Systemically administered AAV9-sTRAIL combats invasive glioblastoma in a patient-derived orthotopic xenograft model
Bibliographic record
Abstract
Adeno-associated virus (AAV) vectors expressing tumoricidal genes injected directly into brain tumors have shown some promise, however, invasive tumor cells are relatively unaffected. Systemic injection of AAV9 vectors provides widespread delivery to the brain and potentially the tumor/microenvironment. Here we assessed AAV9 for potential glioblastoma therapy using two different promoters driving the expression of the secreted anti-cancer agent sTRAIL as a transgene model; the ubiquitously active chicken β-actin (CBA) promoter and the neuron-specific enolase (NSE) promoter to restrict expression in brain. Intravenous injection of AAV9 vectors encoding a bioluminescent reporter showed similar distribution patterns, although the NSE promoter yielded 100-fold lower expression in the abdomen (liver), with the brain-to-liver expression ratio remaining the same. The main cell types targeted by the CBA promoter were astrocytes, neurons and endothelial cells, while expression by NSE promoter mostly occurred in neurons. Intravenous administration of either AAV9-CBA-sTRAIL or AAV9-NSE-sTRAIL vectors to mice bearing intracranial patient-derived glioblastoma xenografts led to a slower tumor growth and significantly increased survival, with the CBA promoter having higher efficacy. To our knowledge, this is the first report showing the potential of systemic injection of AAV9 vector encoding a therapeutic gene for the treatment of brain tumors.
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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) | 0.001 | 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 | 0.001 |
| 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".