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多omics分析揭示了免疫治疗对于不可切除的非小细胞肺癌的敏感性

Rui Wu¹, Kunchen Wei², Xingshuai Huang³

¹Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Thoracic Surgery II, Peking University Cancer Hospital & Institute, Beijing, China.

Frontiers in immunology

|February 24, 2025

概括

这项研究确定了血中的关键代谢物和蛋白质,可以准确预测非小细胞肺癌患者的免疫治疗反应. 这些生物标志物在预测治疗敏感性方面表现出很高的有效性.

科学领域:

在瘤学瘤学.
生物标志物发现发现
免疫治疗是一种免疫疗法.

背景情况:

在不可切除的第三期和第四期非小细胞肺癌 (NSCLC) 中预测免疫治疗反应至关重要.
在治疗前确定可靠的生物标志物可以优化患者对免疫治疗的选择.

研究的目的:

开发使用血代谢物和蛋白质的预测模型,以确定NSCLC患者的免疫治疗益处.
确定用于预测NSCLC中免疫检查点抑制剂的反应的新生物标志物.

主要方法:

从NSCLC患者收集的周围血液血在免疫治疗前对驱动突变呈阴性.
进行非向的代谢和蛋白质组学分析.
利用机器学习 (随机森林,逐步回归) 和后勤回归来构建预测模型.

主要成果:

鉴定了2种不同的代谢物 (5-sulfooxymethylfurfural,Anthranilic acid) 和2种不同的蛋白质 (免疫球蛋白重变量1-45,微纤维相关糖蛋白4).
代谢学模型AUC:0.977; 蛋白质学模型AUC:0.875; 整合性奥米学模型AUC:0.955.
临床风险模型的AUC显著降低,为0.659.

结论:

周围血液等离子体中的代谢和蛋白质生物标志物在预测不可切割的NSCLC中免疫疗法灵敏度方面表现出高的有效性和可靠性.
为了证实这些发现,需要在更大的,基于人口的队列中进行进一步的验证.

关键词:

免疫检查点抑制剂免疫检查点抑制剂代谢生物组的代谢生物组非小细胞肺癌的肺癌.预测模型预测模型蛋白质组学蛋白质组学

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Tumor Immunotherapy

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Immunotherapy is a treatment that boosts or manipulates the immune system to fight diseases, including cancer. For instance, by stimulating an immune response through vaccinations against viruses that cause cancers, like hepatitis B virus and human papillomavirus, these diseases can be prevented. Nonetheless, some cancer cells can avoid the immune system due to their rapid mutation and division. The immune response to many cancers involves three phases: elimination, equilibrium, and escape.