Nature Communications (Sep 2022)
Cost-effective methylome sequencing of cell-free DNA for accurately detecting and locating cancer
- Mary L. Stackpole,
- Weihua Zeng,
- Shuo Li,
- Chun-Chi Liu,
- Yonggang Zhou,
- Shanshan He,
- Angela Yeh,
- Ziye Wang,
- Fengzhu Sun,
- Qingjiao Li,
- Zuyang Yuan,
- Asli Yildirim,
- Pin-Jung Chen,
- Paul Winograd,
- Benjamin Tran,
- Yi-Te Lee,
- Paul Shize Li,
- Zorawar Noor,
- Megumi Yokomizo,
- Preeti Ahuja,
- Yazhen Zhu,
- Hsian-Rong Tseng,
- James S. Tomlinson,
- Edward Garon,
- Samuel French,
- Clara E. Magyar,
- Sarah Dry,
- Clara Lajonchere,
- Daniel Geschwind,
- Gina Choi,
- Sammy Saab,
- Frank Alber,
- Wing Hung Wong,
- Steven M. Dubinett,
- Denise R. Aberle,
- Vatche Agopian,
- Steven-Huy B. Han,
- Xiaohui Ni,
- Wenyuan Li,
- Xianghong Jasmine Zhou
Affiliations
- Mary L. Stackpole
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California at Los Angeles
- Weihua Zeng
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California at Los Angeles
- Shuo Li
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California at Los Angeles
- Chun-Chi Liu
- EarlyDiagnostics, Inc.
- Yonggang Zhou
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California at Los Angeles
- Shanshan He
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California at Los Angeles
- Angela Yeh
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California at Los Angeles
- Ziye Wang
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California at Los Angeles
- Fengzhu Sun
- Department of Quantitative and Computational Biology, University of Southern California
- Qingjiao Li
- The Eighth Affiliated Hospital, Sun Yat-Sen University
- Zuyang Yuan
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California at Los Angeles
- Asli Yildirim
- Department of Microbiology, Immunology and Molecular Genetics, University of California at Los Angeles
- Pin-Jung Chen
- Department of Surgery, University of California at Los Angeles
- Paul Winograd
- Department of Surgery, University of California at Los Angeles
- Benjamin Tran
- Department of Surgery, University of California at Los Angeles
- Yi-Te Lee
- Department of Molecular and Medical Pharmacology, David Geffen School of Medicine, University of California at Los Angeles
- Paul Shize Li
- Westlake High School
- Zorawar Noor
- Department of Medicine, David Geffen School of Medicine, University of California at Los Angeles
- Megumi Yokomizo
- Department of Radiological Sciences, David Geffen School of Medicine, University of California at Los Angeles
- Preeti Ahuja
- Department of Radiological Sciences, David Geffen School of Medicine, University of California at Los Angeles
- Yazhen Zhu
- Department of Molecular and Medical Pharmacology, David Geffen School of Medicine, University of California at Los Angeles
- Hsian-Rong Tseng
- Department of Molecular and Medical Pharmacology, David Geffen School of Medicine, University of California at Los Angeles
- James S. Tomlinson
- Department of Surgery, University of California at Los Angeles
- Edward Garon
- Department of Medicine, David Geffen School of Medicine, University of California at Los Angeles
- Samuel French
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California at Los Angeles
- Clara E. Magyar
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California at Los Angeles
- Sarah Dry
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California at Los Angeles
- Clara Lajonchere
- Jonsson Comprehensive Cancer Center, University of California at Los Angeles
- Daniel Geschwind
- Institute for Precision Health, University of California at Los Angeles
- Gina Choi
- Department of Medicine, David Geffen School of Medicine, University of California at Los Angeles
- Sammy Saab
- Department of Medicine, David Geffen School of Medicine, University of California at Los Angeles
- Frank Alber
- Department of Microbiology, Immunology and Molecular Genetics, University of California at Los Angeles
- Wing Hung Wong
- Department of Statistics, Stanford University
- Steven M. Dubinett
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California at Los Angeles
- Denise R. Aberle
- Department of Radiological Sciences, David Geffen School of Medicine, University of California at Los Angeles
- Vatche Agopian
- Department of Surgery, University of California at Los Angeles
- Steven-Huy B. Han
- Department of Medicine, David Geffen School of Medicine, University of California at Los Angeles
- Xiaohui Ni
- EarlyDiagnostics, Inc.
- Wenyuan Li
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California at Los Angeles
- Xianghong Jasmine Zhou
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California at Los Angeles
- DOI
- https://doi.org/10.1038/s41467-022-32995-6
- Journal volume & issue
-
Vol. 13,
no. 1
pp. 1 – 12
Abstract
Abstract Early cancer detection by cell-free DNA faces multiple challenges: low fraction of tumor cell-free DNA, molecular heterogeneity of cancer, and sample sizes that are not sufficient to reflect diverse patient populations. Here, we develop a cancer detection approach to address these challenges. It consists of an assay, cfMethyl-Seq, for cost-effective sequencing of the cell-free DNA methylome (with > 12-fold enrichment over whole genome bisulfite sequencing in CpG islands), and a computational method to extract methylation information and diagnose patients. Applying our approach to 408 colon, liver, lung, and stomach cancer patients and controls, at 97.9% specificity we achieve 80.7% and 74.5% sensitivity in detecting all-stage and early-stage cancer, and 89.1% and 85.0% accuracy for locating tissue-of-origin of all-stage and early-stage cancer, respectively. Our approach cost-effectively retains methylome profiles of cancer abnormalities, allowing us to learn new features and expand to other cancer types as training cohorts grow.
