Kinetic analysis of oncolytic OrfV-induced innate and adaptive immune responses in a murine model of late-stage ovarian cancer
Bibliographic record
Abstract
Immunotherapies revive host immune responses against tumors by stimulating innate and adaptive immune effector cells with antitumor functions. Thus, detailed studies of immunological cell phenotypes and functions within the tumor microenvironment (TME) following immunotherapy treatments is critical to identifying the determinants of therapeutic success, optimizing treatment regimens, and driving curative outcomes. Oncolytic viruses such as Orf virus (OrfV) are multifunctional biologics that preferentially infect and kill cancer cells while simultaneously causing inflammation that drives anticancer immune responses. Here, we describe the immunological impact of OrfV on the ascites TME in a preclinical model of advanced-stage epithelial ovarian cancer. OrfV promoted the infiltration of several immune effector cells with increased expression of activation markers and effector cytokines into the ascites TME, which correlated with reduced ascites tumor burden and improved survival. The kinetics of the immune response and change in tumor burden following OrfV therapy revealed an optimal re-administration time to sustain antitumor immunity, further extending survival. The data presented highlight the importance of investigating immune response kinetics following immunotherapy and demonstrate that detailed kinetic profiling of immune responses can reveal novel insights into mechanisms of action that can guide the development of more effective therapies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".