Tumor and germline testing with next generation sequencing in epithelial ovarian cancer: a prospective paired comparison using an 18‐gene panel
Bibliographic record
Abstract
Genetic testing in epithelial ovarian cancer (EOC) in Ontario includes germline next-generation sequencing (NGS) for 19 genes. Additionally, tumor tissue undergoes reflex NGS testing for BRCA1/2 to assess eligibility for PARPi. Although parallel testing confers advantages, this model duplicates healthcare resources. Here, we prospectively assessed the feasibility of tumor-first multigene testing by comparing tumor tissue with germline testing of peripheral blood. An 18-gene NGS panel was used to test tumor and germline DNA (n = 106 patients). In 26 patients, 27 tumor Tier I or II variants were identified, with 16/27 (59%) being germline pathogenic variants (PV) (13 BRCA1/2; 3 other genes) and 11/27 (41%) somatic variants (9 BRCA1/2; 2 other). In 51/106 patients, there were no tumor variants (excluding TP53), of which one patient had a germline BRCA1 copy number variant deletion in exon 12. Tumor-first testing detected variant-positive and variant-negative germline cases in 105/106 patients (99.1%). Among 50 BRCA-negative patients, 14/50 (28%) were homologous recombination deficiency (HRD)-positive. Therefore, we demonstrate that multigene NGS tumor-testing is effective in identifying germline variants in EOC with a < 1% false-negative rate.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".