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Deciphering the tumor microenvironment cell–infiltrating landscape reveals microenvironment subtypes and therapeutic potentials for nonsquamous NSCLC
Hao Chen, Tongchao Zhang, Yuan Zhang, Hao Wu, Zhen Fang, Yang Liu, Yang Chen, Zhe Wang, Shengtao Jia, Xingzhao Ji, Liang Shang, Fengying Du, Jin Liu, Ming Lu, Wei Chong
Hao Chen, Tongchao Zhang, Yuan Zhang, Hao Wu, Zhen Fang, Yang Liu, Yang Chen, Zhe Wang, Shengtao Jia, Xingzhao Ji, Liang Shang, Fengying Du, Jin Liu, Ming Lu, Wei Chong
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Research Article Immunology Oncology

Deciphering the tumor microenvironment cell–infiltrating landscape reveals microenvironment subtypes and therapeutic potentials for nonsquamous NSCLC

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Abstract

Recent studies highlighted the clinicopathologic importance of the tumor microenvironment (TME) in delineating molecular attributes and therapeutic potentials. However, the overall TME cell infiltration landscape in nonsquamous non–small cell lung cancer (NSCLC) has not been comprehensively characterized. In this study, we used consensus non-negative matrix factorization molecular subtyping to determine TME cell infiltration patterns and identified 3 TME clusters (TME-C1, -C2, -C3) characterized by distinct clinicopathologic features, infiltrating cells, and biological processes. Proteomics analyses revealed that cyclic GMP-AMP–stimulator of interferon genes immune signaling–mediated protein and phosphorylation levels were significantly upregulated in inflammation-related TME-C2 clusters. The score extracted from the TME-related signature (TMEsig-score) divided patients with NSCLC into high- and low-score subgroups, where a high score was associated with favorable prognosis and immune infiltration. The genomic landscape revealed that patients with low TMEsig-score harbored more somatic copy number alterations and higher mutation frequency of driver genes involving STK11, KEAP1, SMARCA4, and others. Drug sensitivity analyses suggested that tumors with high TMEsig-score were responsible for favorable clinical response to immune checkpoint inhibitor treatment. In summary, this study highlights that comprehensive recognizing of the TME cell infiltration landscape will contribute to enhancing our understanding of TME immune regulation and promote effectiveness of precision biotherapy strategies.

Authors

Hao Chen, Tongchao Zhang, Yuan Zhang, Hao Wu, Zhen Fang, Yang Liu, Yang Chen, Zhe Wang, Shengtao Jia, Xingzhao Ji, Liang Shang, Fengying Du, Jin Liu, Ming Lu, Wei Chong

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Figure 4

TME phenotype-related DEGs in nonsquamous NSCLC.

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TME phenotype-related DEGs in nonsquamous NSCLC.
(A) A total of 657 over...
(A) A total of 657 overlapped differential expressed genes of the 3 TME clusters were recognized as the TME phenotype-related gene signature and shown in a Venn diagram. (B) Unsupervised clustering of TME phenotype-related gene signatures to classify patients into different transcriptomic subtypes, termed as TME gene S1–S3, respectively. The stage, sex, age, TME clusters, and TME signature subtype were used as patient annotations. (C) Functional annotation for TME phenotype-related genes using GO enrichment analysis. The color depth of the bar plots represented the statistical significance of enriched pathways. (D) Metascape enrichment network visualization showed the intracluster and intercluster similarities of enriched terms, up to 20 terms per cluster. Cluster annotations are shown in the color key. (E and F) The survival curves of the TME gene signature subtypes were estimated by the Kaplan-Meier plotter in Meta-GEO and TCGA-LUAD cohort (meta-GEO cohort, P < 0.001; TCGA-LUAD cohort, P = 0.001; log-rank test).

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ISSN 2379-3708

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