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 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".