中南大学陈湘课题组取得一项新突破。他们报道了PD-L1琥珀酰化改变影响黑色素瘤的抗肿瘤免疫反应。相关论文于2025年3月11日发表在《自然—遗传学》杂志上。

肿瘤通过代谢重编程来满足恶性肿瘤所必需的能量、合成和氧化还原需求，通常以糖酵解和乳酸生成增加为特征。

然而，线粒体代谢在肿瘤免疫中的作用尚不清楚。本研究整合了空间转录组学、大量转录组学和蛋白质组学，揭示了代谢物琥珀酰辅酶a与肿瘤免疫之间的密切联系，以及抗程序性细胞死亡蛋白-1 （PD-1）治疗黑色素瘤患者的疗效。通过补充α-酮戊二酸或琥珀酸，提高琥珀酰辅酶a水平，增强T细胞介导的肿瘤消除，无论是在体外还是在体内。在机制上，PD-1的配体（PD-L1）在赖氨酸129位点的琥珀酰化导致其降解。增加肉碱棕榈酰基转移酶1A (CPT1A)，被鉴定为PD-L1的琥珀基转移酶，增强抗肿瘤活性。临床前，高脂血症药物贝唑菲特上调CPT1A，并与CTLA-4单克隆抗体协同抑制肿瘤生长。在临床上，肿瘤中较高的PD-L1和较低的CPT1A水平与更好的抗pd -1治疗反应相关，这提示了预测治疗效果的潜在生物标志物。

附：英文原文

Title: Alterations in PD-L1 succinylation shape anti-tumor immune responses in melanoma

Author: Liang, Long, Kuang, Xinwei, He, Yi, Zhu, Lin, Lau, Poyee, Li, Xin, Luo, Dingan, Gong, Lan, Zhou, Wenbin, Zhang, Fanglin, Liang, Xiaowei, Li, Zhuofeng, Hu, Bin, Liu, Dandan, Ding, Tao, Li, Hui, Zhao, Shuang, Su, Juan, Hung, Mien-Chie, Liu, Jing, Liu, Hong, Chen, Xiang

Issue&Volume: 2025-03-11

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.

DOI: 10.1038/s41588-025-02077-6

Source: https://www.nature.com/articles/s41588-025-02077-6