Glioblastoma cell populations with distinct oncogenic programs release podoplanin as procoagulant extracellular vesicles
Bibliographic record
Abstract
Vascular anomalies, including local and peripheral thrombosis, are a hallmark of glioblastoma (GBM) and an aftermath of deregulation of the cancer cell genome and epigenome. Although the molecular effectors of these changes are poorly understood, the upregulation of podoplanin (PDPN) by cancer cells has recently been linked to an increased risk for venous thromboembolism (VTE) in GBM patients. Therefore, regulation of this platelet-activating protein by transforming events in cancer cells is of considerable interest. We used single-cell and bulk transcriptome data mining, as well as cellular and xenograft models in mice, to analyze the nature of cells expressing PDPN, as well as their impact on the activation of the coagulation system and platelets. We report that PDPN is expressed by distinct (mesenchymal) GBM cell subpopulations and downregulated by oncogenic mutations of EGFR and IDH1 genes, along with changes in chromatin modifications (enhancer of zeste homolog 2) and DNA methylation. Glioma cells exteriorize their PDPN and/or tissue factor (TF) as cargo of exosome-like extracellular vesicles (EVs) shed from cells in vitro and in vivo. Injection of glioma-derived podoplanin carrying extracelluar vesicles (PDPN-EVs) activates platelets, whereas tissue factor carrying extracellular vesicles (TF-EVs) activate the clotting cascade. Similarly, an increase in platelet activation (platelet factor 4) or coagulation (D-dimer) markers occurs in mice harboring the corresponding glioma xenografts expressing PDPN or TF, respectively. Coexpression of PDPN and TF by GBM cells cooperatively affects tumor microthrombosis. Thus, in GBM, distinct cellular subsets drive multiple facets of cancer-associated thrombosis and may represent targets for phenotype- and cell type-based diagnosis and antithrombotic intervention.
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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) | 0.001 | 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".