Validating risk prediction equations for common cancers as possible tools to direct risk-adapted screening and prevention: a cohort study using the UK Biobank
Principal Investigator: Professor Julia Hippisley-Cox
Approved Research ID: 40628
Approval date: August 7th 2018
Screening for cancers may offer a way to diagnose tumours at an early stage that are treatable, and therefore reduce death rates. Screening methods exist for breast and prostate cancers for example, but these attract some controversy as they may identify tumours that would never progress and cause symptoms, or impact on life. Therefore, a significant number of people every year could undergo tests for cancers that may be invasive and uncomfortable, and cause them to receive intensive treatment for no benefit. For example, rather than subjecting every woman aged 50 -70 to a test for breast cancer, risk-adapted strategies could identify those people at higher risk of developing breast cancer, and therefore those who may benefit most from undergoing testing. This can be applied to different cancers as well. Previous research in a sample of almost 5 million individuals has shown that the risk for an individual person developing 11 specific cancers may be predicted by mathematical equations that consider multiple factors about them, such as their age, family history, whether or not they smoke, or how much alcohol they drink. Whilst the results were encouraging, further study using other databases is needed to identify whether or not these tools may be useful to improve screening, or help develop screening for other cancers that do not have a test available yet. Our project will use the UK Biobank database to test if these developed 'QCancer' equations are accurate in predicting the risk of 11 different cancers. By applying the equations to another large database (i.e. the UK Biobank), we will use the equations to try and predict which patients developed breast cancer over a 5-year time period. Then, we will be able to compare this with the observed rates of cancers, and statistical tests can analyse if these are effective tools. This project will last for 12 months. Should any of the prediction equations prove to be effective in calculating who may be at high risk for specific common cancers, then we may be able to use these to make existing screening tests much more effective, help develop tests for other cancers with no screening programme yet, and target therapy to prevent certain cancers developing in people at high risk.