Delta-24-RGD, an Oncolytic Adenovirus, Increases Survival and Promotes Proinflammatory Immune Landscape Remodeling in Models of AT/RT and CNS-PNET
Bibliographic record
Abstract
Abstract Purpose: Atypical teratoid/rhabdoid tumors (AT/RT) and central nervous system primitive neuroectodermal tumors (CNS-PNET) are pediatric brain tumors with poor survival and life-long negative side effects. Here, the aim was to characterize the efficacy and safety of the oncolytic adenovirus, Delta-24-RGD, which selectively replicates in and kills tumor cells. Experimental Design: Delta-24-RGD determinants for infection and replication were evaluated in patient expression datasets. Viral replication and cytotoxicity were assessed in vitro in a battery of CNS-PNET and AT/RT cell lines. In vivo, efficacy was determined in different orthotopic mouse models, including early and established tumor models, a disseminated AT/RT lesion model, and immunocompetent humanized mouse models (hCD34+-NSG-SGM3). Results: Delta-24-RGD infected and replicated efficiently in all the cell lines tested. In addition, the virus induced dose-dependent cytotoxicity [IC50 value below 1 plaque-forming unit (PFU)/cell] and the release of immunogenic markers. In vivo, a single intratumoral Delta-24-RGD injection (107 or 108 PFU) significantly increased survival and led to long-term survival in AT/RT and PNET models. Delta-24-RGD hindered the dissemination of AT/RTs and increased survival, leading to 70% of long-term survivors. Of relevance, viral administration to established tumor masses (30 days after engraftment) showed therapeutic benefit. In humanized immunocompetent models, Delta-24-RGD significantly extended the survival of mice bearing AT/RTs or PNETs (ranging from 11 to 27 days) and did not display any toxicity associated with inflammation. Immunophenotyping of Delta-24-RGD–treated tumors revealed increased CD8+ T-cell infiltration. Conclusions: Delta-24-RGD is a feasible therapeutic option for AT/RTs and CNS-PNETs. This work constitutes the basis for potential translation to the clinical setting.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".