Adaptation of transgene mRNA translation boosts the anticancer efficacy of oncolytic HSV1
Bibliographic record
Abstract
Background Transgenes deliver therapeutic payloads to improve oncolytic virus immunotherapy. Transgenes encoded within oncolytic viruses are designed to be highly transcribed, but protein synthesis is often negatively affected by viral infection, compromising the amount of therapeutic protein expressed. Studying the oncolytic herpes simplex virus-1 (HSV1), we found standard transgene mRNAs to be suboptimally translated in infected cells. Methods Using RNA-Seq reads, we determined the transcription start sites and 5'leaders of HSV1 genes and uncovered the US11 5'leader to confer superior activity in translation reporter assays. We then incorporated this 5’leader into GM-CSF expression cassette in oncolytic HSV1 and compared the translationally adapted oncolytic virus with the conventional, leaderless, virus in vitro and in mice. Results Inclusion of the US11 5’leader in the GM-CSF transgene incorporated into HSV1 boosted translation in vitro and in vivo . Importantly, treatment with US11 5’leader-GM-CSF oncolytic HSV1 showed superior antitumor immune activity and improved survival in a syngeneic mouse model of colorectal cancer as compared with leaderless-GM-CSF HSV1. Conclusions Our study demonstrates the therapeutic value of identifying and integrating platform-specific cis -acting sequences that confer increased protein synthesis on transgene expression.
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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".