ReviewNature reviews. Cancer2025
Genomic and fragmentomic landscapes of cell-free DNA for early cancer detection.
Review in Nature reviews. Cancer, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07304934 (A Prospective Multi-center Cohort Study Based on Deep Learning-based cfDNA Fragment Omics to Verify the TuFEst Model for Axillary Lymph Node Status Assessment in Breast Cancer Patients Undergoing Primary Surgery or Neoadjuvant Therapy), which is not on this map. Cited by 56 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
A Prospective Multi-center Cohort Study Based on Deep Learning-based cfDNA Fragment Omics to Verify the TuFEst Model for Axillary Lymph Node Status Assessment in Breast Cancer Patients Undergoing Primary Surgery or Neoadjuvant Therapy
Who cites it
56 citing papers in PubMed.
- Non-invasive screening in hereditary cancer: a randomized controlled trial to test cell-free DNA-based early detection in the CHARM consortium.European journal of human genetics : EJHG · 2026Trial
- Adjuvant penpulimab in very-high risk clear cell renal cell carcinoma: a prospective, non-randomized, controlled phase II trial with integrated plasma multi-omics analyses.Journal for immunotherapy of cancer · 2026Trial
- Systemic health impact of cancer-associated extracellular vesicles and particles.Nature reviews. Cancer · 2026Review
- Bridging the Precancerous Gap in Colorectal Cancer Through AI-Enhanced Liquid Biopsy, Endoscopy, and Digital Pathology.Cancers · 2026Review
- Cancer in a Drop: Liquid biopsy insights from AACR 2026.The journal of liquid biopsy · 2026Article
- A Noninvasive Circulating Tumor DNA Methylation Classifier to Identify Benign Pulmonary Nodules.Clinical cancer research : an official journal of the American Association for Cancer Research · 2026Article
- DNA Methylation as a Programmable Information Layer: From Molecular Marks to Disease State Engineering.International journal of molecular sciences · 2026Review
- Telomere-driven replicative crisis is driven by large-scale changes in genomic architecture.Genome research · 2026Article
- Circulating tumor DNA in gastrointestinal cancers: promise, pitfalls, and the path forward.EClinicalMedicine · 2026Review
- Applications of Artificial Intelligence in Cancer Diagnosis and Treatment.Cancer medicine · 2026Review
- Exploring per-base quality scores as a surrogate marker of cell-free DNA fragmentome.Briefings in bioinformatics · 2026Article
- Advancing AI for multi-omics and clinical data integration in basic and translational cancer research.Nature reviews. Cancer · 2026Review
- Liquid Biopsy in Primary CNS Tumors: Bridging Biology, Technology, and Clinical Care.Molecular diagnosis & therapy · 2026Review
- Frequency-Domain Transformation of cfDNA End-Motif Profiles Enhances Robust Cancer Detection.Genes · 2026Article
- Harmonizing definitions in liquid biopsy: A terminology framework by the international society of liquid biopsy.The journal of liquid biopsy · 2026Review
- Rethinking ovarian cancer III: the past decade and future directions.Nature reviews. Cancer · 2026Review
- Prognostic significance and pathological correlation analysis of DKK4 in colorectal cancer.Translational cancer research · 2026Article
- Early detection of gastric cancer: a novel circulating microbiome DNA based liquid biopsy assay.NPJ precision oncology · 2026Article
- An improved analytic workflow for serum mtDNA DAMP abundance, fragmentation and heteroplasmic variants: a retrospective analysis of acute respiratory failure patients.Scientific reports · 2026Article
- Ligation-assisted target recycling for DNA nanoswitch biosensors.bioRxiv : the preprint server for biology · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Genomic analyses of cell-free DNA (cfDNA) in plasma are enabling noninvasive blood-based biomarker approaches to cancer detection and disease monitoring. Current approaches for identification of circulating tumour DNA typically use targeted tumour-specific mutations or methylation analyses. An emerging approach is based on the recognition of altered genome-wide cfDNA fragmentation in patients with cancer. Recent studies have revealed a multitude of characteristics that can affect the compendium of cfDNA fragments across the genome, collectively called the 'cfDNA fragmentome'. These changes result from genomic, epigenomic, transcriptomic and chromatin states of an individual and affect the size, position, coverage, mutation, structural and methylation characteristics of cfDNA. Identifying and monitoring these changes has the potential to improve early detection of cancer, especially using highly sensitive multi-feature machine learning approaches that would be amenable to broad use in populations at increased risk. This Review highlights the rapidly evolving field of genome-wide analyses of cfDNA characteristics, their comparison to existing cfDNA methods, and recent related innovations at the intersection of large-scale sequencing and artificial intelligence. As the breadth of clinical applications of cfDNA fragmentome methods have enormous public health implications for cancer screening and personalized approaches for clinical management of patients with cancer, we outline the challenges and opportunities ahead.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.