The traditional chinese medicine monomer Ailanthone improves the therapeutic efficacy of anti-PD-L1 in melanoma cells by targeting c-Jun
Bibliographic record
Abstract
BACKGROUND: C-Jun, a critical component of AP-1, exerts essential functions in various tumors, including melanoma, and is believed to be a druggable target for cancer therapy. Unfortunately, no effective c-Jun inhibitors are currently approved for clinical use. The advent of immune checkpoint inhibitor (ICI) has brought a paradigm shift in melanoma therapy, but more than half of patients fail to exhibit clinical responses. The exploration of new combination therapies has become the current pursuit of melanoma treatment strategy. This study aims to screen out Chinese herbal monomers that can target c-Jun, explore the combined effect of c-Jun inhibitor and ICI, and further clarify the related molecular mechanism. METHODS: We adopted a combinatorial screening strategy, including molecular docking, ligand-based online approaches and consensus quantitative structure-activity relationship (QSAR) model, to filter out c-Jun inhibitors from a traditional Chinese medicine (TCM) library. A mouse melanoma model was used to evaluate the therapeutic efficacy of monotherapy and combination therapy. Multicolor flow cytometry was employed to assess the tumor microenvironment (TME). Multiple in vitro assays were performed to verify down-streaming signaling pathway. CD4 + T-cell differentiation assay was applied to investigate Treg differentiation in vitro. RESULTS: Ailanthone (AIL) was screened out as a c-Jun inhibitor, and inhibited melanoma cell growth by directly targeting c-Jun and promoting its degradation. Surprisingly, AIL also facilitated the therapeutic efficacy of anti-programmed death ligand-1 (PD-L1) in melanoma cells by reducing the infiltration of Tregs in TME. Additionally, AIL treatment inhibited c-Jun-induced PD-L1 expression and secretion. As a consequence, Treg differentiation was attenuated after treatment with AIL through the c-Jun/PD-L1 axis. CONCLUSION: Our findings identified AIL as a novel c-Jun inhibitor, and revealed its previously unrecognized anti-melanoma effects and the vital role in regulating TME by Treg suppression, which provides a novel combination therapeutic strategy of c-Jun inhibition by AIL with ICI. AIL down-regulates c-Jun by reducing its stability, and inhibits the function of Tregs via AIL-c-Jun-PD-L1 pathway, ultimately suppressing melanoma progression and enhancing the efficacy of anti-PD-L1.
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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) | 0.002 | 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 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".