Expression of tumor antigens within an oncolytic virus enhances the anti-tumor T cell response
Bibliographic record
Abstract
Although patients benefit from immune checkpoint inhibition (ICI) therapy in a broad variety of tumors, resistance may arise from immune suppressive tumor microenvironments (TME), which is particularly true of hepatocellular carcinoma (HCC). Since oncolytic viruses (OV) can generate a highly immune-infiltrated, inflammatory TME, OVs could potentially restore ICI responsiveness via recruitment, priming, and activation of anti-tumor T cells. Here we find that on the contrary, an oncolytic vesicular stomatitis virus, expressing interferon-ß (VSV-IFNß), antagonizes the effect of anti-PD-L1 therapy in a partially anti-PD-L1-responsive model of HCC. Cytometry by Time of Flight shows that VSV-IFNß expands dominant anti-viral effector CD8 T cells with concomitant relative disappearance of anti-tumor T cell populations, which are the target of anti-PD-L1. However, by expressing a range of HCC tumor antigens within VSV, combination OV and anti-PD-L1 therapeutic benefit could be restored. Our data provide a cautionary message for the use of highly immunogenic viruses as tumor-specific immune-therapeutics by showing that dominant anti-viral T cell responses can inhibit sub-dominant anti-tumor T cell responses. However, through encoding tumor antigens within the virus, oncolytic virotherapy can generate anti-tumor T cell populations upon which immune checkpoint blockade can effectively work.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".