Gene therapy for malignant gliomas
Bibliographic record
Abstract
Gliomas are the most common primary brain tumours found in adults. The median survival of patients diagnosed with the most malignant form, glioblastoma multiforme (GBM), is 9-12 months and has changed little over the years despite advances in medical technology. Gene therapy may offer new solutions to treat this resistant disease. Hence, we tested three different gene therapy strategies. In our first study, we tested the efficacy of targeted therapy to correct common aberrations found in gliomas including amplification/mutation of receptor tyrosine kinases (RTK) and loss of PTEN, which result in an overactive PI3K/Akt pathway. Without PTEN, FOXO transcription factors are inactivated, and the cell becomes resistant to apoptosis and cell cycle arrest. By using an adenoviral vector (AdV) expressing an activated FOXO1 mutant (AdFOXO1;AAA), we restored apoptosis and cell cycle arrest, reduced tumour volume and prolonged survival in an intracerebral xenograft model. Secondly, we examined the therapeutic capacity of a novel replicating/non-disseminating AdV expressing the fusion protein of cytosine deaminase and uracil phosphoribosyltransferase (CU). CU can convert the non-toxic pro-drug, 5-fluorocytosine (5-FC) to the tissue diffusible chemotherapeutic drug, 5-fluorouracil (5-FU) to target dividing cells. In vitro, the replicating vectors were superior to the non-replicating vectors, but the fully replicating/disseminating vector did not perform considerably better than the replicating/non-disseminating vector suggesting that dissemination may not be advantageous. In vivo, the replicating/non-disseminating vector administered in conjunction with 5-FC prolonged survival in both an athymic and an immunocompetent mouse model. Moreover, an immune bystander effect in vivo was mediated by macrophages and T cells. Lastly, we investigated a method to harness a tool of the immune system, IFN-ß; this cytokine is known to have anti-angiogenic, anti-proliferative, and immunomo
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".