Liquid biopsy has emerged as a promising avenue for developing such biomarkers. While most liquid biopsy approaches focus on analyzing circulating tumor DNA (ctDNA), studying the entire methylome of cell-free DNA (cfDNA) has the potential to reflect signals from both the tumor and the host.
In the current study, Lu and colleagues used the Infinium Methylation EPIC BeadChip array to profile cfDNA methylation in plasma collected from patients in S1314, a multicenter prospective cooperative group study. They developed a machine-learning-based algorithm to analyze differential methylation and created a methylation-based response score (mR-score) that predicted pathologic response to neoadjuvant chemotherapy. By combining this mR-score with circulating bladder DNA fraction calculated from the cfDNA methylome, they successfully predicted therapy response in 79% of patients in the cohort.
Although this was a feasibility study of limited size, it demonstrated the potential of using the entire cfDNA methylome to directly predict clinical outcomes. This is a departure from prior liquid biopsy studies looking at cfDNA methylation, where tumor-specific methylation signatures were used primarily to detect the presence of ctDNA as a surrogate for persistent disease. In contrast, the cfDNA methylome approach considers the totality of methylation patterns from both ctDNA and host cfDNA (e.g. leukocytes), which reflects both tumor and host biology and may ultimately prove valuable for predicting response to immunotherapy. With further validation and development, cfDNA methylome analysis has the potential to expand the molecular insights gained from noninvasive liquid biopsies and to better inform precision management of cancer patients.
Written by: Yi-Tsung Lu, Melissa Plets, Gareth Morrison, Alexander T Cunha, Steven Y Cen, Suhn K Rhie, Kimberly D Siegmund, Siamak Daneshmand, David I Quinn, Joshua J Meeks, Seth P Lerner, Daniel P Petrylak, David McConkey, Thomas W Flaig, Ian M Thompson, Amir Goldkorn
Division of Medical Oncology, Department of Medicine and Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA., SWOG Statistics and Data Management Center, Fred Hutchinson Cancer Research Center, Seattle, WA, USA., Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA., Department of Biochemistry and Molecular Medicine and Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA., Department of Population and Public Health Science, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA., Department of Urology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA., Departments of Urology, Biochemistry, and Molecular Genetics, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA., Scott Department of Urology, Dan L Duncan Cancer Center, Baylor College of Medicine, Houston, TX, USA., Smilow Cancer Center, Yale University, New Haven, CT, USA., Johns Hopkins School of Medicine, Baltimore, MD, USA., University of Colorado, School of Medicine, Aurora, CO, USA., CHRISTUS Medical Center Hospital, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA., Division of Medical Oncology, Department of Medicine and Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
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