Cross-dataset pan-cancer detection by correlating cell-free DNA fragment coverage with open chromatin sites across cell types

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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Publication Details

Journal
Nature Communications
Published
2025-11-21
DOI
https://doi.org/10.1038/s41467-025-66503-3
Primary Topic
Cancer Genomics and Diagnostics
Type
article
Field-Weighted Citation Impact
0.00

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article

Cross-dataset pan-cancer detection by correlating cell-free DNA fragment coverage with open chromatin sites across cell types

Christina Demuth, Søren Besenbacher, Tenna Vesterman Henriksen, Philippe Lamy et al.
Nature Communications
Cancer Genomics and Diagnostics
article

Cross-dataset pan-cancer detection by correlating cell-free DNA fragment coverage with open chromatin sites across cell types

Christina Demuth, Søren Besenbacher, Tenna Vesterman Henriksen, Philippe Lamy, Marijana Nesic, Mads H. Rasmussen, Iver Nordentoft, Kirsten Bouchelouche, Claus L. Andersen, Maria Rusan, Sia V. Lindskrog, Anders J. Skanderup, Ludvig Renbo Olsen, Lars Dyrskjøt, Britt Elmedal Laursen, Denis Odinokov, Amanda Frydendahl, Michael Borre, Mads Ryø Jochumsen, Christina Therkildsen, Karina Dalsgaard Sørensen, Laura Iisager, Simon Buus, Jakob Qvortrup Holsting, Karoline Kondrup
article en

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.

Nature CommunicationsVol. 16(1)
Agency for Science, Technology and Research (SG), Aarhus University (DK), Hvidovre Hospital (DK), Aarhus University Hospital (DK), Genome Institute of Singapore (SG)
Aarhus Universitet, Aarhus Universitets Forskningsfond, Novo Nordisk, Novo Nordisk Fonden
Openalex Percentile: Top 57%
Cancer Genomics and Diagnostics
DOIOpen Access PDFOpenAlex
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