New method uses metabolism to detect cancers

cancer diagnosis

When cancer is detected at an early stage, the rates of survival increase drastically, but today only a few cancer types are screened for. 

An international study led by researchers from Chalmers University of Technology, Sweden, shows that a new, previously untested method can easily find multiple types of newly formed cancers at the same time – including cancer types that are difficult to detect with comparable methods.

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 1.

About Chalmers University of Technology’s new method

Now, in an international collaboration, researchers from Chalmers University of Technology have developed a new method for MCED based on human metabolism. 

The results, published in the scientific journal PNAS, uncover new opportunities for cheaper and more effective cancer screening. In a study of 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 1 cancers in asymptomatic healthy people can be detected with the new method compared to the emerging DNA-based MCED tests.

“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 1 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.

Cheaper, more practical tests

The method is based on a discovery by Gatto and Jens Nielsen at Chalmers University of Technology 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.

“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,” Gatto said.

Important step towards effective multi-cancer tests

The researchers now 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 ground-breaking study that gives us hope that one day society will be able to create screening programs that can detect all cancer types early,” Gatto noted.

“To be able to find more cancer cases early, we know that new tools are needed. These results are very promising because of the demonstrated higher sensitivity across more types of cancer at stage I, using a cost-efficient and accessible technology,” said Eric Jonasch, professor at The University of Texas MD Anderson Cancer Center and co-author of the study.

The method has already taken a step forward through the start-up company Elypta, which is one of Chalmers Ventures’ portfolio companies. Elypta is based on the research presented in the scientific study and focuses on developing and commercializing MCED tests as well as a test for recurrent kidney cancer. In addition, Elypta has developed the measurement methods used to support the study analysis.

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