Adjuvant oncolytic virotherapy for personalized anti-cancer vaccination
Bibliographic record
Abstract
By conferring systemic protection and durable benefits, cancer immunotherapies are emerging as long-term solutions for cancer treatment. One such approach that is currently undergoing clinical testing is a therapeutic anti-cancer vaccine that uses two different viruses expressing the same tumor antigen to prime and boost anti-tumor immunity. By providing the additional advantage of directly killing cancer cells, oncolytic viruses (OVs) constitute ideal platforms for such treatment strategy. However, given that the targeted tumor antigen is encoded into the viral genomes, its production requires robust infection and therefore, the vaccination efficiency partially depends on the unpredictable and highly variable intrinsic sensitivity of each tumor to OV infection. In this study, we demonstrate that anti-cancer vaccination using OVs (Adenovirus (Ad), Maraba virus (MRB), Vesicular stomatitis virus (VSV) and Vaccinia virus (VV)) co-administered with antigenic peptides is as efficient as antigen-engineered OVs and does not depend on viral replication. Our strategy is particularly attractive for personalized anti-cancer vaccines targeting patient-specific mutations. We suggest that the use of OVs as adjuvant platforms for therapeutic anti-cancer vaccination warrants testing for cancer treatment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 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".