First-time evidence that individuals at risk of developing AML can be identified, years before diagnosis

An international group of researchers have discovered that analyzing blood tests for changes in the DNA code can identify individuals at high risk of developing acute myeloid leukaemia (AML) years before diagnosis. The findings were recently published in Nature.

In this study, researchers from the Sanger Institute and the European Bioinformatics Institute collaborated with the European Prospective Investigation into Cancer and Nutrition (EPIC) study; one of the largest cohort studies in the world, with more than half a million participants recruited across 10 European countries and followed for almost 15 years.

They analysed data from the EPIC study; specifically the stored blood DNA from 124 AML patients and compared it to that from 676 people who remained free from AML or a related cancer.

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Remarkably, the team unearthed that many of the individuals who went on to develop AML had particular genetic changes that set them apart from those who did not. The team believe that these changes could be used collectively to develop a predictive test for AML risk.

Further research could allow earlier detection and monitoring of people at risk of AML in the future, and open the prospect of developing ways to reduce the likelihood of developing this cancer.

“Our study provides for the first time evidence that we can identify people at risk of developing AML many years before they actually develop this life-threatening disease. We hope to build on these findings to develop robust screening tests for identifying those at risk and drive research into how to prevent or stall progression towards AML. Our aspiration is that one day AML prevention would provide a compelling alternative to treatment,” George Vassiliou, one of the joint leaders of the project from the Wellcome Sanger Institute and Cambridge University Hospitals NHS Trust (both UK), concluded.

Source: www.sanger.ac.uk/news/view/roots-leukaemia-reveal-possibility-predicting-people-risk