Combination Therapy With Reovirus and Anti-PD-1 Blockade Controls Tumor Growth Through Innate and Adaptive Immune Responses
Bibliographic record
Abstract
Oncolytic reovirus can be delivered both systemically and intratumorally, in both preclinical models and in early phase clinical trials. Reovirus has direct oncolytic activity against a variety of tumor types and antitumor activity is directly associated with immune activation by virus replication in tumors. Immune mechanisms of therapy include both innate immune activation against virally infected tumor cells, and the generation of adaptive antitumor immune responses as a result of in vivo priming against tumor-associated antigens. We tested the combination of local oncolytic reovirus therapy with systemic immune checkpoint inhibition. We show that treatment of subcutaneous B16 melanomas with a combination of intravenous (i.v.) anti-PD-1 antibody and intratumoral (i.t.) reovirus significantly enhanced survival of mice compared to i.t. reovirus (P < 0.01) or anti-PD-1 therapy alone. In vitro immune analysis demonstrated that checkpoint inhibition improved the ability of NK cells to kill reovirus-infected tumor cells, reduced T(reg) activity, and increased the adaptive CD8(+) T-cell-dependent antitumor T-cell response. PD-1 blockade also enhanced the antiviral immune response but through effector mechanisms which overlapped with but also differed from those affecting the antitumor response. Therefore, combination with checkpoint inhibition represents a readily translatable next step in the clinical development of reovirus viroimmunotherapy.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".