ArticleClinical cancer research : an official journal of the American Association for Cancer Research2022
cfTrack: A Method of Exome-Wide Mutation Analysis of Cell-free DNA to Simultaneously Monitor the Full Spectrum of Cancer Treatment Outcomes Including MRD, Recurrence, and Evolution.
Article in Clinical cancer research : an official journal of the American Association for Cancer Research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Correction: cfTrack: A Method of Exome-Wide Mutation Analysis of Cell-free DNA to Simultaneously Monitor the Full Spectrum of Cancer Treatment Outcomes Including MRD, Recurrence, and Evolution.Clinical cancer research : an official journal of the American Association for Cancer Research · 2026Article
- Reducing demographic bias in biomedical machine learning for cancer detection using cfDNA methylation.Genome biology · 2026Article
- Predicting high confidence ctDNA somatic variants with ensemble machine learning models.Scientific reports · 2025Article
- Ultra-sensitive molecular residual disease detection through whole genome sequencing with single-read error correction.EMBO molecular medicine · 2024Article
- Noninvasive Lung Cancer Subtype Classification Using Tumor-Derived Signatures and cfDNA Methylome.Cancer research communications · 2024Article
- Comprehensive tissue deconvolution of cell-free DNA by deep learning for disease diagnosis and monitoring.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- cfSNV: a software tool for the sensitive detection of somatic mutations from cell-free DNA.Nature protocols · 2023Review
Corrections and comments
- Erratum issued
Authors and funding
13 authors.
Funding
Abstract
purposeCell-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
designThree 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.
resultsWe 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.
conclusionsOur results demonstrated that cfTrack can sensitively and specifically monitor the full spectrum of cancer treatment outcomes using exome-wide mutation analysis of cfDNA.
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