Dual-Armed Oncolytic Myxoma Virus Encoding IFN-γ and CD47 Promotes Lymphocyte Infiltration and Tumor Suppression of Syngeneic Murine Melanoma
Bibliographic record
Abstract
Myxoma virus (MyxV) is a rabbit-specific poxvirus. However, its ability to selectively target tumor cells has established it as a safe and effective anticancer therapy. To strengthen its preclinical efficacy, transgenes that can prolong cancer cell infection and enhance anti-tumor effector functions are currently being investigated. We engineered MyxV armed with CD47, to turn on a 'do not eat me' signal within infected cells with actively replicating viruses, and with IFN-γ to further activate host immune anticancer responses. Tumor suppressive activities were significantly enhanced by the dual-armed MyxV_CD47/IFN-γ compared to parental MyxV or single-armed MyxV_CD47 or MyxV_IFN-γ. In addition, significant increases in IFN-γ+ CD8+T-cells and CD4+ T-cells populations within tumor-infiltrating lymphocytes (TIL) were observed after MyxV_CD47/IFN-γ treatment. Notably, all groups treated with MyxV showed a marked reduction in Foxp3+ CD4+ regulatory T-cells (Tregs) within TIL. We also show that MyxV infection induces PD-L1 up-regulation in cancer cells, and combinational treatment of MyxV with anti-mouse PD-L1 antibodies (αPD-L1) further controlled tumor burden and increased survival in the syngeneic melanoma model B16F10. Our data demonstrate that a CD47 and IFNγ dual-armed MyxV is an effective oncolytic viral immunotherapeutic. These findings strongly support further preclinical investigations to develop next-generation MyxV-based immunotherapy approaches.
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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".