Optimizing Ovarian Cancer Treatment and Prevention Through Parallel Germline and Somatic Genetic Testing
Bibliographic record
Abstract
T he number of individuals diagnosed with ovarian cancer is increasing every year, with approximately 314,000 new cases annually. 1There is still no effective screening test for ovarian cancer, 2 and most cases will be diagnosed at an advanced stage. 3The vast majority of ovarian cancers are epithelial, and 10% to 15% of these can be attributed to a germline pathogenic variant (PV) of BRCA1 or BRCA2. 4It is well recognized that BRCA1/2 PVs are associated with very high lifetime risks of developing ovarian cancer (17%-44%) and breast cancer (69%-72%) by 80 years of age. 5 Identifying a BRCA1/2 PV in women diagnosed with ovarian cancer is critical for 2 reasons: (1) they may have family members who can benefit from genetic testing and subsequent risk-reducing interventions to avoid breast and/or ovarian cancer, and (2) there is a significant therapeutic implication, because these women can now be treated with a PARP inhibitor, specifically olaparib, as maintenance therapy after completion of first-line therapy to improve overall survival. 6In addition to BRCA1/2, there are other cancer susceptibility genes (CSGs), such as RAD51C , RAD51D, and BRIP1, which have implications for family members because of moderately elevated ovarian cancer risks in the range of 6% to 13%. 5 These could be detected through multigene panel testing, rather than just BRCA1/2 testing.Finally, there are somatic BRCA PVs in approximately 5% of patients with ovarian cancer 4 who would also benefit from olaparib, and these would be detected by tumor testing specifically for BRCA1/2 PV.To cast the broadest net, multigene panel testing could maximize the identification of families who would benefit from genetic testing and cancer risk-reducing strategies, and tumor testing for BRCA1/2 PV could identify those with somatic BRCA PV who could benefit from olaparib.When compared with traditional family history (FH)-based germline testing, the combination of universal multigene panel testing and tumor testing for somatic BRCA mutations for all patients with ovarian cancer makes sense, because FH can miss up to 50% of mutation carriers. 7However, it is unknown whether the cost of this combination testing strategy, along with olaparib, would be acceptable in the context of our health care system.Manchanda et al 8 report a cost-effectiveness analysis comparing the costs and benefits of unselected panel germline testing and BRCA somatic testing (herein referred to as parallel testing strategy ) versus FH-based criteria in patients with ovarian cancer.They constructed a microsimulation model using United Kingdom-and United States-based data and conducted extensive sensitivity analyses to account for uncertainty around various scenarios.The primary outcome was the incremental cost-effectiveness ratio (ICER), defined as the difference in cost divided by the difference in effectiveness between 2 strategies.Both payer and societal perspectives were adopted, the latter to account for productivity loss.Effectiveness was measured in terms of quality-adjusted life year (QALY) expectancy.In the base case, Manchanda et al 8 assumed the "best case scenario," in which all eligible patients undergo genetic testing and their firstand second-degree relatives undergo genetic testing, but the authors also tested the more "realistic"
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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 | 0.001 |
| 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".