Path2Omics提高了从瘤组织病理学的转录和甲基化预测准确度.
Danh-Tai Hoang1,2, Eldad D Shulman1, Saugato Rahman Dhruba1
1Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, Bethesda, Maryland.
Cancer research
|October 30, 2025
概括
一个人工智能框架Path2Omics预测基因表达和基因甲基化从组织病理学幻灯片. 这种计算方法通过使用常规组织样本来推断治疗决策的分子数据来推进精确瘤学.
科学领域:
- 计算生物学是一种计算生物学.
- 生物信息学是一种生物信息学.
- 人工智能在瘤学中的应用
背景情况:
- 精确瘤学依赖于分子数据来改善治疗结果.
- 从患者样本中获取分子数据通常是昂贵且耗时的.
研究的目的:
- 开发Path2Omics,这是一个深度学习框架,用于从基因病理图像中预测基因表达和甲基化.
- 评估Path2Omics在各种癌症类型和组织保护方法中的表现.
主要方法:
- 开发了Path2Omics的两个组件:一种固定在甲中,嵌入 (FFPE) 模型和一种新鲜冷 (FF) 模型.
- 在30种癌症类型的癌症基因组图谱 (TCGA) 中训练有素的组织病理学模型幻灯片.
- 在七个外部数据集上验证了性能,包括FFPE和FF样本.
主要成果:
- FF模型的表现优于FFPE模型,即使是在FFPE数据集上也是如此.
- 结合FF和FFPE预测的综合模型显示,相比单独使用FFPE模型,FFPE预测有30%的改善.
- Path2Omics准确地预测了大约4,400个基因的基因表达,并在预测生存和治疗反应方面证明了临床相关性.
结论:
- Path2Omics有效地从常规基因病理学幻灯片中推断基因表达和甲基化.
- 该框架具有通过利用可访问的组织数据来推进精确瘤学的巨大潜力.
- 推断的分子数据与实际值密切匹配,支持预测患者结果的临床实用性.
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