posted on 2025-08-04, 07:21authored byDimitrios 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
<p>Supplementary Table S1. Clinical information, sequencing statistics, and ARTEMIS-DELFI scores for cfDNA analyses in Discovery cohort. Supplementary Table S2. Clinical information, sequencing statistics, and ARTEMIS-DELFI scores for cfDNA analyses in Validation cohort. Supplementary Table S3. Summary of mutations detected by plasma-based targeted sequencing panel. Supplementary Table S4. Correlation of TF expression between GBM and other tumor types. Supplementary Table S5. Summary of complete blood count for patients with gliomas or non-neoplastic neurological conditions. Supplementary Table S6. Summary of ChIP-Seq peaks used in DECIFER analysis. Supplementary Table S7. Correlation of TFBS relative coverage differences with expression differences between tumor/cell types and whole blood.</p>
Funding
Dr. Miriam and Sheldon G. Adelson Medical Research Foundation (AMRF)
Gray Foundation
Commonwealth Foundation for Cancer Research Foundation
Mark Foundation For Cancer Research (The Mark Foundation for Cancer Research)
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.
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.