Cancers (Basel). 2025 Aug 15;17(16):2668. doi: 10.3390/cancers17162668.

ABSTRACT

Background/Objectives: Esophageal and esophagogastric junction adenocarcinoma (EADC-EGJA), which mainly develops from Barrett's esophagus (BE), low-grade dysplasia (LGD), and high-grade dysplasia (HGD), has a poor prognosis and several unmet clinical needs, among which is the detection of minimal residual disease (MRD) after endoscopic/surgical resection. Long interspersed nuclear element-1 (LINE-1), a surrogate marker of global methylation, is considered an emerging biomarker for MRD monitoring. The aim of this study was to determine, by LINE-1 methylation analysis, at which carcinogenesis step global methylation is affected and whether this biomarker could be followed in longitudinal to monitor the disease behavior post-surgery. Methods: Cell-free DNA of 90 patients with non-dysplastic Barrett's esophagus (NDBE), HGD/early EADC-EGJA, or locally advanced/advanced EADC-EGJA were analyzed for LINE-1 methylation, by Methylation-Sensitive Restriction Enzyme droplet digital PCR (MSRE-ddPCR). Twenty-six patients were longitudinally studied by repetitive blood sampling. Results: Global hypomethylation increased during carcinogenesis, with significant difference between locally advanced/advanced EADC-EGJA and NDBE patients (p = 0.028). Longitudinal cases confirmed the rareness of hypomethylation in NDBE cases. The majority of HGD/early EADC-EGJA and locally advanced/advanced EADC-EGJA patients showed methylation changes after resection according to clinical status. Conclusions: This study suggests that global hypomethylation occurs just prior to cancer invasiveness and that it is a promising biomarker to monitor MRD.

PMID:40867295 | PMC:PMC12384112 | DOI:10.3390/cancers17162668

J Transl Med. 2025 Aug 27;23(1):964. doi: 10.1186/s12967-025-06993-3.

ABSTRACT

BACKGROUND: Circulating tumour DNA (ctDNA) in liquid biopsies has emerged as a powerful biomarker in cancer patients. Its relative abundance in cell-free DNA serves as a proxy for the overall tumour burden. Here we present GeneBits, a method for cancer therapy monitoring and relapse detection. GeneBits employs tumour-informed enrichment panels targeting 20-100 somatic single-nucleotide variants (SNVs) in plasma-derived DNA, combined with ultra-deep sequencing and unique molecular barcoding. In conjunction with the newly developed computational method umiVar, GeneBits enables accurate detection of molecular residual disease and early relapse identification.

RESULTS: To assess the performance of GeneBits and umiVar, we conducted benchmarking experiments using three different commercial cell-free DNA reference standards. These standards were tested with targeted next-generation sequencing (NGS) workflows from both IDT and Twist, allowing us to evaluate the consistency and accuracy of our approach across different oligo-enrichment strategies. GeneBits achieved comparable depth of coverage across all target sites, demonstrating robust performance independent of the enrichment kit used. For duplex reads with ≥ 4x UMI-family size, umiVar achieved exceptionally low error rates, ranging from 7.4×10^-7 to 7.5×10^-5. Even when including mixed consensus reads (duplex & simplex), error rates remained low, between 6.1×10^-6 and 9×10^-5. Furthermore, umiVar enabled variant detection at a limit of detection as low as 0.0017%, with no false positive calls in mutation-free reference samples. In a reanalysed melanoma cohort, variant allele frequency kinetics closely mirrored imaging results, confirming the clinical relevance of our method.

CONCLUSION: GeneBits and umiVar enable highly accurate therapy and relapse monitoring in plasma as well as identification of molecular residual disease within four weeks of tumour surgery or biopsy. By leveraging small, tumour-informed sequencing panels, GeneBits provides a targeted, cost-effective, and scalable approach for ctDNA-based cancer monitoring. The benchmarking experiments using multiple commercial cell-free DNA reference standards confirmed the high sensitivity and specificity of GeneBits and umiVar, making them valuable tools for precision oncology. UmiVar is available at https://github.com/imgag/umiVar .

PMID:40866952 | PMC:PMC12382282 | DOI:10.1186/s12967-025-06993-3