Going (Reo)Viral: Factors Promoting Successful Reoviral Oncolytic Infection
Bibliographic record
Abstract
Oncolytic viruses show intriguing potential as cancer therapeutic agents. These viruses are capable of selectively targeting and killing cancerous cells while leaving healthy cells largely unaffected. The use of oncolytic viruses for cancer treatments in selected circumstances has recently been approved by the Food and Drug Administration (FDA) of the US and work is progressing on engineering viral vectors for enhanced selectivity, efficacy and safety. However, a better fundamental understanding of tumour and viral biology is essential for the continued advancement of the oncolytic field. This knowledge will not only help to engineer more potent and effective viruses but may also contribute to the identification of biomarkers that can determine which patients will benefit most from this treatment. A mechanistic understanding of the overlapping activity of viral and standard chemotherapeutics will enable the development of better combinational approaches to improve patient outcomes. In this review, we will examine each of the factors that contribute to productive viral infections in cancerous cells versus healthy cells. Special attention will be paid to reovirus as it is a well-studied virus and the only wild-type virus to have received orphan drug designation by the FDA. Although considerable insight into reoviral biology exists, there remain numerous deficiencies in our understanding of the factors regulating its successful oncolytic infection. Here we will discuss what is known to regulate infection as well as speculate about potential new mechanisms that may enhance successful replication. A joint appreciation of both tumour and viral biology will drive innovation for the next generation of reoviral mediated oncolytic therapy.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".