Oncolytic Virotherapy and Immunogenic Cancer Cell Death: Sharpening the Sword for Improved Cancer Treatment Strategies
Bibliographic record
Abstract
Oncolytic viruses are novel immunotherapeutics with increasingly promising outcomes in cancer patient clinical trials. Preclinical and clinical studies have uncovered the importance of virus-induced activation of antitumor immune responses for optimal therapeutic efficacy. Recently, several classes of chemotherapeutics have been shown to cause immunogenic cancer cell death characterized by the release of immunomodulatory molecules that activate antigen-presenting cells and thus trigger the induction of more potent anticancer adaptive immune responses. In preclinical models, several oncolytic viruses induce immunogenic cell death, which is associated with increased cross-priming of tumor-associated antigens. In this review, we discuss the recent advances in immunogenic cancer cell death as induced by chemotherapeutic treatments, including the roles of relevant danger-associated molecular patterns and signaling pathways, and highlighting the significance of the endoplasmic reticulum (ER) stress response. As virtually all viruses modulate both ER stress and cell death responses, we provide perspectives on future research directions that can be explored to optimize oncolytic viruses, alone or in combination with targeted drug therapies, as potent immunogenic cancer cell death-inducing agents. We propose that such optimized virus-drug synergistic strategies will improve the therapeutic outcomes for many currently intractable cancers.
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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) | 0.002 | 0.000 |
| 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 | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".