Mathematically mapping the network of cells in the tumor microenvironment

Mike van Santvoort; Oscar Lapuente Santana; Maria Zopoglou; Constantin Zackl; Francesca Finotello; Pim van der Hoorn; Federica Eduati

doi:10.1016/j.crmeth.2025.100985

Mathematically mapping the network of cells in the tumor microenvironment

Mike van Santvoort, Oscar Lapuente Santana, Maria Zopoglou, Constantin Zackl, Francesca Finotello, Pim van der Hoorn, Federica Eduati (Corresponding author)

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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

Original language	English
Article number	100985
Number of pages	21
Journal	Cell Reports Methods
Volume	5
Issue number	2
Early online date	14 Feb 2025
DOIs	https://doi.org/10.1016/j.crmeth.2025.100985
Publication status	Published - 24 Feb 2025

Funding

The authors acknowledge the support of the Immunoengineering Program of the Institute for Complex Molecular System . F.E. was supported by the Netherlands Organization for Scientific Research (NWO) Gravitation programme IMAGINE! (project number 24.005.009 ). F.F. was supported by the Austrian Science Fund (no. T 974-B30 and FG 2500-B ) and by the Oesterreichische Nationalbank (OeNB) (no. 18496 ). The computational results presented here have been achieved in part using the LEO HPC infrastructure of the University of Innsbruck. The results shown here are in part based on data generated by TCGA Research Network (https://cancergenome.nih.gov). We would like to thank Livy Nijhuis for testing the code. We would like to thank the developers of CODEFACS and LIRICS, and in particular Dr. Kun Wang, for sharing their code to perform comparative analysis. We thank the reviewers for very useful comments and suggestions. The authors acknowledge the support of the Immunoengineering Program of the Institute for Complex Molecular System. F.E. was supported by the Netherlands Organization for Scientific Research (NWO) Gravitation programme IMAGINE! (project number 24.005.009). F.F. was supported by the Austrian Science Fund (FWF) (no. T 974-B30 and FG 2500-B) and by the Oesterreichische Nationalbank (OeNB) (no. 18496). The computational results presented here have been achieved in part using the LEO HPC infrastructure of the University of Innsbruck. The results shown here are in part based on data generated by TCGA Research Network (https://cancergenome.nih.gov). We would like to thank Livy Nijhuis for testing the code. We would like to thank the developers of CODEFACS and LIRICS, and in particular Dr. Kun Wang, for sharing their code to perform comparative analysis. We thank the reviewers for very useful comments and suggestions.

Keywords

CP: cancer biology
CP: systems biology
bulk transcriptomics
cell-cell networks
immunotherapy
ligand-receptor interactions
patient-specific models
random graphs
tumor microenvironment

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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title = "Mathematically mapping the network of cells in the tumor microenvironment",

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{\textquoteright}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",

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Mathematically mapping the network of cells in the tumor microenvironment

Abstract

Funding

Keywords

UN SDGs

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