Targeting CLEC4E in immunosuppressive tumour‐associated macrophages via BET inhibition
Bibliographic record
Abstract
BACKGROUND: Immunosuppressive tumour-associated macrophages (TAMs) represent a promising target for cancer immunotherapy; however, existing TAM-directed therapies have shown limited clinical efficacy. C-type lectin domain family 4 member E (CLEC4E), a pro-inflammatory molecule expressed on macrophages, was recently found to be highly enriched in TAMs. This study aims to elucidate the role of CLEC4E in TAMs and identify potential therapeutic agents targeting CLEC4E, and to clarify the mechanism of Bromodomain and extraterminal domain (BET) inhibitor NHWD-870 in downregulating CLEC4E. METHODS: (control) mice were generated and implanted with melanoma or ovarian cancer models. Single-cell RNA sequencing was performed to characterise macrophage phenotypic changes following CLEC4E knockout, with validation via RT-PCR, flow cytometry and proteomic sequencing. A drug screen identified BET inhibitors targeting CLEC4E, and their mechanisms were further investigated using RNA silencing, Chromatin Immunoprecipitation (ChIP)-seq and luciferase reporter assays. RESULTS: TAM infiltration was associated with poor prognosis. CLEC4E knockout significantly suppressed tumour growth compared to control mice. TAMs from knockout mice exhibited downregulated proliferation markers and upregulated genes related to antigen presentation and pro-inflammatory responses. Mechanistically, CLEC4E deletion inhibited TAM proliferation via the Erk signalling pathway, enhanced TAM‒T cell interactions, and increased granzyme B expression in T cells. The BET inhibitor NHWD-870 was shown to disrupt BRD4‒CEBPβ interaction, leading to downregulation of CLEC4E expression. CONCLUSIONS: TAMs promote an immunosuppressive microenvironment by enhancing their own proliferation and impairing anti-tumour functions, thereby limiting T-cell cytotoxicity. Targeting the BRD4/CEBPβ/CLEC4E axis with BET inhibitors represents a promising therapeutic strategy for reprogramming TAMs and enhancing anti-tumour immunity.
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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".