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基于细胞-细胞通信网络的可解释机器学习预测了癌症患者对免疫检查点抑制剂的反应
Juhun Lee1, Donghyo Kim1, JungHo Kong1
1Department of Life Sciences, Pohang University of Science and Technology, Pohang 790-784, Korea.
Science advances
|January 31, 2024
概括
本研究引入了一种机器学习模型,使用瘤数据的细胞-细胞通信网络来预测患者对免疫检查点抑制剂 (ICI) 的反应. 该模型确定了关键的沟通通路,改善了ICI治疗疗效的预测.
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:
- 免疫检查点抑制剂 (ICI) 已经改变了癌症治疗,但在许多患者中有效性有限.
- 目前用于预测ICI反应的生物标志物不足,需要改进预测工具.
研究的目的:
- 开发一种可解释的机器学习 (ML) 模型,使用患者特异的细胞-细胞通信网络 (CCN) 预测ICI疗效.
- 确定与ICI反应和抵抗相关的关键通信通路和分子参与者.
主要方法:
- 从批量瘤转录组中解码了患者特定的CCN.
- 在11个队列和4种癌症类型的700多名ICI治疗患者样本上训练了一种可解释的ML模型.
- 在黑色素瘤患者的单细胞水平上验证了关键发现.
主要成果:
- 该ML模型准确地预测了四种癌症类型的ICI疗效 (中位数AUROC:0.79).
- 确定免疫细胞化学反应对于ICI反应和结构性细胞生长因子通信对抗性至关重要.
- 确认了黑色素瘤单细胞数据中的关键通信通路和参与者.
结论:
- 使用CCN的基于网络的ML方法可以有效地预测ICI有效性.
- 这种方法提供了对ICI反应和耐药性的生物学机制的洞察.
- 这种方法有可能扩大ICI在癌症患者的临床益处.
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