Developing a Personalized Cancer Nanovaccine Using Coxsackievirus‐Reprogrammed Cancer Cell Membranes for Enhanced Anti‐Tumor Immunity
Bibliographic record
Abstract
Cancer vaccines emerge as a promising approach in immunotherapy, but their efficacy is often hindered by immunosuppressive factors like PD-L1 on tumor cell membranes. To address this challenge, a personalized nanovaccine is developed using membranes from Coxsackievirus B3 (CVB3)-infected 4T1 breast cancer cells combined with heat-deactivated CVB3 (hdCVB3) encapsulated in PLGA nanoparticles (PLGA@hdCVB3I4T1M). RNA sequencing reveals significant upregulation of immune activation-related genes, while protein analysis demonstrates reduced immunosuppressive markers (PD-L1, B7-H3, CD47) and increased immunostimulatory proteins (calreticulin), enhancing immune cell uptake and activation. In vitro and in vivo studies confirm the safety and potent immunostimulatory effects of PLGA@hdCVB3I4T1M, leading to enhanced immune cell infiltration, elevated proinflammatory cytokine production, and robust antitumor responses. The nanovaccine significantly improves tumor suppression and prolongs survival in animal models. Additionally, the inclusion of hdCVB3 amplified immune recognition of both viral and tumor antigens, further enhancing therapeutic efficacy, particularly when combined with oncolytic virotherapy. Mechanistically, this strategy primes the immune system for a more effective and sustained antitumor response. In summary, PLGA@hdCVB3I4T1M effectively stimulates the immune system, overcoming tumor immune evasion. This nanovaccine represents a promising strategy for enhancing cancer immunotherapy and holds strong potential for clinical translation, particularly in combination with oncolytic virotherapy.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".