Sharpening the Edge for Precision Cancer Immunotherapy: Targeting Tumor Antigens through Oncolytic Vaccines
Bibliographic record
Abstract
Cancer immunotherapy represents a promising, modern-age option for treatment of cancers. Among the many immunotherapies being developed, oncolytic viruses (OVs) are slowly moving to the forefront of potential clinical therapeutic agents, especially considering the fact that the first oncolytic virus was recently approved by the Food and Drug Administration for the treatment of melanoma. OVs were originally discovered for their ability to kill cancer cells, but they have emerged as unconventional cancer immunotherapeutics due to their ability to activate a long-term antitumor immune response. This immune response not only eliminates cancer cells but also offers potential for preventing cancer recurrence. A fundamental requirement for the generation of such a strong antitumor T cell response is the recognition of an immunogenic tumor antigen by the antitumor T cell. Several tumor antigens capable of activating these antitumor T cells have been identified and are now being expressed through genetically engineered OVs to potentiate antitumor immunity. With the emergence of novel technologies for identifying tumor antigens and immunogenic epitopes in a myriad of cancers, design of "oncolytic vaccines" expressing highly specific tumor antigens provides a great strategy for targeting tumors. Here, we highlight the various OVs engineered to target tumor antigens and discuss multiple studies and strategies used to develop oncolytic vaccine regimens. We also contend how, going forward, a combination of technologies for identifying novel immunogenic tumor antigens and rational design of oncolytic vaccines will pave the way for the next generation of clinically efficacious cancer 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.004 |
| 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".