Development of a recombinant murine tumour model using hepatoma cells expressing hepatitis C virus nonstructural antigens
Bibliographic record
Abstract
Hepatitis C virus (HCV) chronically infects 2%-3% of the world's population, causing liver disease and cancer with prolonged infection. The narrow host range of the virus, being restricted largely to human hepatocytes, has made the development of relevant models to evaluate the efficacy of vaccines a challenge. We have developed a novel approach to accomplish this by generating a murine hepatoma cell line stably expressing nonstructural HCV antigens which can be used in vitro or in vivo to test HCV vaccine efficacies. These HCV-recombinant hepatoma cells formed large solid-mass tumours when implanted into syngeneic mice, allowing us to test candidate HCV vaccines to demonstrate the development of an HCV-specific immune response that limited tumour growth. Using this model, we tested the therapeutic potential of recombinant anti-HCV-specific vaccines based on two fundamentally different attenuated pathogen vaccine systems-attenuated Salmonella and recombinant adenoviral vector based vaccine. While attenuated Salmonella that secreted HCV antigens limited growth of the HCV-recombinant tumours when used in a therapeutic vaccination trial, replication-competent but noninfectious adenovirus expressing nonstructural HCV antigens showed overall greater survival and reduced weight loss compared to non-replicating nondisseminating adenovirus. Our results demonstrate a model with anti-tumour responses to HCV nonstructural (NS) protein antigens and suggest that recombinant vaccine vectors should be explored as a therapeutic strategy for controlling HCV and HCV-associated cancers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".