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对癌症进行可解释转移学习耐药性:候选目标识别候选目标识别

Wenjie Zhang¹, Xisong Wu², Liang Chen¹

¹School of Medicine, Chongqing University, Chongqing 400030, China.

Current issues in molecular biology

|September 29, 2025

概括

我们开发了一种泛癌模型来预测瘤的耐药性,识别肺癌中TFF1等关键生物标志物. 这种方法有助于精确瘤学,通过揭示瘤特异性抵抗机制.

科学领域:

计算生物学和生物信息学
基因组学和转录基因组学
癌症研究和精确瘤学研究.

背景情况:

由于不同的分子机制,瘤耐药性在不同癌症类型中具有很高的变异性.
预测和理解耐药性对于有效的癌症治疗和个性化医学至关重要.

研究的目的:

开发一种泛癌转移学习框架,用于预测瘤的耐药性.
确定具有临床意义的新型瘤特异性耐药生物标志物.

主要方法:

一个剩余变量自编码器 (Res VAE) 骨干被整合到一个转移学习框架中.
五种模型,包括三种新型大语言模型 (LLM) 集成的VAE (VAE_LL,VAE_LS,VAE_LD) 和两种组合方法 (RF,XGB) 在癌症药物敏感性基因组学 (GDSC) 数据集上进行了训练.
在5个TCGA队列 (1,836名患者) 上,对180个药物癌症预测任务进行了验证.

主要成果:

VAE_LD模型表现出卓越的性能,在GDSC基准上实现了平均AUC0.81和F1得分0.92的平均AUC.
在多种癌症类型的临床验证中,VAE_LD保持了强大的预测能力.
解释分析确定了临床上重要的生物标志物,如肺腺癌中的TFF1和质母细胞瘤中的OPALIN,LTF,IL2RA,SLC17A7,与对特定药物的耐药性相关 (Gefitinib,Temozolomide).

结论:

VAE_LD模型提供了一种高性能和可解释的方法,用于预测泛癌症的耐药性.
该框架有助于发现针对性治疗的可操作生物标志物.
这项研究提供了一个强大的计算工具,用于推进精密瘤学应用.

关键词:

深度学习是一种深度学习.药物耐药性耐药性药物耐药性基因表达特征基因表达特征模型解释解释模型解释预测性标记物预测性标记物

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