A new miRNA-Modified coxsackievirus B3 inhibits triple negative breast cancer growth with improved safety profile in immunocompetent mice
Bibliographic record
Abstract
Coxsackievirus B3 (CVB3) displays great oncolytic activity against various cancer cells. Previously, we demonstrated that adding targeting sequences (TS) of miR-145/143, which are downregulated in cancer compared with normal cells, into CVB3 genome drastically attenuates tissue toxicity, while retaining its oncolytic activity towards lung tumor. Here we extended to assess miR-modified CVB3 in breast cancer therapy. We generated a new miRNA-CVB3 by inserting TS of muscle-specific miR-1 and pancreas-selective miR-216 into the above miR-145/143-modified CVB3. We found that this newly established CVB3 (termed miR-CVB3-1.1) is safe without triggering noticeable pathogenesis when applied to immunocompetent mice. In vitro studies revealed that miR-CVB3-1.1 can infect and lyse a wide range of breast cancer cells. Animal experiments using a syngeneic breast cancer mouse model showed that intratumoral inoculation of miR-CVB3-1.1 significantly suppresses tumor growth and metastasis, associated with productive viral growth and enhanced immune cell infiltration in the tumor microenvironment. Moreover, we observed substantially reduced toxicity and prolonged survival in mice treated with miR-CVB3-1.1 compared with wild-type CVB3. Together, our results support miR-CVB3-1.1 as a promising candidate, which can be further evaluated for clinical treatment of breast cancer.
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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) | 0.000 | 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 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".