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Background <p>Extrachromosomal circular DNA has emerged as a pivotal factor in tumor biology, contributing to intratumor heterogeneity, oncogene amplification, and tumor evolution. Despite its relevance, the presence and molecular characteristics of plasma-derived cell-free extrachromosomal circular DNA (eccDNA) in cancer patients remain insufficiently explored.</p> Methods <p>In this study, we profiled plasma-derived cell-free eccDNA from a multi-cancer cohort consisting of 413 cancer patients and 239 healthy individuals. We analyzed eccDNA fragment size distributions using a patient-derived xenograft (PDX) mouse model and identified fragment size features to distinguish tumor-derived eccDNA from non-tumor-derived eccDNA. We further performed in silico fragment size selection and developed a gene-based annotation method to characterize the gene content carried by cell-free eccDNA across different cancer types. By utilizing these cell-free eccDNA signatures, we developed ScanTecc (screening cancer types with cell-free eccDNA), a machine learning-based approach for cancer detection and tissue-of-origin classification. The classification performance of ScanTecc was further evaluated at the individual sample level using multiple machine learning classifiers, including adaptive boosting and logistic regression.</p> Results <p>We observed a significantly higher abundance and longer fragment lengths of eccDNA in cancer patients’ plasma. Analysis of the PDX mouse model revealed a distinct fragment size threshold of approximately 1,000 base pairs that effectively differentiates tumor-derived eccDNA from non-tumor-derived counterparts. Following fragment size selection, gene-based annotation of large-sized eccDNA revealed cancer type–specific enrichment of tumor-associated genes. Leveraging these features, ScanTecc achieved an overall AUC of 0.92 for distinguishing cancer patients from healthy individuals, with consistently high performance across disease stages, including stage I (AUC = 0.92) and stage IV (AUC = 0.93). ScanTecc also enabled accurate tissue-of-origin classification and achieved an overall AUC of 0.79 in identifying specific cancer types, with AUC values ranging from 0.70 for gastric cancer to 0.81 for ovarian cancer.</p> Conclusions <p>Our study establishes a multi-cancer plasma cell-free eccDNA landscape and introduces a non-invasive cancer screening framework based on cell-free eccDNA features, highlighting the potential of plasma cell-free eccDNA for early cancer detection and tumor classification.</p>

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Detection of primary cancer types via fragment size selection in circulating cell-free extrachromosomal circular DNA

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月6日

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  • Jingwen Fang,
  • Songwen Luo,
  • Shouzhen Li,
  • Yehong Xu,
  • Jing Wang,
  • Benjie Shan,
  • Mingjun Hu,
  • Qiaoni Yu,
  • Wen Zhang,
  • Ke Liu,
  • Yunying Shao,
  • Jiaxuan Yang,
  • YouYang Zhou,
  • Guangtao Xu,
  • Xinfeng Yao,
  • Ruoming Sun,
  • Mengyuan Zhang,
  • Kun Li,
  • Xihai Xu,
  • Yongliang Zhang,
  • Zhihong Zhang,
  • Xinghua Han,
  • Yueyin Pan,
  • Chuang Guo,
  • Kun Qu

摘要

Background

Extrachromosomal circular DNA has emerged as a pivotal factor in tumor biology, contributing to intratumor heterogeneity, oncogene amplification, and tumor evolution. Despite its relevance, the presence and molecular characteristics of plasma-derived cell-free extrachromosomal circular DNA (eccDNA) in cancer patients remain insufficiently explored.

Methods

In this study, we profiled plasma-derived cell-free eccDNA from a multi-cancer cohort consisting of 413 cancer patients and 239 healthy individuals. We analyzed eccDNA fragment size distributions using a patient-derived xenograft (PDX) mouse model and identified fragment size features to distinguish tumor-derived eccDNA from non-tumor-derived eccDNA. We further performed in silico fragment size selection and developed a gene-based annotation method to characterize the gene content carried by cell-free eccDNA across different cancer types. By utilizing these cell-free eccDNA signatures, we developed ScanTecc (screening cancer types with cell-free eccDNA), a machine learning-based approach for cancer detection and tissue-of-origin classification. The classification performance of ScanTecc was further evaluated at the individual sample level using multiple machine learning classifiers, including adaptive boosting and logistic regression.

Results

We observed a significantly higher abundance and longer fragment lengths of eccDNA in cancer patients’ plasma. Analysis of the PDX mouse model revealed a distinct fragment size threshold of approximately 1,000 base pairs that effectively differentiates tumor-derived eccDNA from non-tumor-derived counterparts. Following fragment size selection, gene-based annotation of large-sized eccDNA revealed cancer type–specific enrichment of tumor-associated genes. Leveraging these features, ScanTecc achieved an overall AUC of 0.92 for distinguishing cancer patients from healthy individuals, with consistently high performance across disease stages, including stage I (AUC = 0.92) and stage IV (AUC = 0.93). ScanTecc also enabled accurate tissue-of-origin classification and achieved an overall AUC of 0.79 in identifying specific cancer types, with AUC values ranging from 0.70 for gastric cancer to 0.81 for ovarian cancer.

Conclusions

Our study establishes a multi-cancer plasma cell-free eccDNA landscape and introduces a non-invasive cancer screening framework based on cell-free eccDNA features, highlighting the potential of plasma cell-free eccDNA for early cancer detection and tumor classification.