Oncolytic reovirus as a combined antiviral and anti-tumour agent for the treatment of liver cancer
Bibliographic record
Abstract
OBJECTIVE: Oncolytic viruses (OVs) represent promising, proinflammatory cancer treatments. Here, we explored whether OV-induced innate immune responses could simultaneously inhibit HCV while suppressing hepatocellular carcinoma (HCC). Furthermore, we extended this exemplar to other models of virus-associated cancer. DESIGN AND RESULTS: Clinical grade oncolytic orthoreovirus (Reo) elicited innate immune activation within primary human liver tissue in the absence of cytotoxicity and independently of viral genome replication. As well as achieving therapy in preclinical models of HCC through the activation of innate degranulating immune cells, Reo-induced cytokine responses efficiently suppressed HCV replication both in vitro and in vivo. Furthermore, Reo-induced innate responses were also effective against models of HBV-associated HCC, as well as an alternative endogenous model of Epstein-Barr virus-associated lymphoma. Interestingly, Reo appeared superior to the majority of OVs in its ability to elicit innate inflammatory responses from primary liver tissue. CONCLUSIONS: We propose that Reo and other select proinflammatory OV may be used in the treatment of multiple cancers associated with oncogenic virus infections, simultaneously reducing both virus-associated oncogenic drive and tumour burden. In the case of HCV-associated HCC (HCV-HCC), Reo should be considered as an alternative agent to supplement and support current HCV-HCC therapies, particularly in those countries where access to new HCV antiviral treatments may be limited.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".