Tumor vascularization is critical for oncolytic vaccinia virus treatment of peritoneal carcinomatosis
Bibliographic record
Abstract
Peritoneal carcinomatosis (PC) represents a significant clinical challenge for which there are few treatment options. Oncolytic viruses are ideal candidates for PC treatment because of their high tumor specificity, excellent safety profile and suitability for peritoneal delivery. Here, we described the use of vvDD-SR-RFP, a recombinant vaccinia virus, in xenograft and syngeneic models of colorectal PC. Colorectal cancer cell lines were highly susceptible to vvDD-SR-RFP replication and cytotoxicity. Intraperitoneal delivery of vvDD-SR-RFP on Day 12 to mice with colorectal carcinomatosis significantly improved survival whereas survival was not improved following virus treatment on Day 8, when tumors were smaller. Immunohistochemistry revealed early tumors had a poorly distributed network of blood vessels and lower proliferation index compared to later tumors. Virus infection was also restricted to tumor rims following Day 8 treatment, whereas it was disseminated in tumors treated on Day 12. Additionally, direct infection of tumor endothelium was observed and virus infection correlated with a loss of endothelial staining and induction of cell death. Our results demonstrate that tumor vasculature has a critical role in virus delivery and tumor response. This will have significant implications in the clinical setting, both in understanding timing of therapies and in designing combination treatment strategies.
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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".