For a study, researchers sought to present a method for monitoring the full spectrum of cancer treatment outcomes, including minimal residual disease (MRD), recurrence, evolution, and second primary cancers, using whole-exome sequencing (WES) of cfDNA. To compare the detection performance of MRD/recurrence and second primary cancers, 3 simulation datasets were created from 26 cancer patients. cfDNA samples (n=76) from cancer patients (n=35) with 6 different cancer types were used for performance validation during various treatments. In addition, investigators presented cfTrack, a cancer monitoring method based on cfDNA. Using WES data’s broad genome coverage, cfTrack can detect MRD and cancer recurrence by integrating signals from a patient’s known clonal tumor mutations.
Furthermore, cfTrack detects tumor evolution and second primary cancers by identifying emerging tumor mutations for the first time. Several machine learning and statistical denoising techniques were used to improve detection power. In detecting recurrence in samples with tumor fractions of 0.05%, cfTrack achieved an average AUC of 99% on the validation dataset and 100% on the independent dataset using simulation data. Furthermore, cfTrack had an AUC of 88% in detecting second primary cancers in samples with tumor fractions of 0.2%. Using real-world data, cfTrack accurately monitors tumor evolution during treatment, which previous methods could not do. Using exome-wide mutation analysis of cfDNA, the study group demonstrated that cfTrack can sensitively and specifically monitor the full spectrum of cancer treatment outcomes.