Oncolytic Measles Virus Encoding MicroRNA for Targeted RNA Interference
Bibliographic record
Abstract
Virotherapy is a promising, novel form of cancer immunotherapy currently being investigated in pre-clinical and clinical settings. While generally well-tolerated, the anti-tumor potency of oncolytic virus-based monotherapies needs to be improved further. One of the major factors limiting the replication efficiency of oncolytic viruses are the antiviral defense pathways activated by tumor cells. In this study, we have designed and validated a universal expression cassette for artificial microRNAs that can now be adapted to suppress genes of interest, including potential resistance factors. Transcripts are encoded as a primary microRNA for processing via the predominantly nuclear RNase III Drosha. We have engineered an oncolytic measles virus encoding this universal expression cassette for artificial microRNAs. Virally encoded microRNA was expressed in the range of endogenous microRNA transcripts and successfully mediated target protein suppression. However, absolute expression levels of mature microRNAs were limited when delivered by an oncolytic measles virus. We demonstrate that measles virus, in contrast to other cytosolic viruses, does not induce translocation of Drosha from the nucleus into the cytoplasm, potentially resulting in a limited processing efficiency of virus-derived, cytosolically delivered artificial microRNAs. To our knowledge, this is the first report demonstrating functional expression of microRNA from oncolytic measles viruses potentially enabling future targeted knockdown, for instance of antiviral factors specifically in tumor cells.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".