Current Immunotherapeutic Strategies to Enhance Oncolytic Virotherapy
Bibliographic record
Abstract
Oncolytic viruses (OV) represent a promising strategy to augment the spectrum of cancer therapeutics. For efficacy, they rely on two general mechanisms: tumor-specific infection/cell-killing, followed by subsequent activation of the host's adaptive immune response. Numerous OV genera have been utilized in clinical trials, ultimately culminating in the 2015 Food and Drug Administration approval of a genetically engineered herpes virus, Talminogene laherparepvec (T-VEC). It is generally accepted that OV as monotherapy have only modest clinical efficacy. However, due to their ability to elicit specific antitumor immune responses, they are prime candidates to be paired with other immune-modulating strategies in order to optimize therapeutic efficacy. Synergistic strategies to enhance the efficacy of OV include augmenting the host antitumor response through the insertion of therapeutic transgenes such as GM-CSF, utilization of the prime-boost strategy, and combining OV with immune-modulatory drugs such as cyclophosphamide, sunitinib, and immune checkpoint inhibitors. This review provides an overview of these immune-based strategies to improve the clinical efficacy of oncolytic virotherapy.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".