CancerDetector: ultrasensitive and non-invasive cancer detection at the resolution of individual reads using cell-free DNA methylation sequencing data

Wenyuan Li; Qingjiao Li; Shuli Kang; Mary Same; Yonggang Zhou; Carol Sun; Chun-Chi Liu; Lea Matsuoka; Linda Sher; Wing Hung Wong; Frank Alber; Xianghong Jasmine Zhou

doi:10.1093/nar/gky423

CancerDetector: ultrasensitive and non-invasive cancer detection at the resolution of individual reads using cell-free DNA methylation sequencing data

Nucleic Acids Res. 2018 Sep 6;46(15):e89. doi: 10.1093/nar/gky423.

Authors

Wenyuan Li¹, Qingjiao Li¹, Shuli Kang², Mary Same¹, Yonggang Zhou¹, Carol Sun³, Chun-Chi Liu⁴, Lea Matsuoka⁵, Linda Sher⁶, Wing Hung Wong^{7

8}, Frank Alber², Xianghong Jasmine Zhou^{1

9}

Affiliations

¹ Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, CA 90095, USA.
² Molecular and Computational Biology Program, Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089, USA.
³ Oak Park High School, Oak Park, CA 91377, USA.
⁴ Institute of Genomics and Bioinformatics, National Chung Hsing University, Taiwan 40227, Republic of China.
⁵ Division of Hepatobiliary Surgery & Liver Transplantation, Department of Surgery, Vanderbilt University Medical Center, Nashville, TN 37232, USA.
⁶ Department of Surgery, University of Southern California, Keck School of Medicine, Los Angeles, Los Angeles, CA 90033, USA.
⁷ Department of Statistics, Stanford University, Stanford, CA 94305, USA.
⁸ Department of Health Research & Policy, Stanford University, Stanford, CA 94305, USA.
⁹ Institute for Quantitative and Computational Biosciences, University of California at Los Angeles, Los Angeles, CA 90095, USA.

Abstract

The detection of tumor-derived cell-free DNA in plasma is one of the most promising directions in cancer diagnosis. The major challenge in such an approach is how to identify the tiny amount of tumor DNAs out of total cell-free DNAs in blood. Here we propose an ultrasensitive cancer detection method, termed 'CancerDetector', using the DNA methylation profiles of cell-free DNAs. The key of our method is to probabilistically model the joint methylation states of multiple adjacent CpG sites on an individual sequencing read, in order to exploit the pervasive nature of DNA methylation for signal amplification. Therefore, CancerDetector can sensitively identify a trace amount of tumor cfDNAs in plasma, at the level of individual reads. We evaluated CancerDetector on the simulated data, and showed a high concordance of the predicted and true tumor fraction. Testing CancerDetector on real plasma data demonstrated its high sensitivity and specificity in detecting tumor cfDNAs. In addition, the predicted tumor fraction showed great consistency with tumor size and survival outcome. Note that all of those testing were performed on sequencing data at low to medium coverage (1× to 10×). Therefore, CancerDetector holds the great potential to detect cancer early and cost-effectively.

Publication types

Research Support, N.I.H., Extramural
Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

Algorithms*
Cell-Free Nucleic Acids / chemistry
Cell-Free Nucleic Acids / genetics*
Computational Biology / methods*
CpG Islands / genetics
DNA Methylation*
DNA, Neoplasm / chemistry
DNA, Neoplasm / genetics
High-Throughput Nucleotide Sequencing / methods
Humans
Neoplasms / blood
Neoplasms / diagnosis*
Neoplasms / genetics
ROC Curve
Reproducibility of Results

Substances

Cell-Free Nucleic Acids
DNA, Neoplasm

Grants and funding

U01 HL108634/HL/NHLBI NIH HHS/United States