Evidence for differential viral oncolytic efficacy in an in vitro model of epithelial ovarian cancer metastasis
Bibliographic record
Abstract
Epithelial ovarian cancer is unique among most carcinomas in that metastasis occurs by direct dissemination of malignant cells traversing throughout the intraperitoneal fluid. Accordingly, we test new therapeutic strategies using an in vitro three-dimensional spheroid suspension culture model that mimics key steps of this metastatic process. In the present study, we sought to uncover the differential oncolytic efficacy among three different viruses-Myxoma virus, double-deleted vaccinia virus, and Maraba virus-using three ovarian cancer cell lines in our metastasis model system. Herein, we demonstrate that Maraba virus effectively infects, replicates, and kills epithelial ovarian cancer (EOC) cells in proliferating adherent cells and with slightly slower kinetics in tumor spheroids. Myxoma virus and vaccinia viruses infect and kill adherent cells to a much lesser extent than Maraba virus, and their oncolytic potential is almost completely attenuated in spheroids. Myxoma virus and vaccinia are able to infect and spread throughout spheroids, but are blocked in the final stages of the lytic cycle, and oncolytic-mediated cell killing is reactivated upon spheroid reattachment. Alternatively, Maraba virus has a remarkably reduced ability to initially enter spheroid cells, yet rapidly infects and spreads throughout spheroids generating significant cell killing effects. We show that low-density lipoprotein receptor expression in ovarian cancer spheroids is reduced and this controls efficient Maraba virus binding and entry into infected cells. Taken together, these results are the first to implicate the potential impact of differential viral oncolytic properties at key steps of ovarian cancer metastasis.
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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".