The Early Detection of Multiple Cancers By Mrs Vera West
Not to see this blog as in any way anti-medicine, we report of good developments with medical and scientific advances. Thus, a research group at Chalmers University of Technology, Sweden, has found a new way of detecting multiple types of newly formed cancers at the same time. As cancer may lead to high mortalities in late stages, early detection of cancers is an advancement. The new method is based upon the discovery that a specific type of sugar (glycosaminoglycans), can be used as biomarkers to detect cancers, even in the early stages, much better than the other methods such as DNA fragment detection. Changes in the specific sugars are then analysed by machine algorithms.
In reflection, it would be a much better world if technologically based medicine concentrated upon issues like this, rather than all those critiqued at the blog almost every day. Faith in mainstream medicine would be restored, at least to some degree, even though we would remain ever-vigilant.
“An international study led by researchers from the Chalmers University of Technology in Sweden, shows that a new, previously untested method can easily detect multiple types of newly formed cancers at the same time – including cancer types that are difficult to find with comparable methods.
Cancer is more difficult to cure when detected at a late stage, and mortality rates can be high. When cancer is detected at an early stage, the rates of survival increase drastically, but current screening only looks for a few types of cancer. Additionally, current screening tests are cancer type-specific, which means that patients need to be tested for each cancer type separately.
Multi-Cancer Early Detection (MCED) is an emerging research area. 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 (stage I).
Through this international collaboration, researchers from Chalmers developed a new method for multi-cancer early detection that is instead based on human metabolism, uncovering new opportunities for cheaper and more effective cancer screening.
The method is based on a discovery by Dr. Francesco Gatto and Prof. Jens Nielsen at Chalmers approximately a decade ago, showing that glycosaminoglycans – a type of sugar that plays an important role in metabolism – are excellent biomarkers to noninvasively detect cancer. 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 study looked at 1, 260 participants, both those healthy and previously diagnosed with cancer. 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.
"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, who is 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," he added.
As a next step, the researchers hope to conduct a study with more participants to further develop and confirm the method’s potential for screening use.
The study findings have been published in the scientific journal PNAS.
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