Oncolytic viruses sensitize human tumor cells for NY-ESO-1 tumor antigen recognition by CD4+ effector T cells.
Bibliographic record
Abstract
Oncolytic immunotherapy using oncolytic viruses (OV) has been shown to stimulate the antitumor immune response by inducing the release of tumor-associated antigens (TAA) and danger signals from the dying infected tumor cells. In this study, we sought to determine if the lysis of tumor cells induced by different OV: measles virus, vaccinia virus, vesicular stomatitis virus, herpes simplex type I virus, adenovirus or enterovirus, has consequences on the capacity of tumor cells to present TAA, such as NY-ESO-1. We show that the co-culture of NY-ESO-1neg/HLA-DP4pos melanoma cells with NY-ESO-1pos/HLA-DP4neg melanoma cells infected and killed by different OV induces an intercellular transfer of NY-ESO-1 that allows the recognition of NY-ESO-1neg/HLA-DP4pos tumor cells by an HLA-DP4/NY-ESO-1(157–170)-specific CD4+ cytotoxic T cell clone, NY67. We then confirmed this result in a second model with an HLA-DP4+ melanoma cell line that expresses a low amount of NY-ESO-1. Recognition of this cell line by the NY67 clone is largely increased in the presence of OV productive infection. Altogether, our results show for the first time another mechanism of stimulation of the anti-tumor immune response by OV, via the loading of tumor cells with TAA that sensitizes them for direct recognition by specific effector CD4+ T cells, supporting the use of OV for cancer immunotherapy.
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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".