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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 5

Construction of the TME score and exploration of its biological relevance.

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Construction of the TME score and exploration of its biological relevanc...
(A) Identification of the cutoff point of the TMEsig-score subgroup in nonsquamous NSCLC. TMEsig-score with the highest standardized log-rank statistics was regarded as the optimal cutoff point. (B) Kaplan-Meier curves for high versus low TMEsig-score subgroups in meta-GEO cohort (log-rank test, P < 0.001). (C) Kaplan-Meier curves for high versus low TMEsig-score patient subgroups in TCGA-LUAD cohort (log-rank test, P < 0.001). (D and E) Violin plot showing the distribution of TMEsig-score in different TME clusters (D) and TCGA expression subtypes (E). The differences between the 3 groups were compared through the Kruskal-Wallis test (both P < 0.001). (F) Spearman’s correlation between TMEsig-score and ImmuneScore in TCGA-LUAD cohort (r = 0.67). (G) Comparison of TMEsig-score group with CPTAC-annotated immune group in CPTAC-LUAD proteomics cohort (Fisher’s exact test). (H) Heatmap shows correlation of TMEsig-score with STING-related molecules (IRF3, TBK1, cGAS), inflammatory cytokines (CCL5, CXCL10, CD8A, GZMA), and immune checkpoint molecules (CD274, TAP1) in the CPTAC-LUAD cohort. (I) The nonsquamous NSCLC cell subsets of 10 highest and lowest TMEsig-score in CCLE data set. (J) Western blot analyses of cGAS-STING pathway–related molecules in selected ATCC-sourced cell lines of different TMEsig-score subgroups. (K) Comparison of relative level of cGAS-STING pathway–related molecules in different TMEsig-score cell subsets by Western blot. Data represented with mean ± SD. The differences between the 2 groups were compared through the Student’s t test (*P < 0.05, **P < 0.01, ***P < 0.001).

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