Susceptibility of ascites tumor cells to ex-vivo killing by the oncolytic virus JX-963 in epithelial ovarian cancer
Bibliographic record
Abstract
e16537 Background: Epithelial ovarian cancer (EOC) has a poor prognosis, and novel therapies are urgently needed. One-third of patients with EOC will develop clinical ascites, an adverse prognostic factor. The ascitic fluid is rich in tumor cells which can be purified and used as a valuable source of patient material for in vitro analysis. In this study, we describe a method to evaluate the efficacy of ascitic tumor cell killing by the oncolytic virus, JX-963 (vaccinia strain) currently approved for use in a NCIC-CTG phase I trial. Methods: Research ethics approval and patient consent was obtained for this study. 15 samples were collected prospectively with relevant clinicopathologic information. Infection with JX-963 was performed using viral doses ranging from a multiplicity of infection (MOI) of 0.5 to 8. Following a seven-day infection period, viability was assessed using the Alamar Blue metabolic assay and tracked by virally encoded green fluorescent protein (eGFP) expression, immunohistochemistry (IHC) and flow cytometry. Results: A standardized protocol was developed for the collection and purification of EOC cells from patient ascites for subsequent infection and quantification of viral killing. Qualitative analysis using phase contrast imaging of the total cell content showed greater opaque dead cell aggregates with increasing MOI over time. Although variable between individual samples, there was a strong correlation between escalating MOI and enhanced cell death in all patient ascites samples, when quantified by Alamar Blue assay and confirmed by flow cytometry and IHC. Similar cell killing profiles by JX-963 were observed for ascites tumor cells derived from both chemotherapy-naïve patients and chemotherapy-exposed (>1 prior line of chemotherapy) patients. Conclusions: EOC cells from patient ascites show effective but differential susceptibility to viral oncolysis by the JX-963 virus that appears to be independent of prior exposure to chemotherapy. The relevance of this work is that in an individual patient, this assay will allow the testing of panels of oncolytic viruses to identify candidate viral therapeutics which may predict response to treatment. No significant financial relationships to disclose.
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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".