A team of researchers at the University of Wisconsin–Madison have successfully combined genomics and machine learning in the quest to develop accessible tests that allow earlier detection of cancer.
In a study published this week in Science Translational Medicine and led by Muhammed Murtaza, professor of surgery at the UW School of Medicine and Public Health, researchers used a machine-learning model to examine blood plasma for DNA fragments from cancer cells. The technique, which uses readily available lab materials, detected cancers at an early stage among most of the samples they studied.
The approach hinges on analyzing fragments of cell-free DNA, commonly found in plasma, which is the liquid portion of blood.
It accurately distinguished people with any stage of cancer from healthy individuals 91% of the time. In addition, the model accurately identified samples from patients with stage 1 cancer in 87% of cases, suggesting it holds promise for detecting cancer in early stages.
“My hope,” Murtaza adds, “is that with additional development, this work will lead to a blood test for cancer detection and monitoring that will be available clinically in the next 2-5 years for at least some conditions, and ultimately be accessible for patients with limited healthcare resources in the U.S. and around the world.”
Read the full story on UW–Madison's website .
Science Translational Medicine
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