On Apr. 6, 2026, scientists from the University of California, Los Angeles (UCLA) announce they have developed a simple and cost-effective blood test that, in early studies, shows promise in detecting multiple cancers, various liver conditions and organ abnormalities simultaneously by analyzing DNA fragments circulating in the bloodstream. The test, described in the journal Proceedings of the National Academy of Sciences, could offer a powerful and more affordable approach to early disease detection and comprehensive health monitoring.

“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.” The new method, called MethylScan, works by analyzing cell-free DNA (cfDNA), tiny fragments of genetic material released into the blood when cells die. Because cells from every organ shed DNA into the bloodstream, cfDNA carries molecular signals that reflect what is happening throughout the body.

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.

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. 

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.