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cfTrack: Exome-wide mutation analysis of cell-free DNA to simultaneously monitor the full spectrum of cancer treatment outcomes: MRD, recurrence, and evolution Shuo Li, Weihua Zeng, Xiaohui Ni, Yonggang Zhou, Mary Stackpole, Zorawar Noor, Zuyang Yuan, Edward Garon, Steven Dubinett, Wenyuan Li, and Xianghong Zhou |
| Abstract |
Purpose: Plasma cell-free DNA (cfDNA) offers a noninvasively approach to monitor cancer. Here we develop a method using whole-exome sequencing (WES) of cfDNA for simultaneously monitoring of the full spectrum of cancer treatment outcomes, including MRD, recurrence, evolution, and second primary cancer.
Experimental Design: The simulation data of cancer patients with recurrence (n=968), second primary cancers (n=72), or complete remission (n=150) were used for evaluating the performance. As a further validation, plasma samples (n=24) from eight non-small-cell lung cancer (NSCLC) patients were used for validating the performance on monitoring the outcomes of immunotherapy 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 detects MRD and cancer recurrence by integrating signals across the 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 detects recurrence in samples with tumor fraction > 0.025% with > 95% sensitivity and 96% specificity, and second primary cancers in samples with tumor fraction > 0.1% with approximately 75% sensitivity and 94% specificity. In the NSCLC patients, cfTrack achieves accurate and comprehensive monitoring of tumor evolution during treatment, which cannot be accomplished by previous methods.
Conclusion: Our results demonstrated that cfTrack can sensitively and specifically monitor the full spectrum of cancer treatment outcomes using exome-wide mutation analysis.
| 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)
