Divergent evolution of temozolomide resistance in glioblastoma stem cells is reflected in extracellular vesicles and coupled with radiosensitization
Bibliographic record
Abstract
Background: Glioblastoma (GBM) is almost invariably fatal due to failure of standard therapy. The relapse of GBM following surgery, radiation, and systemic temozolomide (TMZ) is attributed to the ability of glioma stem cells (GSCs) to survive, evolve, and repopulate the tumor mass, events on which therapy exerts a poorly understood influence. Methods: Here we explore the molecular and cellular evolution of TMZ resistance as it emerges in vivo (xenograft models) in a series of human GSCs with either proneural (PN) or mesenchymal (MES) molecular characteristics. Results: We observed that the initial response of GSC-initiated intracranial xenografts to TMZ is eventually replaced by refractory growth pattern. Individual tumors derived from the same isogenic GSC line expressed divergent and complex profiles of TMZ resistance markers, with a minor representation of O6-methylguanine DNA methyltransferase (MGMT) upregulation. In several independent TMZ-resistant tumors originating from MES GSCs we observed a consistent diminution of mesenchymal features, which persisted in cell culture and correlated with increased expression of Nestin, decline in transglutaminase 2 and sensitivity to radiation. The corresponding mRNA expression profiles reflective of TMZ resistance and stem cell phenotype were recapitulated in the transcriptome of exosome-like extracellular vesicles (EVs) released by GSCs into the culture medium. Conclusions: Intrinsic changes in the tumor-initiating cell compartment may include loss of subtype characteristics and reciprocal alterations in sensitivity to chemo- and radiation therapy. These observations suggest that exploiting therapy-induced changes in the GSC phenotype and alternating cycles of therapy may be explored to improve GBM outcomes.
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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".