Detection of splicing aberrations caused by BRCA1 and BRCA2 sequence variants encoding missense substitutions: implications for prediction of pathogenicity
Bibliographic record
Abstract
Missense substitutions in high-risk cancer susceptibility genes create clinical uncertainty in the genetic counseling process. Multifactorial likelihood classification approaches and in vitro assays are useful for the classification of exonic sequence variants in BRCA1 and BRCA2, but these currently rely on the assumption that changes in protein function are the major biological mechanism of pathogenicity. This study investigates the potentially pathogenic role of aberrant splicing for exonic variants predicted to encode missense substitutions using patient-derived RNA. No splicing aberrations were identified for BRCA1c.5054C>T and BRCA2c.7336A>G, c.8839G>A, and c.9154C>T. However, RT-PCR analysis identified a major splicing aberration for BRCA1c.4868C>G(p.Ala1623Gly), a variant encoding a missense substitution considered likely to be neutral. Splicing aberrations were also observed for BRCA2c.7988A>T(p.Glu2663Val) and c.8168A>G(p.Asp2723Gly), but both variant and wildtype alleles were shown to be present in full-length mRNA transcripts, suggesting that variant protein may be translated. BRCA2 protein function assays indicated that BRCA2p.Glu2663Val, p.Asp2723Gly and p.Arg3052Trp missense proteins have abrogated function consistent with pathogenicity. Multifactorial likelihood analysis provided evidence for pathogenicity for BRCA1 c.5054C>T(p.Thr1685Ile) and BRCA2c.7988A>T(p.Glu2663Val), c.8168A>G(p.Asp2723Gly) and c.9154C>T(p.Arg3052Trp), supporting experimentally derived evidence. These findings highlight the need for improved bioinformatic prediction of splicing aberrations and to refine multifactorial likelihood models used to assess clinical significance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".