Project overview

Despite advances in screening, prevention, and treatment, more than 40,000 women still die of breast cancer each year in the United States. Using the All of Us Research Program’s diverse and expansive dataset, Elizabeth Burnside, MD, and Irene Ong, PhD, are working to create a more personalized, risk-based approach to breast cancer screening that could help decrease the impact that breast cancer has on women and families and improve outcomes for those affected.

Their Precision Health Outcomes Realized (PHOR)* research project works to harness the power of predictive modeling to better understand what variables are most helpful in assessing whether or not someone will develop breast cancer. It pulls from a wide variety of data types within the Researcher Workbench — including health and lifestyle surveys, electronic health records, whole genome sequences, and more — and uses machine learning and statistical analysis to evaluate how the combination of these factors work together to shape an individual’s unique risk profile. The power of the All of Us dataset, according to Burnside and Ong, is not only its size and scope – with more than 6,000 reported breast cancer cases as of publication date of this article – but also its diversity, with more than 2,000 of the reported breast cancer cases coming from Black, Hispanic, and other non-white individuals. By combining all of the different types of data in a case-control cohort, Burnside and Ong have been able to create algorithms to systematize the determination of breast cancer risk. Their goal is to empower patients and doctors to plan screening through a more individualized lens.