通过多omics和可解释的机器学习解读特定环境的Axitinib逃生通路
Samriddhi Gupta1, Khyati Patni1, Simarpreet Kaur2
1Department of Computational Biology, Indraprastha Institute of Information Technology Delhi (IIIT-Delhi), New Delhi, 110020, India.
Journal of translational medicine
|November 13, 2025
概括
癌症中的阿克西替尼抗性是特定于环境的. 多态和人工智能揭示了血液癌症和固体瘤的不同适应性,指导了个性化重新敏感化策略.
科学领域:
- 在瘤学瘤学.
- 计算生物学 计算生物学
- 基因组学就是基因组学.
背景情况:
- 针对癌症的向疗法,如阿克西替尼 (Axitinib) 面临抗药性,限制了疗效.
- 由于分子适应,患者对Axitinib的反应是异质的.
- 综合的多学科分析对于理解抵抗机制至关重要.
研究的目的:
- 通过使用多omics方法来定义Axitinib耐药性的机制.
- 为了确定补偿性生存途径,限制阿克西替尼的疗效.
- 为Axitinib响应开发预测模型.
主要方法:
- 高通量转录基因和蛋白质基因分析~1000个泛癌细胞系.
- 机器学习框架来预测细胞系特定的药物反应.
- 可解释AI (LIME) 识别阻力特征和分类型发现的层次聚类.
主要成果:
- 在44种药物中,阿克西蒂尼布显示出最高的预测准确度.
- 机器学习可靠地从多omics数据中对抗性/敏感细胞系进行了分类.
- 确定了两种不同的阿克西替尼抗药亚型:血液癌症 (代谢,生长因子) 和固体瘤 (低氧适应,ECM重塑,EMT,免疫逃避).
结论:
- 抗阿克西提尼布的耐药性是由组织和特定环境的适应驱动的.
- 多omics和可解释的AI揭示了不同的抵抗策略.
- 需要根据瘤环境量身定制的精确再敏感化方法.
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