Evolution of Oncolytic Viruses: Novel Strategies for Cancer Treatment
Bibliographic record
Abstract
Many viruses have documented oncolytic activity, with the first evidence observed clinically over a decade ago. In recent years, there has been a resurgence of interest in the field of oncolytic viruses. Viruses may be innately oncotropic, lacking the ability to cause disease in people or they may require engineering to allow selective tumor targeting and attenuation of pathogenicity. Following infection of a neoplastic cell, several events may occur, including direct viral oncolysis, apoptosis, necrotic cell death and autophagic cellular demise. Of late, a large body of work has recognized the ability of oncolytic viruses (OVs) to activate the innate and adaptive immune system, as well as directly killing tumors. The production of viruses expressing transgenes encoding for cytokines, colony-stimulating factors, costimulatory molecules and tumor-associated antigens has been able to further incite immune responses against target tumors. Multiple OVs are now in the advanced stages of clinical trials, with several individual viruses having completed their respective trials with positive results. This review introduces the multiple mechanisms by which OVs are able to act as an antineoplastic therapy, either on their own or in combination with other more traditional treatment modalities. The full benefit and the place where OVs will be integrated into standard-of-care therapies will be determined with ongoing studies ranging from the laboratory to the patient. With various different viruses now in the clinic this therapeutic option is beginning to prove its worth, and the versatility of these agents means further innovative and novel applications will continue to be developed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.000 |
| 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".