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Updated: May 27, 2025

Visualization, Quantification, and Mapping of Immune Cell Populations in the Tumor Microenvironment

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数学地图化瘤微环境中的细胞网络

Mike van Santvoort¹, Óscar Lapuente-Santana², Maria Zopoglou³

¹Department of Mathematics and Computer Science, Eindhoven University of Technology, PO Box 513, Eindhoven 5600MB, the Netherlands; Institute for Complex Molecular Systems, Eindhoven University of Technology, PO Box 513, Eindhoven 5600MB, the Netherlands.

Cell reports methods

|February 15, 2025

概括

我们开发了RaCInG,这是一种使用随机图的新方法,可以从大量RNA测序数据中生成个性化的细胞-细胞相互作用网络. 这种方法揭示了与免疫治疗反应相关的患者特异性瘤微环境特征.

科学领域:

计算生物学计算生物学
系统生物学系统生物学
癌症研究癌症研究

背景情况:

细胞与细胞相互作用 (CCI) 网络对于了解疾病至关重要,但当前的方法往往是概括性的,缺少患者特定的细节.
现有的方法聚合数据或专注于细胞类型,限制了对个体患者异质性和局部网络结构的洞察力.

研究的目的:

引入RaCInG (随机细胞相互作用生成器),一种用于推断个性化的CCI网络的新型计算模型.
为了利用先前对联体受体相互作用和大量RNA测序数据的知识,为患者特定的网络构建.

主要方法:

RaCInG使用基于随机图的方法来建模CCI网络.
该模型整合了连接体-受体相互作用先验与来自8,683名癌症患者的大量RNA测序数据.
网络特征被提取来分析瘤微环境.

主要成果:

在8,683名癌症患者中,RaCInG生成了与瘤微环境相关的643个网络特征.
这些特征显示出与免疫反应和癌症亚型的显著关联.
该模型能够预测和解释免疫疗法反应,证明了与现有方法的稳定性和一致性.

结论:

RaCInG提供了一个强大的工具,用于阐明癌症患者特定的网络动态.
这些发现为癌症生物学和个性化治疗反应提供了新的见解.
RaCInG有可能推进癌症和其他生物医学领域复杂的CCI的研究.

关键词:

CP:癌症生物学癌症生物学 CP: 系统生物学.大量转录组学转录组学细胞-细胞网络的网络.免疫疗法免疫疗法结合体-受体相互作用针对患者的特定模型随机图表随机图表的使用瘤微环境是一个微环境.

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