Modification of<i>BRCA1</i>-Associated Breast and Ovarian Cancer Risk by<i>BRCA1</i>-Interacting Genes
Bibliographic record
Abstract
Inherited BRCA1 mutations confer elevated cancer risk. Recent studies have identified genes that encode proteins that interact with BRCA1 as modifiers of BRCA1-associated breast cancer. We evaluated a comprehensive set of genes that encode most known BRCA1 interactors to evaluate the role of these genes as modifiers of cancer risk. A cohort of 2,825 BRCA1 mutation carriers was used to evaluate the association of haplotypes at ATM, BRCC36, BRCC45 (BRE), BRIP1 (BACH1/FANCJ), CTIP, ABRA1 (FAM175A), MERIT40, MRE11A, NBS1, PALB2 (FANCN), RAD50, RAD51, RAP80, and TOPBP1, and was associated with time to breast and ovarian cancer diagnosis. Statistically significant false discovery rate (FDR) adjusted P values for overall association of haplotypes (P(FDR)) with breast cancer were identified at ATM (P(FDR) = 0.029), BRCC45 (P(FDR) = 0.019), BRIP1 (P(FDR) = 0.008), CTIP (P(FDR) = 0.017), MERIT40 (P(FDR) = 0.019), NBS1 (P(FDR) = 0.003), RAD50 (P(FDR) = 0.014), and TOPBP1 (P(FDR) = 0.011). Haplotypes at ABRA1 (P(FDR) = 0.007), BRCC45 (P(FDR) = 0.016 and P(FDR) = 0.005 in two haplotype blocks), and RAP80 (P(FDR) < 0.001) were associated with ovarian cancer risk. Overall, the data suggest that genomic variation at multiple loci that encode proteins that interact biologically with BRCA1 are associated with modified breast cancer and ovarian cancer risk in women who carry BRCA1 mutations.
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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) | 0.002 | 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".