Monday, April 25, 2016
Adding Risk Markers Improves Breast Cancer Risk Prediction
Adding a genetic risk score, mammographic density, and hormone levels — all biologic markers of risk — can significantly improve upon the current models for breast cancer risk prediction, according to data presented here at the American Association for Cancer Research (AACR) Annual Meeting 2016.
"Several prior studies have evaluated the added predictive value of a genetic risk score or mammography or both to improve upon the current Gail model, but none has used all three risk markers," presenter Xuehong Zhang, MD, ScD, assistant professor of medicine at Harvard Medical School, Boston, Massachusetts, told Medscape Medical News.
"We have shown that while each of the three biological markers can improve risk prediction, all three together improved the model the most," he added.
Dr Zhang was referring to the Gail and the Rosner- Colditz models, which have been validated and are currently used for predicting risk for breast cancer. They also are useful for making chemoprevention and screening recommendations.
The Gail model takes into account traditional risk factors, such as age, age at first menstrual period, age at first birth, history of breast cancer, and atypical hyperplasia on biopsy. The Rosner-Colditz model includes these factors plus body mass index, alcohol intake, and other well-known reproductive factors.
Dr Zhang and his colleagues conducted a nested case-control study within the Nurses' Health Study (NHS) and NHS II. Collectively, the investigation included 4006 case patients/7874 control patients for the Gail model, and 2665 case patients/5455 controls patients for the Rosner-Colditz model.
The genetic risk score was calculated on the basis of 67 single-nucleotide polymorphisms. Mammographic density was assessed among women who provided blood samples. Estrogen, testosterone, and prolactin levels were measured using prediagnostic plasma samples.