转移学习与多组学集成和深度神经网络揭示了癌症中耐药性机制
Semih Alpsoy1,2, Osman Ugur Sezerman3
1Department of Molecular Biotechnology, Türkisch-Deutsche Universität, Istanbul, Turkey.
Scientific reports
|November 27, 2025
概括
这项研究使用深度学习和多omics数据来预测癌症药物反应和识别耐药机制. 这种方法成功地预测了药物敏感性,并揭示了一些常见的癌症药物耐药性的关键途径.
科学领域:
- 计算生物学是一种计算生物学.
- 基因组学就是基因组学.
- 癌症研究 癌症研究
背景情况:
- 耐药性是癌症治疗的一个主要障碍.
- 预测药物反应和理解耐药机制对于个性化医学至关重要.
研究的目的:
- 开发一种基于深度神经网络 (DNN) 的转移学习 (TL) 方法,用于预测癌症药物反应.
- 通过使用多omics集成 (MI) 发现药物耐药性的基础分子机制.
主要方法:
- 综合基因表达,体突变和拷贝数异常 (CNA) 数据与药物反应概况.
- 采用基于DNN的TL方法,接受了癌症药物敏感性基因组学 (GDSC) 数据的培训.
- 在独立的活体PDX百科全书 (PDX) 和活体癌症基因组图谱 (TCGA) 数据集上的验证预测.
- 进行路径丰富分析和费舍尔精确测试 (FET) 以确定抗性机制和与突变/CNA相关的关联.
主要成果:
- 泛药模型在预测药物反应 (AUCPR) 中取得了卓越的表现.
- 鉴定了LDHB介导的pyruvate代谢和FYN介导的焦点粘附在帕克利塔塞尔耐药性中,以及PINK1-介导的mitophagy在5-FU耐药性.
- 在LDHB和PINK1中发现了CNA之间的关联,以及对分别帕克利塔塞尔和5-FU的耐药性.
- 发现了帕克利塔克塞尔和塞图西马布之间共享的耐药性机制.
结论:
- 基于DNN的TL方法显示了跨不同数据集对药物反应的强大预测能力.
- 路径丰富分析为复杂的药物耐药性机制提供了重要的生物学见解.
- 结果与现有文献一致,验证了该模型在癌症研究中的实用性.
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