Sun Tianyue, Chen Yan, Chen Ying-Xuan
State Key Laboratory for Oncogenes and Related Genes, Division of Gastroenterology and Hepatology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
Department of Gastroenterology, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China.
Front Immunol. 2025 May 22;16:1575829. doi: 10.3389/fimmu.2025.1575829. eCollection 2025.
Colorectal cancer (CRC), the third most common cancer worldwide, often shows limited responsiveness to immunotherapy due to its predominantly immune-excluded phenotype. Despite increasing insights into the complex tumor microenvironment (TME), the metabolic heterogeneity of CRC cells and their interactions with tumor-infiltrating immune cells remain poorly understood.
We analyzed 46,374 epithelial cells from 17 CRC patients treated with PD-1 blockade to develop an amino acid (AA) metabolism score using the AUCell algorithm. This score was applied to a separate single-cell RNA sequencing (scRNA-seq) dataset from 23 CRC patients to investigate cell-cell interactions and functions of tumor-infiltrating immune cells, revealing distinct immune TME landscapes shaped by tumor metabolism. An co-culture assay of CRC cells and CD8 T cells was performed to validate the findings. Additionally, LASSO and Cox regression analyses were conducted to construct an AA metabolism-related risk score for predicting prognosis and drug sensitivity across multiple bulk transcriptome cohorts.
This study identified a link between elevated amino acid metabolism in CRC epithelial cells and resistance to PD-1 blockade therapy. A 31-gene AA score was developed by intersecting differentially expressed genes between responders and non-responders to PD-1 blockade with amino acid metabolism-related genes from the Molecular Signature Database (MSigDB). Using this score, 23 additional CRC samples were classified into high and low AA score groups. Comparative analysis revealed that the low AA group exhibited a more robust immune response, characterized by a greater number and stronger cell-cell interactions. Tumor-infiltrating immune cells in this group demonstrated enhanced activation and anti-tumor functions. Furthermore, CD8 T cells showed increased Granzyme B levels when co-cultured with CRC cells in which Psat1 or Shmt2 was knocked down. Finally, a machine learning-derived risk score based on six genes was established to translate single-cell findings to bulk transcriptomes. This risk score was found to correlate with immune checkpoint expression and immune cell infiltration, with potential implications for predicting prognosis and drug sensitivity.
Our findings highlight the role of elevated epithelial amino acid metabolism in shaping an immune-suppressive microenvironment, offering insights for patient stratification and therapeutic decision-making.
结直肠癌（CRC）是全球第三大常见癌症，由于其主要为免疫排除表型，对免疫疗法的反应通常有限。尽管对复杂的肿瘤微环境（TME）的认识不断增加，但CRC细胞的代谢异质性及其与肿瘤浸润免疫细胞的相互作用仍知之甚少。
我们分析了17例接受PD-1阻断治疗的CRC患者的46374个上皮细胞，使用AUCell算法得出氨基酸（AA）代谢评分。该评分应用于来自23例CRC患者的另一个单细胞RNA测序（scRNA-seq）数据集，以研究肿瘤浸润免疫细胞的细胞间相互作用和功能，揭示由肿瘤代谢塑造的不同免疫TME景观。进行了CRC细胞和CD8 T细胞的共培养试验以验证结果。此外，进行了LASSO和Cox回归分析，以构建与AA代谢相关的风险评分，用于预测多个批量转录组队列的预后和药物敏感性。
本研究确定了CRC上皮细胞中氨基酸代谢升高与对PD-1阻断治疗的抗性之间的联系。通过将PD-1阻断反应者和无反应者之间的差异表达基因与来自分子特征数据库（MSigDB）的氨基酸代谢相关基因相交，得出了一个31基因的AA评分。使用该评分，另外23个CRC样本被分为高AA评分组和低AA评分组。比较分析显示，低AA组表现出更强的免疫反应，其特征是细胞间相互作用的数量更多、更强。该组中的肿瘤浸润免疫细胞表现出增强的活化和抗肿瘤功能。此外，当与敲低了Psat1或Shmt2的CRC细胞共培养时CD8 T细胞显示颗粒酶B水平增加。最后，建立了基于六个基因的机器学习衍生风险评分，以将单细胞研究结果转化为批量转录组。发现该风险评分与免疫检查点表达和免疫细胞浸润相关，对预测预后和药物敏感性具有潜在意义。
我们的研究结果突出了上皮氨基酸代谢升高在塑造免疫抑制微环境中的作用，为患者分层和治疗决策提供了见解。
