Topographic mapping of the glioblastoma proteome reveals a triple-axis model of intra-tumoral heterogeneity

Abstract

Glioblastoma is an aggressive form of brain cancer with well-established patterns of intra-tumoral heterogeneity implicated in treatment resistance and progression. While regional and single cell transcriptomic variations of glioblastoma have been recently resolved, downstream phenotype-level proteomic programs have yet to be assigned across glioblastoma’s hallmark histomorphologic niches. Here, we leverage mass spectrometry to spatially align abundance levels of 4,794 proteins to distinct histologic patterns across 20 patients and propose diverse 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. Moreover, we highlight differential drug sensitivities and relative chemoresistance in glioblastoma cell lines with enhanced KRAS programs. Importantly, these pharmacological differences are less pronounced in transcriptional glioblastoma subgroups suggesting that this model may provide insights for targeting heterogeneity and overcoming therapy resistance.

Review of Current Human Genome-Scale Metabolic Models for Brain Cancer and Neurodegenerative Diseases

“…These five regions were mapped recently to a proteomic model of three pathways (KRAS-, MYC-, and hypoxia). The KRAS-, MYC, and hypoxia pathways were identified with three main phenotypes: migration, proliferation, and altered metabolism, respectively [ 148 ]. Consequently, building a multi-regional reconstruction for GBM could identify the metabolic regional heterogeneity and vulnerability.…”

Integrating computational pathology and proteomics to address tumor heterogeneity

“…Similarly, our group has also recently used a comparable morphoproteomic approach to generate a spatially preserved proteomic atlas of glioblastoma through careful microdissection and LC-MS/MS profiling of the classic histomorphologic hallmarks of this aggressive cancer [42]. Indeed, this analysis showed that different Integrating AI and proteomics to address tumor heterogeneity 449 glioblastoma regions can be compartmentalized into subpopulations where glioma cells prioritize migration and infiltration or proliferation and expansion.…”

Niche deconvolution of the glioblastoma proteome reveals a distinct infiltrative phenotype within the proneural transcriptomic subgroup

“…Towards addressing this, we recently used liquid chromatography tandem mass spectrometry (LC-MS/MS) to develop a spatially-defined proteomic atlas of glioblastoma. Specifically, we microscopically isolated and profiled the aforementioned tumor regions (CT, MVP, PAN, IT) and adjacent brain tissue from the leading edge (LE), allowing components of the glioblastoma proteome to be aligned with their relevant histomorphologic niches (n = 77 regions from 20 individual patients) 11 , 12 . Moreover, because protein patterns provide a reliable downstream phenotypic readout of biological function, these regional profiles could also serve as valuable reference sets to estimate niche contributions of bulk tissue-derived profiles.…”