这页已由机器翻译。其他页面可能仍然显示为英文。 View in English

基于人工智能的多模式预测术后辅助免疫治疗对泌尿腺癌的益处:来自III期多中心随机IMvigor010试验的结果

Xiatong Huang ^1,2,3, Wenjun Qiu ¹, Yuyun Kong ¹

¹Department of Oncology Nanfang Hospital Southern Medical University Guangzhou Guangdong China.
²Cancer Center the Sixth Affiliated Hospital School of Medicine South China University of Technology Foshan China.
³Foshan Key Laboratory of Translational Medicine in Oncology The Sixth Affiliated Hospital School of Medicine South China University of Technology Foshan China.
⁴Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation Department of Ultrasound Sun Yat-sen Memorial Hospital Sun Yat-sen University Guangzhou China.
⁵The First School of Clinical Medicine Southern Medical University Guangzhou Guangdong China.
⁶Department of Pathology Nanfang Hospital Southern Medical University Guangzhou Guangdong China.
⁷Department of Diagnostic and Interventional Radiology University of Tsukuba Ibaraki Japan.
⁸Department of Oncology Zhujiang Hospital Southern Medical University Guangzhou Guangdong China.
⁹Department of Gastroenterology Nanfang Hospital Southern Medical University Guangzhou China.
¹⁰Department of Cardiology Nanfang Hospital Southern Medical University Guangzhou Guangdong China.
¹¹Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation Guangdong-Hong Kong Joint Laboratory for RNA Medicine Department of Medical Oncology Sun Yat-sen Memorial Hospital Sun Yat-sen University Guangzhou China.
¹²Guangdong Provincial Key Laboratory of Cancer Pathogenesis and Precision Diagnosis and Treatment AI and joint Big Data Laboratory Department of Medical Oncology Sun Yat-sen Memorial Hospital Sun Yat-sen University Shanwei China.
¹³Department of Breast Surgery The First Affiliated Hospital Jinan University Guangzhou China.
¹⁴Institute for AI in Medicine and faculty of Medicine Macau University of Science and Technology Taipa Macao China.

⁰Department of Oncology Nanfang Hospital Southern Medical University Guangzhou Guangdong China.

Medcomm +

|

2025年八月27日

在PubMed上查看摘要

概括

一种人工智能驱动的生物标志物UAIscore比现有的测试更好地预测尿癌患者的免疫治疗反应. 将UAIscore与其他生物标志物的结合提高了准确性,识别了最受益于治疗的患者.

科学领域

癌症学
免疫疗法
医学的人工智能

背景情况

循环瘤DNA (ctDNA) 有助于识别肌肉侵入性泌尿腺癌 (MIUC) 患者的辅助免疫治疗.
来自手术样本的转录组数据对免疫治疗反应的预后价值在MIUC中未得到充分研究.

研究的目的

开发人工智能 (AI) 驱动的生物标志物,使用转录组数据预测泌尿器癌的免疫治疗反应.
将新型AI生物标志物的预测性能与现有的生物标志物如ctDNA,瘤突变负担 (tTMB) 和PD-L1进行比较.

主要方法

利用IMvigor010试验中的转录和ctDNA数据开发了一个AI生物标志物,UAIscore.
在免疫治疗反应分层的患者中评估UAIscore,ctDNA,tTMB和PD- L1的预测性能.
综合多模式生物标志物以评估预测准确性的改进.
分析了分子亚型,并阐明了特定途径 (NF-κB,TNF-α) 在免疫疗法耐药性的作用.

主要成果

与观察相比,人工智能驱动的UAIscore对接受阿特佐利祖马布的患者预测了更好的结果.
作为一个独立的预测生物标志物,UAIscore的表现始终优于ctDNA,tTMB和PD-L1.
将UAIscore与ctDNA,tTMB和PD-L1结合起来提高了预测准确度.
这种类型对免疫疗法表现出敏感性,可能是由于免疫透率高,缺氧率低.
在免疫增强的瘤中,NF-κB和TNF-α通路被确定为免疫疗法耐药性的调解者.

结论

该UAIscore是一个有价值的AI驱动的生物标志物,用于分层尿癌患者,这些患者可能会对辅助免疫治疗做出反应.
综合多种生物标志物,包括UAIscore,可以更好地预测免疫疗法的疗效.
了解分子亚型和途径参与为增强免疫疗法的组合疗法提供了机理性见解.

关键词:

辅助免疫疗法生物标志物决策树模型预测得分泌尿器癌

相关概念视频

Tumor Immunotherapy

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.