Crosstalk In: crosstalk-aware inference of tumor microenvironment cell infiltration

Le résumé fourni par la source

BACKGROUND: The tumor microenvironment (TME) is a complex ecosystem whose cellular composition and interactions shape tumor progression, therapeutic response, and patient prognosis. However, most bulk deconvolution methods infer cell-type composition while treating cell types independently. METHODS: In this paper, we develop CrosstalkIn, a crosstalk-aware bulk deconvolution framework that constructs patient-specific cell-cell networks by integrating Gene Ontology (GO)-based functional similarity and protein-protein interaction (PPI)-based molecular relationships. A modified random walk with restart algorithm is then applied to calculate cell Infiltration Scores (InScores). RESULTS: Benchmark results on two flow-cytometry-validated cohorts demonstrate that CrosstalkIn achieves superior deconvolution performance, with the largest or second-largest Spearman correlations for most evaluated cell types. In lower-grade glioma, CrosstalkIn-derived InScores identify survival-associated cell types, generate accurate prognostic risk scores, and stratify patients into distinct survival groups. Across multiple adenocarcinoma cohorts, the risk score shows consistent prognostic value, particularly in advanced-stage patients. In melanoma, CrosstalkIn improves immunotherapy response prediction and identifies biologically interpretable cell-type biomarkers. CONCLUSION: CrosstalkIn provides a robust framework for crosstalk-aware inference of TME cell infiltration from bulk transcriptomic data and supports cancer prognosis and immunotherapy response prediction.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.