The contribution of pathogenic variants in breast cancer susceptibility genes to familial breast cancer risk
Bibliographic record
Abstract
Abstract Understanding the gene-specific risks for development of breast cancer will lead to improved clinical care for those carrying germline mutations in cancer predisposition genes. We sought to detail the spectrum of mutations and refine risk estimates for known and proposed breast cancer susceptibility genes. Targeted massively-parallel sequencing was performed to identify mutations and copy number variants in 26 known or proposed breast cancer susceptibility genes in 2134BRCA1/2-negative women with familial breast cancer (proband with breast cancer and a family history of breast or ovarian cancer) from a largely European–Caucasian multi-institutional cohort. Case–control analysis was performed comparing the frequency of internally classified mutations identified in familial breast cancer women to Exome Aggregation Consortium controls. Mutations were identified in 8.2% of familial breast cancer women, including mutations in high-risk (odds ratio > 5) (1.4%) and moderate-risk genes (2 < odds ratio < 5) (2.9%). The remaining familial breast cancer women had mutations in proposed breast cancer genes (1.7%), Lynch syndrome genes (0.5%), and six cases had two mutations (0.3%). Case–control analysis demonstrated associations with familial breast cancer forATM, PALB2, andTP53mutations (odds ratio > 3.0,p < 10−4),BARD1mutations (odds ratio = 3.2,p = 0.012), andCHEK2truncating mutations (odds ratio = 1.6,p = 0.041). Our results demonstrate that approximately 4.7% ofBRCA1/2negative familial breast cancer women have mutations in genes statistically associated with breast cancer. We classifiedPALB2andTP53as high-risk,ATMandBARD1as moderate risk, andCHEK2truncating mutations as low risk breast cancer predisposition genes. This study demonstrates that large case–control studies are needed to fully evaluate the breast cancer risks associated with mutations in moderate-risk and proposed susceptibility genes.
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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 | 0.001 | 0.004 |
| 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".