Remodeling the tumor immune microenvironment with oncolytic viruses expressing miRNAs
Bibliographic record
Abstract
MiRNAs (miRNA, miR) play important functions in the tumor microenvironment (TME) by silencing gene expression through RNA interference. They are involved in regulating both tumor progression and tumor suppression. The pathways involved in miRNA processing and the miRNAs themselves are dysregulated in cancer. Consequently, they have become attractive therapeutic targets as underscored by the plethora of miRNA-based therapies currently in pre-clinical and clinical studies. It has been shown that miRNAs can be used to improve oncolytic viruses (OVs) and enable superior viral oncolysis, tumor suppression and immune modulation. In these cases, miRNAs are empirically selected to improve viral oncolysis, which translates into decreased tumor growth in multiple murine models. While this infectious process is critical to OV therapy, optimal immunomodulation is crucial for the establishment of a targeted and durable effect, resulting in cancer eradication. Through numerous mechanisms, OVs elicit a strong antitumor immune response that can also be further improved by miRNAs. They are known to regulate components of the immune TME and promote effector functions, antigen presentation, phenotypical polarization, and varying levels of immunosuppression. Reciprocally, OVs have the power to overcome the limitations encountered in canonical miRNA-based therapies. They deliver therapeutic payloads directly into the TME and facilitate their amplification through selective tumoral tropism and abundant viral replication. This way, off-target effects can be minimized. This review will explore the ways in which miRNAs can synergistically enhance OV immunotherapy to provide the basis for future therapeutics based on this versatile combination platform.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".