Targeting Metabolism for Noninvasive Multi-Cancer Early Detection Tests
The survival rate of cancer can drastically increase when detection is available at an early stage. However, only a few cancer types are screened for today, such as breast and colorectal cancer, despite the advantages conferred by population-wide cancer screening and early cancer detection.
Liquid biopsy is one of the non-invasive methods that can provide information about cancerous tumors based on the presence of tumor-associated DNA or proteins in body fluids. Recently, a new study published in PNAS suggests that a single test measuring metabolic markers in blood and urine samples could detect multiple cancer types cost-effectively.
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Challenges of Liquid Biopsies
Tissue biopsies are currently the gold standard for cancer diagnoses, but repeated biopsies present a challenge due to their invasive nature. On the other hand, liquid biopsies could be a promising alternative for cancer diagnosis and monitoring due to their noninvasive nature.
As tumor cells grow, they shed DNA, proteins, and other metabolic byproducts that tests can detect in the blood and other body fluids. Liquid biopsy refers to the detection of circulating tumor cells or tumor cell products in body fluids.
A considerable number of liquid biopsies developed so far detect circulating tumor cell DNA. However, genitourinary cancers, such as bladder, prostate, and kidney cancers, and brain cancers, such as gliomas, do not shed DNA and, thus, cannot be detected in plasma samples. Moreover, these tests have shown low accuracy in detecting early-stage cancer.
Although some recent improvements, including simultaneously assessing the circulating levels of tumor DNA and proteins, have helped improve diagnostic accuracy, the high costs eventually limited wide-population screening.
Glycosaminoglycans as the Biomarker of Cancer Detection
Now, scientists have developed a new method for multi-cancer early detection based on human metabolism. The team targeted glycosaminoglycans, a type of sugar that is an important part of our metabolism, as an excellent biomarker to detect cancer non-invasively. Scientists developed a machine learning method using algorithms to find cancer-indicating changes in glycosaminoglycans. As the method uses comparatively small volumes of blood or urine, it seems more practical and cheaper to screen cancer widely.
In a study recruiting 1260 participants, the researchers first discovered that the new method could detect all 14 cancer types in the test. Next, they showed that the test detected twice as many stage I cancers in asymptomatic healthy people with the new method compared to the emerging DNA-based Multi-Cancer Early Detection(MCED) tests. The next focus of researchers would be confirming the method’s potential for screening use by recruiting even more participants.
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