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.

Plasma cell-free DNA (cfDNA) is a noninvasive biomarker for cell death of all organs. Deciphering the tissue origin of cfDNA can reveal abnormal cell death because of diseases, which has great clinical potential in disease detection and monitoring. Despite the great promise, the sensitive and accurate quantification of tissue-derived cfDNA remains challenging to existing methods due to the limited characterization of tissue methylation and the reliance on unsupervised methods. To fully exploit the clinical potential of tissue-derived cfDNA, here we present one of the largest comprehensive and high-resolution methylation atlas based on 521 noncancer tissue samples spanning 29 major types of human tissues. We systematically identified fragment-level tissue-specific methylation patterns and extensively validated them in orthogonal datasets. Based on the rich tissue methylation atlas, we develop the first supervised tissue deconvolution approach, a deep-learning-powered model, cfSort , for sensitive and accurate tissue deconvolution in cfDNA. On the benchmarking data, cfSort showed superior sensitivity and accuracy compared to the existing methods. We further demonstrated the clinical utilities of cfSort with two potential applications: aiding disease diagnosis and monitoring treatment side effects. The tissue-derived cfDNA fraction estimated from cfSort reflected the clinical outcomes of the patients. In summary, the tissue methylation atlas and cfSort enhanced the performance of tissue deconvolution in cfDNA, thus facilitating cfDNA-based disease detection and longitudinal treatment monitoring.

No abstract

Purpose: Cell-free DNA (cfDNA) offers a noninvasive approach to monitor cancer. Here we develop a method using whole-exome sequencing (WES) of cfDNA for simultaneously monitoring the full spectrum of cancer treatment outcomes, including minimal residual disease (MRD), recurrence, evolution, and second primary cancers. Experimental Design: Three simulation datasets were generated from 26 patients with cancer to benchmark the detection performance of MRD/recurrence and second primary cancers. For further validation, cfDNA samples (n = 76) from patients with cancer (n = 35) with six different cancer types were used for performance validation during various treatments. Results: We present a cfDNA-based cancer monitoring method, named cfTrack. Taking advantage of the broad genome coverage of WES data, cfTrack can sensitively detect MRD and cancer recurrence by integrating signals across known clonal tumor mutations of a patient. In addition, cfTrack detects tumor evolution and second primary cancers by de novo identifying emerging tumor mutations. A series of machine learning and statistical denoising techniques are applied to enhance the detection power. On the simulation data, cfTrack achieved an average AUC of 99% on the validation dataset and 100% on the independent dataset in detecting recurrence in samples with tumor fractions ≥0.05%. In addition, cfTrack yielded an average AUC of 88% in detecting second primary cancers in samples with tumor fractions ≥0.2%. On real data, cfTrack accurately monitors tumor evolution during treatment, which cannot be accomplished by previous methods. Conclusions: Our results demonstrated that cfTrack can sensitively and specifically monitor the full spectrum of cancer treatment outcomes using exome-wide mutation analysis of cfDNA.