ArticleNature communications2025
Cross-dataset pan-cancer detection by correlating cell-free DNA fragment coverage with open chromatin sites across cell types.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
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Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Blood-Based Circulating Tumor DNA Assays for Early Colorectal Cancer Detection: A Systematic Review of Performance in Asymptomatic Screening Populations.International journal of cancer · 2026Pooled it
- Cell-free DNA genomic and fragmentomic features for early outcome prediction in large B cell lymphoma.Cell reports. Medicine · 2026Article
- Immune response to DNA and RNA: structural insights, molecular mechanisms, and therapeutic targeting.Molecular biomedicine · 2026Review
- A scalable deep-learning framework for cancer detection using cell-free DNA shallow whole-genome sequencing.Science advances · 2026Article
- Review
- cfDECOR: a novel approach for estimating cell type contributions to cfDNA based on chromatin accessibility patterns.Scientific reports · 2026Article
- Cross-dataset pan-cancer detection by correlating cell-free DNA fragment coverage with open chromatin sites across cell types.Nature communications · 2025Article
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Authors and funding
25 authors.
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Abstract
The fragmentation patterns of whole genome sequenced cell-free DNA are promising features for tumor-agnostic cancer detection. However, systematic biases challenge their cross-cohort generalization. We introduce LIONHEART, an open source cancer detection method specifically optimized to generalize across datasets. The method correlates bias-corrected cfDNA fragment coverage across the genome with the locations of accessible chromatin regions from 898 cell and tissue type features. We use these correlations to detect changes in the cell-free DNA cell type composition caused by cancer. We test LIONHEART on nine datasets and fourteen cancer types (1106 non-cancer controls, 1449 cancers) obtained from different studies and show that it can distinguish cancer samples from non-cancer controls across cohorts with ROC AUC scores ranging from 0.62-0.95 (mean = 0.83, std = 0.12). We further validate the method on an external dataset, achieving a ROC AUC of 0.917.
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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.