Abstract 2598: Predicting breast and ovarian cancer risks for BRCA1 and BRCA2 mutation carriers using polygenic risk scores
Notice bibliographique
Résumé
Abstract Women who carry a pathogenic mutation in the BRCA1 or BRCA2 gene are at high risk of breast (BC) and ovarian cancer (OC). Their clinical management usually includes invasive risk-reducing interventions with substantial side effects. Improved personalized cancer risk estimates may help to identify women at particularly high risk or with high risk of disease at early ages who may benefit from early intervention as well as women at lower risk who may opt to delay surgery or chemoprevention. Genome-wide association studies have identified >100 common genetic variants that are associated with BC or OC risks. Several of these variants are also individually associated with risk of BC or OC for BRCA1 and BRCA2 mutation carriers. However, no study has evaluated the combined effects of all the known common genetic variants on BC or OC risk for BRCA1/2 mutation carriers. We constructed polygenic risk scores (PRS) based on results of genetic association studies conducted in the general population. Each PRS was formed by the sum of the number of risk alleles across the variants weighted by their log-Odds Ratio estimate from population-based studies of BC or OC. We investigated 3 PRS for BC (overall, estrogen receptor (ER) positive, and ER-negative) and one PRS for OC. We used data for 15,252 BRCA1 and 8,211 BRCA2 female carriers. The association of each PRS with BC or OC risk was evaluated using a weighted cohort analysis with time to diagnosis as the outcome and estimated the Hazard Ratios (HR) per standard deviation increase in the PRS. All PRS were significantly associated with cancer risks for BRCA1/2 carriers. The PRS for ER-negative BC displayed the strongest association with BC risk in BRCA1 carriers (HR = 1.29 [1.25-1.33], p = 8×10−64). In BRCA2 carriers, the strongest association was seen for the overall BC PRS (HR = 1.26 [1.21-1.31], p = 3×10−27). The OC PRS was strongly associated with OC risk for both BRCA1 and BRCA2 carriers. These relative risks translate to large differences in absolute risks for carriers: e.g., the OC risk was 6% by age 80 for BRCA2 carriers at the 10th percentile of the OC PRS compared with 19% risk for those at the 90th percentile of PRS. Our findings demonstrate that BC and OC PRS derived from studies in the general population are predictive of cancer risks in BRCA1 and BRCA2 carriers. Incorporation of the PRS into risk prediction models would improve risk prediction and hence inform decisions on cancer risk management. Citation Format: Karoline Kuchenbaecker, Jacques Simard, Kenneth Offit, Fergus J. Couch, Douglas F. Easton, Georgia Chenevix-Trench, Antonis C. Antoniou, Consortium of Investigators of Modifiers of BRCA1/2. Predicting breast and ovarian cancer risks for BRCA1 and BRCA2 mutation carriers using polygenic risk scores. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 2598.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,007 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,002 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 source (Gemma direct ou Codex distillé), 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 ».