Supplementation with α-ketoglutarate improved the efficacy of anti-PD1 melanoma treatment through epigenetic modulation of PD-L1
Bibliographic record
Abstract
Patients with advanced melanoma have shown an improved outlook after anti-PD1 therapy, but the low response rate restricts clinical benefit; therefore, enhancing anti-PD1 therapeutic efficacy remains a major challenge. Here, our findings showed a significantly increased abundance of α-KG in healthy controls, anti-PD1-sensitive melanoma-bearing mice, and anti-PD1-sensitive melanoma patients; moreover, supplementation with α-KG enhanced the efficacy of anti-PD1 immunotherapy and increased PD-L1 expression in melanoma tumors via STAT1/3. We also found that supplementation with α-KG significantly increased the activity of the methylcytosine dioxygenases TET2/3, which led to an increased 5-hydroxymethylcytosine (5-hmC) level in the PD-L1 promoter. As a consequence, STAT1/3 binding to the PD-L1 promoter was stabilized to upregulate PD-L1 expression. Importantly, single-cell sequencing of preclinical samples and analysis of clinical data revealed that TET2/3-STAT1/3-CD274 signaling was associated with sensitivity to anti-PD1 treatment in melanoma. Taken together, our results provide novel insight into α-KG's function in anti-PD1 treatment of melanoma and suggest supplementation with α-KG as a novel promising strategy to improve the efficacy of anti-PD1 therapy.
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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".