Methylome sequencing of cell-free DNA and RRBS of solid tissue

Early cancer detection by cell-free DNA (cfDNA) faces multiple challenges: low fraction of tumor cfDNA, molecular heterogeneity of cancer, and sample sizes not sufficient to reflect diverse patient population. We develop a cancer detection approach to address these challenges. It consists of an assay, cfMethyl-Seq, for cost-effective sequencing of cfDNA 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 system 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, thereby permitting the classification models to learn new features as training cohorts grow, and expanding their scope to other cancer types.

Type: Other
Archiver: European Genome-Phenome Archive (EGA)

2 Datasets 3 Publications

Click on a Dataset ID in the table below to learn more, and to find out who to contact about access to these data

Dataset ID	Description	Technology	Samples
EGAD00001009001	RRBS data for solid tumors and adjacent normal tissues	HiSeq X Ten	328
EGAD00001009003	cfMethyl-Seq libraries were generated for 479 cfDNA samples and were sequenced with 150 bp paired-end reads.	HiSeq X Ten	479

Publications	Citations
Cost-effective methylome sequencing of cell-free DNA for accurately detecting and locating cancer. Stackpole ML, Zeng W, Li S, Liu CC, Zhou Y, He S, Yeh A, Wang Z, Sun F, Li Q, Yuan Z, Yildirim A, Chen PJ, Winograd P, Tran B, Lee YT, Li PS, Noor Z, Yokomizo M, Ahuja P, Zhu Y, Tseng HR, Tomlinson JS, Garon E, French S, Magyar CE, Dry S, Lajonchere C, Geschwind D, Choi G, Saab S, Alber F, Wong WH, Dubinett SM, Aberle DR, Agopian V, Han SB, Ni X, Li W, Zhou XJ. Nat Commun 13: 2022 5566	27
Comprehensive tissue deconvolution of cell-free DNA by deep learning for disease diagnosis and monitoring. Li S, Zeng W, Ni X, Liu Q, Li W, Stackpole ML, Zhou Y, Gower A, Krysan K, Ahuja P, Lu DS, Raman SS, Hsu W, Aberle DR, Magyar CE, French SW, Han SB, Garon EB, Agopian VG, Wong WH, Dubinett SM, Zhou XJ. Proc Natl Acad Sci U S A 120: 2023 e2305236120	9
Noninvasive Lung Cancer Subtype Classification Using Tumor-Derived Signatures and cfDNA Methylome. Li S, Li W, Liu B, Krysan K, Dubinett SM. Cancer Res Commun 4: 2024 1738-1747	0