Spatial and temporal epithelial ovarian cancer cell heterogeneity impacts Maraba virus oncolytic potential
Bibliographic record
Abstract
BACKGROUND: Epithelial ovarian cancer exhibits extensive interpatient and intratumoral heterogeneity, which can hinder successful treatment strategies. Herein, we investigated the efficacy of an emerging oncolytic, Maraba virus (MRBV), in an in vitro model of ovarian tumour heterogeneity. METHODS: Four ovarian high-grade serous cancer (HGSC) cell lines were isolated and established from a single patient at four points during disease progression. Limiting-dilution subcloning generated seven additional subclone lines to assess intratumoral heterogeneity. MRBV entry and oncolytic efficacy were assessed among all 11 cell lines. Low-density receptor (LDLR) expression, conditioned media treatments and co-cultures were performed to determine factors impacting MRBV oncolysis. RESULTS: Temporal and intratumoral heterogeneity identified two subpopulations of cells: one that was highly sensitive to MRBV, and another set which exhibited 1000-fold reduced susceptibility to MRBV-mediated oncolysis. We explored both intracellular and extracellular mechanisms influencing sensitivity to MRBV and identified that LDLR can partially mediate MRBV infection. LDLR expression, however, was not the singular determinant of sensitivity to MRBV among the HGSC cell lines and subclones. We verified that there were no apparent extracellular factors, such as type I interferon responses, contributing to MRBV resistance. However, direct cell-cell contact by co-culture of MRBV-resistant subclones with sensitive cells restored virus infection and oncolytic killing of mixed population. CONCLUSIONS: Our data is the first to demonstrate differential efficacy of an oncolytic virus in the context of both spatial and temporal heterogeneity of HGSC cells and to evaluate whether it will constitute a barrier to effective viral oncolytic therapy.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".