Towards developing biomarkers for glioblastoma multiforme: a proteomics view

Jayaram, Savita; Gupta, Manoj Kumar; Polisetty, Ravindra Varma; Cho, William C.; Sirdeshmukh, Ravi

doi:10.1586/14789450.2014.939634

Cited by 34 publications

(29 citation statements)

References 106 publications

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“…These observations may guide further explorations of the complexities associated with this emission, including the composition of EGFRvIII containing oncosomes (63), associated biomarkers (64,65), as well as potential for a broader biological effect resulting from interactions between EGFRvIII-driven EVs, tumor microenvironment and recipient cells (12,66).…”

Section: Discussionmentioning

confidence: 99%

The Impact of Oncogenic EGFRvIII on the Proteome of Extracellular Vesicles Released from Glioblastoma Cells

Choi

Montermini

Kim

et al. 2018

Molecular & Cellular Proteomics

122

133

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Epidermal growth factor receptor (EGFR)EGFR SummaryGlioblastoma multiforme (GBM) is a highly aggressive and heterogeneous form of primary brain tumors, driven by a complex repertoire of oncogenic alterations, including the constitutively active epidermal growth factor receptor (EGFRvIII). EGFRvIII impacts both cell-intrinsic and non-cell autonomous aspects of GBM progression, including cell invasion, angiogenesis and modulation of the tumor microenvironment. This is, at least in part, attributable to the release and intercellular trafficking of extracellular vesicles (EVs), heterogeneous membrane structures containing multiple bioactive macromolecules. Here we analyzed the impact of EGFRvIII on the profile of glioma EVs using isogenic tumor cell lines, in which this oncogene exhibits a strong transforming activity. We observed that EGFRvIII expression alters the expression of EV-regulating genes (vesiculome) and EV properties, including their protein composition. Using mass spectrometry, quantitative proteomic analysis and Gene Ontology terms filters, we observed that EVs released by EGFRvIIItransformed cells were enriched for extracellular exosome and focal adhesion related proteins.Among them, we validated the association of pro-invasive proteins (CD44, BSG, CD151) with EVs of EGFRvIII expressing glioma cells, and down-regulation of exosomal markers (CD81 and CD82) relative to EVs of EGFRvIII-negative cells. Nano-flow cytometry revealed that the EV output from individual glioma cell lines was highly heterogeneous, such that only a fraction of vesicles contained specific proteins. Notably, cells expressing EGFRvIII released EVs double positive for CD44/BSG, and these proteins also co-localized in cellular filopodia. We also detected the expression of homophilic adhesion molecules and increased homologous EV uptake by EGFRvIII-positive glioma cells. These results suggest that oncogenic EGFRvIII reprograms the proteome and uptake of GBM-related EVs, a notion with considerable implications for their biological activity and properties relevant for the development of EV-based cancer biomarkers.

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Section: Discussionmentioning

confidence: 99%

The Impact of Oncogenic EGFRvIII on the Proteome of Extracellular Vesicles Released from Glioblastoma Cells

Choi

Montermini

Kim

et al. 2018

Molecular & Cellular Proteomics

122

133

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“…Glioblastoma multiforme (GBM), one of the most invasive and fatal brain tumours that develops from glial cells, can severely affects the central nervous system and general health [1]. The percent 5-year surviving rate was estimated to be 33.2% between 2008 and 2014 according to statistics from the Surveillance, Epidemiology and End Results (SEER) database and the Centers for Disease Control and Prevention's National Center for Health Statistics (https://seer.cancer.gov/ csr/1975_2015/).…”

Section: Introductionmentioning

confidence: 99%

Machine‐learning based radiogenomics analysis of MRI features and metagenes in glioblastoma multiforme patients with different survival time

Liao

Cai²,

Tian

et al. 2019

J Cellular Molecular Medi

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Background This study aimed to examine multi‐dimensional MRI features’ predictability on survival outcome and associations with differentially expressed Genes ( RNA Sequencing) in groups of glioblastoma multiforme ( GBM ) patients. Methods Radiomics features were extracted from segmented lesions of T2‐ FLAIR MRI data of 137 GBM patients. Radiomics features include intensity, shape and textural features in seven classes were included in the analysis. Patients were divided into two groups depending on their survival time (shorter or longer than 1‐year survival). Four different machine learning algorithms were implemented to construct the prediction models. Features with top importance (importance >0.04) were selected to construct the prediction model using the model with the best performance. The interactions between image features and genomics were then analysed with Pearson's correlation analysis. Results The GBDT model with 72 features with highest importance had the highest accuracy of 0.81 on both short and long survival time classes, and the area under the curve ( AUC ) of the receiver operative characteristic ( ROC ) of the short and long survival time class were 0.79 and 0.81. Six metagenes showed significant interactive effect ( P < 0.05), and Pearson's correlation analysis revealed that three of these metagenes ( TIMP 1 , ROS 1 EREG ) showed moderate (0.3 < | r | < 0.5) or high correlation (| r | > 0.5) with image features. Conclusion Radiogenomics analysis shows that MRI features are predictive of survival outcomes, and image features are highly associated with selective metagenes. Radiogenomics analysis is a useful method for optimizing clinical diagnosis and selecting effective treatments.

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“…Research on exosomes and other secreted membrane vesicles has expanded in the last decade due to their putative role in therapeutics (27), disease pathogenesis (28) and biomarker discovery (29, 30). These vesicles should be precisely characterized biophysically prior to investigating their role as intercellular messengers, biomarkers or therapeutic tools.…”

Section: Introductionmentioning

confidence: 99%

Physical characterization and profiling of airway epithelial derived exosomes using light scattering

Kesımer

Gupta

2015

Methods

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Exosomes and other extracellular vesicles have been gaining interest during the last decade due to their emerging role in biology and, disease pathogenesis and their biomarker potential. Almost all published research related to exosomes and other extracellular vesicles include some form of physical characterization. Therefore, these vesicles should be precisely profiled and characterized physically before studying their biological role as intercellular messengers, biomarkers or therapeutic tools. Using a combination of light scattering techniques, including dynamic light scattering (DLS) and multi-angle laser light scattering combined with size exclusion separation (SEC-MALLS), we physically characterized and compared distinct extracellular vesicles derived from the apical secretions of two different cultured airway epithelial cells. The results indicated that epithelial cells release vesicles with distinct physical properties and sizes. Human primary tracheobronchial cell culture (HTBE) derived vesicles have a hydrodynamic radius (Rh) of approximately 340 nm while their radius of gyration (Rg) is approximately 200 nm. Electron microscopy analysis, however, revealed that their spherical component is 40-100 nm in size, and they carry filamentous, entangled membrane mucins on their surface that increases their overall radius. The mucin decoration on the surface defines their size and charge as measured using light scattering techniques. Their surface properties mirror the properties of the cells from which they are derived. This may provide a unique tool for researchers to elucidate the unanswered questions in normal airway biology and innate and adaptive defense, including the remodeling of airways during inflammation, tumorigenesis and metastasis.

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Towards developing biomarkers for glioblastoma multiforme: a proteomics view

Cited by 34 publications

References 106 publications

The Impact of Oncogenic EGFRvIII on the Proteome of Extracellular Vesicles Released from Glioblastoma Cells

The Impact of Oncogenic EGFRvIII on the Proteome of Extracellular Vesicles Released from Glioblastoma Cells

Machine‐learning based radiogenomics analysis of MRI features and metagenes in glioblastoma multiforme patients with different survival time

Physical characterization and profiling of airway epithelial derived exosomes using light scattering

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