Enhanced multicancer screening assay through whole-genome methylation sequencing-based multimodal cell-free DNA analysis

The rapid and accurate detection of multiple cancers presents considerable challenges, especially for stage I disease, due to the low concentration and heterogeneous nature of circulating tumor DNA. Here we introduce an enhanced multicancer screening assay that integrates whole-genome methylation sequencing with an innovative multimodal analytical framework for cell-free DNA. The ensemble machine learning model integrates four specific cell-free DNA characteristics: average methylation fraction, copy number variation, fragment size ratio and fragment size distribution. The model underwent testing on 1415 samples, encompassing eight primary cancer types and healthy controls. The sensitivity was 93.2%, and the specificity was 95%. The test demonstrated effectiveness in detecting cancers at early stages. The sensitivity was 92.3% for stage I and 92.2% for stage II. The multimodal technique successfully combined average methylation fraction's sensitivity to early epigenetic signals with fragmentomic characteristics. This facilitated the differentiation between healthy individuals and those with early stage cancer. The model achieved an accuracy rate of 85.7% in the top 2 category for correctly identifying the tissue of origin. The results confirm that whole-genome methylation sequencing-based multimodal analysis can improve multicancer early detection technology and revolutionize cancer screening methods.

Authors

Institutions

Publication Details

Journal
Experimental & Molecular Medicine
Published
2026-04-21
DOI
https://doi.org/10.1038/s12276-026-01674-7
Primary Topic
Cancer Genomics and Diagnostics
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Enhanced multicancer screening assay through whole-genome methylation sequencing-based multimodal cell-free DNA analysis

Wonshik Han, Kwang Hyun Kim, Young Sik Park, Yongjun Cha et al.
Experimental & Molecular Medicine
Cancer Genomics and Diagnostics
article

Enhanced multicancer screening assay through whole-genome methylation sequencing-based multimodal cell-free DNA analysis

Wonshik Han, Kwang Hyun Kim, Young Sik Park, Yongjun Cha, Seongmun Jeong, Yujin Jeon, Se‐Hoon Lee, Hyun Goo Woo, Hayoon Lee, Joo Kyung Park, Sanghoo Lee, Young‐Woo Kim, Duhee Bang, Dayoung Go, Sungho Kim, Yong-Jin Kim, Hwang-Phill Kim, Sang-Hyun Song, Tae-You Kim, Han-Byoel Lee
article en

Abstract

The rapid and accurate detection of multiple cancers presents considerable challenges, especially for stage I disease, due to the low concentration and heterogeneous nature of circulating tumor DNA. Here we introduce an enhanced multicancer screening assay that integrates whole-genome methylation sequencing with an innovative multimodal analytical framework for cell-free DNA. The ensemble machine learning model integrates four specific cell-free DNA characteristics: average methylation fraction, copy number variation, fragment size ratio and fragment size distribution. The model underwent testing on 1415 samples, encompassing eight primary cancer types and healthy controls. The sensitivity was 93.2%, and the specificity was 95%. The test demonstrated effectiveness in detecting cancers at early stages. The sensitivity was 92.3% for stage I and 92.2% for stage II. The multimodal technique successfully combined average methylation fraction's sensitivity to early epigenetic signals with fragmentomic characteristics. This facilitated the differentiation between healthy individuals and those with early stage cancer. The model achieved an accuracy rate of 85.7% in the top 2 category for correctly identifying the tissue of origin. The results confirm that whole-genome methylation sequencing-based multimodal analysis can improve multicancer early detection technology and revolutionize cancer screening methods.

Experimental & Molecular MedicineVol. 58(4)
Seoul National University of Science and Technology (KR), Ewha Womans University (KR), Seoul National University (KR), Yong In University (KR), Cancer Research Institute (US), Yonsei University (KR), SUNY Upstate Medical University (US), Samsung (South Korea) (KR), Samsung Medical Center (KR), Seoul National University Hospital (KR), National Cancer Center (KR), National University College (PR), Ajou University (KR), Sungkyunkwan University (KR)
National Research Foundation, Korea Health Industry Development Institute, National Research Foundation of Korea, Ministry of Science and ICT, South Korea, Advanced Research Projects Agency
Openalex Percentile: Top 20%
Cancer Genomics and Diagnostics
DOIOpen Access PDFOpenAlex
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.