The Use of Oncolytic Vaccinia Viruses in the Treatment of Cancer: A New Role for an Old Ally?
Bibliographic record
Abstract
The use of genetically engineered, tumor-targeting viruses as oncolytic agents has recently emerged as a promising new area for the development of novel cancer therapies. The first viruses to enter the clinic, such as ONYX-015 (an oncolytic adenovirus), provided evidence both for the safety and for the anti-tumor potential of this approach. The results of these early trials have also allowed investigators to examine the limitations of these viruses and to develop potentially far more effective approaches. In this review the development of such next generation viruses, in particular the potential use of strains of vaccinia virus, will be discussed. Vaccinia has an enormous history of use in humans and possesses many of the features felt to be beneficial for the creation of a successful virotherapy agent. It causes no known disease in humans, yet is capable of infecting almost all cell types with a subsequent rapid and lytic infection, which subsequently induces a vigorous local CTL immune response at the site of infection. Vaccinia also displays natural tumor tropism, and several approaches have been used to further limit viral replication to tumor cells and to optimize the immune response induced at the site of the tumor. Finally, the large cloning capacity of vaccinia allows for the addition of multiple foreign genes into the viral genome. This has been exploited to increase the bystander effect of the virus by immune modulation or by expression of pro-drug converting enzymes as well as to incorporate safety controls and reporters for in vivo molecular imaging. Initial clinical trials with these viruses further highlights their potential as the next generation of oncolytic agents and as highly effective future cancer therapies.
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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 | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.007 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".