Prognostic and predictive determinants in high-grade gliomas: integrating tumor-intrinsic biology with patient and system-level factors - Davalan, William; Alkins, Ryan (Frontiers in Neurology, 2025)
Adult-type high-grade gliomas (HGGs) represent a biologically heterogeneous and clinically aggressive class of primary central nervous system tumors, characterized by diffuse infiltration, therapeutic resistance, and poor prognosis. Contemporary advances in molecular neuro-oncology have redefined prognostic stratification, shifting from purely histopathological frameworks to integrated molecular classification. This narrative review critically examines the intrinsic biological determinants of prognosis in HGGs, as delineated in the 2021 World Health Organization Classification of Tumors of the Central Nervous System, which differentiates glioblastoma (IDH-wildtype), astrocytoma (IDH-mutant), and oligodendroglioma (IDH-mutant, 1p/19q-codeleted) based on distinct molecular signatures. We examine the prognostic and therapeutic relevance of canonical biomarkers, alongside emerging molecular alterations and autophagy-related gene expression. In addition, we explore the tumor microenvironment and immune landscape of HGGs, and highlight the growing role of radiogenomics and artificial intelligence in integrating imaging with multi-omics data for personalized risk stratification. Beyond tumor-intrinsic biology, increasing attention is being directed toward patient-level and system-level determinants that shape prognosis. This review also synthesizes current evidence on the impact of demographic, clinical, therapeutic, and socio-economic factors influencing survival in patients with HGGs. A multidimensional approach to prognostication that integrates molecular, clinical, and contextual data is therefore essential for both improving survival and advancing health equity. By synthesizing established and emerging prognostic insights, this review underscores the critical role of tumor-intrinsic biology in guiding precision oncology approaches and developing biologically informed prognostic frameworks for patients with HGGs, while supporting the integration of non-biological determinants into clinical frameworks.