PD-L1 enhances the efficacy of the oncolytic virus VSVΔ51
Bibliographic record
Abstract
Abstract Oncolytic viruses (OVs) are a novel immunotherapy showing great promise in the treatment of cancer. Due to the impaired anti-viral response in cancer cells, OVs such as VSVΔ51 preferentially replicate in cancer cells relative to healthy cells. PD-L1 is a surface protein that binds to the inhibitory checkpoint receptor PD-1 to inhibit anti-cancer immunity in vivo. Previous work has suggested that PD-L1 has the ability to inhibit type I interferon signaling. Given that the type I interferon pathway is responsible for inducing the cellular anti-viral response, we hypothesized that PD-L1 will affect the replication of the OV VSVΔ51 in cancer cells. To this end, a PD-L1 knockout line was generated from the PD-L1-expressing mouse prostate cancer cell line TRAMP-C2 by CRISPR/Cas9 (TRAMP-C2 Cd274 −/−). Indeed, WT TRAMP-C2 is more susceptible to VSVΔ51 infection and oncolysis compared to TRAMP-C2 Cd274 −/− cells, and TRAMP-C2 Cd274 −/− cells exhibit severe defects in viral replication and virion production. Similar results have been observed in a different cell type/line. Mechanistically, TRAMP-C2 Cd274 −/− secrete greater amounts of IFN-β compared to WT TRAMP-C2 post-infection (with subsequent enhanced expression of IFN-stimulated anti-viral genes), and have altered signaling in response to IFN-β stimulation and VSVΔ51 infection. Similar results are observed following treatment with the viral mimic, poly(I:C). Importantly, all differences in infection between WT TRAMP-C2 and TRAMP-C2 Cd274 −/− are abolished when the activity of IFNAR is blocked. Lastly, preliminary evidence suggests that CD80 surface expression is required for the function of PD-L1. Ultimately, we aim to characterize PD-1-independent functions of PD-L1.
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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) | 0.002 | 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".