Therapy-Induced MHC I Ligands Shape Neo-Antitumor CD8 T Cell Responses during Oncolytic Virus-Based Cancer Immunotherapy
Bibliographic record
Abstract
Oncolytic viruses (OVs), known for their cancer-killing characteristics, also overturn tumor-associated defects in antigen presentation through the MHC class I pathway and induce protective neo-antitumor CD8 T cell responses. Nonetheless, whether OVs shape the tumor MHC-I ligandome remains unknown. Here, we investigated if an OV induces the presentation of novel MHC I-bound tumor antigens (termed tumor MHC-I ligands). Using comparative mass spectrometry (MS)-based MHC-I ligandomics, we determined differential tumor MHC-I ligand expression following treatment with oncolytic reovirus in a murine ovarian cancer model. In vitro, we found that reovirus changes the tumor ligandome of cancer cells. Concurrent multiplexed quantitative proteomics revealed that the reovirus-induced changes in tumor MHC-I ligand presentation were mostly independent of their source proteins. In an in vivo model, tumor MHC-I ligands induced by reovirus were detectable not only in tumor tissues but also the spleens (a source of antigen-presenting cells) of tumor-bearing mice. Most importantly, therapy-induced MHC-I ligands stimulated antigen-specific IFNγ responses in antitumor CD8 T cells from mice treated with reovirus. These data show that therapy-induced MHC-I ligands may shape underlying neo-antitumor CD8 T cell responses. As such, they should be considered in strategies promoting the efficacy of OV-based cancer immunotherapies.
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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 | 0.000 |
| 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".