Patterns and Frequency of Pathogenic Germline Variants Among Prostate Cancer Patients Utilizing Multi-Gene Panel Genetic Testing
Bibliographic record
Abstract
Background: Germline genetic testing (GGT) has significant implications in the management of patients with prostate cancer (PCa). Herein, we report on patterns and frequency of pathogenic/likely pathogenic germline variants (P/LPGVs) among newly diagnosed Arab patients with PCa. Methods: Patients meeting the National Comprehensive Cancer Network (NCCN) eligibility criteria for GGT were offered a 19-gene PCa panel or an expanded 84-gene multi-cancer panel. Results: During the study period, 231 patients were enrolled; 107 (46.3%) had metastatic disease at diagnosis. In total, 17 P/LPGVs were detected in 17 patients (7.4%). Among the 113 (48.9%) patients who underwent GGT with the 19-gene panel, eight (7.1%) had P/LPGVs, compared to nine (7.6%) of the 118 (51.1%) who did GGT through the expanded 84-gene panel (P = 0.88). Variant of uncertain significance (VUS) rate was higher (n = 73, 61.9%) among the group who underwent expanded 84-gene panel testing compared to those who underwent the 19-gene PCa panel (n = 35, 30.9%) (P = 0.001). P/LPGVs in DNA damage repair (DDR) genes, most frequently BRCA2 , CHEK2 and TP53 , were the most common P/LPGVs findings. Conclusion: This study is the first to characterize the germline genetic profile of an Arab population with PCa. All detected P/LPGVs were potentially actionable, with most variants able to be detected with a PCa-specific panel. World J Oncol. 2024;15(5):801-808 doi: https://doi.org/10.14740/wjon1896
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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 | 0.002 |
| 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) | 0.001 | 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".