NEW YORK – A Chinese research team has shown that their method for profiling mutation and DNA methylation patterns in circulating cell-free DNA (cfDNA) can detect hepatocellular carcinoma (HCC) liver cancer cases.
“[W]e developed the Mutation Capsule Plus (MCP) technology, supporting parallel profiling of mutations and methylation changes (MCP profiling) and de novo discovery of methylation markers through CpG tandems target amplification,” co-senior and co-corresponding authors Yuchen Jiao, Hong Zhao, and Chunfeng Qu, investigators affiliated with the Chinese Academy of Medical Sciences and Peking Union Medical College, and their colleagues wrote.
As they reported in Science Translational Medicine on Wednesday, the researchers initially used a methylation detection and profiling approach known as “CpG tandem target amplification” (CTTA) to find informative methylation marks in pre-MCP libraries generated from cfDNA in blood plasma samples from 30 individuals with HCC and 30 HCC-free control individuals.
By combining such methylation clues with DNA mutation patterns found with MCP in samples from 60 HCC cases and 60 unaffected controls, the team established an MCP-based HCC detection algorithm based on 10 informative methylation marks and sequences at a handful of protein-coding or promoter regions that outperformed biomarkers based on methylation- or mutation-alone.
After testing the model in that training cohort, the investigators applied it to another 58 individuals with HCC and 198 without, where the approach appeared to have around 90 percent sensitivity and 94 percent specificity.
When they applied it to prospectively collected samples from more than 300 individuals with symptom-free hepatitis B virus infections, meanwhile, the researchers reported that their parallel mutation and methylation profiling approach could find HCC cases with 80 percent sensitivity and 94 percent specificity. In that HBV cohort, the cfDNA strategy unearthed all but one of the five known HCC cases.
“The [MCP] technology preserves and amplifies the information of mutations and methylation changes in a single pre-MCP library, which supports multiple tests of different downstream applications, including the genome-wide screening of previously unidentified methylation biomarkers, and parallel profiling of mutations and methylation changes,” the authors explained, noting that the “multiplex test feature does not sacrifice sensitivity because the MCP profiling of the pre-MCP library is comparable to the direct profiling of the original cfDNA sample.”
More broadly, the MCP method made it possible to systematically tally the DNA methylation and mutation shifts that tend to turn up in circulating tumor DNA, including individual base changes, long insertions and deletions, and other more complicated mutation types.
“These findings demonstrate that the MCP technology has potential for the discovery and validation of multiomics biomarkers for the noninvasive detection of cancer,” the authors reported, noting that “comprehensive profiling approach allowed a comparison of cfDNA-based biomarkers, revealing the complementary pattern of the methylation and mutation biomarkers for HCC detection.”
The team is not the first to turn to cfDNA as a strategy for detecting HCC cases, including those found in high-risk individuals with viral hepatitis or liver cirrhosis. In a Cancer Discovery study published earlier this month, for example, investigators at Johns Hopkins University and elsewhere described a DELFI machine learning-based method for classifying HCC risk based on cfDNA fragmentome patterns in blood plasma samples.
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