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e-Poster Display Session

5P - Uncovering the evolution of glioblastoma proteome landscape from primary to the recurrent stage for development of novel diagnostic and predictive biomarkers

Date

09 Dec 2020

Session

e-Poster Display Session

Presenters

Nazanin Tatari

Citation

Annals of Oncology (2020) 31 (suppl_7): S1417-S1424. 10.1016/annonc/annonc389

Authors

N. Tatari1, S. Khan2, J. Livingstone2, C. Venugopal1, J. Chan3

Author affiliations

  • 1 Stem Cell and Cancer Research Institute, McMaster University, Hamilton/CA
  • 2 Princess Margaret Cancer Centre, University health Network, University of Toronto, Toronto/CA
  • 3 University of Calgary, Calgary/CA
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Abstract 5P

Background

Glioblastoma (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.

Methods

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 will be assessed using CRISPR KO studies and the top hit will be selected for pre-clinical testing.

Results

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.

Conclusions

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.

Legal entity responsible for the study

The authors.

Funding

McMaster University and University of Toronto.

Disclosure

All authors have declared no conflicts of interest.

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