Targeted Inflammation During Oncolytic Virus Therapy Severely Compromises Tumor Blood Flow
Bibliographic record
Abstract
Oncolytic viruses (OVs) are selected or designed to eliminate malignancies by direct infection and lysis of cancer cells. In contrast to this concept of direct tumor lysis by viral infection, we observed that a significant portion of the in vivo tumor killing activity of two OVs, vesicular stomatitis virus (VSV) and vaccinia virus is caused by indirect killing of uninfected tumor cells. Shortly after administering the oncolytic virus we observed limited virus infection, coincident with a loss of blood flow to the interior of the tumor that correlated with induction of apoptosis in tumor cells. Transcript profiling of tumors showed that virus infection resulted in a dramatic transcriptional activation of pro-inflammatory genes including the neutrophil chemoattractants CXCL1 and CXCL5. Immunohistochemical examination of infected tumors revealed infiltration by neutrophils correlating with chemokine induction. Depletion of neutrophils in animals prior to VSV administration eliminated uninfected tumor cell apoptosis and permitted more extensive replication and spreading of the virus throughout the tumor. Taken all together, these results indicate that targeted recruitment of neutrophils to infected tumor beds enhances the killing of malignant cells. We propose that activation of inflammatory cells can be used for enhancing the effectiveness of oncolytic virus therapeutics, and that this approach should influence the planning of therapeutic doses.
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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".