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通过半监督转移学习从批量到单细胞转录基因组,在瘤内识别耐药单个细胞

Kaishun Huang¹, Hui Liu²

¹College of Computer and Information Engineering, Nanjing Tech University, Nanjing, 211800, Jiangsu, China.

Communications biology

|March 31, 2025

概括

我们开发了SSDA4Drug,这是一种使用单细胞RNA测序 (scRNA-seq) 预测癌细胞耐药性的新方法. 这种工具准确地识别出耐药亚群和动态药物反应,推进精准医学.

科学领域:

基因组学就是基因组学.
计算生物学计算生物学
癌症研究癌症研究

背景情况:

瘤复发和转移通常是由耐药细胞驱动的.
单细胞RNA测序 (scRNA-seq) 提供了关于细胞药物反应的见解.
预测药物反应的现有scRNA-seq方法存在局限性.

研究的目的:

开发一种先进的方法,利用scRNA-seq数据推断耐药癌细胞.
为了提高单细胞药物反应的预测准确度.
在瘤中识别动态和可逆的药物反应状态.

主要方法:

提议的SSDA4Drug,一个半监督的几次射击转移学习方法.
利用半监督的对抗域适应用于从批量和单细胞转录组数据中提取特征.
从大量细胞系转移药物敏感性知识到单细胞.

主要成果:

与最先进的方法相比,SSDA4Drug在多个scRNA-seq数据集中表现出更高的性能.
在单细胞药物反应预测中实现了显著的性能提升,使用最小的标记数据.
准确地回顾了动态药物反应,并确定了肺癌中可逆性耐药性状态.
预测的药物反应与口腔状细胞癌的进化模式保持一致.
使用SHAP值和集成梯度,确定了参与前列腺癌耐药性的关键基因.

结论:

SSDA4Drug是一种强大的工具,用于识别耐药瘤细胞亚群.
该方法准确地预测单细胞药物反应和动态耐药性.
这些发现支持精准医学的进步和用于癌症治疗的新药开发.

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