Germline testing and somatic tumor testing for <i>BRCA1/2</i> pathogenic variants in ovarian cancer: What is the optimal sequence of testing?
Bibliographic record
Abstract
10585 Background: In 2020 ASCO recommended that all women with epithelial ovarian cancer have germline testing (GT) for BRCA1/2 mutations, and those without a germline pathogenic variant (PV) should have somatic tumor testing (TT), to determine eligibility for PARP inhibitor (PARPi) therapy (GT-TT strategy). An alternate strategy is to start with tumor testing first, and to conduct germline testing only in those with a PV in the tumor, or a significant family history (TT-GT strategy). The objective was to conduct a cost-effectiveness analysis comparing the 2 testing strategies. Methods: A Markov Monte Carlo simulation model compared the costs (USD) and benefits of the 2 testing strategies. According to local empiric data, a sufficient tissue sample for TT was available in 99% of cases, otherwise the patient would only have GT. Sensitivity of TT was 99% for detecting germline PV. Only those with BRCA1/2 PV were eligible for PARPi. Primary outcomes included the number of women eligible for PARPi, with progression-free years of life (PFLY) gained based on SOLO1 data, and the incremental cost-effectiveness ratio (ICER). Monte Carlo simulation estimated the number of women who would have GT and TT, and the total with germline or somatic BRCA1/2 PV eligible for PARPi. Sensitivity analyses accounted for uncertainty around various parameters. Results: The GT-TT strategy was more effective but more costly than TT-GT in identifying patients eligible for PARPi. Table summarizes the average lifetime costs, benefits, and Monte Carlo simulation estimates for 10,000 women diagnosed with advanced epithelial ovarian cancer annually in the USA. The incremental benefit from the GT-TT strategy would be achieved at substantial cost to the health care system, with an ICER of $119,340 per PFLY gained relative to the TT-GT strategy. The results were highly sensitive to the sensitivity of TT to detect germline PV, and the costs of GT and TT. Assuming that GT was less than 50% of the cost of TT, the sensitivity of TT had to exceed 98% for the TT-GT strategy to be cost-effective. Conclusions: Although the ASCO recommended strategy of BRCA germline testing followed by tumor testing for those without a pathogenic variant may be more effective in identifying ovarian cancer patients for PARP inhibitor therapy, it is more costly. The ASCO strategy is justified if the sensitivity of tumor testing is not sufficiently high. However, assuming high tumor testing performance rates, tumor testing first followed by germline testing if there is a PV in the tumor and/or family history is a cost-effective strategy.[Table: see text]
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".