Public Health Mode Personalized Medicine: Risk Stratification for Breast Cancer Screening
Notice bibliographique
Résumé
Background: Breast cancer (BC) screening, primarily age-based, is a major public health program in many wealthy countries. At the same time, there is a dramatic increase in using genetics to support personalized medicine. These two approaches would seem antithetical. However, they can join powerfully with the possibility of using genetic information as the basis for a major shift from age-based to a risk-based BC screening programs. Aim: To assess the prospective cost-effectiveness of such a shift to risk-based BC screening requires representative population data on the relationships among a woman's age when a risk assessment is done, her family history of cancer in the context of pedigree data, and specific features of her genotype - comprising both the presence of rare genetic mutations like BRCA1/2 and recently derived polygenic risk scores. We use our newly developed Genetic Mixing Model (GMM) to estimate this joint distribution as the initial step in assessing the prospective cost-effectiveness of risk stratified BC screening in Canada. Methods: BOADICEA is a BC risk stratification algorithm already in wide use around the world, and in particular in Ontario, for high risk screening. A new version of BOADICEA incorporating a polygenic risk score has recently (will have) been published. We embedded the new core BOADICEA algorithm into the GMM. GMM thus provides the empirical foundation for assessing risk stratification for a representative population by constructing an estimate of the multivariate joint distribution of family history, presence of rare genetic mutations including BRCA1/2, and a polygenic risk score, derived from genome-wide association studies. Results: Using a polygenic risk score (PRS) would be far more useful for stratifying women according to their risk of breast cancer than the two most commonly used indicators at present: family history and rare genetic mutations. We have assessed a variety of combinations of these genetic indicators, in combination with offering universal risk assessment to women in Canada at various ages, and using different thresholds for categorizing women as being at high risk. The optimal age for risk assessment is in the 35 to 40 range. And the PRS is substantially more useful than family history or rare mutations for stratifying women for screening intensity by their risk of BC. Conclusion: Shifting from the current public health approach of primarily age-based screening for breast cancer, to one based on risk stratification, especially making use of recent advances in assessing polygenic risk, offers major potential benefits.
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 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 | 0,001 | 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 ».