The effects of integrating reproductive and genome-wide polygenic risk information into novel clinical prediction models for breast cancer incidence and mortality
Breast cancer is the most common cancer in women, and also the most common cancer that women die from. Understanding which women are at higher risk of developing breast cancer, or dying from breast cancer, might help change decisions around the best ways to screen these women. It could also identify women that may benefit from being prescribed inexpensive and safe medications that reduce the risk of them developing breast cancer in the first place. Our group has been developing risk equations that estimate these risks, using general practice and hospital data. However, some types of information, such as genetic data, or 'reproductive' factors like the number of children a women has had, are not widely available in the original data but could help predict women's breast cancer risks with more accuracy. This project has two aims - first, it will evaluate how well these risk equations work in a group of women (UK Biobank) that is separate to the data in which they were developed. Then, it will assess how the performance of these models changes when we include additional genetic and reproductive information. We are interested in the overall performance of these equations in all women, and also how well they work in different ethnic groups. We expect this project to last for 12 months. Its results may be useful to inform new ways of screening women, or finding women that may benefit from lifestyle changes or medications to reduce their breast cancer risk.