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>cfDNA fragmentation and mutations in plasma of patients with HGGs. <b>A,</b> The table shows the number and percentage of patients analyzed and detected with the ARTEMIS–DELFI assay, the tumor-guided mutational panel approach, or both. <b>B,</b> Histogram representation of the sensitivity of HGG detection by the mutation panel or the ARTEMIS–DELFI approach. The score threshold corresponding to 90% specificity is applied to determine the samples detected by ARTEMIS–DELFI approach. The analytical specificity of the tumor-informed mutation panel assay is 99.9%. <b>C,</b> Fragment length distribution of tumor-specific mutant fragments vs. fragments with no mutations, germline mutations or known clonal hematopoiesis of indeterminate potential (CHIP) mutations. Tumor-specific mutations are residing in shorter fragments than wild-type (WT) or nontumor mutant fragments (median length of 146 bp vs. 167 bp, Wilcoxon <i>P</i> < 0.0001).</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.