Researchers at the Institute of Cancer Research (ICR), London, UK, and the University of Cambridge have developed a new tool to predict the risk of developing prostate cancer in men.
The new tool, named CanRisk-Prostate, is already in use by healthcare specialists for predicting the risk of developing breast and ovarian cancers across the world.
It helps to reduce unnecessary and potentially invasive testing for people who are at very low risk of developing the disease.
The first comprehensive prostate cancer model was developed using the information on genetic and cancer family history obtained from approximately 17,000 families affected by prostate cancer.
To predict future risks, the model makes use of the rare genetic faults in moderate-to-high-risk genes as well as a risk score based on 268 other common low-risk variants and detailed family cancer histories.
Researchers found that the predicted risk of developing the disease was higher for men whose fathers had been diagnosed with prostate cancer, which is 42% if the father was diagnosed at 50 years and 27% if diagnosed at 80 years.
Men with genetic faults were also at higher risk. The researchers found that 54% of men with an alteration in the BRCA2 gene would develop the disease, although individuals with BRCA2 gene faults were at substantially lower risk if they had a small number of low-risk variants.
Clinicians will be able to use any combination of cancer family history and rare and common genetic variants to provide a personalised risk prediction.
Dr Tommy Nyberg from the University of Cambridge Medical Research Council (MRC) Biostatistics Unit said: “We’ve created the most comprehensive tool to date for predicting a man’s risk of developing prostate cancer.
“We hope this will help clinicians and genetic counsellors assess their clients’ risk and provide the appropriate follow-up.
“Over the next 12 months, we aim to build this tool into the widely used CanRisk tool, which will facilitate the risk-based clinical management of men seen in family cancer clinics and enable risk-adapted early detection approaches to the population at large.”