Metal‐Phenolic Nanocloaks on Cancer Cells Potentiate STING Pathway Activation for Synergistic Cancer Immunotherapy
Bibliographic record
Abstract
Abstract Due to the presence of natural neoantigens, autologous tumor cells hold great promise as personalized therapeutic vaccines. Yet autologous tumor cell vaccines require multi‐step production that frequently leads to the loss of immunoreactive antigens, causing insufficient immune activation and significantly hampering their clinical applications. Herein, we introduce a novel whole‐cell cancer vaccine by cloaking cancer cells with lipopolysaccharide‐decorated manganese(II)‐phenolic networks (MnTA nanocloaks) to evoke tumor‐specific immune response for highly efficacious synergistic cancer immunotherapy. The natural polyphenols coordinate with Mn 2+ and immediately adhere to the surface of individual cancer cells, thereby forming a nanocloak and encapsulating tumor neoantigens. Subsequent decoration with lipopolysaccharide induces internalization by dendritic cells, where Mn 2+ ions are released in the cytosol, further facilitating the activation of the stimulator of the interferon genes (STING) pathway. Highly effective tumor suppression was observed by combining the nanocloaked cancer cell treatment with anti‐programmed cell death ligand 1 (anti‐PD‐L1) antibodies‐mediated immune checkpoint blockade therapy. Our work demonstrates a universal yet simple strategy to engineer a cell‐based nanobiohybrid system for enhanced 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) | 0.002 | 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".