Do BARD1 Mutations Confer an Elevated Risk of Prostate Cancer?
Bibliographic record
Abstract
The current cancer testing gene panels tend to be comprehensive rather than site-specific. BARD1 is one of the genes commonly included in the multi-cancer testing panels. Mutations in BARD1 confer an increase in the risk for breast cancer, but it is not studied whether or not they predispose to prostate cancer. To establish if BARD1 mutations also predispose to prostate cancer, we screened BARD1 in 390 Polish patients with hereditary prostate cancer. No truncating mutations were identified by sequencing. We also genotyped 5715 men with unselected prostate cancer, and 10,252 controls for three recurrent BARD1 variants, including p.Q564X, p.R658C and p.R659=. Neither variant conferred elevated risk of prostate cancer (ORs between 0.84 and 1.15, p-values between 0.57 and 0.93) nor did they influence prostate cancer characteristics or survival. We conclude that men with a BARD1 mutation are not at elevated prostate cancer risk. It is not justified to inform men about increased prostate cancer risk in case of identification of a BARD1 mutation. However, a female relative of a man with a BARD1 mutation may benefit from this information and be tested for the mutation, because BARD1 is a breast cancer susceptibility gene.
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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".