Potentiating Cancer Immunotherapy Using an Oncolytic Virus
Bibliographic record
Abstract
Oncolytic viruses (OVs) are highly immunogenic and this limits their use in immune-competent hosts. Although immunosuppression may improve viral oncolysis, this gain is likely achieved at the cost of antitumoral immunity. We have developed a strategy wherein the immune response against the OV leads to enhanced therapeutic outcomes. We demonstrate that immunization with an adenoviral (Ad) vaccine before treatment with an oncolytic vesicular stomatitis virus (VSV) expressing the same tumor antigen (Ag) leads to significantly enhanced antitumoral immunity. Intratumoral replication of VSV was minimally attenuated in Ad-immunized hosts but extending the interval between treatments reduced the attenuating effect and further increased antitumoral immunity. More importantly, our combination approach shifted the immune response from viral Ags to tumor Ags and further reduced OV replication in normal tissues, leading to enhancements in both efficacy and safety. These studies also highlight the benefits of using a replicating, OV to boost a pre-existing antitumoral immune response as this approach generated larger responses versus tumor Ag in tumor-bearing hosts than could be achieved in tumor-free hosts. This strategy should be applicable to other vector combinations, tumor Ags, and tumor targets.
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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".