Evidence map›Paper›PMID 41542078›Full record

ArticleComputational and structural biotechnology journal2025

TMEtyper: A computational method for tumor microenvironment subtyping with applications in immunotherapy.

Yaru Miao, Tong Zhou, Yan Li, Jie Yao, Yanghui Bi, Ruiping Zhang

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In one paragraph

Article in Computational and structural biotechnology journal, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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3 · Its place in the literature

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1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Yaru MiaoInstitute of Medical Technology, Shanxi Medical University, Taiyuan, China.
Tong ZhouAcademy of Medical Sciences, Shanxi Medical University, Taiyuan, China.
Yan LiAcademy of Medical Sciences, Shanxi Medical University, Taiyuan, China.
Jie YaoShanxi Provincial Center for Disease Control and Prevention, Taiyuan, China.
Yanghui BiShanxi Bethune Hospital Gene Sequencing Center, Shanxi Academy of Medical Sciences, Taiyuan, China.
Ruiping ZhangThe Radiology Department of Shanxi Provincial People's Hospital Affiliated to Shanxi Medical University, Taiyuan 030001, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: The heterogeneity of the tumor microenvironment (TME) is a critical determinant of outcomes in immune checkpoint blockade (ICB) therapy. However, robust methodological frameworks for systematically characterizing this heterogeneity and identifying causal regulators of treatment response are still lacking. Methods: We developed TMEtyper, a comprehensive computational framework for TME characterization. This was achieved by constructing a pan-cancer TME signature that integrates cellular compositions, pathway activities, and intercellular communication networks. We employed consensus clustering coupled with topological feature extraction to delineate seven distinct TME subtypes. Key hub genes specific to each subtype were identified through an integrative machine learning approach, and their regulatory mechanisms were elucidated using structural causal modeling. Results: TMEtyper integrates 231 TME signatures to characterize the TME via network-based clustering, defining seven subtypes with distinct prognostic implications. Its analytical pipeline combines ensemble machine learning with a convolutional neural network for robust subtype classification and employs structural causal modeling to reconstruct underlying regulatory networks. Validation across 11 independent immunotherapy cohorts confirmed its strong predictive power, with the Lymphocyte-Rich Hot subtype being consistently associated with superior clinical outcomes. TMEtyper is implemented as an open-source R package with an interactive web interface, facilitating TME analysis and biomarker discovery for the research community. Conclusions: TMEtyper establishes an integrative framework that advances TME characterization beyond conventional classifications, delivering both biological insights and clinical utility. Its deployment as an accessible analytical resource opens new avenues for personalized immunotherapy strategies and biomarker development.

Indexed as

ImmunotherapyNeural networkRegulatory networksStructural causal modelTMEtyperTumor microenvironment

Identifiers

PMID41542078
PMCPMC12799947

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.