BIOM-24. NONINVASIVE DETECTION OF GLIOMAS VIA PLASMA CFDNA FRAGMENTOME PROFILING

Abstract BACKGROUND Delayed diagnosis of brain tumors is common and negatively impacts patient outcomes. A noninvasive, blood-based assay for early detection may accelerate clinical intervention and improve prognosis. This study explores genome-wide cell-free DNA (cfDNA) fragmentome profiling for the detection of brain tumors, focusing on gliomas. METHODS Genome-wide cfDNA fragmentation profiles and repeat element landscapes were analyzed from plasma samples of individuals with brain tumors (n = 148) and controls without cancer (n = 357). A machine learning classifier was trained on cfDNA fragmentome features to distinguish tumor-bearing individuals and validated in an independent, prospectively collected cohort. To elucidate the cellular origins of cfDNA signals, we developed a statistical framework that integrates cfDNA coverage at transcription factor binding sites (TFBS) with pan-tissue and pan-cancer transcription factor expression profiles. RESULTS The classifier demonstrated robust performance in detecting brain tumors across all glioma grades, achieving an area under the ROC curve (AUC) of 0.90 (95% CI, 0.87–0.93). Fragmentation signatures reflected contributions from both tumor-derived cfDNA and immunologically relevant leukocyte populations, consistent with the immunosuppressive environment of brain tumors. These cfDNA features were reproducible and stable across cohorts. CONCLUSIONS Plasma cfDNA fragmentome analysis enables accurate, noninvasive detection of gliomas and reveals reproducible fragmentation changes associated with brain malignancy. These findings support the potential clinical utility of cfDNA fragmentomics as a liquid biopsy platform for earlier diagnosis and disease monitoring in neuro-oncology.