Detection of Brain Cancer Using Genome-wide Cell-free DNA Fragmentomes
Dimitrios Mathios, Noushin Niknafs, Akshaya V. Annapragada, Ernest J. Bobeff, Elaine J. Chiao, Kavya Boyapati, Keerti Boyapati, Sarah Short, Adrianna L. Bartolomucci, Stephen Cristiano, Shashikant Koul, Nicholas A. Vulpescu, Leonardo Ferreira, Jamie E. Medina, Daniel C. Bruhm, Vilmos Adleff, Małgorzata Podstawka, Patrycja Stanisławska, Chul‐Kee Park, Judy Huang, Gary L. Gallia, Henry Brem, Debraj Mukherjee, Justin M. Caplan, Jon Weingart, Christopher M. Jackson, Michael Lim, Jillian Phallen, Robert B. Scharpf, Victor E. Velculescu
Abstract
Diagnostic delays in patients with brain cancer are common and can impact patient outcome. Development of a blood-based assay for detection of brain cancers could accelerate brain cancer diagnosis. In this study, we analyzed genome-wide cell-free (cfDNA) fragmentomes, including fragmentation profiles and repeat landscapes, from the plasma of individuals with (n = 148) or without (n = 357) brain cancer. Machine learning analyses of cfDNA fragmentome features detected brain cancer across all-grade gliomas (AUC = 0.90; 95% confidence interval, 0.87-0.93), and these results were validated in an independent prospectively collected cohort. cfDNA fragmentome changes in patients with gliomas represented a combination of fragmentation profiles from glioma cells and altered white blood cell populations in the circulation. These analyses reveal the properties of cfDNA in patients with brain cancer and open new avenues for noninvasive detection of these individuals. SIGNIFICANCE: Brain cancer is one of the deadliest and most challenging cancers to detect with liquid biopsy approaches in blood, hampering efforts for earlier noninvasive diagnosis. We have developed a machine learning genome-wide cfDNA fragmentation method that provides a sensitive and accessible approach for brain cancer detection.