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

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 6

Genetic alternations between high and low TMEsig-score subgroups based on TCGA-LUAD cohort.

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Genetic alternations between high and low TMEsig-score subgroups based o...
(A) Cosine similarity analysis of extracted 3 mutational signatures against the 30 identified signatures in Catalogue of Somatic Mutations in Cancer (COSMIC, v2) with heatmap illustration. (B) The proportion of extracted TCGA-LUAD mutational signatures (smoking-, APOBEC-, and MMR-related signature) across different TMEsig-score subgroups (P = 0.003, χ2 test). (C) Mutational landscape of SMGs in TCGA-LUAD stratified by low (left panel) versus high TMEsig-score (right panel) subgroups. Individual patients are represented in each column. The upper bar plot showed TMB. Mutational frequencies of SMGs in different TMEsig subtypes were depicted in 2 sides of the plot and highlighted in red for those statistically significant. TME cluster, age, stage, sex, smoking status, and mutational signatures were shown as patient annotations. (D) Relative distribution of SCNA in TMEsig-score high versus low subgroups (P < 0.001, Wilcoxon rank-sum test). (E) Significant amplifications and deletions of CNVs were detected and compared between the TMEsig-score low and high subgroups. (F and G) Differences of fraction genome altered (FGA) and segment numbers values in different TMEsig-score groups (P < 0.001, Wilcoxon’s rank-sum test).