Synergistic Viro-chemoimmunotherapy in Breast Cancer Enabled by Bioengineered Immunostimulatory Exosomes and Dual-Targeted Coxsackievirus B3
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Breast cancer’s immunosuppressive environment hinders effective immunotherapy, but oncolytic viruses hold promise for addressing this challenge by targeting tumor cells and altering the microenvironment. Yet, neutralizing antibodies and immune clearance impede their clinical utility. This study explored microRNA-modified coxsackievirus B3 (miR-CVB3), an innovative oncolytic virus, and its potential in breast cancer treatment. It investigated miR-CVB3’s impact on immune-related proteins and utilized exosomes as both protective shields and delivery carriers. Results demonstrated miR-CVB3’s capacity to reshape immune-related protein profiles toward a more immunostimulatory state and enhance exosome-mediated immune cell activation. Notably, cancer cell-released exosomes encapsulating miR-CVB3 (ExomiR-CVB3) maintained its antitumor cytotoxicity and bolstered its immunostimulatory effects. Moreover, ExomiR-CVB3 shielded miR-CVB3 from neutralizing antibodies and rapid immune clearance when it was systemically administered. Building on these findings, ExomiR-CVB3 was engineered with the AS1411 aptamer and doxorubicin (ExomiR-CVB3/DoxApt), enhancing therapeutic efficacy. This notable approach, combining genomic modification, aptamer surface decoration, and doxorubicin addition, demonstrated safe delivery of CVB3 to cancer cells. Comprehensive in vitro and in vivo analyses revealed selective breast cancer cell targeting, cell death induction, and significant immune cell infiltration within the tumor microenvironment while sparing healthy organs. In summary, this study highlights ExomiR-CVB3/DoxApt as a pioneering breast cancer treatment strategy adaptable for diverse cancer types, offering a potent and versatile approach to reshaping cancer immunotherapy.
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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".