Examining the Concordance of Detection of Hereditary Cancer Gene Variants Between Blood, Tumour, and Normal Tissue in Patients with High-Grade Serous Ovarian Carcinoma
Bibliographic record
Abstract
BACKGROUND/OBJECTIVES: Access to genetic counselling and BRCA1/2 germline testing is standard of care for patients with high-grade serous ovarian carcinoma (HGSOC). While tumour testing reliably detects pathogenic variants in hereditary cancer genes, it cannot distinguish somatic from germline variants. Concurrent testing of non-cancerous (normal) tissue obtained during surgery may improve triage for germline testing and clinical genetics referral. This study evaluated the concordance of inherited variant detection among tumour, normal tissue, and blood to determine whether archived normal tissue can reliably identify germline pathogenic variants. METHODS: Patients with HGSOC who had a pathogenic variant identified by targeted Next Generation Sequencing (NGS) tumour testing and underwent germline hereditary cancer gene panel (HCP) testing between April 2019 and November 2020 were included. HCP testing was performed on formalin-fixed, paraffin-embedded normal tissue from the original resection. Variant results were compared across tumour, normal tissue, and germline (blood) samples to determine concordance, false-negative, and false-positive rates. RESULTS: Forty-one patients had confirmed tumour variants in BRCA1/2 or other HCP genes. Of these, 24 harboured a corresponding germline pathogenic variant. Archived normal tissue was available for 23 of these 24 cases, and all germline variants were detected in normal tissue, showing 100% concordance. Among the 17 patients without germline variants, all corresponding normal tissue samples were negative, also demonstrating 100% concordance. No false positives or negatives were identified. CONCLUSIONS: NGS testing of normal tissue at surgical resection reliably identifies germline pathogenic variants in patients with HGSOC. Incorporating this approach may help triage patients for clinical genetics assessment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".