Identification of fifteen novel germline variants in the<i>BRCA1</i>3′UTR reveals a variant in a breast cancer case that introduces a functional<i>miR-103</i>target site
Bibliographic record
Abstract
Mutations in the BRCA1 gene confer a substantial increase in breast cancer risk, yet routine clinical genetic screening is limited to the coding regions and intron-exon boundaries, precluding the identification of mutations in noncoding and untranslated regions (UTR). As 3'UTR mutations can influence cancer susceptibility by altering protein and microRNA (miRNA) binding regions, we screened the BRCA1 3'UTR for mutations in a large series of BRCA-mutation negative, population and clinic-based breast cancer cases, and controls. Fifteen novel BRCA1 3'UTR variants were identified, the majority of which were unique to either cases or controls. Using luciferase reporter assays, three variants found in cases, c.* 528G>C, c.* 718A>G, and c.* 1271T>C and four found in controls, c.* 309T>C, c.* 379G>A, c.* 823C>T, and c.* 264C>T, reduced 3'UTR activity (P < 0.02), whereas two variants found in cases, c.* 291C>T and c.* 1139G>T, increased 3'UTR activity (P < 0.01). Three case variants, c.* 718A>G, c.* 800T>C, and c.* 1340_1342delTGT, were predicted to create new miRNA binding sites and c.* 1340_1342delTGT caused a reduction (25%, P = 0.0007) in 3'UTR reporter activity when coexpressed with the predicted targeting miRNA, miR-103. This is the most comprehensive identification and analysis of BRCA1 3'UTR variants published to date.
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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 | 0.000 |
| 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".