Inferring tissue origin from cell-free DNA in metastatic cancer.
4 affiliations
- 1School of Clinical Medicine, Shandong Second Medical University, Weifang, China.
- 2The First Affiliated Hospital of Shantou University Medical College, Shantou, Guangdong, China.
- 3Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China. [email protected].
- 4Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China. [email protected].
Abstract
A major clinical challenge in metastatic cancer management is determining not only the presence of tumor-derived cell-free DNA (cfDNA) but also the tissue lineage and organ context reflected by circulating cfDNA signals. CfDNA supports such inference by retaining molecular signatures specific to its tissue-of-origin, including DNA methylation, 5-hydroxymethylcytosine (5hmC), fragmentomic profiles, nucleosome positioning patterns, and histone modifications. This review examines the biological principles underlying cfDNA-based tissue-of-origin determination, the computational frameworks used to interpret these signals, and the key barriers to clinical translation. We additionally compare representative deconvolution and classification strategies, including linear and probabilistic models, machine/deep learning, and single-cell-informed reference approaches. Despite substantial progress, clinical integration remains hindered by low tumor fractions, heterogeneous contributions from multiple tissues, background noise from nonmalignant cell death, and a lack of robust prospective validation. We further emphasize that source estimates in metastatic plasma should be interpreted in the context of mixed tissue contributions, differential shedding, reference design, and model uncertainty, rather than as direct measures of anatomical metastatic burden.
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BibTeX
@article{shu2026inferring,
title = {{Inferring tissue origin from cell-free DNA in metastatic cancer.}},
author = {Anlian Shu and Yikun Li and Qixin Sun and Jinming Yu and Feifei Teng},
journal = {Cancer metastasis reviews},
year = {2026},
month = oct,
doi = {10.1007/s10555-026-10386-3},
url = {https://europepmc.org/article/MED/42905206},
note = {CC BY-NC-ND},
}
RIS
TY - JOUR
TI - Inferring tissue origin from cell-free DNA in metastatic cancer.
AU - Anlian Shu
AU - Yikun Li
AU - Qixin Sun
AU - Jinming Yu
AU - Feifei Teng
PY - 2026
DA - 2026/10/21
JO - Cancer metastasis reviews
DO - 10.1007/s10555-026-10386-3
UR - https://europepmc.org/article/MED/42905206
AB - A major clinical challenge in metastatic cancer management is determining not only the presence of tumor-derived cell-free DNA (cfDNA) but also the tissue lineage and organ context reflected by circulating cfDNA signals. CfDNA supports such inference by retaining molecular signatures specific to its tissue-of-origin, including DNA methylation, 5-hydroxymethylcytosine (5hmC), fragmentomic profiles, nucleosome positioning patterns, and histone modifications. This review examines the biological principles underlying cfDNA-based tissue-of-origin determination, the computational frameworks used to interpret these signals, and the key barriers to clinical translation. We additionally compare representative deconvolution and classification strategies, including linear and probabilistic models, machine/deep learning, and single-cell-informed reference approaches. Despite substantial progress, clinical integration remains hindered by low tumor fractions, heterogeneous contributions from multiple tissues, background noise from nonmalignant cell death, and a lack of robust prospective validation. We further emphasize that source estimates in metastatic plasma should be interpreted in the context of mixed tissue contributions, differential shedding, reference design, and model uncertainty, rather than as direct measures of anatomical metastatic burden.
SN - 0167-7659
SN - 1573-7233
ER -