Brief Communication; A Heterologous Oncolytic Bacteria-Virus Prime-Boost Approach for Anticancer Vaccination in Mice
Bibliographic record
Abstract
Anticancer vaccination is becoming a popular therapeutic approach for patients with cancers expressing common tumor antigens. One variation on this strategy is a heterologous virus vaccine where 2 viruses encoding the same tumor antigen are administered sequentially to prime and boost antitumor immunity. This approach is currently undergoing clinical investigation using an adenovirus (Ad) and the oncolytic virus Maraba (MRB). In this study, we show that Listeria monocytogenes can be used in place of the Ad to obtain comparable immune priming efficiency before MRB boosting. Importantly, the therapeutic benefits provided by our heterologous L. monocytogenes-MRB prime-boost strategy are superior to those conferred by the Ad-MRB combination. Our study provides proof of concept for the heterologous oncolytic bacteria-virus prime-boost approach for anticancer vaccination and merits its consideration for clinical testing.
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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) | 0.000 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".