Targeting of Interferon-Beta to Produce a Specific, Multi-Mechanistic Oncolytic Vaccinia Virus
Bibliographic record
Abstract
BACKGROUND: Oncolytic viruses hold much promise for clinical treatment of many cancers, but a lack of systemic delivery and insufficient tumor cell killing have limited their usefulness. We have previously demonstrated that vaccinia virus strains are capable of systemic delivery to tumors in mouse models, but infection of normal tissues remains an issue. We hypothesized that interferon-beta (IFN-beta) expression from an oncolytic vaccinia strain incapable of responding to this cytokine would have dual benefits as a cancer therapeutic: increased anticancer effects and enhanced virus inactivation in normal tissues. We report the construction and preclinical testing of this virus. METHODS AND FINDINGS: In vitro screening of viral strains by cytotoxicity and replication assay was coupled to cellular characterization by phospho-flow cytometry in order to select a novel oncolytic vaccinia virus. This virus was then examined in vivo in mouse models by non-invasive imaging techniques. A vaccinia B18R deletion mutant was selected as the backbone for IFN-beta expression, because the B18R gene product neutralizes secreted type-I IFNs. The oncolytic B18R deletion mutant demonstrated IFN-dependent cancer selectivity and efficacy in vitro, and tumor targeting and efficacy in mouse models in vivo. Both tumor cells and tumor-associated vascular endothelial cells were targeted. Complete tumor responses in preclinical models were accompanied by immune-mediated protection against tumor rechallenge. Cancer selectivity was also demonstrated in primary human tumor explant tissues and adjacent normal tissues. The IFN-beta gene was then cloned into the thymidine kinase (TK) region of this virus to create JX-795 (TK-/B18R-/IFN-beta+). JX-795 had superior tumor selectivity and systemic intravenous efficacy when compared with the TK-/B18R- control or wild-type vaccinia in preclinical models. CONCLUSIONS: By combining IFN-dependent cancer selectivity with IFN-beta expression to optimize both anticancer effects and normal tissue antiviral effects, we were able to achieve, to our knowledge for the first time, tumor-specific replication, IFN-beta gene expression, and efficacy following systemic delivery in preclinical models.
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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".