Oncolytic viruses for cancer immunotherapy
Bibliographic record
Abstract
In this review, we discuss the use of oncolytic viruses in cancer immunotherapy treatments in general, with a particular focus on adenoviruses. These serve as a model to elucidate how versatile viruses are, and how they can be used to complement other cancer therapies to gain optimal patient benefits. Historical reports from over a hundred years suggest treatment efficacy and safety with adenovirus and other oncolytic viruses. This is confirmed in more contemporary patient series and multiple clinical trials. Yet, while the first viruses have already been granted approval from several regulatory authorities, room for improvement remains.As good safety and tolerability have been seen, the oncolytic virus field has now moved on to increase efficacy in a wide array of approaches. Adding different immunomodulatory transgenes to the viruses is one strategy gaining momentum. Immunostimulatory molecules can thus be produced at the tumor with reduced systemic side effects. On the other hand, preclinical work suggests additive or synergistic effects with conventional treatments such as radiotherapy and chemotherapy. In addition, the newly introduced checkpoint inhibitors and other immunomodulatory drugs could make perfect companions to oncolytic viruses. Especially tumors that seem not to be recognized by the immune system can be made immunogenic by oncolytic viruses. Logically, the combination with checkpoint inhibitors is being evaluated in ongoing trials. Another promising avenue is modulating the tumor microenvironment with oncolytic viruses to allow T cell therapies to work in solid tumors.Oncolytic viruses could be the next remarkable wave in cancer immunotherapy.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".