A Cell-Surface Membrane Protein Signature for Glioblastoma

Ghosh, Dhimankrishna; Funk, Cory C.; Caballero, Juan; Shah, Nameeta; Rouleau, Katherine; Earls, John C.; Soroceanu, Liliana; Foltz, Greg; Cobbs, Charles S.; Price, Nathan D.; Hood, Leroy

doi:10.1016/j.cels.2017.03.004

Cited by 42 publications

(36 citation statements)

References 70 publications

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“…Studying proteins exposed on the surface of cells requires biochemical approaches that enrich for membrane proteins. Plasma membrane proteins on intact cells can be first biotinylated and then the isolated fraction subjected to mass spectrometry ( Ghosh et al, 2017 ; Shin et al, 2003 ). Membranes and all their constituents can be also extracted from lysed cells by means of a Percoll/sucrose density gradient ( Lund et al, 2009 ).…”

Section: Discussionmentioning

confidence: 99%

Methods for Systematic Identification of Membrane Proteins for Specific Capture of Cancer-Derived Extracellular Vesicles

Zaborowski

Lee

et al. 2019

Cell Reports

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SUMMARY Analysis of cancer-derived extracellular vesicles (EVs) in biofluids potentially provides a source of disease biomarkers. At present there is no procedure to systematically identify which antigens should be targeted to differentiate cancer-derived from normal host cell-derived EVs. Here, we propose a computational framework that integrates information about membrane proteins in tumors and normal tissues from databases: UniProt, The Cancer Genome Atlas, the Genotype-Tissue Expression Project, and the Human Protein Atlas. We developed two methods to assess capture of EVs from specific cell types. (1) We used palmitoylated fluorescent protein (palmtdTomato) to label tumor-derived EVs. Beads displaying antibodies of interest were incubated with conditioned medium from palmtdTomato-expressing cells. Bound EVs were quantified using flow cytometry. (2) We also showed that membrane-bound Gaussia luciferase allows the detection of cancer-derived EVs in blood of tumor-bearing animals. Our analytical and validation platform should be applicable to identify antigens on EVs from any tumor type.

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Section: Discussionmentioning

confidence: 99%

Methods for Systematic Identification of Membrane Proteins for Specific Capture of Cancer-Derived Extracellular Vesicles

Zaborowski

Lee

et al. 2019

Cell Reports

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“…Levels of tricarboxylic acid cycle enzymes such as isocitrate dehydrogenase and aldehyde dehydrogenase decrease in response to antiangiogenic therapy, suggesting the cells evade death by increasing glycolysis ( Demeure et al, 2016 ). Additionally, a systems-based statistical analysis of a proteomic and transcriptomic signature of GBM was identified, concluding a strong link between GBM invasive properties and the TGF- β signaling pathways ( Ghosh et al, 2017 ). Targeting these pathways may inhibit GBM proliferation; however, target validation is necessary to rule out proteins that do not drive tumor growth.…”

Section: Emerging Targets In Glioblastomamentioning

confidence: 99%

Current Challenges and Opportunities in Treating Glioblastoma

et al. 2018

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Glioblastoma multiforme (GBM), the most common and aggressive primary brain tumor, has a high mortality rate despite extensive efforts to develop new treatments. GBM exhibits both intra- and intertumor heterogeneity, lending to resistance and eventual tumor recurrence. Large-scale genomic and proteomic analysis of GBM tumors has uncovered potential drug targets. Effective and “druggable” targets must be validated to embark on a robust medicinal chemistry campaign culminating in the discovery of clinical candidates. Here, we review recent developments in GBM drug discovery and delivery. To identify GBM drug targets, we performed extensive bioinformatics analysis using data from The Cancer Genome Atlas project. We discovered 20 genes, BOC, CLEC4GP1, ELOVL6, EREG, ESR2, FDCSP, FURIN, FUT8-AS1, GZMB, IRX3, LITAF, NDEL1, NKX3-1, PODNL1, PTPRN, QSOX1, SEMA4F, TH, VEGFC, and C20orf166AS1 that are overexpressed in a subpopulation of GBM patients and correlate with poor survival outcomes. Importantly, nine of these genes exhibit higher expression in GBM versus low-grade glioma and may be involved in disease progression. In this review, we discuss these proteins in the context of GBM disease progression. We also conducted computational multi-parameter optimization to assess the blood-brain barrier (BBB) permeability of small molecules in clinical trials for GBM treatment. Drug delivery in the context of GBM is particularly challenging because the BBB hinders small molecule transport. Therefore, we discuss novel drug delivery methods, including nanoparticles and prodrugs. Given the aggressive nature of GBM and the complexity of targeting the central nervous system, effective treatment options are a major unmet medical need. Identification and validation of biomarkers and drug targets associated with GBM disease progression present an exciting opportunity to improve treatment of this devastating disease.

