Cytokine Conditioning Enhances Systemic Delivery and Therapy of an Oncolytic Virus
Bibliographic record
Abstract
Optimum clinical protocols require systemic delivery of oncolytic viruses in the presence of an intact immune system. We show that preconditioning with immune modulators, or loading virus onto carrier cells ex vivo, enhances virus-mediated antitumor activity. Our early trials of systemic reovirus delivery showed that after infusion reovirus could be recovered from blood cells--but not from plasma--suggesting that rapid association with blood cells may protect virus from neutralizing antibody. We therefore postulated that stimulation of potential carrier cells directly in vivo before intravenous viral delivery would enhance delivery of cell-associated virus to tumor. We show that mobilization of the CD11b(+) cell compartment by granulocyte macrophage-colony stimulating factor immediately before intravenous reovirus, eliminated detectable tumor in mice with small B16 melanomas, and achieved highly significant therapy in mice bearing well-established tumors. Unexpectedly, cytokine conditioning therapy was most effective in the presence of preexisting neutralizing antibody. Consistent with this, reovirus bound by neutralizing antibody effectively accessed monocytes/macrophages and was handed off to tumor cells. Thus, preconditioning with cytokine stimulated recipient cells in vivo for enhanced viral delivery to tumors. Moreover, preexisting neutralizing antibody to an oncolytic virus may, therefore, even be exploited for systemic delivery to tumors in the clinic.
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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) | 0.002 | 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".