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 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) | 0.001 | 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".