Modulation of Reoviral Cytolysis (I): Combination Therapeutics
Bibliographic record
Abstract
Patients with stage IV gastric cancer suffer from dismal outcomes, a challenge especially in many Asian populations and for which new therapeutic options are needed. To explore this issue, we used oncolytic reovirus in combination with currently used chemotherapeutic drugs (irinotecan, paclitaxel, and docetaxel) for the treatment of gastric and other gastrointestinal cancer cells in vitro and in a mouse model. Cell viability in vitro was quantified by WST-1 assays in human cancer cell lines treated with reovirus and/or chemotherapeutic agents. The expression of reovirus protein and caspase activity was determined by flow cytometry. For in vivo studies, athymic mice received intratumoral injections of reovirus in combination with irinotecan or paclitaxel, after which tumor size was monitored. In contrast to expectations, we found that reoviral oncolysis was only poorly correlated with Ras pathway activation. Even so, the combination of reovirus with chemotherapeutic agents showed synergistic cytopathic effects in vitro, plus enhanced reovirus replication and apoptosis. In vivo experiments showed that reovirus alone can reduce tumor size and that the combination of reovirus with chemotherapeutic agents enhances this effect. Thus, we find that oncolytic reovirus therapy is effective against gastric cancer. Moreover, the combination of reovirus and chemotherapeutic agents synergistically enhanced cytotoxicity in human gastric cancer cell lines in vitro and in vivo. Our data support the use of reovirus in combination with chemotherapy in further clinical trials, and highlight the need for better biomarkers for reoviral oncolytic responsiveness.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".