Researchers introduced a new method called MethylScan for detecting multiple diseases from a single blood sample, according to an April 7 study published in the journal PNAS. The technique aims to improve accuracy and reduce costs by removing background signals from healthy DNA, allowing clearer detection of cancer, liver disease, and tissue damage.
This development is important because traditional cell-free DNA (cfDNA) testing faces challenges due to the high proportion of healthy DNA fragments in blood samples. These fragments can obscure rare disease signals, especially during early stages when tumor-derived cfDNA is present at very low levels. The cost of deep sequencing required for reliable detection has limited broader clinical use.
The MethylScan approach uses specialized enzymes known as methylation-sensitive restriction enzymes (MSREs) to selectively remove hypomethylated regions typical of healthy white blood cells. By targeting over 154,000 specific regions and including control sites for normalization, the method increases the signal-to-noise ratio for disease markers while reducing sequencing needs. Clinical validation involved over one thousand plasma and tissue samples from both cancer patients—covering liver, lung, ovarian, and stomach cancers—and noncancer individuals at risk due to chronic conditions.
Results showed that MethylScan could detect cancer signals with high sensitivity even when tumor fractions were as low as 0.05%. In multicancer detection across four types, the test achieved an average area under the receiver operating characteristic curve (AUROC) of 0.938 at a specificity of 98%, with sensitivity rates above 63% overall and more than half in early-stage cases. The method also classified various liver diseases with nearly 85% accuracy and distinguished patient ancestry with over 97% accuracy among White and Asian participants.
By enabling comprehensive analysis using small amounts of cfDNA from blood samples while lowering costs through efficient background depletion, MethylScan may help pave the way toward holistic diagnostics that can identify multiple health conditions simultaneously.
