Genetic and molecular analyses of candidate germline <i>BRIP1/FANCJ</i> variants implicated in breast and ovarian cancer
Bibliographic record
Abstract
ABSTRACT Five rare variants in BRIP1/FANCJ , initially reported in ovarian (OC) or breast (BC) cancer cases by the adult hereditary cancer clinics, were investigated for their candidacy as clinically relevant variants. These variants were investigated genetically in a population exhibiting genetic drift and molecularly assayed for biological impact. Using in silico tools, population-based genetic databases and other resources, three of the five reported BRIP1 variants were likely to be damaging: c.797C>T; p.Thr266Met, c.2087C>T; p.Pro696Leu and c.2990_2993delCAAA; p.Thr997ArgfsTer61. The carrier frequencies ranged from 0-0.7% in ancestry defined cancer groups comprised of 47 OC families, 49 hereditary breast and ovarian cancer syndrome families, 142 hereditary breast cancer syndrome families, 435 sporadic OC cases and 563 sporadic BC cases and 0-0.2% in 1025 population-matched controls. Multiple carriers of the same variants were identified in additional population-matched cancer cases. Of the five reported BRIP1 variants, p.Thr266Met, p.Pro696Leu and p.Thr997ArgfsTer61, which were predicted to be damaging, conferred cellular sensitivity to mitomycin C and cisplatin unlike p.Ser139Ala and p.Ala406Ser. Collectively, our investigation implicates BRIP1 c.797C>T; p.Thr266Met, c.2087C>T; p.Pro696Leu and p.Thr997ArgfsTer61 as deleterious variants in OC and BC.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".