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从基因组学学习患者相似性,用于精确瘤学

Maha Shady^1,2,3,4, Brendan Reardon^3,4, Sharon Jiang^2,3,4

¹Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.

medRxiv : the preprint server for health sciences

|December 25, 2025

概括

一个新的深度学习模型使用瘤基因组数据识别患者的相似性. 这种方法有助于治疗决策,特别是对于缺乏生物标志物或患有罕见癌症的患者.

科学领域:

在瘤学瘤学.
生物信息学是一种生物信息学.
计算生物学计算生物学

背景情况:

精确瘤学依赖于分子生物标志物,但许多患者缺乏可操作的目标或有效的治疗方法.
患者相似性方法可以通过分析来自大型患者队伍的综合瘤概况和临床数据来增强决策支持.

研究的目的:

开发一个深度学习框架,用瘤基因组资料来测量患者的相似性.
评估患者子组和社区与乳腺癌和泛癌环境中的治疗结果的关联.
评估该模型对没有可操作生物标志物和未知原发性癌症 (CUP) 的患者的实用性.

主要方法:

利用来自第三级癌症中心的现实世界临床基因组数据.
开发了一种深度学习模型,以嵌入瘤基因组资料并测量患者的相似性.
评估患者子组和社区的治疗结果的关联.

主要成果:

该模型确定了具有已知和新疗法关联的临床意义的患者群.
来自患者社区的治疗轨迹比偶然预期的更频繁.
对于缺乏可操作生物标志物的患者和未知初级 (CUP) 诊断的癌症,已证明有用.
展示了随着时间的推移不断学习和分析的潜力.

结论:

基于相似性的框架将复杂的数据转化为可操作的洞察力,用于精密瘤学.
这种方法增强了临床医生的判断力,并支持以患者为中心的决策.
为精密瘤学中的实时学习决策支持模型提供了基础.

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