BRCA Tumor Analysis as Molecular Screening for Germline Testing
Bibliographic record
Abstract
Background: In patients with advanced high-grade serous ovarian cancer (HGSOC) and prostate adenocarcinoma, the identification of somatic/germline BRCA1/2 mutations allows new therapeutic opportunities. To estimate the prevalence of somatic and germline BRCA1/2 mutations in non-mucinous high grade ovarian/fallopian tube/peritoneal extraovarian cancer (NMHGOC) and prostate adenocarcinoma. Methods: Prevalence was established by analyzing patients with NMHGOC or prostate adenocarcinoma, with a BRCA1/2 study in the tumor between 2017 and 2018. Whether a germline study had been carried out was subsequently reviewed. Results: 10 patients out of 43 (23.3%) with NMHGOC had a BRCA1/2 mutation in the tumor. 9 patients (20.9%) presented a BRCA1/2 mutation in the germline setting (2 without tumor result due to limited tissue sample). 3 patients (6.9%) had only somatic mutations. 30% of the mutations in the tumor were, therefore, somatic mutations. Of the 9 patients with prostate adenocarcinoma, 2 (22.2%) had a BRCA2 mutation in the tumor. While 1 (11.1%) had the mutation in the germline setting, 1 patient (11.1%) had only somatic mutations. Conclusion: In our series, the prevalence of somatic and germline BRCA1/2 mutations in NMHGOC is similar to that reported in the literature. Whereas somatic mutations are only present at the neoplastic tissue, the rate of mutations in the tumor is higher than in the germline setting. A more effective diagnostic and predictive strategy could be achieved with tumor BRCA analysis as the first attempt. Initial results in prostate adenocarcinoma point to the same conclusion for this tumor.
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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 | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".