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The $20 Blood Test Revolutionizing Cancer Detection


MethylScan blood test correctly classified 85% of liver diseases, reducing the need for invasive liver biopsies.

UCLA’s new blood test called MethylScan uses DNA fragments to accurately detect multiple cancers and liver cirrhosis from a single blood draw.
By analyzing DNA methylation patterns in cell-free DNA (cfDNA), this innovative liquid biopsy can identify liver, lung, ovarian, and stomach cancers, alongside conditions like liver cirrhosis and hepatitis.(1 Trusted Source
Toward the simultaneous detection of multiple diseases with a highly cost-effective cell-free DNA methylome test

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Published in the Proceedings of the National Academy of Sciences, this study highlights a more affordable approach to early cancer detection and comprehensive health monitoring. MethylScan acts as a health radar, tracing molecular signals back to their tissue of origin to improve diagnostic accuracy and patient outcomes.

Why Methylation Patterns are More Effective for Multi-Organ Screening

“Early detection is crucial,” said Dr. Jasmine Zhou, the study’s senior author, a professor of pathology and laboratory medicine and investigator at the UCLA Health Jonsson Comprehensive Cancer Center. “Survival rates are far higher when cancers are caught before they spread. If you detect cancer at stage one, outcomes are dramatically better than at stage four.”

“Every day, 50 to 70 billion cells in our body die. They don’t just disappear, their DNA goes into the bloodstream,” Zhou said. “That means we already have information from all our organs circulating in the blood.”

The idea of using blood to detect cancer, sometimes called a liquid biopsy, isn’t new. Some tests already look for mutations in tumor DNA to screen for certain cancers. But those tests often focus on a limited number of genetic changes and can be expensive, in part because they require deep sequencing to detect faint tumor signals.

Instead of searching for mutations, the UCLA team examined DNA methylation, chemical tags attached to DNA that help regulate gene activity. Methylation patterns differ by tissue type and can change when cells become cancerous or diseased.

Reducing Sequencing Costs: Toward a $20 Multi-Cancer Blood Test

“DNA methylation reflects the health status of a tissue,” said Dr. Wenyuan Li, a professor of pathology and laboratory medicine at UCLA and co-corresponding author of the study. “It’s a very informative signal.”

The challenge is that most cell-free DNA in the bloodstream doesn’t come from tumors or injured organs. About 80% to 90% originates from normal blood cells. That creates background noise, making it difficult and costly to detect the relatively rare fragments that might signal early cancer.

To address that, the researchers built on past work to develop a technique to remove much of the background DNA before sequencing. Using specialized enzymes, they selectively cut away unmethylated DNA fragments that largely come from blood cells. By designing a genome-wide hybridization panel, the captured DNA fragments are enriched for methylated DNA from solid organs, including potentially diseased ones.

By removing the noise, the researchers say they can dramatically reduce the amount of sequencing needed, lowering costs while maintaining sensitivity. Achieving an effective sequencing depth of 300× per sample requires only 5 Gb of data, which would cost less than $20 if the price per gigabase is under $4.

Using Machine Learning to Decode Complex DNA Methylation Data

To test the accuracy of MethylScan, the researchers analyzed blood samples from 1,061 people, including patients with liver, lung, ovarian and stomach cancers; individuals with liver diseases such as hepatitis B, hepatitis C, alcohol-related liver disease and metabolic-associated liver disease; people with benign lung nodules; and healthy participants. Machine learning algorithms were then applied to analyze the complex methylation data.

For multi-cancer detection, the test achieved a high level of overall accuracy. At a specificity of 98%, meaning few false positives, it detected about 63% of cancers across all stages and roughly 55% of early-stage cancers.

The test also performed well in liver cancer surveillance among high-risk individuals, including those with liver cirrhosis or HBV, detecting nearly 80% of cases at a specificity of just over 90%, meaning a less than 10% false positive rate.

Beyond simply detecting cancer, the methylation patterns helped identify where in the body a signal was coming from, known as the tissue of origin. “Being able to trace signals back to their source is important because a positive blood test needs to be followed by imaging or other diagnostic procedures directed at the right organ,” said Li.

Detecting Early Organ Damage in the Liver and Lungs Without Prior Diagnosis

MethylScan can work like a health radar for the body. By reading DNA signals in the blood, it can tell when specific organs, such as the liver or lungs, are under stress or damaged, even without knowing the disease in advance.

The researchers also showed that the blood test could distinguish between different types of liver disease, including viral hepatitis and metabolic-associated liver disease. It correctly classified about 85% of patients, suggesting blood-based DNA testing could reduce the need for invasive liver biopsies.

Although larger prospective trials will be needed to confirm its performance in real-world screening, Zhou said the work represents an important step toward a single, affordable blood assay that can detect a broad spectrum of diseases earlier and more comprehensively than current methods allow.

“This study demonstrates that blood-based methylation profiling can deliver clinically meaningful information across multiple diseases,” said Zhou. “It’s an exciting advancement that brings us closer to realizing the dream of a single assay for universal disease detection.”

Reference:

  1. Toward the simultaneous detection of multiple diseases with a highly cost-effective cell-free DNA methylome test – (https://www.pnas.org/doi/10.1073/pnas.2518347123)

Source-Eurekalert

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