Maraba virus-vectored cancer vaccines represent a safe and novel therapeutic option for cats
Bibliographic record
Abstract
Abstract Direct killing of malignant cells combined with induction of tumour-specific immune responses makes oncolytic vaccines attractive for cancer therapy. We previously developed a heterologous cancer immunization strategy that utilized a replication-defective adenovirus-vectored primary vaccine encoding a tumour antigen followed by boosting with a replication-competent Maraba virus expressing the same antigen. To assess the safety of oncolytic Maraba virus-based booster vaccines and inform the design of clinical trials, we conducted translational studies in cats, which have immune systems that are similar to people and spontaneously develop cancers of comparable types and etiologies. A dose of Maraba virus up to 2.5 × 1011 pfu per cat was well-tolerated, with adverse effects limited to mild, transient pyrexia, weight loss, neutropenia, lymphopenia and thrombocytopenia. Maraba viral genomes were present in some urine, stool and most plasma samples up to one week post-infection, but no infectious viruses were recovered. Post-mortem analysis showed one heart, one lung and all spleen samples contained Maraba virus genomes. No replication-competent viruses were recovered from any tissues. Post-mortem histopathological analyses revealed hyperplasia of lymphoid tissues, but no abnormal lesions were attributed to vaccination. This study demonstrated that Maraba virus-vectored cancer vaccines were well-tolerated and supports their use in treating cats.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".