作为基于深度学习的免疫亚型和预测免疫检查点阻塞反应在三阴性乳腺癌中的关键特征,CXCL9
Juan Li1, Biao Xu2, Qingwu Shi3
1Institute of Traditional Chinese Medicine Literature, Nanjing University of Chinese Medicine, 138 Xianlin Road, Nanjing, Jiangsu 210023, China; Qingyuan Hospital of Traditional Chinese Medicine, Guangzhou University of Chinese Medicine Affiliated Hospital, 10 Qiaobei Road, Qingyuan, Guangdong 511500, China.
International immunopharmacology
|February 27, 2026
概括
这项研究使用深度学习开发了一种针对三阴性乳腺癌 (TNBC) 的新型免疫分类. 鉴定出CXCL9是免疫检查点阻塞 (ICB) 反应的关键生物标志物,受 IDO1.1 的调节.
科学领域:
- 在瘤学瘤学.
- 免疫学 免疫学 免疫学
- 生物信息学是一种生物信息学.
背景情况:
- 免疫检查点封锁 (ICB) 为三阴性乳腺癌 (TNBC) 治疗提供了希望.
- 目前基于PD-L1和TMB的TNBC中ICB疗效的预测策略存在局限性.
- 在TNBC中准确选择患者接受ICB治疗仍然是一个挑战.
研究的目的:
- 使用多omics数据开发一种新的TNBC免疫分类系统.
- 在TNBC亚型中确定与ICB反应相关的关键生物标志物.
- 阐明瘤微环境中已识别的生物标志物的调节机制.
主要方法:
- 来自GEO,TCGA和GTEx的综合多omics数据.
- 采用深度学习驱动的无监督聚类 (AE-K-手段) 来识别TNBC免疫亚型.
- 利用差异表达分析,机器学习和单细胞RNA测序 (scRNA-seq) 来进行特征识别和验证.
主要成果:
- 将TNBC分为三个不同的亚型,在整体存活率和免疫异质性方面存在显著差异.
- 确定了CXCL9作为一个关键特征,在髓状细胞中高度表达,在响应者和非响应者之间显著不同.
- 证明IDO1抑制可提高巨细胞中CXCL9表达的调节,这表明一种调节机制.
结论:
- 开发了一种新型的TNBC免疫分类,提高了ICB反应的预测潜力.
- 确定CXCL9是TNBC中ICB反应的关键生物标志物,受到IDO1.1的监管.
- 这些发现支持精确免疫疗法策略和IDO1抑制剂与ICB在TNBC治疗中的潜在组合.
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