通过将 Interlace™ 全局甲基化发现与下一代测序检测 Labcorp Plasma Complete™ 相结合来增强循环肿瘤 DNA 的检测

Genome-wide methylation patterns are a powerful biomarker. The detection of aberrant methylation has many oncological applications, including using tumor DNA for cancer detection and therapy selection. In liquid biopsies, the ability to detect small amounts of circulating tumor DNA (ctDNA) is crucial to enable earlier diagnosis, monitor minimal residual disease, and assess treatment response. This study evaluated Interlace, an enzymatic technology that enriches unmethylated DNA molecules, to detect ctDNA. This methodology uses an engineered methyltransferase enzyme to target and tag unmethylated CpG sites across the genome. Libraries are then prepared from these tagged regions and sequenced while simultaneously allowing unenriched product to be utilized for downstream molecular applications such as hybrid capture. Interlace was run in parallel with Labcorp Plasma Complete (LPC), a 521-gene next-generation sequencing (NGS) hybrid capture assay that detects genetic alterations in cell free DNA (cfDNA) from the plasma of cancer patients. LPC reports single nucleotide variants (SNVs), insertions and deletions (indels), translocations, copy number amplifications, microsatellite instability (MSI), and blood tumor mutation burden (bTMB). To evaluate the analytical performance of the two tests in parallel, a sample cohort (n=45) including cfDNA reference samples, noncancerous wild-type controls, cancer cell lines, and cfDNA from cancer patients was processed. A head-to-head comparison of LPC results for samples processed with and without the Interlace assay demonstrated 96.7% positive percent agreement and 99.5% positive predictive value. Interlace was able to detect tumor DNA down to 0.1% tumor content in a dilution series created by mixing sheared tumor and wild-type cell line DNA, representative of ctDNA, at target concentrations from 0% to 10%. Using identified differentially methylated regions as input into unsupervised clustering or a proprietary machine learning model, Interlace was able to differentiate blinded cancer cfDNA from cancer cell lines from wild-type samples. Multiomics platforms that evaluate multiple analytes from a single sample, as described here, are powerful tools to provide a more comprehensive view of patient samples. The combination of LPC for somatic variant reporting with Interlace methylation-based ctDNA detection may allow for increased sensitivity in reporting circulating tumor DNA in plasma samples from cancer patients.