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
PURPOSE In 2020, ASCO recommended that all women with epithelial ovarian cancer have germline testing for BRCA1/2 mutations, and those without a germline pathogenic variant (PV) should have somatic tumor testing to determine eligibility for a poly (ADP-ribose) polymerase inhibitor. Consequently, the majority of patients with ovarian cancer will have both germline testing and somatic testing. An alternate strategy is tumor testing first and then germline testing if there is a PV in the tumor and/or significant family history. The objective was to conduct a cost-effectiveness analysis comparing the two testing strategies. METHODS The Markov model compared the costs (US dollars) and benefits of two testing strategies for newly diagnosed ovarian cancer: (1) ASCO strategy and (2) tumor testing triage for germline testing. Data were applied from SOLO-1, and costs were from wholesale acquisition prices, Medicare, and published sources. Sensitivity analyses accounted for uncertainty around various parameters. Monte Carlo simulation estimated the number tested and identified with germline and somatic BRCA PV for olaparib maintenance treatment annually in the US population. RESULTS The ASCO strategy was more effective but more costly than tumor testing triage in identifying patients for olaparib, with an incremental cost-effectiveness ratio of $281,296 US dollars per progression-free life year gained. Assuming 10,000 eligible patients with ovarian cancer annually, Monte Carlo simulation yielded comparable numbers of patients with BRCA PV in the germline and tumor with the ASCO and tumor testing triage strategies (2,080 v 2,062, respectively), but substantially higher number of patients tested using the ASCO strategy (8,052 v 3,076). CONCLUSION The ASCO strategy may identify more BRCA PVs but is not cost-effective. Tumor testing in epithelial ovarian cancer as triage for germline testing is the favored strategy in this health care system.
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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 | 0.006 | 0.030 |
| 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 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".