Predicting Prostate Cancer in Primary Care Settings

Summary

One in eight men in the UK will be diagnosed with prostate cancer in their lifetime, and the disease causes more than 12,000 deaths a year.

Our research aims to improve the accuracy and speed of prostate cancer diagnosis. By improving how cases are identified, healthcare professionals could reduce unnecessary referrals and painful biopsies, while speeding up diagnosis for prostate cancer.

What are we doing?

We are analysing the impact of combining a patient’s genetic information, alongside test results from the commonly used Prostate-Specific Antigen (PSA) test. By developing a genetic risk score, the study aims to improve the referral process for cancer patients. This will help to ensure people with high genetic risk, but slightly low PSA results are appropriately referred, while those with slightly raised PSA results but low genetic risk can avoid unnecessary biopsies.

How are we doing it?

Our research is being carried out through the statistical analysis of patient data, to assess whether combining genetic risk score with the PSA test gives more accurate predictions of prostate cancer than the PSA test alone.

1. Data Collection: using existing data from a study on PSA testing for prostate cancer, which includes genetic information and PSA test results.

2. Combining Information: merging genetic risk scores (GRS) with PSA results to see if this combined approach improves prediction of prostate cancer compared to using PSA alone.

3. Comparison Models: testing different models, comparing the combined approach with using PSA alone and with other factors like age.

4. Analysis: understanding if combining GRS and PSA enhances the ability to predict prostate cancer cases more accurately.

What happens next?

Following completion of the study, we plan to pursue larger follow-up grants and hope to inspire and plan future projects, including a health economic evaluation, testing in more diverse international datasets, including a feasibility study for real-world implementation of genetic risk scores in prostate cancer diagnosis.