Combination of IAP Antagonists and TNF-α-Armed Oncolytic Viruses Induce Tumor Vascular Shutdown and Tumor Regression
Bibliographic record
Abstract
Smac mimetic compounds (SMCs) are anti-cancer drugs that antagonize Inhibitor of Apoptosis proteins, which consequently sensitize cancer cells to death in the presence of proinflammatory ligands such as tumor necrosis factor alpha (TNF-α). SMCs synergize with the attenuated oncolytic vesicular stomatitis virus (VSVΔ51) by eliciting an innate immune response, which is dependent on the endogenous production of TNF-α and type I interferon. To improve on this SMC-mediated synergistic response, we generated TNF-α-armed VSVΔ51 to produce elevated levels of this death ligand. Due to ectopic expression of TNF-α from infected cells, a lower viral dose of TNF-α-armed VSVΔ51 combined with treatment of the SMC LCL161 was sufficient to improve the survival rate compared to LCL161 and unarmed VSVΔ51 co-therapy. This improved response is attributed to a bystander effect whereby the spread of TNF-α from infected cells leads to the death of uninfected cells in the presence of LCL161. In addition, the treatments induced vascular collapse in solid tumors with a concomitant increase of tumor cell death, revealing another mechanism by which cytokine-armed VSVΔ51 in combination with LCL161 can kill tumor cells. Our studies demonstrate the potential for cytokine-engineered oncolytic virus and SMCs as a new combination immunotherapy for cancer treatment.
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".