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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 and Xianghong Jasmine Zhou # |
| # Corresponding author |
| * Joint first authors |
| 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 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 patterns 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 burden. Testing CancerDetector on real plasma data demonstrated its high sensitivity and specificity in detecting tumor DNAs. In addition, the predicted tumor burden 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 (1X to 10X). Therefore, CancerDetector holds the great potential to detect cancer early and cost-effectively.
| Code |
This software is only for academia users. Users are prohibited from transferring this software to others. For commercial users, please contact Prof. Jasmine Zhou (xjzhou@mednet.ucla.edu)
CancerDetector License and Download
