Integration of Molecular and Radiologic Features to Understand Breast Cancer Risk: Implications for Risk Assessment and Prevention

Breast cancer is the number one cause of cancer in women. Although many risk factors for breast cancer have been identified, only some are modifiable. Since 2004, incidence rates have been rising 0.3% year, highlighting the lack of successful prevention strategies. As a cancer epidemiologist, my goal is to identify risk factors for breast cancer to better predict which women are at the highest risk of breast cancer. Current risk prediction models used in the clinics, do not perform well in discriminating women at high risk of breast cancer. I will discuss three novel risk factors for breast cancer: early life body size, mammographic density and vitamin D supplementation. Being heavier earlier in life is inversely associated with later risk of breast cancer. Understanding the mechanism by which adiposity in early life influences adult breast cancer risk has the opportunity to lead to prevention opportunities. Mammographic density is one of the strongest risk factors for breast cancer. Including mammographic density and other novel risk factors have improved our ability to accurately predict individuals at risk for breast cancer. Several epidemiologic studies have examined circulating vitamin D levels and breast cancer; however, the evidence is inconsistent as to whether vitamin D can reduce risk or improve cancer survival. To address gaps in knowledge and limitations of other studies, we conducted an ancillary study in the context of a randomized trial of high dose vitamin D on mammographic features and breast tissue. Integrating molecular and radiologic features in the context of epidemiologic studies, we can better understand how risk factors influence cancer etiology and progression.
 

Learning Objectives:

1. Clarify the importance of life course and considering timing of exposures.
2. Explain mammographic density as risk factor for breast cancer.
3. Classify how integrative epidemiology can help us to understand how risk factors influence disease.