Deletion of immunomodulatory genes as a novel approach to oncolytic vaccinia virus development
Bibliographic record
Abstract
Vaccinia virus (VV) has emerged as a promising platform for oncolytic virotherapy. Many clinical VV candidates, such as the double-deleted VV, vvDD, are engineered with deletions that enhance viral tumor selectivity based on cellular proliferation rates. An alternative approach is to exploit the dampened interferon-based innate immune responses of tumor cells by deleting one of the many VV immunomodulatory genes expressed to dismantle the antiviral response. We hypothesized that such a VV mutant would be attenuated in non-tumor cells but retain the ability to effectively propagate in and kill tumor cells, yielding a tumor-selective oncolytic VV with significant anti-tumor potency. In this study, we demonstrated that VVs with a deletion in one of several VV immunomodulatory genes (N1L, K1L, K3L, A46R, or A52R) have similar or improved in vitro replication, spread, and cytotoxicity in colon and ovarian cancer cells compared to vvDD. These deletion mutants are tumor selective, and the best performing candidates (ΔK1L, ΔA46R, and ΔA52R VV) are associated with significant improvement in survival, as well as immunomodulation, within the tumor environment. Overall, we show that exploiting the diminished antiviral responses in tumors serves as an effective strategy for generating tumor-selective and potent oncolytic VVs, with important implications in future oncolytic virus (OV) design.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 | 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 teacher head, 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".