Abstract PO052: Uncovering the evolution of Glioblastoma proteome landscape from primary to the recurrent stage for development of novel diagnostic and predictive biomarkers
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AbstractGlioblastoma (GBM) is characterized by extensive cellular and genetic heterogeneity. A wealth of literature describes the biology of primary GBM (p-GBM), but we currently lack an understanding of how GBM evolves through therapy to become a very different tumor at recurrence, which may explain why therapies against p-GBM fail to work in recurrent GBM (r-GBM). Therefore, to understand the evolution of r-GBM, we aimed to characterize patient-matched p-GBM and r-GBM proteome and identify potential therapeutic targets for r-GBM. We collected one of the world’s largest patient-matched p-GBM and r-GBM samples from the Hamilton Health Sciences for gene expression profiling, proteomic analyses and tissue microarray (TMA) construction. Nano-String analysis was performed for GBM subtype identification. Furthermore, patient demographics was generated for survival analysis. The top potential therapeutic targets for r-GBM were identified by proteomic analysis and were validated on TMA using immunohistochemistry. The essentiality of each protein in r-GBM were assessed using CRISPR KO studies and the top hit were selected for pre-clinical testing. 6798 proteins were detected by shotgun, label-free proteomic analyses. Differential expression analysis on the surface proteins revealed a distinct set of proteins overexpressed in r-GBM among which 7 proteins were selected as top potential therapeutic targets for r-GBM. Besides, the patients were grouped based on survival rate and the differential expression analysis revealed significantly enriched proteins and pathways in short-term survivors which cause aggressive phenotypes in GBM. In addition, consensus clustering identified five protein clusters which show distinction between primary vs recurrent tumors. Our data also strongly supports a preponderance of immune regulatory/suppressive genes as important drivers of r-GBM. This study resulted in identification of diagnostic and predictive biomarkers which is extremely complementary and instructive for the development of new poly-therapeutic paradigms for GBM patients at the recurrent level and will lead to improvement of patient’s survival.Citation Format: Nazanin Tatari, Shahbaz Khan, Julie Livingstone, Chitra Venugopal, Jennifer Chan, Cynthia Hawkins, John Provias, Jian-Qiang Lu, Kjetil Ask, Thomas Kislinger, Sheila Singh. Uncovering the evolution of Glioblastoma proteome landscape from primary to the recurrent stage for development of novel diagnostic and predictive biomarkers [abstract]. In: Proceedings of the AACR Virtual Special Conference on the Evolving Tumor Microenvironment in Cancer Progression: Mechanisms and Emerging Therapeutic Opportunities; in association with the Tumor Microenvironment (TME) Working Group; 2021 Jan 11-12. Philadelphia (PA): AACR; Cancer Res 2021;81(5 Suppl):Abstract nr PO052.
