Engineering Rapalog-Inducible Genetic Switches Based on Split-T7 Polymerase to Regulate Oncolytic Virus-Driven Production of Tumour-Localized IL-12 for Anti-Cancer Immunotherapy
Bibliographic record
Abstract
The approval of different cytokines as anti-neoplastic agents has been challenged by dose-limiting toxicities. Although reducing dose levels affords improved tolerability, efficacy is precluded at these suboptimal doses. Strategies combining cytokines with oncolytic viruses have proven to elicit potent survival benefits in vivo, despite promoting rapid clearance of the oncolytic virus itself. Herein, we developed an inducible expression system based on a Split-T7 RNA polymerase for oncolytic poxviruses to regulate the spatial and temporal expression of a beneficial transgene. This expression system utilizes approved anti-neoplastic rapamycin analogues for transgene induction. This treatment regimen thus offers a triple anti-tumour effect through the oncolytic virus, the induced transgene, and the pharmacologic inducer itself. More specifically, we designed our therapeutic transgene by fusing a tumour-targeting chlorotoxin (CLTX) peptide to interleukin-12 (IL-12), and demonstrated that the constructs were functional and cancer-selective. We next encoded this construct into the oncolytic vaccinia virus strain Copenhagen (VV-iIL-12mCLTX), and were able to demonstrate significantly improved survival in multiple syngeneic murine tumour models through both localized and systemic virus administration, in combination with rapalogs. In summary, our findings demonstrate that rapalog-inducible genetic switches based on Split-T7 polymerase allow for regulation of the oncolytic virus-driven production of tumour-localized IL-12 for improved anti-cancer immunotherapy.
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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".