Taking a Stab at Cancer; Oncolytic Virus-Mediated Anti-Cancer Vaccination Strategies
Bibliographic record
Abstract
Vaccines have classically been used for disease prevention. Modern clinical vaccines are continuously being developed for both traditional use as well as for new applications. Typically thought of in terms of infectious disease control, vaccination approaches can alternatively be adapted as a cancer therapy. Vaccines targeting cancer antigens can be used to induce anti-tumour immunity and have demonstrated therapeutic efficacy both pre-clinically and clinically. Various approaches now exist and further establish the tremendous potential and adaptability of anti-cancer vaccination. Classical strategies include ex vivo-loaded immune cells, RNA- or DNA-based vaccines and tumour cell lysates. Recent oncolytic virus development has resulted in a surge of novel viruses engineered to induce powerful tumour-specific immune responses. In addition to their use as cancer vaccines, oncolytic viruses have the added benefit of being directly cytolytic to cancer cells and thus promote antigen recognition within a highly immune-stimulating tumour microenvironment. While oncolytic viruses are perfectly equipped for efficient immunization, this complicates their use upon previous exposure. Indeed, the host's anti-viral counter-attacks often impair multiple-dosing regimens. In this review we will focus on the use of oncolytic viruses for anti-tumour vaccination. We will explore different strategies as well as ways to circumvent some of their limitations.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".