Optimizing Ovarian Cancer Treatment and Prevention Through Parallel Germline and Somatic Genetic Testing
Notice bibliographique
Résumé
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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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».