Integrative modeling of tumor genomes and epigenomes for enhanced cancer diagnosis by cell-free DNA

Mingyun Bae, Gyuhee Kim, Tae-Rim Lee, Jin Mo Ahn, Hyunwook Park, Sook Ryun Park, Ki Byung Song, Eunsung Jun, Dongryul Oh, Jeong-Won Lee, Young Sik Park, Ki-Won Song, Jeong-Sik Byeon, Bo Hyun Kim, Joo Hyuk Sohn, Min Hwan Kim, Gun Min Kim, Eui Kyu Chie, Hyun-Cheol Kang, Sun-Young Kong, Sang Myung Woo, Jeong Eon Lee, Jai Min Ryu, Junnam Lee, Dasom Kim, Chang-Seok Ki, Eun-Hae Cho () and Jung Kyoon Choi ()
Additional contact information
Mingyun Bae: Department of Bio and Brain Engineering, KAIST
Gyuhee Kim: Department of Bio and Brain Engineering, KAIST
Tae-Rim Lee: Genome Research Center, GC Genome
Jin Mo Ahn: Genome Research Center, GC Genome
Hyunwook Park: Department of Bio and Brain Engineering, KAIST
Sook Ryun Park: University of Ulsan College of Medicine
Ki Byung Song: Asan Medical Center, University of Ulsan College of Medicine
Eunsung Jun: Asan Medical Center, University of Ulsan College of Medicine
Dongryul Oh: Sungkyunkwan University School of Medicine
Jeong-Won Lee: Sungkyunkwan University School of Medicine
Young Sik Park: Seoul National University Hospital
Ki-Won Song: University of Ulsan College of Medicine
Jeong-Sik Byeon: University of Ulsan College of Medicine
Bo Hyun Kim: National Cancer Center
Joo Hyuk Sohn: Yonsei University College of Medicine
Min Hwan Kim: Yonsei University College of Medicine
Gun Min Kim: Yonsei University College of Medicine
Eui Kyu Chie: Seoul National University College of Medicine
Hyun-Cheol Kang: Seoul National University College of Medicine
Sun-Young Kong: National Cancer Center
Sang Myung Woo: National Cancer Center
Jeong Eon Lee: Samsung Medical Center
Jai Min Ryu: Samsung Medical Center
Junnam Lee: Genome Research Center, GC Genome
Dasom Kim: Genome Research Center, GC Genome
Chang-Seok Ki: Genome Research Center, GC Genome
Eun-Hae Cho: Genome Research Center, GC Genome
Jung Kyoon Choi: Department of Bio and Brain Engineering, KAIST

Nature Communications, 2023, vol. 14, issue 1, 1-15

Abstract: Abstract Multi-cancer early detection remains a key challenge in cell-free DNA (cfDNA)-based liquid biopsy. Here, we perform cfDNA whole-genome sequencing to generate two test datasets covering 2125 patient samples of 9 cancer types and 1241 normal control samples, and also a reference dataset for background variant filtering based on 20,529 low-depth healthy samples. An external cfDNA dataset consisting of 208 cancer and 214 normal control samples is used for additional evaluation. Accuracy for cancer detection and tissue-of-origin localization is achieved using our algorithm, which incorporates cancer type-specific profiles of mutation distribution and chromatin organization in tumor tissues as model references. Our integrative model detects early-stage cancers, including those of pancreatic origin, with high sensitivity that is comparable to that of late-stage detection. Model interpretation reveals the contribution of cancer type-specific genomic and epigenomic features. Our methodologies may lay the groundwork for accurate cfDNA-based cancer diagnosis, especially at early stages.

Date: 2023
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DOI: 10.1038/s41467-023-37768-3

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