Nanoshield Architecture Harnessing Neoantigen-Targeting Peptides Enables Durable Post-surgical Glioma Immunotherapy
Bibliographic record
Abstract
Despite advances in immunotherapy, its efficacy against postoperative glioma recurrence remains limited. Here, we present a neoantigen-targeting peptide nanoshield that synergizes with glioma resection to eliminate residual tumor cells and prevent relapse. The nanoshield architecture is constructed using a multicationic protein (MCP) as the structural scaffold, which is assembled with the mutated isocitrate dehydrogenase 1 (muIDH1) neoantigen. The nanoshield vaccine enables lysosome-escaping muIDH1 delivery and inflammasome-mediated immune activation, generating polyfunctional CD8 + T cells. The results demonstrate superior and durable immunogenicity, with a 3-fold increase in CD8 + T cells and a 6-fold in vivo retention profile compared to free peptide controls, respectively. This leads to significant reduction in tumor size in prophylactic and therapeutic glioma models. Notably, it achieves over 40% improvement in terms of postoperative recurrence-free survival through combining the nanovaccine with antiprogrammed death-1 (aPD-1) therapy. Our immunotherapeutic strategy induces potent antitumor immunity, offering promising clinical potential for postoperative management.
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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 | 0.000 |
| 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".