Enabling whole-genome DNA methylation-based classification of central nervous system tumors

Objectives: DNA methylation profiling using array-based platforms has proven invaluable for classifying central nervous system (CNS) tumors, especially those with challenging or atypical morphologies. However, existing classification frameworks remain restricted to array-based inputs, interrogating only a subset of CpG sites and limiting diagnostic and prognostic resolution. Whole-genome methylation sequencing methods such as whole-genome bisulfite sequencing (WGBS) and enzymatic methyl-seq (EM-seq) offer near-complete methylome coverage, but their integration into established classifiers is lacking. This study aimed to develop MethylInsight, a web-based platform designed to adapt a widely recognized CNS tumor classification framework to whole-genome data. Methods: MethylInsight converts WGBS and EM-seq signals into array-compatible beta values, enabling compatibility with established classifiers. A Random Forest-based model with logistic regression calibration was trained on 3,905 CNS tumor and control samples spanning 82 tumor subtypes and nine control tissue classes. Performance was evaluated using five-fold cross-validation and internal validation on 22 matched patient samples. The platform also incorporates t-distributed stochastic neighbor embedding (t-SNE) visualizations for contextualizing newly profiled samples against a reference cohort. Results: MethylInsight demonstrated robust classification performance across tumor classes, achieving an area under the ROC curve (AUC) of 0.961, comparable to the DKFZ (0.966) and NM (0.964) classifiers. Cross-validation showed uniformly high accuracy, including for glioblastoma (GBM), a challenging subtype, with sensitivity and specificity of 0.924 and 0.940, respectively. Calibration reduced the estimated error rate from 5.88% to 2.95%. Validation across platforms showed strong concordance, with 21 of 22 paired datasets achieving >80% Pearson correlation, and top-ranked predictions matched for all but three pairs, which still shared overlapping top-two predictions. Conclusions: MethylInsight enables whole-genome methylation data integration into CNS tumor classification, overcoming limitations of array-based methods. By supporting EM-seq inputs and providing calibrated probabilities and intuitive t-SNE visualizations, MethylInsight enhances diagnostic precision and tumor stratification. The platform is freely accessible at https://inocras.methylclassifier.com.