NONINVASIVE CLASSIFICATION OF CENTRAL NERVOUS SYSTEM TUMORS USING CFDNA METHYLATION PROFILING: DEVELOPMENT AND VALIDATION OF A MULTICLASS RANDOM FOREST MODEL
Recommended Citation
Noushmehr H, Done B, Herrgott G, Snyder J, deCarvalho A, Walbert T, Castro A. NONINVASIVE CLASSIFICATION OF CENTRAL NERVOUS SYSTEM TUMORS USING CFDNA METHYLATION PROFILING: DEVELOPMENT AND VALIDATION OF A MULTICLASS RANDOM FOREST MODEL. Neuro Oncol 2025; 27(Supplement 5):v32.
Document Type
Conference Proceeding
Publication Date
11-11-2025
Publication Title
Neuro Oncol
Keywords
biopsy, central nervous system, central nervous system neoplasms, ependymoma, pituitary neoplasms, complement system proteins, genome, glioma, meningioma, methylation, diagnosis, diagnostic imaging, neoplasms, surveillance, medical, tissue specimen, cell-free dna, liquid biopsy, epigenome, random forest
Abstract
BACKGROUND: Accurate classification of central nervous system (CNS) tumors using tissue samples has been well-established. However, a noninvasive approach utilizing liquid biopsy samples offers significant advantages for patient diagnosis and monitoring. This study aims to develop a multiclass classification model for CNS tumors based on circulating cell-free DNA (cfDNA) methylation profiles obtained from blood samples. METHODS: We performed genome-wide methylation profiling (EPIC array v1.0) on 227 serum liquid biopsy specimens from patients with CNS tumors, including glioma (n=109), meningioma (n=81), ependymoma (n=23), and pituitary tumors (n=14). A random forest classifier was trained on 155 samples, while 72 samples were reserved for independent testing (held-out set). Publicly available methylome data from CNS tumor tissue samples were used to further validate the tumor-specific signatures derived from the blood samples. RESULTS: Our random forest model demonstrated a classification accuracy of 91% on the held-out test set, confirming the presence of tumor-specific methylation signatures in blood samples from patients with CNS tumors. Tumor-specific methylation signatures detected in liquid biopsy samples were able to discriminate between related tumors in an independent set of tissue samples. CONCLUSIONS: These findings suggest that liquid biopsy-derived cfDNA methylation signatures are a promising noninvasive tool for the classification and surveillance of CNS tumors, offering an alternative or complement to tissue-based and imaging methods.
Volume
27
Issue
Supplement 5
First Page
v32
