Integrated multiomic diagnostics in central nervous system tumors from clinical readiness to practical prioritization

Central nervous system (CNS) tumors represent a biologically heterogeneous group of neoplasms in which accurate classification is essential for prognosis, therapeutic planning, and clinical trial eligibility. Contemporary diagnosis increasingly incorporates molecular findings alongside histopathology and immunohistochemistry, particularly through targeted genomic testing and DNA methylation profiling. However, the clinical maturity of different omic modalities varies substantially. Genomic alterations and DNA methylation classifiers currently have the strongest evidence base and the clearest role in routine or referral-center diagnostic practice, whereas transcriptomic, proteomic, metabolomic, spatial, and artificial intelligence-assisted approaches remain more selective or investigational. This narrative review summarizes the diagnostic contributions of genomics, epigenomics, transcriptomics, proteomics, and metabolomics in CNS tumors, with emphasis on clinical readiness, diagnostic yield, practical limitations, and evidence gaps. We distinguish improved diagnostic classification from proven improvement in patient outcomes, since outcome-level evidence remains limited for many multi-omic approaches. We also propose a practical prioritization framework for selecting molecular tests according to tumor type, patient age, tissue availability, diagnostic uncertainty, and laboratory resources. Multi-omic diagnostics are best understood not as universal comprehensive profiling, but as a tiered and context-dependent approach in which established molecular tools are applied first and emerging modalities are reserved for selected unresolved or research-informed scenarios.