对比学习揭示了与免疫疗法反应相关的肺腺癌瘤微环境中的细胞相互作用和形态
Hortense Le1,2, Nicolas Coudray3,4, Anna Yeaton5
1Department of Pathology, NYU Grossman School of Medicine, New York, USA. hortense.le@nyulangone.org.
Scientific reports
|October 2, 2025
概括
研究人员绘制了肺腺癌 (LUAD) 社区的地图,以了解瘤微环境 (TME) 相互作用. 特定的细胞邻居表型 (cn-HPCs) 预测患者的生存率和免疫治疗反应.
科学领域:
- 在瘤学瘤学.
- 计算生物学 计算生物学
- 免疫学 免疫学 免疫学
背景情况:
- 肺腺癌 (LUAD) 呈现出各种瘤微环境 (TME) 特征,影响预后和免疫治疗反应.
- 了解LUAD TME中的细胞相互作用和空间结构对于临床分层至关重要.
研究的目的:
- 通过使用对比学习来开发LUAD细胞社区的公正地图.
- 确定细胞邻居 (cn-HPCs) 的组织形态表型集群,并评估它们的预后和预测意义.
主要方法:
- 应用了对比学习来创建LUAD TME的细胞邻里图谱.
- 分析了已识别的cn-HPCs,免疫类型,基因模块和通路丰富之间的关联.
- 在独立的LUAD免疫疗法队列中验证的发现.
主要成果:
- 鉴定出具有显著预后价值的独特cn-HPCs;cn-HPC 0与免疫激活和有利的生存有关,而cn-HPC 23与亡和不良结果有关.
- cn-HPCs反映了关键的LUAD过程,包括免疫调节,细胞生长和炎症.
- cn-HPC 23在一个独立的队列中证明了免疫治疗反应的预测价值.
结论:
- 通过cn-HPCs对TME进行细粒度空间分析,为预测免疫疗法益处提供了补充价值.
- cn-HPCs跨越了基因病理模式和临床结果,支持它们在LUAD预后和治疗分层中的使用.
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