肉瘤微环境细胞状态和生态系统与预后有关,并预测免疫治疗的反应
Ajay Subramanian1, Neda Nemat-Gorgani1, Timothy J Ellis-Caleo2
1Department of Radiation Oncology, Stanford University, Stanford, CA, USA.
Nature cancer
|March 1, 2024
概括
研究人员使用机器学习在软组织肉瘤中识别了23种细胞状态,发现了三种不同的细胞社区. 一个社区预测免疫治疗的反应,帮助治疗策略为肉瘤患者.
科学领域:
- 在瘤学瘤学.
- 计算生物学 计算生物学
- 基因组学就是基因组学.
背景情况:
- 在软组织肉瘤 (STS) 中表征多种恶性和侧膜细胞状态,以及它们与患者结果的联系,在固定的样本中具有挑战性.
- 批量转录组与临床数据提供了一种大规模的方法来理解肉瘤的复杂性.
研究的目的:
- 使用机器学习框架,识别肉瘤内的基本细胞状态和细胞生态系统.
- 为了将这些已识别的状态和生态系统与患者的结果和基因组变化相关联.
主要方法:
- 使用EcoTyper,一种机器学习框架,在大量的转录组上使用软组织肉瘤患者的临床注释.
- 识别和验证了瘤特异性,转录定义的细胞状态和保存的细胞群落 (生态型).
主要成果:
- 鉴定并验证了23种肉瘤特异性细胞状态,在数据集中预测了许多患者的结果.
- 发现了与基因组变化和不同的临床结果相关的三个保存的细胞生态型.
- 一种生态型,包括与瘤相关的巨细胞和类似上皮质的恶性细胞,预测了对免疫检查点抑制的反应.
结论:
- 这些发现使得在软组织肉瘤中大规模识别细胞状态和生态系统成为可能.
- 确定了一个特定的生态型,可以预测免疫疗法反应,并可能指导肉瘤患者的治疗决策.
- 结果可能有助于识别可能受益于免疫治疗的患者,并为新的治疗策略提供信息.
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