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从表观遗传特征推断 T 细胞的发育层次和功能状态,使用深度学习

Hongyan Chen¹, Dongxue Yan¹, Jie Sun¹

¹School of Biomedical Engineering, National Clinical Research Center for Ocular Diseases, Eye Hospital, Wenzhou Medical University, Wenzhou 325027, China.

Journal of chemical information and modeling

|March 28, 2024

概括

DeepEpiTEX是一种新的深度学习框架,使用表观遗传数据对耗尽的T细胞 (TEX) 进行分类. 该工具有助于了解癌症免疫力,并预测针对个性化治疗策略的免疫治疗反应.

科学领域:

免疫学免疫学免疫学
计算生物学计算生物学
基因组学就是基因组学.

背景情况:

耗尽的T细胞 (TEX) 在抗癌免疫力和免疫疗法的有效性方面至关重要.
了解瘤微环境 (TME) 中的TEX异质性对于个性化免疫疗法至关重要.

研究的目的:

开发一个计算框架,DeepEpiTEX,从表观遗传特征推断TEX发育层次和功能状态.
为了确定与患者存活率和免疫治疗反应相关的TEX子集.

主要方法:

DeepEpiTEX利用在DNA甲基化,microRNA和lncRNA表达数据上训练的深度神经网络.
该框架在TCGA泛癌数据上进行了培训,并在外部队列上进行了验证.
分析的重点是推断TEX发育层次和功能状态.

主要成果:

在30种癌症类型中,DeepEpiTEX确定了5个最佳的TEX子集,在30种癌症类型中存在显著的生存差异.
该框架证明了在各种癌症队列中具有普遍性和适用性.
发现了特定的TEX子集与对免疫检查点阻塞疗法的反应之间的潜在联系.

结论:

表观遗传调节在TEX中发挥着重要作用.
DeepEpiTEX提供了一个强大的工具,可以在各种疾病背景下对TEX进行分类.
识别免疫有利的TEX子集可能会预测免疫疗法的更大益处.

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