Reactivating T cell immunity in Wnt-hyperactivated non-small cell lung cancer through a supramolecular droplet of carnosic acid and peptide
Bibliographic record
Abstract
The Wnt/β-catenin signaling pathway is renowned for its contribution to the immunosuppressive microenvironment in non-small cell lung cancer (NSCLC). Consequently, inhibiting this pathway has emerged as a promising strategy to enhance immune activation and reinstate T cell responses in cancer treatment. In this study, we initially investigate the metabolic characteristics of Wnt-hyperactivated NSCLC using mass spectroscopic detection in a mouse in-situ model and unveil its significant feature of acid accumulation at tumor sites. Building upon this discovery, we design an acid-sensitive peptide-carnosic acid (CA) supramolecular droplet (Pep1@CA), which leverages the acidic microenvironment characteristic of NSCLC for controlled release. By doing so, we aim to enhance targeting efficiency while minimizing off-target effects. As anticipated, Pep1@CA demonstrates potent tumor-specific inhibition of the Wnt signaling pathway and effectively reactivates T cell immunity in Wnt-hyperactivated NSCLC. Importantly, comprehensive in vivo evaluations reveal significant antitumor efficacy alongside excellent biosafety profiles. Collectively, this study provides a therapeutic strategy with promising clinical translational potential for targeting the Wnt signaling pathway and offers theoretical support for its application in immunotherapy. This innovative approach underscores that targeting pathways beyond traditional immunotherapy can also activate tumor immunity, thereby expanding the potential of cancer immunotherapy.
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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".