Bibliographic record
Abstract
Over the past several decades, immunotherapy as a novel cancer treatment has made tremendous progress. Several categories of immunotherapy have emerged, all designed to stimulate the patients’ immune system to fight cancer. Cancer vaccines, specifically virus-based cancer vaccines, is a subcategory of immunotherapy that can trigger both innate and adaptive immune response by targeting tumor-specific and tumor-associated antigens. This review summarizes the mechanisms of different types of virus-based cancer vaccines, including inactivated/live attenuated/subunit vaccines, oncolytic virus vaccines, and viral vector vaccines. Furthermore, this review introduces the clinical applications of virus-based cancer vaccines, including oncolytic virus vaccine T-VEC against metastasized melanoma, and Pexa-Vec and PROSTVAC that are currently in clinical trials. Virus-based cancer vaccines have shown encouraging results in numerous studies in enhancing overall survival, relapse-free survival, and overall response rate among patients with solid tumors. This review underscores the necessity for future research aimed at improving the efficacy of virus-based cancer vaccines and investigating combination therapies with other immunotherapies to achieve optimal treatment outcomes.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".