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Ground-Breaking New Method for Multi-Cancer Early Detection Is More Practical and Cheaper

By LabMedica International staff writers
Posted on 08 Dec 2022
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Image: The multi-cancer early detection method measures cancer-indicating changes in so-called glycosaminoglycans (Photo courtesy of Chalmers)
Image: The multi-cancer early detection method measures cancer-indicating changes in so-called glycosaminoglycans (Photo courtesy of Chalmers)

Cancer is one of the deadliest diseases in the world and is more difficult to cure when detected at a late stage. When cancer is detected at an early stage, the rates of survival increase drastically, but today only a few cancer types are screened for. Finding effective methods for early detection of several types of cancer at the same time, so-called Multi-Cancer Early Detection (MCED), is an emerging research area. Today's established screening tests are cancer type-specific, which means that patients need to be tested for each cancer type separately. Emerging MCED tests under development are usually based on genetics, for example measuring DNA fragments from tumors circulating in the blood. But DNA-based methods can only detect some types of cancer and have limited ability to find tumors at the earliest stage, so called stage I.

Now, in an international collaboration, researchers from Chalmers University of Technology (Gothenburg, Sweden) have developed a new method for MCED that is instead based on human metabolism. The results uncover new opportunities for cheaper and more effective cancer screening. In a study totaling 1,260 participants, the researchers first discovered that the new method could detect all 14 cancer types that were tested. Next, they showed that twice as many stage I cancers in asymptomatic healthy people can be detected with the new method compared to the emerging DNA-based MCED tests.

The method is based on a discovery at Chalmers almost 10 years ago: that so-called glycosaminoglycans – a type of sugar that is an important part of our metabolism – are excellent biomarkers to detect cancer noninvasively. The researchers developed a machine learning method in which algorithms are used to find cancer-indicating changes in the glycosaminoglycans. The method uses comparatively small volumes of blood or urine, which makes them more practical and cheaper to use. In the next step, the researchers hope to be able to conduct a study with even more participants to further develop and confirm the method’s potential for screening use.

"This is a previously unexplored method, and thanks to the fact that we have been able to test it in a large population, we can show that it is effective in finding more stage I cancers and more cancer types. The method makes it possible to find cancer types that are not screened for today and cannot be found with DNA-based MCED tests, such as brain tumors and kidney cancer," said Francesco Gatto, a visiting researcher at the Department of Biology and Biological Engineering at Chalmers and one of the study's authors. "The fact that the method is comparatively simple means that the cost will be significantly low, ultimately enabling more people to have access to and take the test."

Related Links:
Chalmers University of Technology

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