Improved oncolytic activity of a reovirus mutant that displays enhanced virus spread due to reduced cell attachment
Bibliographic record
Abstract
Wild-type reovirus serotype 3 Dearing (T3wt), a non-pathogenic intestinal virus, has shown promise as a cancer therapy in clinical trials, but it would benefit from an increased potency. Given that T3wt is naturally adapted to the intestinal environment (rather than tumors), we genetically modified reovirus to improve its infectivity in cancer cells. Various reovirus mutants were created, and their oncolytic potency was evaluated in vitro using plaque size as a measure of virus fitness in cancer cells. Notably, Super Virus 5 (SV5), carrying five oncolytic mutations, displayed the largest plaques in breast cancer cells among the mutants tested, indicating the potential for enhancing oncolytic potency through the combination of mutations. Furthermore, in a HER2+ murine breast cancer model, mice treated with SV5 exhibited superior tumor reduction and increased survival compared with those treated with PBS or T3wt. Intriguingly, SV5 did not replicate faster than T3wt in cultured cells but demonstrated a farther spread relative to T3wt, attributed to its reduced attachment to cancer cells. These findings highlight the significance of increased virus spread as a crucial mechanism for improving oncolytic virus activity. Thus, genetic modifications of reovirus hold the potential for augmenting its efficacy in cancer therapy.
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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 | 0.001 |
| 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".