Characteristics of Oncolytic Vesicular Stomatitis Virus Displaying Tumor-Targeting Ligands
Bibliographic record
Abstract
We sought proof of principle that tumor-targeting ligands can be displayed on the surface of vesicular stomatitis virus (VSV) by engineering its glycoprotein. Here, we successfully rescued VSVs displaying tumor vasculature-targeting ligands. By using a rational approach, we investigated various feasible insertion sites on the G protein of VSV (VSV-G) for display of tumor vasculature-targeting ligands, cyclic RGD (cRGD) and echistatin. We found seven sites on VSV-G that tolerated insertion of the 9-residue cRGD peptide, two of which could tolerate insertion of the 49-amino acid echistatin domain. All of the ligand-displaying viruses replicated as well as the parental virus. In vitro studies demonstrated that the VSV-echistatin viruses specifically bound to targeted integrins. Since the low-density lipoprotein receptor (LDLR) was recently identified as a major receptor for VSV, we investigated the entry of ligand-displaying viruses after masking LDLR. The experiment showed that the modified viruses can enter the cell independently of LDLR, whereas entry of unmodified virus is significantly blocked by a specific monoclonal antibody against LDLR. Both parental and ligand-displaying viruses displayed equal oncolytic efficacies in a syngeneic mouse myeloma model. We further demonstrated that single-chain antibody fragments against tumor-specific antigens can be inserted at the N terminus of the G protein and that corresponding replication-competent VSVs can be rescued efficiently. Overall, we demonstrated that functional tumor-targeting ligands can be displayed on replication-competent VSVs without perturbing viral growth and oncolytic efficacy. This study provides a rational foundation for the future development of fully retargeted oncolytic VSVs.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".