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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 2

Association of TME cell–infiltrating patterns with immune signature and prognosis in NSCLC.

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Association of TME cell–infiltrating patterns with immune signature and ...
(A) Unsupervised clustering of TME cell landscape for 681 lung cancer patients in meta-GEO cohort. Stage, sex, age, GEO database cohort, and TME cluster are shown as patient annotations. The numbers of patients with TME-C1, TME-C2, and TME-C3 phenotypes are 292, 253, and 136, respectively. (B) 3D UMAP projection of NSCLC tumors per TME subtype (subnetworks) based on unsupervised clustering. (C–F) Indicator of immune infiltration level was compared by Kruskal-Wallis test among 3 TME clusters, including immune score (C), tumor purity (D), T cell immune GEP score (E), and PD-L1 expression (F). When P < 0.05, pairwise comparisons were made using Dunn’s test with Benjamini-Hochberg adjustment for multiple comparisons. (G) Kaplan-Meier curves for overall survival of 3 TME phenotypes in NSCLC meta-GEO cohort. (Log-rank test, P < 0.001.) (H) Forest plot representation of multivariate Cox model depicted association between TME clusters and overall survival (OS) after being adjusted for age, sex, and stage. Square data markers indicate estimated hazard ratios (HRs) and the length of the horizontal line represented the 95% confidence interval for each variable. (I) Kaplan-Meier curves for OS of 3 TME clusters in TCGA-LUAD cohort; log-rank test, P = 0.016. The numbers of patients in TME-C1, TME-C2, and TME-C3 clusters are 199, 195, and 106, respectively. (J) Forest plot representation of multivariate Cox model–calculated association between OS and TME cluster with other clinical factors taken into account.

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