Myxoma virus and oncolytic virotherapy: a new biologic weapon in the war against cancer
Bibliographic record
Abstract
Oncolytic virotherapy is an innovative alternative to more conventional cancer therapies. The ability of some viruses to specifically target and kill malignant cancerous cells while leaving normal tissue unscathed has opened a large repertoire of new and selective cancer killing therapeutic candidates. Poxviruses, such as vaccinia virus, have a long history of use in humans as live vaccines and have more recently been studied as potential platforms for delivery of immunotherapeutics and attenuated variants of vaccinia have been explored as oncolytic candidates. In contrast, the poxvirus myxoma virus is a novel oncolytic candidate that has no history of use in humans directly, as it has a distinct and absolute host species tropism to lagomorphs (rabbits). Myxoma virus has been recently shown to be able to also selectively infect and kill human tumor cells, a unique tropism that is linked to dysregulated intracellular signalling pathways found in the majority of human cancers. This review outlines the existing knowledge on the tropism of myxoma virus for human cancer cells, as well as preclinical data exhibiting its ability to infect and clear tumors in animal models of cancer. This is an exciting new therapeutic option for treating cancer, and myxoma virus joins a growing group of oncolytic virus candidates that are being developed as a new class of cancer therapies in man.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".