The Impact of Oncogenic EGFRvIII on the Proteome of Extracellular Vesicles Released from Glioblastoma Cells
Bibliographic record
Abstract
Glioblastoma 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 EGFRvIII-transformed 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 downregulation 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 (including EGFRvIII). Notably, cells expressing EGFRvIII released EVs double positive for CD44/BSG, and these proteins also colocalized 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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".