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“…Proteogenomic profiling of GBM tissues showed high expression of CD44 [23] and recent GBM systematic analysis further supported CD44 as a GBM cell surface antigen [26].…”

Section: Identifying Existing Drugs Targeting Gbm Signature and Cd44 mentioning

confidence: 77%

“…CD44 is a transmembrane receptor and has multifaceted functions in both normal and disease physiology. OMICS studies have identified CD44 to be overexpressed in many types of cancer including glioblastoma [26,27]. Based on our analysis, CD44 seems particularly important as it can be both identified in transcriptomics and proteomics-based approaches, among the top hub gene and whose high expression correlate with poor disease-free survival.…”

Section: Co-expression Network Of Cd44mentioning

confidence: 80%

Integration of RNA-Seq and proteomics data identifies glioblastoma multiforme surfaceome signature

Syafruddin

Nazarie

Moidu

et al. 2020

Preprint

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BackgroundGlioblastoma multiforme (GBM) is a highly lethal, stage IV brain tumor with a prevalence of approximately 2 per 10000 people globally. The cell surface proteins or surfaceome play significant roles in modulating cancer phenotypes and acting as information gateway in many oncogenic signaling pathways. Hence, surfaceome are attractive targets for cancer therapy due to their direct accessibility to drugs. Nonetheless, a comprehensive GBM surfaceome landscape has not been fully defined. Thus, the aim of this study is to define GBM-specific surfaceome genes and identify key cell surface genes that could potentially be developed as novel GBM biomarkers, or therapeutically targeted.MethodsWe integrated the RNA-Seq data from TCGA GBM (n=166) and GTEx normal brain cortex (n=408) databases to identify the significantly dysregulated surfaceome in GBM. This was followed by integrative analysis that combines transcriptomics, proteomics and protein-protein interaction network data to prioritize the high-confidence GBM surfaceome signature.ResultsAmong the 2,381 significantly dysregulated genes in GBM, 395 genes were classified as surfaceome. Via the integrative analysis, we identified 6 high-confidence GBM molecular signature, HLA-DRA, CD44, SLC1A5, EGFR, ITGB2, PTPRJ, which were significantly upregulated in GBM. The expression of these genes were validated in an independent transcriptomics database, which confirmed their upregulated expression in GBM. Importantly, high expression of CD44, PTPRJ and HLA-DRA is significantly associated with poor disease-free survival. Lastly, using the Drugbank database, we identified several clinically-approved drugs targeting the GBM molecular signature suggesting potential drug repurposing.ConclusionsIn summary, we identified and highlighted the key GBM surface-enriched repertoires that could be biologically relevant in supporting GBM pathogenesis. These genes could be further interrogated experimentally in future studies that could lead to efficient GBM diagnostic/prognostic markers or a therapeutic regimen to treat GBM.

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A Cell-Surface Membrane Protein Signature for Glioblastoma

Cited by 42 publications

References 70 publications

Methods for Systematic Identification of Membrane Proteins for Specific Capture of Cancer-Derived Extracellular Vesicles

Methods for Systematic Identification of Membrane Proteins for Specific Capture of Cancer-Derived Extracellular Vesicles

Current Challenges and Opportunities in Treating Glioblastoma

Integration of RNA-Seq and proteomics data identifies glioblastoma multiforme surfaceome signature

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