Immunogenic cell death: The cornerstone of oncolytic viro-immunotherapy
Bibliographic record
Abstract
According to the World Health Organization, cancer is one of the leading global health concerns, causing nearly 10 million deaths in 2020. While classical chemotherapeutics produce strong cytotoxicity on cancer cells, they carry limitations of drug resistance and off-target effects and sometimes fail to elicit adequate antitumor protection against tumor relapse. Additionally, most cancer cells have developed various ways to escape immune surveillance. Nevertheless, novel anticancer strategies such as oncolytic viro-immunotherapy can trigger immunogenic cell death (ICD), which can quickly grasp the attention of the host defense machinery, resulting in an ensuing antitumor immune response. Specifically, oncolytic viruses (OVs) can infect and destroy targeted cancer cells and stimulate the immune system by exposing pathogen-associated molecular patterns (PAMPs) and damage-associated molecular patterns (DAMPs) to promote inflammatory reactions, and concomitantly prime and induce antitumor immunity by the release of neoantigens from the damaged cancer cells. Thus, OVs can serve as a novel system to sensitize tumor cells for promising immunotherapies. This review discusses the concept of ICD in cancer, centralizing ICD-associated danger signals and their consequence in antitumor responses and ICD induced by OVs. We also shed light on the potential strategies to enhance the immunogenicity of OVs, including the use of genetically modified OVs and their combination with ICD-enhancing agents, which are helpful as forthcoming anticancer regimens.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".