Mathematically mapping the network of cells in the tumor microenvironment DOI Creative Commons

Mike van Santvoort,

Óscar Lapuente-Santana, Maria Zopoglou

et al.

Cell Reports Methods, Journal Year: 2025, Volume and Issue: unknown, P. 100985 - 100985

Published: Feb. 1, 2025

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

Language: Английский

Mathematically mapping the network of cells in the tumor microenvironment DOI Creative Commons

Mike van Santvoort,

Óscar Lapuente-Santana, Maria Zopoglou

et al.

Cell Reports Methods, Journal Year: 2025, Volume and Issue: unknown, P. 100985 - 100985

Published: Feb. 1, 2025

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

Language: Английский

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