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New tool could help target prostate cancer testing at men at greatest risk

prostate cancer patient

Scientists have created a comprehensive tool for predicting an individual’s risk of developing prostate cancer, which they say will help ensure that those men at greatest risk will receive the appropriate testing while reducing unnecessary – and potentially invasive – testing for those at very low risk.

CanRisk-Prostate, developed by researchers at the University of Cambridge and The Institute of Cancer Research, London, will be incorporated into the group’s CanRisk web tool, which has recently recorded its one millionth risk prediction. The free tool is already used by healthcare professionals worldwide to help predict the risk of developing breast and ovarian cancers.

The need for more accurate screening tools

Prostate cancer is the most common type of cancer in men. Around 52,000 men are diagnosed with the disease each year in the UK, and there are more than 12,000 deaths. Over three-quarters of men diagnosed with prostate cancer survive for over ten years, but this proportion has barely changed over the past decade in the UK.

Testing for prostate cancer typically involves a blood test that looks for a protein known as a prostate-specific antigen (PSA), made only by the prostate gland – but PSA testing lacks accuracy. According to the NHS, around three in four men with a raised PSA level will not have cancer. Further tests, such as tissue biopsies or MRI scans, are therefore required to confirm a diagnosis.

A model relying on rare genetic faults

Inherited faulty versions of genes such as BRCA2, HOXB13 and possibly BRCA1 are associated with moderate-to-high risk of prostate cancer, though such faults are rare in the population. There are also several hundred more common genetic variants that each confer a lower risk, but in aggregate act like a ‘volume control’ that moderates or increases prostate cancer risk.

The researchers have developed the first comprehensive prostate cancer model using genetic and cancer family history data from almost 17,000 families affected by prostate cancer. The model uses data on rare genetic faults in moderate-to-high-risk genes and a risk score based on 268 common low-risk variants, together with detailed cancer family history, to predict the future risks.

The new study describing the model is published in the Journal of Clinical Oncology.

Predicting risk of prostate cancer

Using the model, the team found that the predicted risk of developing prostate cancer was higher for men who had a father diagnosed with prostate cancer – 27 per cent if the father was diagnosed at an older age (80 years) but as high as 42 per cent if the father was diagnosed at a young age (50 years).

The risks were considerably higher for men with genetic faults. Some 54 per cent of men who carried an alteration in the BRCA2 gene would develop prostate cancer. However, among men with BRCA2 gene faults, the risks were substantially lower if they also had a small number of the low-risk variants, but much higher if they also had a large number of the low-risk variants.

The researchers believe that, in practice, clinicians will be able to use any combination of cancer family history, rare and common genetic variants to provide a personalised risk prediction.

A prostate risk clinic has been established at the Early Detection and Diagnosis centre at The Institute of Cancer Research (ICR) and The Royal Marsden NHS Foundation Trust to translate these findings into targeted screening programmes.

So far, the data used to develop CanRisk-Prostate has been from men of European ancestry. Next, the team hope to be able to include data from men of other ethnicities as further research is undertaken.

The research was supported by the Cancer Research UK-funded CanRisk programme. Additional support for CanRisk-Prostate was provided by Prostate Cancer UK, the ICR, the Everyman Campaign, National Cancer Research Network UK, National Cancer Research Institute, NIHR Cambridge Biomedical Research Centre and the NIHR Biomedical Research Centre at the ICR and The Royal Marsden.

The most accurate computer model to date

Professor Ros Eeles, Professor of Oncogenetics at the ICR and Clinical Consultant at The Royal Marsden, co-author on the study said:

“This is an important step forward as it will enable clinicians to have conversations with men about their individual risk of prostate cancer based on the most accurate computer model to date. This will help them in making decisions about screening.”

Professor Antonis Antoniou from the Department of Public Health and Primary Care at the University of Cambridge said:

“Prostate cancer is the most common cancer in men in the UK, but population-wide screening based on PSA isn’t an option: these tests are often falsely positive, which means that many men would then be biopsied unnecessarily. Also, many prostate tumours identified by PSA tests are slow-growing and would not have been life-threatening. The treatment of these tumours may do more harm than good.

“What we need is a way of identifying those men who are at greatest risk, allowing us to target screening and diagnostic tests where they are most needed, while also reducing the harms for those men who have low risk of the disease. This is what CanRisk-Prostate aims to do. For the first time, it combines information on the genetic makeup and prostate cancer family history, the main risk factors for the disease, to provide personalised cancer risks.”

Dr Tommy Nyberg from the MRC Biostatistics Unit at Cambridge 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.”


Ros Eeles prostate cancer screening
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