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Identification and validation of tumor environment phenotypes in lung adenocarcinoma by integrative genome-scale analysis

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Abstract

Purpose

To comprehensively elucidate the landscape of the tumor environment (TME) of lung adenocarcinoma (LUAD), which has a profound impact on prognosis and response to immunotherapy.

Methods and materials

Using a large dataset of LUAD patients from The Cancer Genome Atlas, Gene Expression Omnibus database (GEO), and our institution (n = 1411), we estimated the infiltration pattern of 24 immune cell populations in each sample and systematically correlated the TME phenotypes with genomic traits and clinicopathologic characteristics.

Results

The LUAD microenvironment was classified into two distinct TME clusters (A and B), and a random forest classifier model was constructed. TMEcluster A was characterized by sparse distribution of immune cell infiltration, relatively low levels of immunomodulators and slightly higher mutation load. By contrast, enrichment of both cytotoxic T cells and immunosuppressor cells was observed in TMEcluster B. Moreover, several immune-related cytokines or markers including IFN-γ, TNF-β, and several immune checkpoint molecules such as PD-L1 were also upregulated in TMEcluster B. Multivariable Cox analysis revealed that the TMEcluster was an independent prognostic factor (TMEcluster B vs. A, hazard ratio = 0.68, 95% confidence interval = 0.50–0.91, p = 0.010). These findings were all externally validated in the data from the GEO database and our institution.

Conclusions

Our findings describe a comprehensive landscape of LUAD immune infiltration pattern and integrate several previously proposed biomarkers associated with distinct immunophenotypes, thus shedding light on how tumors interact with immune microenvironment. Our results may guide a more precise immune therapeutic strategy for LUAD patients.

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Acknowledgements

We thank International Science Editing (https://www.internationalscienceediting.com) for editing this manuscript.

Funding

This work was supported by the Science and Technology Commission of Shanghai Municipality under Grant [No. 17ZR1405200] and Zhengyi Scholar Foundation of School of Basic Medical Sciences, Fudan University under Grant [No. S22-11; https://basicmed.fudan.edu.cn/].

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Author notes

  1. Guoshu Bi, Zhencong Chen and Xiaodong Yang contributed equally to this work.

Authors and Affiliations

  1. Department of Thoracic Surgery, Zhongshan Hospital, Fudan University, No. 180 Fenglin Rd, Xuhui District, Shanghai, 200032, China

    Guoshu Bi, Zhencong Chen, Xiaodong Yang, Jiaqi Liang, Zhengyang Hu, Yunyi Bian, Qihai Sui, Cheng Zhan & Hong Fan

  2. Department of Biostatistics, Public Health, Fudan University, Shanghai, 200000, China

    Runmei Li

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  1. Guoshu Bi
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Contributions

Conception and Design were contributed by GB, ZC, XY, CZ, and HF. Collection and assembly of data were contributed by GB, ZC, and XY. Data analysis and interpretation were contributed by GB, ZC, and XY. Drafting of the manuscript was contributed by GB, ZC, and XY. Critical revision of the manuscript for important intellectual content was contributed by CZ and HF. Final approval of the manuscript and submission were contributed by GB, ZC, XY, JL, ZH, YB, QS, RL, YZ, YH, TL, CZ, and HF.

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Correspondence to Cheng Zhan or Hong Fan.

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Supplementary Figure 1.

A Overview of the study design. B Consensus matrixes of all GC cohorts for each k (k = 2–4), displaying the clustering stability using 1000 iterations of hierarchical clustering. C Correlation between the 24 types of TME immune cells in LUAD patients from TCGA. (PNG 4913 kb)

Supplementary Figure 2.

Principal components analysis performed on all the patients who have been classified into TME cluster A or B. The first two principal components demonstrated the distinct characteristic of the two clusters. Blue circle indicates patients in TMEcluster A, while red triangle indicates patients in TMEcluster B. (PNG 344 kb)

Supplementary Figure 3.

A The cnetplot showing the differentially expressed genes and corresponding enriched pathways in GO database. B, C Gene set enrichment analysis (GSEA) showing the significantly enriched pathways in TMEcluster B (B) and A (C). (PNG 17863 kb)

Supplementary Figure 4.

Heatmap showing published biological pathway signatures involved in cancer development and their differential activation pattern across the TME clusters by the use of ssGSEA. (PNG 4852 kb)

Supplementary Figure 5.

A The volcano plot showing the differentially expressed miRNAs across the TME clusters. B, C Details on the genomic alterations in TMEcluster A (B) and B (C). (PNG 2563 kb)

Supplementary Figure 6.

Immune microenvironment traits in the TME clusters in patients from GEO and our institution. A and E Violin plots showing the expression profiling of the immune-related genes in the POPLAR study and cytolytic activity (CYT) score defined by Rooney et al in patients from GEO (A) and our institution (E). The differences between every two groups were compared through the Wilcoxon test. p values indicated. B and F Relative expression level of molecules potentially involved in initiation of innate immunity (left) and MHC-I/II antigen-presenting process (right) in patients from GEO (B) and our institution (F). C and G Relative expression level of immune co-inhibitors (left) and co-stimulators (right) in patients from GEO (C) and our institution (G). D and H Violin plots showing the CD8+ T cells/Treg ratio in the two TME clusters in patients from GEO (D) and our institution (H). I, J The expression pattern of the four important immune checkpoint molecules in TMEcluster A and B in patients from GEO (I) and our institution (J). Within each group, the thick lines in the boxes represent the median value. The bottom and top of the boxes are the 25th and 75th percentiles (interquartile range). The whiskers encompass 1.5 times the interquartile range. The statistical difference of two TME clusters was compared through the Wilcoxon test. ****, p<0.0001. (PNG 3374 kb)

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Bi, G., Chen, Z., Yang, X. et al. Identification and validation of tumor environment phenotypes in lung adenocarcinoma by integrative genome-scale analysis. Cancer Immunol Immunother 69, 1293–1305 (2020). https://doi.org/10.1007/s00262-020-02546-3

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