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将临床基因测序转化为瘤亚型的基础表示

JungHo Kong, Ingoo Lee, Dean Boecher

bioRxiv : the preprint server for biology

|September 18, 2025

概括

MutationProjector是一个人工智能模型,将瘤突变配置文件转化为癌症亚型,以改善诊断和治疗. 它准确地预测治疗反应,并识别关键生物标志物,推进精确瘤学.

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.
人工智能在瘤学中的应用

背景情况:

基因测序是癌症治疗的标准,但将序列与治疗联系起来仍然很困难.
开发人工智能模型来解释复杂的基因组数据对于个性化医学至关重要.

研究的目的:

介绍MutationProjector,一个人工智能基础模型,用于将瘤突变配置文件转化为癌症亚型表示.
证明模型在预测治疗反应和分类转移方面的实用性.

主要方法:

预训练 MutationProjector 对来自3万多个瘤的基因组变异和分子知识.
通过重建已存在的遗传特征来评估模型的概括性.
评估预测免疫疗法和化疗反应的性能,并对转移进行分类.

主要成果:

MutationProjector准确地重建遗传资料,显示出强大的概括能力.
该模型从改变的分子路径中确定亚型表示,从而实现可解释性.
在预测免疫治疗反应,化疗反应和转移分类方面取得了领先的表现.

结论:

MutationProjector有效地将瘤突变配置文件转化为可操作的癌症亚型信息.
该模型对改善癌症诊断,治疗选择和生物标志物发现有广泛的影响.
确定了诸如KMT2A和KRAS-STK11变化的关键生物标志物,影响免疫治疗反应.

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