Conditionally replicative adenovirus as a therapy for malignant peripheral nerve sheath tumors
Bibliographic record
Abstract
Oncolytic adenoviruses (Ads) stand out as a promising strategy for the targeted infection and lysis of tumor cells, with well-established clinical utility across various malignancies. This study delves into the therapeutic potential of oncolytic Ads in the context of neurofibromatosis type 1 (NF1)-associated malignant peripheral nerve sheath tumors (MPNSTs). Specifically, we evaluate Conditionally Replicative Adenoviruses (CRAds) driven by the Cyclooxygenase 2 (COX2) promoter, as selective agents against MPNSTs, demonstrating their preferential targeting of MPNST cells compared to non-malignant Schwann cell control. COX2-driven CRAds, particularly those with modified fiber-knobs exhibit superior binding affinity towards MPNST cells and demonstrate efficient and preferential replication and lysis of MPNST cells, with minimal impact on non-malignant control cells. In vivo experiments involving intratumoral CRAd injections in immunocompromised mice with human MPNST xenografts significantly extend survival and reduce tumor growth rate compared to controls. Moreover, in immunocompetent mouse models with MPNST-like allografts, CRAd injections induce a robust infiltration of CD8+ T cells into the tumor microenvironment (TME), indicating the potential to promote a pro-inflammatory response. These findings underscore oncolytic Ads as promising, selective, and minimally toxic agents for MPNST therapy, warranting further exploration.
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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".