Dataset Information

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

ABSTRACT: 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.

INSTRUMENT(S): Q Exactive HF

ORGANISM(S): Homo Sapiens (human)

TISSUE(S): Brain

DISEASE(S): Brain Cancer

SUBMITTER: Ugljesa Djuric

LAB HEAD: Phedias Diamandis

PROVIDER: PXD029290 | Pride | 2021-11-02

REPOSITORIES: Pride

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

Lam K H Brian KHB Leon Alberto J AJ Hui Weili W Lee Sandy Che-Eun SC Batruch Ihor I Faust Kevin K Klekner Almos A Hutóczki Gábor G Koritzinsky Marianne M Richer Maxime M Djuric Ugljesa U Diamandis Phedias P

Nature communications 20220110 1

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 histo ...[more]

PMID: 35013227

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Project description:Background: Molecular profiling of diffuse gliomas has provided significant insights into the pathogenesis, classification and prognostication of these malignancies. However, previous molecular studies of glioma have largely focused on genomic readouts and targeted proteomic profiling technologies. Consequently, proteomic and downstream functional landscape of gliomas in general, and molecular subgroups in particular, remains largely unexplored. Here, we utilize liquid chromatography tandem mass spectrometry to profile genomically-defined cohorts of gliomas spanning the full range of World Health Organization (WHO) grades. Methods: Bulk frozen tissue and purified micro-dissected regions from formalin-fixed paraffin-embedded (FFPE) tissues were assembled and utilized to define robust proteomic signatures of both low grade, infiltrative and high-grade tumors. As a final analysis, primary tumor tissue was compared with both IDH-mutated and IDH-wildtype glioblastoma stem cell (GSC) lines to further overcome tissue heterogeneity and pinpoint proteins differences likely arising in the relevant glial cellular drivers of tumor development. Results: In aggregate, 5,496 unique proteins over 3 glioma cohorts were identified, and span common molecular subclasses based on IDH and 1p19q co-deletion status and all four WHO grades. Supervised clustering highlights substantial proteome and systems-level pathway differences between different genetically defined glioma subtypes and WHO grades. By using bulk tumor statistical analysis, 833 proteins distinguish different WHO grade tumors, while FFPE tumor dissection reveals 287 proteins in GBMs with abundance changes according to IDH mutation status. Using our integrative approach, calcium signaling, proteins of the endoplasmic reticulum and extracellular integrin proteins are most conserved proteomic markers that distinguish aggressive, IDH-wt, from IDH-mut GBM tumors in primary and tissue culture models gliomagenesis. Conclusions: This proteomic survey provides the largest and most diverse unbiased protein-based brain tumor resource to date. Current treatments for glial tumors are largely non-specific and overlap between genomic subtypes and WHO grades. Our analysis provides early insight into the vast downstream and epigenetic protein-level differences within this molecular framework. Given the central position proteins occupy in driving biology and phenotype, further characterization of the substantial proteomic diversity that exist between the molecular subtypes and grades of gliomas, proteomics may help define more personalized prognostic and predictive biomarkers for precision care.

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