人工智能驱动的变体注释用于乳腺癌精密瘤学
Kriti Shukla1, Yue Wang2, Philip M Spanheimer3,4,5
1Department of Chemistry, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.
Clinical and translational science
|October 23, 2025
概括
这项研究引入了一个AI/ML框架来解释乳腺癌中的基因组变异,通过分析变异结构和功能来确定新的治疗点. 它通过分类以前未知的变异来提高精确瘤学的精度,以便更好地分层患者.
科学领域:
- 基因组变异解释的解释
- 精确瘤学 精确瘤学
- 计算生物学 计算生物学
背景情况:
- 在乳腺癌中解释基因组变异是具有挑战性的,特别是对于缺乏临床指导的罕见变异.
- 目前的方法专注于常见的突变,留下许多未知意义的变体未经分类.
研究的目的:
- 为系统识别与ESR1和EZH2活动等关键表型相关的乳腺癌变异开发AI/ML框架.
- 整合多样化的数据集 (基因组,转录组,结构,药物反应) 进行结构信息化的变异分类方法.
主要方法:
- 利用CCLE/DepMap和TCGA数据集分析了超过12,000种乳腺癌变体.
- 采用AI/ML框架整合多omics数据以识别具有共享功能后果的结构聚类突变.
- 从基于频率的分类转移到基于结构的分析.
主要成果:
- 确定了PIK3CA,TP53和其他基因变异与ESR1信号传递之间的新兴关联,影响了内分泌治疗反应预测.
- 在意想不到的基因组环境中发现了EZH2相关的变异,这表明了潜在的表观遗传疗法标.
- 通过结构知情分类,扩大了潜在可操作突变的目录.
结论:
- AI/ML框架提供了一个可扩展的,临床相关的方法来加速乳腺癌的变异注释.
- 这些发现为药物敏感性和耐药性提供了新的见解,使得改善患者分层和药物重新用途成为可能.
- 这种方法重新定义了癌症变异解释,桥梁基因组学,功能生物学和精密医学,用于个性化治疗.
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