Mar 19 2010
Confusion Over Utility of Common Genetic Variations in Breast Cancer Risk Prediction
A paper published in the New England Journal of Medicine (NEJM) this week, entitled “Performance of Common Genetic Variants in Breast-Cancer Risk Models,” has led several media outlets to declare that common genetic variants have nothing to add when it comes to predicting breast cancer risk. Here we’ll explain how the results of this study have been misinterpreted.
Researchers from the National Cancer Institute and other institutions looked at data from 5,590 women with breast cancer and 5,998 women without the disease. These women, all between 50 and 79 years old, had participated in one of five studies, four in the United States and one in Poland. Since it’s known already which women have or have had breast cancer, and which have not, the researchers were able to use their data, both genetic and non-genetic, to test the predictive power of different types of risk calculations.
The study tested five different models. The “demographic model” considered only age, year of entry into the study and which study the woman was originally part of. The “nongenetic model” added in several variables that are part of the so-called “Gail model,” which is the standard model used in clinical practice today to counsel women about their risk. These variables were the number of first-degree relatives with a diagnosis of breast cancer, age at menarche, age at first live birth, and number of previous breast biopsies. Two models used the demographic information plus genetic information for 10 SNPs (they differed in details of how the genetic risk score was calculated). Finally, the “inclusive model” combined demographics, the Gail model and the genetic information.
The genetic models and the nongenetic model performed about the same, with genetics doing just a little bit better. Perhaps not surprisingly, the best model was the one that used the most information. With the inclusive model, which is based on genetic and non-genetic information, there was a net 12% improvement in risk classification over the nongenetic model for women with breast cancer. Continue Reading »









