRCA-associated cancer risk is likely not markedly increased for individuals who carry a truncating variant in BRCA1 exons 9 or 10, or any other BRCA1 allele that permits 20-30% of tumor suppressor function. More generally, our findings highlight the importance of assessing naturally occurring alternative splicing for clinical evaluation of variants in disease-causing genes.
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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 it