Glioblastoma Bystander Cell Therapy: Improvements in Treatment and Insights into the Therapy Mechanisms
Bibliographic record
Abstract
A preclinical model of glioblastoma (GB) bystander cell therapy using human adipose mesenchymal stromal cells (hAMSCs) is used to address the issues of cell availability, quality, and feasibility of tumor cure. We show that a fast proliferating variety of hAMSCs expressing thymidine kinase (TK) has therapeutic capacity equivalent to that of TK-expressing hAMSCs and can be used in a multiple-inoculation procedure to reduce GB tumors to a chronically inhibited state. We also show that up to 25% of unmodified hAMSCs can be tolerated in the therapeutic procedure without reducing efficacy. Moreover, mimicking a clinical situation, tumor debulking previous to cell therapy inhibits GB tumor growth. To understand these striking results at a cellular level, we used a bioluminescence imaging strategy and showed that tumor-implanted therapeutic cells do not proliferate, are unaffected by GCV, and spontaneously decrease to a stable level. Moreover, using the CLARITY procedure for tridimensional visualization of fluorescent cells in transparent brains, we find therapeutic cells forming vascular-like structures that often associate with tumor cells. In vitro experiments show that therapeutic cells exposed to GCV produce cytotoxic extracellular vesicles and suggest that a similar mechanism may be responsible for the in vivo therapeutic effectiveness of TK-expressing hAMSCs.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".