Epigenome-wide Association Study of Breast Cancer Using Prospectively Collected Sister Study Samples
A partir d'échantillons sanguins prélevés sur 298 femmes ayant développé un cancer du sein dans les cinq ans après leur inclusion dans la cohorte "Sister Study" et sur 612 témoins, cette étude identifie une signature qui, basée sur la méthylation de l'ADN sur des îlots CpG, est susceptible d'être utile au diagnostic et à l'estimation du risque d'un cancer du sein
Background Previous studies have suggested DNA methylation in blood is a potential epigenetic marker of cancer risk, but this has not been evaluated on a genome-wide scale in prospective studies for breast cancer.
Methods We measured DNA methylation at 27578 CpGs in blood samples from 298 women who developed breast cancer 0 to 5 years after enrollment in the Sister Study cohort and compared them with a random sample of 612 cohort women who remained cancer free. We also genotyped women for nine common polymorphisms associated with breast cancer.
Results We identified 250 differentially methylated CpGs (dmCpGs) between case subjects and noncase subjects (false discovery rate [FDR] Q < 0.05). Of these dmCpGs, 75.2% were undermethylated in case subjects relative to noncase subjects. Women diagnosed within 1 year of blood draw had small but consistently greater divergence from noncase subjects than did women diagnosed at more than 1 year. Gene set enrichment analysis identified Kyoto Encyclopedia of Genes and Genomes cancer pathways at the recommended FDR of Q less than 0.25. Receiver operating characteristic analysis estimated a prediction accuracy of 65.8% (95% confidence interval = 61.0% to 70.5%) for methylation, compared with 56.0% for the Gail model and 58.8% for genome-wide association study polymorphisms. The prediction accuracy of just five dmCpGs (64.1%) was almost as good as the larger panel and was similar (63.1%) when replicated in a small sample of 81 women with diverse ethnic backgrounds.
Conclusions Methylation profiling of blood holds promise for breast cancer detection and risk prediction.
Journal of the National Cancer Institute , résumé, 2013