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Background <p>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.</p> Methods <p>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).</p> Results <p>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.</p> Conclusion <p>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.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月18日

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  • Qianbei Yi,
  • Jiaqi Yuan,
  • Zheng Ye,
  • Peng Xu,
  • Wenbin Liu

摘要

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.