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

K. H. Brian Lam, Alberto J. Leon, Weili Hui, Sandy Che-Eun Lee, Ihor Batruch, Kevin Faust, Almos Klekner, Gábor Hutóczki, Marianne Koritzinsky, Maxime Richer, Ugljesa Djuric and Phedias Diamandis ()
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K. H. Brian Lam: University of Toronto
Alberto J. Leon: Princess Margaret Cancer Center, University Health Network
Weili Hui: Princess Margaret Cancer Center, University Health Network
Sandy Che-Eun Lee: Princess Margaret Cancer Center, University Health Network
Ihor Batruch: Mount Sinai Hospital
Kevin Faust: Princess Margaret Cancer Center, University Health Network
Almos Klekner: Faculty of Medicine, University of Debrecen
Gábor Hutóczki: Faculty of Medicine, University of Debrecen
Marianne Koritzinsky: Princess Margaret Cancer Center, University Health Network
Maxime Richer: Centre Hospitalier Universitaire de Sherbrooke, 3001, 12e avenue Nord
Ugljesa Djuric: University of Toronto
Phedias Diamandis: University of Toronto

Nature Communications, 2022, vol. 13, issue 1, 1-14

Abstract: 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.

Date: 2022
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DOI: 10.1038/s41467-021-27667-w

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