TY - JOUR
T1 - Detection of Brain Cancer Using Genome-wide Cell-free DNA Fragmentomes
AU - Mathios, Dimitrios
AU - Niknafs, Noushin
AU - Annapragada, Akshaya V.
AU - Bobeff, Ernest J.
AU - Chiao, Elaine J.
AU - Boyapati, Kavya
AU - Boyapati, Keerti
AU - Short, Sarah
AU - Bartolomucci, Adrianna L.
AU - Cristiano, Stephen
AU - Koul, Shashikant
AU - Vulpescu, Nicholas A.
AU - Ferreira, Leonardo
AU - Medina, Jamie E.
AU - Bruhm, Daniel C.
AU - Adleff, Vilmos
AU - Podstawka, Małgorzata
AU - Stanisławska, Patrycja
AU - Park, Chul Kee
AU - Huang, Judy
AU - Gallia, Gary L.
AU - Brem, Henry
AU - Mukherjee, Debraj
AU - Caplan, Justin M.
AU - Weingart, Jon
AU - Jackson, Christopher M.
AU - Lim, Michael
AU - Phallen, Jillian
AU - Scharpf, Robert B.
AU - Velculescu, Victor E.
N1 - Publisher Copyright:
© 2025 The Authors; Published by the American Association for Cancer Research.
PY - 2025/8/1
Y1 - 2025/8/1
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/105012957134
U2 - 10.1158/2159-8290.CD-25-0074
DO - 10.1158/2159-8290.CD-25-0074
M3 - Article
C2 - 40299319
AN - SCOPUS:105012957134
SN - 2159-8274
VL - 15
SP - 1593
EP - 1608
JO - Cancer discovery
JF - Cancer discovery
IS - 8
ER -