Synergistic Interaction Between Oncolytic Viruses Augments Tumor Killing
Bibliographic record
Abstract
A major barrier to all oncolytic viruses (OVs) in clinical development is cellular innate immunity, which is variably active in a spectrum of human malignancies. To overcome the heterogeneity of tumor response, we combined complementary OVs that attack cancers in distinct ways to improve therapeutic outcome. Two genetically distinct viruses, vesicular stomatitis virus (VSV) and vaccinia virus (VV), were used to eliminate the risk of recombination. The combination was tested in a variety of tumor types in vitro, in immunodeficient and immunocompetent mouse tumor models, and ex vivo, in a panel of primary human cancer samples. We found that VV synergistically enhanced VSV antitumor activity, dependent in large part on the activity of the VV B18R gene product. A recombinant version of VSV expressing the fusion-associated small-transmembrane (p14FAST) protein also further enhanced the ability of VV to spread through an infected monolayer, resulting in a "ping pong" oncolytic effect wherein each virus enhanced the ability of the other to replicate and/or spread in tumor cells. Our strategy is the first example where OVs are rationally combined to utilize attributes of different OVs to overcome the heterogeneity of malignancies and demonstrates the feasibility of combining complementary OVs to improve therapeutic outcome.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".