Antiviral humoral immunity: Enemy or ally of viral immunotherapy?
Bibliographic record
Abstract
Oncolytic viruses are gaining traction as novel cancer immunotherapy tools given their ability to selectively target transformed cells. While direct tumor debulking was historically considered their primary mode of action, it is now appreciated that antitumor immunity significantly contributes to therapeutic efficacy. While T cells play a key role, less is known about humoral immunity in oncolytic virotherapy. While systemic delivery is the clinically preferred route for therapy administration, most oncolytic viruses are delivered directly to the tumor to avoid neutralization by pre-existing or therapy-induced immunity. In this review, we discuss emerging data showing the contribution of antiviral immunity to oncolytic activity along with growing evidence that questions dogma surrounding inhibitory activity of neutralizing antibodies. We further discuss how route of administration, tumor vascularization, host and cellular range, and oncolytic virus mechanism of action influence the role of the humoral immune response to therapy outcomes. We end the discussion with additional factors to consider, such as regulatory B cells, immunoglobulin isotype, Fc-mediated functions and the importance of choosing the right pre-clinical model that may contribute to overall therapy outcomes that are not routinely considered in pre-clinical and clinical studies of viral 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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".