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基于机器学习的泛癌研究,对BRAF抑制剂耐药性的分类和机制进行了研究

Yuhang Zhao¹, Kai Yang¹, Yujun Chen¹

¹Department of Integrative Medicine, Huashan Hospital, Fudan University, Shanghai, China.

Translational cancer research

|January 16, 2025

概括

机器学习模型预测癌症患者的V-raf鼠肉瘤病毒性瘤基因同源B1 (BRAF) 抑制剂耐药性. 这种方法可以识别高风险个体和潜在的生物标志物,改善患者的预后和治疗策略.

科学领域:

在瘤学瘤学.
基因组学就是基因组学.
生物信息学是一种生物信息学.

背景情况:

病毒性瘤基因同源B1 (BRAF) 抑制剂 (BRAFi) 对V-raf鼠肉瘤耐药性影响~15%的癌症患者,导致复发和不良结果.
确定患有BRAFi耐药性高风险的患者对于有效的治疗策略至关重要.

研究的目的:

开发一种基于机器学习 (ML) 的方法来识别BRAFi耐药性高风险的患者.
发现与BRAFi耐药性相关的潜在生物标志物.

主要方法:

利用来自CCLE和GDSC数据库的235个泛癌细胞系的RNA测序和IC50数据.
确定了37个与BRAFi耐药性相关的差异表达基因.
开发并验证了ML模型,用于将细胞系分为耐药和敏感组.

主要成果:

使用ML建立了37个基因表达模型,在外部验证中展示了强大的性能.
确定了特定的基因 (例如,AOX1,OXTR,H2AC13,TBX2,SLC2A4) 作为BRAFi耐药性的潜在生物标志物.
较高的OXTR,H2AC13,TBX2表达和较低的SLC2A4表达是BRAFi耐药性的独立风险因素,并与预后不佳有关.

结论:

一个外部验证的基于ML的基因表达模型可以预测BRAFi耐药性.
研究结果提供了对BRAFi耐药性机制的见解,并有助于识别针对性治疗的高风险患者.

关键词:

抗 BRAF 抑制剂的耐药性不同表达基因的基因差异表达机器学习 (ML) 是指机器学习.胰腺癌是一种癌症.预后预后预后

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