Genetic Modifications That Expand Oncolytic Virus Potency
Bibliographic record
Abstract
Oncolytic viruses (OVs) are a promising type of cancer therapy since they selectively replicate in tumor cells without damaging healthy cells. Many oncolytic viruses have progressed to human clinical trials, however, their performance as monotherapy has not been as successful as expected. Importantly, recent literature suggests that the oncolytic potential of these viruses can be further increased by genetically modifying the viruses. In this review, we describe genetic modifications to OVs that improve their ability to kill tumor cells directly, to dismantle the tumor microenvironment, or to alter tumor cell signaling and enhance anti-tumor immunity. These advances are particularly important to increase virus spread and reduce metastasis, as demonstrated in animal models. Since metastasis is the principal cause of mortality in cancer patients, having OVs designed to target metastases could transform cancer therapy. The genetic alterations reported to date are only the beginning of all possible improvements to OVs. Modifications described here could be combined together, targeting multiple processes, or with other non-viral therapies with potential to provide a strong and lasting anti-tumor response in cancer patients.
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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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".