Immunological Effects and Viral Gene Expression Determine the Efficacy of Oncolytic Measles Vaccines Encoding IL-12 or IL-15 Agonists
Bibliographic record
Abstract
Tumor-targeted immunomodulation using oncolytic viral vectors is currently being investigated as a promising strategy in cancer therapy. In a previous study, we showed that a measles virus Schwarz vaccine strain (MeVac) vector encoding an interleukin-12 fusion protein (FmIL-12) is an effective immunotherapy in the MC38cea murine colon adenocarcinoma model. We hypothesized that MeVac encoding interleukin-15 may mediate enhanced T and NK cell responses and thus increase the therapeutic efficacy, especially in NK cell-controlled tumors. Therefore, we generated MeVac vectors encoding an interleukin-15 superagonist, FmIL-15. Replication and oncolytic capacity, transgene expression, and functionality of MeVac FmIL-15 vectors were validated in vitro. Effects on the tumor immune landscape and therapeutic efficacy of both FmIL-12 and FmIL-15 vectors were studied in the MC38cea and B16hCD46 tumor models. Treatment with MeVac FmIL-15 increased T and NK cell infiltration in both models. However, MeVac FmIL-12 showed more robust viral gene expression and immune activation, resulting in superior anti-tumor efficacy. Based on these results, MeVac encoding a human IL-12 fusion protein was developed for future clinical translation.
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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".