Mathematically mapping the network of cells in the tumor microenvironment.

IF 4.3 Q1 BIOCHEMICAL RESEARCH METHODS
Cell Reports Methods Pub Date : 2025-02-24 Epub Date: 2025-02-14 DOI:10.1016/j.crmeth.2025.100985
Mike van Santvoort, Óscar Lapuente-Santana, Maria Zopoglou, Constantin Zackl, Francesca Finotello, Pim van der Hoorn, Federica Eduati
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引用次数: 0

Abstract

Cell-cell interaction (CCI) networks are key to understanding disease progression and treatment response. However, existing methods for inferring these networks often aggregate data across patients or focus on cell-type level interactions, providing a generalized overview but overlooking patient heterogeneity and local network structures. To address this, we introduce "random cell-cell interaction generator" (RaCInG), a model based on random graphs to derive personalized networks leveraging prior knowledge on ligand-receptor interactions and bulk RNA sequencing data. We applied RaCInG to 8,683 cancer patients to extract 643 network features related to the tumor microenvironment and unveiled associations with immune response and subtypes, enabling prediction and explanation of immunotherapy responses. RaCInG demonstrated robustness and showed consistencies with state-of-the-art methods. Our findings highlight RaCInG's potential to elucidate patient-specific network dynamics, offering insights into cancer biology and treatment responses. RaCInG is poised to advance our understanding of complex CCI s in cancer and other biomedical domains.

肿瘤微环境中细胞网络的数学映射。
细胞-细胞相互作用(CCI)网络是了解疾病进展和治疗反应的关键。然而,现有的推断这些网络的方法通常是汇总患者之间的数据或专注于细胞类型水平的相互作用,提供了一个笼统的概述,但忽略了患者的异质性和局部网络结构。为了解决这个问题,我们引入了“随机细胞-细胞相互作用生成器”(RaCInG),这是一个基于随机图的模型,可以利用配体-受体相互作用的先验知识和大量RNA测序数据来派生个性化网络。我们将race应用于8,683例癌症患者,提取了与肿瘤微环境相关的643个网络特征,揭示了与免疫反应和亚型的关联,从而能够预测和解释免疫治疗反应。赛车表现出健壮性,并与最先进的方法保持一致。我们的研究结果强调了RaCInG在阐明患者特异性网络动力学方面的潜力,为癌症生物学和治疗反应提供了见解。RaCInG将推进我们对癌症和其他生物医学领域复杂CCI的理解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Cell Reports Methods
Cell Reports Methods Chemistry (General), Biochemistry, Genetics and Molecular Biology (General), Immunology and Microbiology (General)
CiteScore
3.80
自引率
0.00%
发文量
0
审稿时长
111 days
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