Therapeutic limitations of oncolytic VSVd51-mediated miR-199a-5p delivery in triple negative breast cancer models
Bibliographic record
Abstract
Triple-negative breast cancer (TNBC) metastasis is driven, in part, by the epithelial-to-mesenchymal transition (EMT), a process critical for cancer cell migration and invasion. Current treatment options, including immunotherapies and targeted therapies, demonstrate limited efficacy in this aggressive disease, underscoring the need for innovative therapeutic approaches. Here, we present a novel approach integrating oncolytic virotherapy with RNA interference by engineering two variants of vesicular stomatitis virus (VSVd51) expressing pri- or pre-miR-199a-5p, a microRNA implicated in the regulation of EMT. We demonstrate that both viral constructs are functional and capable of overexpressing mature miR-199a-5p. In the human TNBC cell line MDA-MB-231, both viral variants inhibited the expression of ZEB1, a transcription factor central to EMT. However, in the mouse TNBC cell line 4T1, miR-199a-5p delivered via VSVd51 failed to disrupt EMT-related gene expression. In vivo testing of VSVd51-pre-miR-199 in the syngeneic BALB/c-4T1 mouse model revealed no significant survival benefits or reduction in tumor growth, even when coupled with primary tumor resection. Additional in vivo testing in immunodeficient mice using the MDA-MB-231 xenograft model showed no effect on tumor reduction. Our study highlights the challenges of integrating miRNA-based strategies with oncolytic viruses in a cancer context-specific manner and underscores the importance of vector selection and tumor model compatibility for therapeutic synergy.
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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 | 0.001 | 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".