Autophagy in Tumor Immunity and Viral-Based Immunotherapeutic Approaches in Cancer
Bibliographic record
Abstract
Autophagy is a fundamental catabolic process essential for the maintenance of cellular and tissue homeostasis, as well as directly contributing to the control of invading pathogens. Unsurprisingly, this process becomes critical in supporting cellular dysregulation that occurs in cancer, particularly the tumor microenvironments and their immune cell infiltration, ultimately playing a role in responses to cancer therapies. Therefore, understanding "cancer autophagy" could help turn this cellular waste-management service into a powerful ally for specific therapeutics. For instance, numerous regulatory mechanisms of the autophagic machinery can contribute to the anti-tumor properties of oncolytic viruses (OVs), which comprise a diverse class of replication-competent viruses with potential as cancer immunotherapeutics. In that context, autophagy can either: promote OV anti-tumor effects by enhancing infectivity and replication, mediating oncolysis, and inducing autophagic and immunogenic cell death; or reduce OV cytotoxicity by providing survival cues to tumor cells. These properties make the catabolic process of autophagy an attractive target for therapeutic combinations looking to enhance the efficacy of OVs. In this article, we review the complicated role of autophagy in cancer initiation and development, its effect on modulating OVs and immunity, and we discuss recent progress and opportunities/challenges in targeting autophagy to enhance oncolytic viral immunotherapy.
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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".