Identification of Somatic Gene Signatures in Circulating <scp>Cell-Free DNA</scp> Associated with Disease Progression in Metastatic Prostate Cancer by a Novel Machine Learning Platform

Abstract

The progression from castration-sensitive to castration-resistant prostate cancer is characterized by worse prognosis and there is a pressing need for targeted drugs to prevent or delay this transition. This study used machine learning algorithms to examine the cell-free DNA of patients to identify alterations to specific pathways and genes associated with progression. Detection of these alterations in cell-free DNA may overcome the challenges associated with obtaining tumor bone biopsies and allow contemporary investigation of combinatorial therapies that target these aberrations.