Critical Interactions between Immunogenic Cancer Cell Death, Oncolytic Viruses, and the Immune System Define the Rational Design of Combination Immunotherapies
Bibliographic record
Abstract
Oncolytic viruses (OVs) are multimodal cancer therapeutics, with one of their dominant mechanisms being in situ vaccination. There is a growing consensus that optimal cancer therapies should generate robust tumor-specific immune responses. Immunogenic cell death (ICD) is a paradigm of cellular demise culminating in the spatiotemporal release of danger-associated molecular patterns that induce potent anticancer immunity. Alongside traditional ICD inducers like anthracycline chemotherapeutics and radiation, OVs have emerged as novel members of this class of therapeutics. OVs replicate in cancers and release tumor Ags, which are perceived as dangerous because of simultaneous expression of pathogen-associated molecular patterns that activate APCs. Therefore, OVs provide the target Ags and danger signals required to induce adaptive immune responses. This review discusses why OVs are attractive candidates for generating ICD, biological barriers limiting their success in the clinic, and groundbreaking strategies to potentiate ICD and antitumor immunity with rationally designed OV-based combination 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".