BET inhibitor in combination with BCG vaccine enhances antitumor efficacy and orchestrates T cell reprogramming for melanoma
Bibliographic record
Abstract
Immunotherapy shows remarkable benefits in treating melanoma, yet existing approaches achieve limited overall responses. Here, we show that a combination of bromodomain and extra-terminal protein family inhibitor, NHWD-870, and Bacillus Calmette-Guérin vaccine is a promising therapeutic strategy for melanomas. Single-cell transcriptome analyses and functional experiments show that the combination therapy significantly inhibited tumor growth by reprogramming T cells toward an immune-activated state, enhancing their cytotoxicity, preventing their exhaustion, and increasing the recruitment of them into the tumor microenvironment. We identify the molecule, MT1, as a direct downstream target of BRD4, which is effectively suppressed by NHWD-870. Furthermore, our findings are reinforced by a humanized patient-derived xenograft (PDX) model, which exhibits notable antitumor effects in humanized tumor-bearing mice treated with the combination therapy. Our study underscores the immense potential of this therapeutic approach for clinical practice, offering promising prospects in overcoming the limitations of current treatments. • BET inhibitor, combined with BCG vaccine, inhibits the progression of melanoma • This combination enhances the antitumor effect of T cells in the tumor microenvironment • This combination transfers the tumor microenvironment from “cold tumor” to “hot tumor” Wang et al. illustrates the antitumor effect of the BET inhibitor NHWD-870 combined with Bacillus Calmette-Guérin. The combination reprograms T cells toward an immune-activated state. Mt1 , a marker associated with T cell exhaustion, is a direct downstream target of BRD4, which is effectively suppressed by NHWD-870, thereby inhibiting tumor growth.
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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) | 0.000 | 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".