Breast Cancer Prevention in the Era of Precision Medicine
Menée sur 67 054 participantes d'origine européenne dont 33 673 atteintes d'un cancer du sein, cette étude évalue la performance d'un système de score, basé sur la présence de 77 polymorphismes à simple nucléotide, pour prédire le risque de développer la maladie
After the cloning of the second breast cancer gene in 1995 (BRCA2), gene researchers took divergent paths. Some searched for new and rare genes while others sought to explain unusual cancer clusters, but at Cambridge University, a group of genetic epidemiologists devoted their energies to the development of a polygenic model for breast cancer (1). They sought to account for the missing heritability through a model that postulated that there were many genes, each of which contributed in a small way to a woman’s vulnerability. The effort was facilitated through the completion of a genomic map of nucleotide variants (HapMap) and the engineering of a DNA chip that secured many thousands of single nucleotide polymorphisms (SNPs) from across the genome. The first genome-wide association study (GWAS) for breast cancer was published by the Cambridge group in 2007 (2), and there have been many others since.
Doug Easton and his Cambridge colleagues engendered an extraordinary collaborative spirit, which culminates in a landmark paper boasting 218 authors (3) published in this issue of the Journal. The fruits of the enterprise are presented as a genetic risk assessment model that incorporates 77 SNPs. The authors use the model to generate personal risk scores and thereby stratify women according to their lifetime risk. The paper by Mavvadat et al. is important, because it enables us to evaluate the clinical utility of …
Journal of the National Cancer Institute , éditorial, 2015