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

Author:

Li Shuo123ORCID,Zeng Weihua1,Ni Xiaohui3ORCID,Zhou Yonggang1,Stackpole Mary L.123ORCID,Noor Zorawar S.4,Yuan Zuyang1,Neal Adam56,Memarzadeh Sanaz56789,Garon Edward B.4ORCID,Dubinett Steven M.149101112,Li Wenyuan1,Zhou Xianghong Jasmine1

Affiliation:

1. 1Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, California.

2. 2Bioinformatics Interdepartmental Graduate Program, University of California at Los Angeles, Los Angeles, California.

3. 3EarlyDiagnostics Inc., Los Angeles, California.

4. 4Department of Medicine, David Geffen School of Medicine at UCLA, Los Angeles, California.

5. 5Department of Obstetrics and Gynecology, David Geffen School of Medicine at UCLA, Los Angeles, California.

6. 6UCLA Eli and Edythe Broad Center of Regenerative Medicine and Stem Cell Research, University of California Los Angeles, Los Angeles, California.

7. 7UCLA Jonsson Comprehensive Cancer Center, University of California Los Angeles, Los Angeles, California.

8. 8Molecular Biology Institute, University of California Los Angeles, Los Angeles, California.

9. 9VA Greater Los Angeles Health Care System, Los Angeles, California.

10. 10Department of Pulmonary and Critical Care Medicine, David Geffen School of Medicine at UCLA, Los Angeles, California.

11. 11Department of Molecular and Medical Pharmacology, David Geffen School of Medicine at UCLA, Los Angeles, California.

12. 12Department of Microbiology, Immunology and Molecular Genetics, University of California at Los Angeles, Los Angeles, California.

Abstract

AbstractPurpose: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.

Funder

NCI

National Science Foundation Graduate Research Fellowship

Department of Veterans Affairs Merit Award

Publisher

American Association for Cancer Research (AACR)

Subject

Cancer Research,Oncology

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