ArticleNature communications2022
Tumor fractions deciphered from circulating cell-free DNA methylation for cancer early diagnosis.
Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 37 papers.
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Who cites it
37 citing papers in PubMed.
- Role of ctDNA Tumor Fraction in Selecting Immunotherapy-Based Regimens in Advanced Non-Small Cell Lung Cancer.Clinical cancer research : an official journal of the American Association for Cancer Research · 2026Article
- Integrating artificial intelligence into cancers of unknown primary diagnosis and treatment.iScience · 2026Review
- Integrating DNA methylation and clinical features to predict prostate cancer prognosis.BMC cancer · 2026Article
- Longitudinal cell-free DNA methylome and fragmentome profiles in health uncover signatures of cell type and demographic origin.Genome medicine · 2026Article
- Protocol for non-invasive tumor monitoring and diagnosis based on interpretable deep learning.STAR protocols · 2026Article
- Prognostic Significance of Blood-Based Multicancer Detection in Circulating Tumor DNA: Five-Year Outcomes Analysis.JCO precision oncology · 2026Observational
- Cancer-Like Fragmentomic Characteristics of Somatic Variants in Cell-Free DNA.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Circulating cell-free DNA and circulating tumor DNA as a prognostic biomarker in metastatic spine disease, is there a role?Neurosurgical review · 2026Review
- Naturally-occurring DNA fragment termini correlate with methylation at CpG sites in hair and blood plasma cell-free DNA.BMC genomics · 2026Article
- Liquid biopsies in precision oncology for older adults with cancer.NPJ precision oncology · 2026Review
- Leveraging cfDNA fragmentomic features for the early detection of colorectal cancer.Frontiers in immunology · 2026Article
- OncoCan: a liquid biopsy assay for cell-free DNA quantification in canine plasma to support cancer prognosis.Frontiers in veterinary science · 2026Article
- From multi-omics to deep learning: advances in cfDNA-based liquid biopsy for multi-cancer screening.Biomarker research · 2025Review
- Oral delivery of aptamer-decorated SICTERS Raman probes for colonoscopy-guided resection and photothermal immunization of microtumors.Science advances · 2025Article
- Artificial intelligence in cancer: applications, challenges, and future perspectives.Molecular cancer · 2025Review
- DNA methylation and machine learning: challenges and perspective toward enhanced clinical diagnostics.Clinical epigenetics · 2025Review
- Value of circulating free DNA methylation in the diagnosis of esophageal cancer: A systematic review and meta-analysis.Oncology letters · 2025Article
- Accurate and Efficient Detection of Nasopharyngeal Carcinoma Using Multi-Dimensional Features of Plasma Cell-Free DNA.Head & neck · 2025Article
- Efficient and accurate non-invasive tumor monitoring and diagnosis by interpretable deep learning.iScience · 2025Article
- Integrating cell-free DNA methylation of SEPT9 and SFRP2 into a machine learning model for early diagnosis of HCC.Biomarkers in medicine · 2025Article
Corrections and comments
- Erratum issued
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Tumor-derived circulating cell-free DNA (cfDNA) provides critical clues for cancer early diagnosis, yet it often suffers from low sensitivity. Here, we present a cancer early diagnosis approach using tumor fractions deciphered from circulating cfDNA methylation signatures. We show that the estimated fractions of tumor-derived cfDNA from cancer patients increase significantly as cancer progresses in two independent datasets. Employing the predicted tumor fractions, we establish a Bayesian diagnostic model in which training samples are only derived from late-stage patients and healthy individuals. When validated on early-stage patients and healthy individuals, this model exhibits a sensitivity of 86.1% for cancer early detection and an average accuracy of 76.9% for tumor localization at a specificity of 94.7%. By highlighting the potential of tumor fractions on cancer early diagnosis, our approach can be further applied to cancer screening and tumor progression monitoring.
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