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<p>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.</p>

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年11月21日

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

摘要

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.