| Title: |
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 EvolutioncfTrack: Comprehensive Cancer Monitoring Using cfDNA |
| Authors: |
Li, Shuo; Zeng, Weihua; Ni, Xiaohui; Zhou, Yonggang; Stackpole, Mary L; Noor, Zorawar S; Yuan, Zuyang; Neal, Adam; Memarzadeh, Sanaz; Garon, Edward B; Dubinett, Steven M; Li, Wenyuan; Zhou, Xianghong Jasmine |
| Source: |
Clinical Cancer Research, vol 28, iss 9 |
| Publisher Information: |
eScholarship, University of California |
| Publication Year: |
2022 |
| Collection: |
University of California: eScholarship |
| Subject Terms: |
32 Biomedical and Clinical Sciences (for-2020); 3211 Oncology and Carcinogenesis (for-2020); Health Disparities (rcdc); Human Genome (rcdc); Cancer Genomics (rcdc); Cancer (rcdc); Genetics (rcdc); Cancer (hrcs-hc); Biomarkers; Tumor (mesh); Cell-Free Nucleic Acids (mesh); Exome (mesh); Humans (mesh); Mutation (mesh); Neoplasm Recurrence; Local (mesh); Neoplasm; Residual (mesh); Neoplasms; Second Primary (mesh); Treatment Outcome (mesh); Exome Sequencing (mesh) |
| Time: |
1841 - 1853 |
| Description: |
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. |
| Document Type: |
article in journal/newspaper |
| File Description: |
application/pdf |
| Language: |
unknown |
| Relation: |
qt9mt8g3fx; https://escholarship.org/uc/item/9mt8g3fx; https://escholarship.org/content/qt9mt8g3fx/qt9mt8g3fx.pdf |
| DOI: |
10.1158/1078-0432.ccr-21-1242 |
| Availability: |
https://escholarship.org/uc/item/9mt8g3fx; https://escholarship.org/content/qt9mt8g3fx/qt9mt8g3fx.pdf; https://doi.org/10.1158/1078-0432.ccr-21-1242 |
| Rights: |
CC-BY-NC-ND |
| Accession Number: |
edsbas.BE521E5C |
| Database: |
BASE |