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路径2Omics:从瘤组织病理学增强的转录和甲基化预测准确性

Danh-Tai Hoang, Eldad D Shulman, Saugato Rahman Dhruba

bioRxiv : the preprint server for biology

|June 26, 2025

概括

一个深度学习模型Path2Omics从23种癌症类型的组织病理学幻灯片中预测基因表达和甲基化. 这种具有成本效益的方法通过从组织样本中获得更快,更容易获得的分子洞察力来推进精密瘤学.

科学领域:

计算生物学是一种计算生物学.
基因组学就是基因组学.
病理学病理学病理学

背景情况:

精确瘤学依赖于分子数据,但获得它是昂贵和耗时的.
组织病理学幻灯片在临床环境中很容易获得.

研究的目的:

开发Path2Omics,这是一个深度学习模型,可以从组织病理学幻灯片中预测基因表达和甲基化.
为精密瘤学提供具有成本效益和快速的分子洞察力.

主要方法:

在23个TCGA队列中的8007名患者的20497个幻灯片 (FFPE和FF) 上训练了Path2Omics.
集成单独的FFPE和FF模型,以提高预测准确度.
在七个独立的队列上外部验证.

主要成果:

从FFPE幻灯片中,Path2Omics强有力的预测了平均近5000个基因,超过了以前的模型.
在外部队列上,基因预测比仅使用FFPE的模型增加了30%.
推导基因表达有效预测患者的生存率和治疗反应,与实际值相比.

结论:

Path2Omics提供了一种快速且具有成本效益的方法,可以从组织病理学幻灯片中获得分子洞察力.
这种深度学习方法可以通过利用随时可用的组织数据显著提升精确瘤学.

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