Implementing and Optimizing Universal Germline Genetic Testing for Patients With Prostate Cancer in Clinical Practice
Bibliographic record
Abstract
OBJECTIVE: To advocate for universal germline genetic testing (UGGT) in prostate cancer and provide practical recommendations for its implementation. METHODS: Although guidelines for germline genetic testing in prostate cancer have progressed, usage remains limited and inconsistent due to barriers including access, cost, and variable guideline adherence. These issues prevent some patients with germline pathogenic/likely pathogenic variants from benefiting from risk assessment, precision therapies (eg, PARP inhibitors, PD-1 inhibitors), and potential clinical trials. Despite these benefits, studies indicate that germline genetic testing use remains low, especially in prostate cancer care. The PROCLAIM trial (Shore et al, 2023) highlighted that nearly half of patients with pathogenic variants are missed under National Comprehensive Cancer Network guidelines, particularly impacting non-white patients and those with incomplete family history data. Additional racial and socioeconomic disparities further hinder access and variant interpretation accuracy. Given these challenges, UGGT for all prostate cancer patients has been proposed to improve care equity and decision-making. In March 2024, prostate cancer experts convened to discuss strategies for UGGT implementation. RESULTS: The outcome of that meeting includes recommendations for integrating UGGT into oncology and urology practices and have been outlined in this paper. CONCLUSION: To maximize the benefits while mitigating the potential risks of UGGT, it is essential to address implementation details, including careful gene panel selection, variants of uncertain significance reporting and management, appropriate genetics follow-up, and seamless integration of test reports into electronic medical records for accessibility by patients and providers.
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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 | 0.013 | 0.063 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".