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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 3

Clinical characteristics and biological processes among 3 TME cell infiltration patterns.

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Clinical characteristics and biological processes among 3 TME cell infil...
(A) An overview of the association between TME clusters and nonsquamous NSCLC clinical and molecular characteristics in TCGA cohort. (B) The mutational proportion of STK11 and KEAP1 among 3 TME clusters (Fisher’s exact test). (C and D) SCNA (C) and aneuploidy score (D) was compared between 3 TME clusters (Kruskal-Wallis H test followed by Dunn’s test for pairwise comparisons). (E) Heatmap shows the top enriched biological pathways calculated by GSVA algorithm in distinct TME phenotypes. Hallmark gene set (h.all.v7.0) curated from Molecular Signatures Database (MSigDB) was regarded as the reference gene signatures. (F) GSEA plots showing the cGAS-STING-IRF3–mediated gene sets were enriched in TME-C2. (G) Comparison of TME clusters with CPTAC annotated immune groups in CPTAC-LUAD proteomics cohort (Fisher’s exact test). (H) Comparison of the protein expression levels of STING pathway-related molecules among 3 TME clusters in CPTAC cohort. (I and J) Violin plot shows the phospho-IRF3 (S157) and phospho-NFKBIE (S157) protein levels among different TME clusters. The bottom and top of the boxes were the 25th and 75th percentiles (interquartile range). The whiskers encompassed 1.5 times the interquartile range. The statistical difference of 3 gene clusters was compared through the Kruskal-Wallis H test. ***P < 0.001. PI, proximal-inflamed; PP, proximal-proliferation; TRU, terminal respiratory unit.

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