The emergence of combinatorial strategies in the development of RNA oncolytic virus therapies
Bibliographic record
Abstract
Oncolytic viruses (OVs) represent an exciting new biological approach to cancer therapy. In particular, RNA viruses have emerged as potent agents for oncolytic virotherapy because of their capacity to specifically target and destroy tumour cells while sparing normal cells and tissues. Several barriers remain in the development of OV therapy, including poor penetration into the tumour mass, inefficient virus replication in primary cancers, and tumour-specific resistance to OV-mediated killing. The combination of OVs with cytotoxic agents, such as small molecule inhibitors of signalling or immunomodulators, as well as stealth delivery of therapeutic viruses have shown promise as novel experimental strategies to overcome resistance to viral oncolysis. These agents complement OV therapy by unblocking host pathways, delivering viruses with greater efficiency and/or increasing virus proliferation at the tumour site. In this review, we summarize recent development of these concepts, the potential obstacles, and future prospects for the clinical utilization of RNA OVs in cancer therapy.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 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".