Updated project metadata. Glioblastoma (GBM) is an aggressive form of brain cancer with well-established patterns of intra-tumoral heterogeneity implicated in treatment resistance, recurrence and progression. While regional and single cell transcriptomic variations of GBM have been recently resolved, downstream phenotype-level proteomic programs have yet to be assigned to specific niches. Here, we leverage mass spectrometry to spatially align abundance levels of 4,794 proteins to GBM’s hallmark histomorphologic niches across 20 patients and define distinct molecular programs operational within these regional tumor compartments. Using machine learning, we overlay concordant transcriptional information, and define two distinct proteogenomic programs, MYC- and KRAS-axis hereon, that cooperate with hypoxia to produce a tri-dimensional model of intra-tumoral heterogeneity. Importantly, we show using multiple cohorts, that GBMs with an enhanced KRAS component harbor a more clinically aggressive and infiltrative phenotype. Conversely, tumor cells enriched along the MYC axis where mutually exclusive and had a distinct proliferative program. Moreover, by applying both experimental and computational approaches to link each of these distinct molecular axes with potential pharmacological therapies, we highlight differential drug sensitivities and a notable relative chemoresistance in GBM cell lines with enhanced KRAS programs. Importantly, pharmacological differences were less evident when using traditional expression-based subgroups supporting thattopographic phenotypic mapping ofGBM, and the proposed axes may provide new insights for targeting heterogeneity and overcoming therapy resistance in this aggressive disease.