Research question: Will a model combining metabolomics, plasma protein, polygenic risk scores and traditional risk factors better define breast cancer risk and screening start age than current methods?
Objective: The main aim of this research project is to develop a new, more accurate way to predict a woman’s personal risk of developing breast cancer. We will create a model that combines information from four key areas: the levels of small-molecule metabolites and the levels of proteins in a person’s blood, their genetic risk score for breast cancer, and traditional risk factors like lifestyle and family history. Through this integrated model, we aim to assess each woman’s disease risk and determine a more personalized age for initiating breast cancer screening. This means recommending earlier screening for high-risk individuals and delayed screening for low-risk individuals.
Scientific Rationale: Breast cancer is the most common cancer in women worldwide. However, a woman’s individual risk can vary significantly. Traditional risk factors only explain a part of this difference. New technologies now allow us to gain a deeper understanding of risk. Specific metabolic dysregulations (e.g., related to insulin resistance, lipid metabolism, oxidative stress) are closely linked to breast cancer. Proteins in the blood can reflect very early changes in the body related to cancer development, while a polygenic risk score can measure inherited genetic susceptibility. By integrating these powerful new types of data with traditional factors, we believe we can create a much clearer and more individualised picture of breast cancer risk. The UK Biobank, with its vast amount of health and genetic data from hundreds of thousands of women, provides a unique opportunity to build and test this comprehensive model.
