Risks of Breast, Ovarian, and Contralateral Breast Cancer for $\textit{BRCA1}$ and $\textit{BRCA2}$ Mutation Carriers
Notice bibliographique
Résumé
$\textbf{Importance}$ The clinical management of $\textit{BRCA1}$ and $\textit{BRCA2}$ mutation carriers requires accurate, prospective cancer risk estimates. $\textbf{Objectives}$ To estimate age-specific risks of breast, ovarian, and contralateral breast cancer for mutation carriers and to evaluate risk modification by family cancer history and mutation location. $\textbf{Design, Setting, and Participants}$ Prospective cohort study of 6036 $\textit{BRCA1}$ and 3820 $\textit{BRCA2}$ female carriers (5046 unaffected and 4810 with breast or ovarian cancer or both at baseline) recruited in 1997-2011 through the International $\textit{BRCA1/2}$ Carrier Cohort Study, the Breast Cancer Family Registry and the Kathleen Cuningham Foundation Consortium for Research into Familial Breast Cancer, with ascertainment through family clinics (94%) and population-based studies (6%). The majority were from large national studies in the United Kingdom (EMBRACE), the Netherlands (HEBON), and France (GENEPSO). Follow-up ended December 2013; median follow-up was 5 years. $\textbf{Exposures}$ $\textit{BRCA1/2}$ mutations, family cancer history, and mutation location. $\textbf{Main Outcomes and Measures}$ Annual incidences, standardized incidence ratios, and cumulative risks of breast, ovarian, and contralateral breast cancer. $\textbf{Results}$ Among 3886 women (median age, 38 years; interquartile range [IQR], 30-46 years) eligible for the breast cancer analysis, 5066 women (median age, 38 years; IQR, 31-47 years) eligible for the ovarian cancer analysis, and 2213 women (median age, 47 years; IQR, 40-55 years) eligible for the contralateral breast cancer analysis, 426 were diagnosed with breast cancer, 109 with ovarian cancer, and 245 with contralateral breast cancer during follow-up. The cumulative breast cancer risk to age 80 years was 72% (95% CI, 65%-79%) for $\textit{BRCA1}$ and 69% (95% CI, 61%-77%) for $\textit{BRCA2}$ carriers. Breast cancer incidences increased rapidly in early adulthood until ages 30 to 40 years for $\textit{BRCA1}$ and until ages 40 to 50 years for $\textit{BRCA2}$ carriers, then remained at a similar, constant incidence (20-30 per 1000 person-years) until age 80 years. The cumulative ovarian cancer risk to age 80 years was 44% (95% CI, 36%-53%) for $\textit{BRCA1}$ and 17% (95% CI, 11%-25%) for $\textit{BRCA2}$ carriers. For contralateral breast cancer, the cumulative risk 20 years after breast cancer diagnosis was 40% (95% CI, 35%-45%) for $\textit{BRCA1}$ and 26% (95% CI, 20%-33%) for $\textit{BRCA2}$ carriers (hazard ratio [HR] for comparing $\textit{BRCA2}$ vs $\textit{BRCA1}$, 0.62; 95% CI, 0.47-0.82; P=.001 for difference). Breast cancer risk increased with increasing number of first- and second-degree relatives diagnosed as having breast cancer for both $\textit{BRCA1}$ (HR for ≥2 vs 0 affected relatives, 1.99; 95% CI, 1.41-2.82; P<.001 for trend) and $\textit{BRCA2}$ carriers (HR, 1.91; 95% CI, 1.08-3.37; P=.02 for trend). Breast cancer risk was higher if mutations were located outside vs within the regions bounded by positions c.2282-c.4071 in $\textit{BRCA1}$ (HR, 1.46; 95% CI, 1.11-1.93; P=.007) and c.2831-c.6401 in $\textit{BRCA2}$ (HR, 1.93; 95% CI, 1.36-2.74; P<.001). $\textbf{Conclusions and Relevance}$ These findings provide estimates of cancer risk based on $\textit{BRCA1}$ and $\textit{BRCA2}$ mutation carrier status using prospective data collection and demonstrate the potential importance of family history and mutation location in risk assessment.
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Comment cette classification a été obtenuedéplier
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 | 0,000 |
| 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 ».