Alterations in PD-L1 succinylation shape anti-tumor immune responses in melanoma
Bibliographic record
Abstract
Tumors undergo metabolic reprogramming to meet the energetic, synthetic and redox demands essential for malignancy, often characterized by increased glycolysis and lactate production. However, the role of mitochondrial metabolism in tumor immunity remains unclear. The present study integrates spatial transcriptomics, bulk transcriptomics and proteomics, revealing a strong link between the metabolite succinyl-CoA and tumor immunity as well as the efficacy of anti-programmed cell death protein-1 (PD-1) therapy in patients with melanoma. Elevated succinyl-CoA levels, through α-ketoglutarate or succinate supplementation, enhanced T cell-mediated tumor elimination, both in vitro and in vivo. Mechanistically, succinylation of the ligand of PD-1 (PD-L1) at lysine 129 led to its degradation. Increased carnitine palmitoyltransferase 1A (CPT1A), identified as a succinyltransferase for PD-L1, boosted anti-tumor activity. Preclinically, bezafibrate, a hyperlipidemia drug, upregulated CPT1A and synergized with CTLA-4 monoclonal antibody to inhibit tumor growth. Clinically, higher PD-L1 and lower CPT1A levels in tumors correlated with better anti-PD-1 therapy responses, suggesting potential biomarkers for prediction of treatment efficacy. Succinylation of PD-L1 by carnitine palmitoyltransferase 1A (CPT1A) in melanoma leads to its degradation and enhanced T cell-dependent killing in vitro. Increasing CPT1A levels synergizes with anti-CTLA-4 treatment to suppress tumor growth in a mouse melanoma model.
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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".