Potential for Improving Potency and Specificity of Reovirus Oncolysis with Next-Generation Reovirus Variants
Bibliographic record
Abstract
Viruses that specifically replicate in tumor over normal cells offer promising cancer therapies. Oncolytic viruses (OV) not only kill the tumor cells directly; they also promote anti-tumor immunotherapeutic responses. Other major advantages of OVs are that they dose-escalate in tumors and can be genetically engineered to enhance potency and specificity. Unmodified wild type reovirus is a propitious OV currently in phase I-III clinical trials. This review summarizes modifications to reovirus that may improve potency and/or specificity during oncolysis. Classical genetics approaches have revealed reovirus variants with improved adaptation towards tumors or with enhanced ability to establish specific steps of virus replication and cell killing among transformed cells. The recent emergence of a reverse genetics system for reovirus has provided novel strategies to fine-tune reovirus proteins or introduce exogenous genes that could promote oncolytic activity. Over the next decade, these findings are likely to generate better-optimized second-generation reovirus vectors and improve the efficacy of oncolytic reotherapy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".