空间多组学和深度学习揭示了肝细胞癌中免疫治疗反应和耐药性的指纹
Zhenqin Wu1,2, Joseph Boen3, Sonali Jindal4
1Enable Medicine, Menlo Park 94025, CA USA.
bioRxiv : the preprint server for biology
|July 15, 2025
概括
研究人员开发了一种深度学习模型,以预测肝癌中免疫治疗反应. 该模型确定了与非响应相关的空间特征,如限制性,为更好的患者分层铺平了道路.
科学领域:
- 在瘤学瘤学.
- 免疫治疗是一种免疫疗法.
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
背景情况:
- 较高的不反应率和不清楚的抗药机制限制了免疫治疗的有效性.
- 检查点抑制是一种关键的癌症免疫疗法策略,患者的结果可变.
研究的目的:
- 开发用于肝细胞癌 (HCC) 免疫治疗反应的预测框架.
- 通过空间分析来阐明免疫治疗反应和耐药性的多原子景观.
主要方法:
- 在免疫治疗前后对HCC组织进行空间转录和蛋白质分析.
- 开发一个可解释的,多式联络深度学习框架,特别是图形神经网络,用于签名提取.
- 利用空间蛋白质组数据来预测患者的治疗反应.
主要成果:
- 图形神经网络实现了高预测性能 (ROC-AUC>0.9) 患者治疗反应.
- 确定了与免疫疗法反应和耐药性相关的关键预测特征和空间转录基因特征.
- 在非响应者中发现了一个具有限制性细胞外矩阵因子的"接口",将瘤和淋巴结合物分开.
结论:
- 这项研究提供了一个多原子的"指纹" (SPARC) 免疫疗法反应和耐药机制在HCC.
- 这些发现为改善癌症免疫治疗中的患者分层和个性化治疗策略奠定了基础.
- 突出了空间多态学和深度学习在理解复杂的治疗反应方面的潜力.
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