Review on the Role of BRCA Mutations in Genomic Screening and Risk Stratification of Prostate Cancer
Bibliographic record
Abstract
(1) Background: Somatic and germline alterations can be commonly found in prostate cancer (PCa) patients. The aim of our present study was to perform a comprehensive review of the current literature in order to examine the impact of BRCA mutations in the context of PCa as well as their significance as genetic biomarkers. (2) Methods: A narrative review of all the available literature was performed. Only "landmark" publications were included. (3) Results: Overall, the number of PCa patients who harbor a BRCA2 mutation range between 1.2% and 3.2%. However, BRCA2 and BRCA1 mutations are responsible for most cases of hereditary PCa, increasing the risk by 3-8.6 times and up to 4 times, respectively. These mutations are correlated with aggressive disease and poor prognosis. Gene testing should be offered to patients with metastatic PCa, those with 2-3 first-degree relatives with PCa, or those aged < 55 and with one close relative with breast (age ≤ 50 years) or invasive ovarian cancer. (4) Conclusions: The individualized assessment of BRCA mutations is an important tool for the risk stratification of PCa patients. It is also a population screening tool which can guide our risk assessment strategies and achieve better results for our patients and their families.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".