Abstract BACKGROUND The molecular landscape of adult diffuse glioma has been extensively characterized by gene expression and DNA methylation profiling, but less attention has been paid to somatic copy number alteration (SCNA) data. This study aimed to give a rigorous, survival-focused analysis of glioma genome-wide SCNA data that builds on our previous work. METHODS Detailed survival analyses were conducted on the substructure of UMAP projections of all TCGA glioma (Nf1092), exclusively astrocytic glioma (Nf914), and exclusively IDH-wildtype glioma (Nf528). Results were validated with data from the Glioma Longitudinal Analysis Consortium (GLASS) (Nf224). Clinical factors such as age and MGMT methylation were tested in multivariate survival analyses. RESULTS A UMAP projection of TCGA glioma SCNA data generated three distinct clusters composed of entirely oligodendroglioma, predominantly IDH-mutant astrocytoma (C-IDHmut-astro), and predominantly IDH-wildtype glioma (C-IDHwt), respectively. For astrocytic tumors, cluster assignment was independently prognostic of IDH status (p< 0.001): TCGA IDH-mutant astrocytomas that clustered in C-IDHwt had poorer outcomes than their counterparts (p< 0.04) and IDH-wildtype tumors that clustered in C-IDHmut-astro fared better than those that did not (p< 0.01). The distribution of GLASS astrocytic tumors, which is skewed for better survival, supported our results in IDH-wildtype glioblastoma (p< 0.001, Fisher’s). Among four distinct subclusters of TCGA IDH-wildtype glioblastomas, the largest was significantly or marginally significantly negatively prognostic compared to each other cluster (p=0.048, p=0.059, p=0.027) and their combination (p=0.002). In the GLASS dataset, inclusion in the largest subcluster was also prognostic (p=0.013) and similar trends were observed between individual clusters (p=0.21, p=0.036, p=0.083). Furthermore, membership to the largest cluster was independently prognostic of MGMT methylation status and several published IDH-wildtype glioblastoma subtypes in the TCGA. CONCLUSIONS Unsupervised learning of genome-wide SCNA has prognostic implications for astrocytic glioma. SCNA cluster membership is independently prognostic of MGMT methylation status.