Recent progress in combination therapy of oncolytic vaccinia virus
Bibliographic record
Abstract
In recent years, oncolytic viruses have emerged as promising agents for treating various cancers. An oncolytic virus is a non-pathogenic virus that, due to genetic manipulation, tends to replicate in and cause lysis of cancerous cells while leaving healthy cells unaffected. Among these viruses, vaccinia virus is an attractive platform for use as an oncolytic platform due to its 190 Kb genome with a high capacity for encoding therapeutic payloads. Combining oncolytic VV therapy with other conventional cancer treatments has been shown to be synergistic and more effective than monotherapies. Additionally, OVV can be used as a vector to deliver therapeutic payloads, alone or in combination with other treatments, to increase overall efficacy. Here, we present a comprehensive analysis of preclinical and clinical studies that have evaluated the efficacy of oncolytic vaccinia viruses in cancer immunotherapy. We discuss the outcomes of these studies, including tumor regression rates, overall survival benefits, and long-term responses. Moreover, we provide insights into the challenges and limitations associated with oncolytic vaccinia virus- based therapies, including immune evasion mechanisms, potential toxicities, and the development of resistance.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".