Enhanced Control of Oncolytic Measles Virus Using MicroRNA Target Sites
Bibliographic record
Abstract
Measles viruses derived from the live-attenuated Edmonton-B vaccine lineage are currently investigated as novel anti-cancer therapeutics. In this context, tumor specificity and oncolytic potency are key determinants of the therapeutic index. Here, we describe a systematic and comprehensive analysis of a recently developed post-entry targeting strategy based on the incorporation of microRNA target sites (miRTS) into the measles virus genome. We have established viruses with target sites for different microRNA species in the 3′ untranslated regions of either the N , F , H , or L genes and generated viruses harboring microRNA target sites in multiple genes. We report critical importance of target-site positioning with proximal genomic positions effecting maximum vector control. No relevant additional effect of six versus three miRTS copies for the same microRNA species in terms of regulatory efficiency was observed. Moreover, we demonstrate that, depending on the microRNA species, viral mRNAs containing microRNA target sites are directly cleaved and/or translationally repressed in presence of cognate microRNAs. In conclusion, we report highly efficient control of measles virus replication with various miRTS positions for development of safe and efficient cancer virotherapy and provide insights into the mechanisms underlying microRNA-mediated vector control.
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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".