ArticleExperimental & molecular medicine2023
Cancer signature ensemble integrating cfDNA methylation, copy number, and fragmentation facilitates multi-cancer early detection.
Article in Experimental & molecular medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers, 1 of them a synthesis that pooled it.
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Who cites it
35 citing papers in PubMed, 1 synthesis or guideline pooled it.
- [Liquid Biopsy Revolutionizes the Precise Management of Tumors Across the Entire Course: Current Situation and Future Prospects].Sichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical science edition · 2026Pooled it
- Review
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- Circulating DNA reveals nucleosome occupancy patterns that are associated with nucleosome-DNA affinity and are affected in cancer.Genome medicine · 2026Article
- Evaluation of NID2 and CDO1 methylation for ovarian cancer screening.Oncology letters · 2026Article
- From Spatial Epigenomes to Clinical Diagnostics: Integrative Methylomics Across Scales and Modalities.International journal of molecular sciences · 2026Review
- Prognostic value of PRAME gene promoter methylation and expression in hepatocellular carcinoma.Clinical epigenetics · 2026Article
- Enhanced multicancer screening assay through whole-genome methylation sequencing-based multimodal cell-free DNA analysis.Experimental & molecular medicine · 2026Article
- Detection of known gene fusions in cancer cell lines using whole-genome bisulfite sequencing data.Scientific reports · 2026Article
- Early Cancer Detection: What's Going on and What's Next.MedComm · 2026Review
- Deep learning-based non-invasive profiling of tumor transcriptomes from cell-free DNA for precision oncology.bioRxiv : the preprint server for biology · 2026Article
- High-Grade Serous Ovarian Carcinoma in the Genomics Era: Current Applications, Challenges and Future Directions.International journal of molecular sciences · 2026Review
- Artificial intelligence (AI) and machine learning (ML) in ovarian cancer: transforming detection, treatment, and prevention.Journal of ovarian research · 2026Review
- The best from both disciplines: integrating human and microbial signatures from whole genome sequencing to advance cancer diagnostics.mSystems · 2026Review
- Gene amplification in the premalignant stages of non-small cell lung cancer development.Frontiers in oncology · 2026Review
- Circulating methylated promoters of HK2 and EGFR as biomarkers in the early detection of cancer.Frontiers in oncology · 2026Article
- Liquid biopsies for detection, characterization, and interception of therapy resistance in thoracic malignancies.Frontiers in oncology · 2026Review
- Early detection of multiple cancers: the era of methylation-based liquid biopsy.Frontiers in oncology · 2026Review
- A novel liquid-liquid cfDNA extraction method for targeted sequencing with colorectal cancer patient samples: a pilot study.Frontiers in oncology · 2026Article
- Multi-cancer early detection: from promise to practice and the next frontier.Cancer biology & medicine · 2025Article
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21 authors.
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Abstract
Cell-free DNA (cfDNA) sequencing has demonstrated great potential for early cancer detection. However, most large-scale studies have focused only on either targeted methylation sites or whole-genome sequencing, limiting comprehensive analysis that integrates both epigenetic and genetic signatures. In this study, we present a platform that enables simultaneous analysis of whole-genome methylation, copy number, and fragmentomic patterns of cfDNA in a single assay. Using a total of 950 plasma (361 healthy and 589 cancer) and 240 tissue samples, we demonstrate that a multifeature cancer signature ensemble (CSE) classifier integrating all features outperforms single-feature classifiers. At 95.2% specificity, the cancer detection sensitivity with methylation, copy number, and fragmentomic models was 77.2%, 61.4%, and 60.5%, respectively, but sensitivity was significantly increased to 88.9% with the CSE classifier (p value < 0.0001). For tissue of origin, the CSE classifier enhanced the accuracy beyond the methylation classifier, from 74.3% to 76.4%. Overall, this work proves the utility of a signature ensemble integrating epigenetic and genetic information for accurate cancer detection.
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