Reovirus Virotherapy Overrides Tumor Antigen Presentation Evasion and Promotes Protective Antitumor Immunity
Bibliographic record
Abstract
Tumor-associated immunosuppressive strategies, such as lack of tumor antigen recognition and failure of lymphocyte activation and homing, resist the development of tumor-specific immunity and hamper the immune response-mediated elimination of cancerous cells. In this report, we show that reovirus virotherapy overrides such a tumor immune evasion and establishes clinically meaningful antitumor immunity capable of protecting against subsequent tumor challenge. Reovirus-mediated destruction of tumor cells facilitates the recognition of tumor antigens by promoting the display of otherwise inaccessible tumor-specific immunogenic peptides on the surface of dendritic cells (DC). Furthermore, on exposure to reovirus, DCs produce IL-1α, IL-1β, IL-6, IL-12p40/70, IL-17, CD30L, eotaxin, GM-CSF, KC, MCP-1, MCP-5, M-CSF, MIG, MIP-1α, RANTES, TNF-α, VCAM-1, VSGF, CXCL-16, AXL, and MCP-2; undergo maturation; and migrate into the tumor microenvironment along with CD8 T cells. These reovirus-activated DCs also acquire the capacity to prime tumor antigen-specific transgenic T cells in vitro and intrinsic antitumor T-cell response in vivo. Further, reovirus virotherapy augments the efficacy of DC- or T cell-based anticancer immunotherapies and synergistically enhances the survival in tumor-bearing mice. Most importantly, antitumor cellular immune responses initiated during reovirus oncotherapy protect the host against subsequent tumor challenge in a reovirus-independent but antigen-dependent manner. These reovirus oncotherapy-initiated antitumor immune responses represent an anticancer therapeutic entity that can maintain a long-term cancer-free health even after discontinuation of therapy.
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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".