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An immunogenic NSCLC microenvironment is associated with favorable survival in lung adenocarcinoma.

ABSTRACT: The tumor microenvironment consists of an intricately organized system through which immune cells and cancer cells may communicate to regulate anti-tumor immunogenicity. To this end, non-small cell lung cancer (NSCLC) has been shown to activate a variety of immunological mechanisms, thereby broadening our understanding of lung cancer immunobiology. However, while recent work has highlighted the importance of NSCLC immunology and prognosis, studies have not yet examined the tumor microenvironment (TME) globally in regards to the survival outcomes between two major NSCLC subtypes: lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC). In the present study, we identify an immunogenic tumor microenvironment state in NSCLC that is enriched for the lung adenocarcinoma subtype. By utilizing TME cell enrichment scores and RNA-seq expression data, we show that the inflamed TME is associated with favorable patient survival in lung adenocarcinoma, but this does not hold true for lung squamous cell carcinoma. Moreover, differentially regulated pathways between immune-inflamed and immune-excluded tumors within LUAD and LUSC were not subtype specific. Instead, immune-inflamed LUSC samples possessed elevated immune checkpoint marker expression when compared to those of the LUAD samples, thereby offering a putative explanation for our prognostic observations. These results shed light on the immunological prognostic effects within lung cancer and may encourage further TME exploration between these two subtypes as the landscape of NSCLC therapy progresses.

SUBMITTER: Givechian KB

PROVIDER: S-EPMC6442995 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature

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An immunogenic NSCLC microenvironment is associated with favorable survival in lung adenocarcinoma.

Givechian Kevin B KB Garner Chad C Benz Steve S Song Bing B Rabizadeh Shahrooz S Soon-Shiong Patrick P

Oncotarget 20190305 19

The tumor microenvironment consists of an intricately organized system through which immune cells and cancer cells may communicate to regulate anti-tumor immunogenicity. To this end, non-small cell lung cancer (NSCLC) has been shown to activate a variety of immunological mechanisms, thereby broadening our understanding of lung cancer immunobiology. However, while recent work has highlighted the importance of NSCLC immunology and prognosis, studies have not yet examined the tumor microenvironment ...[more]

PMID: 30956762

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Project description:BackgroundLung adenocarcinoma (LUAD) is the most common histological type of lung cancers, which is the primary cause of cancer-related mortality worldwide. Growing evidence has suggested that tumor microenvironment (TME) plays a pivotal role in tumorigenesis and progression. Hence, we investigate the correlation of TME related genes with LUAD prognosis.MethodThe information of LUAD gene expression data was obtained from The Cancer Genome Atlas (TCGA). According to their immune/stromal scores calculated by the ESTIMATE algorithm, differentially expressed genes (DEGs) were identified. Then, we performed univariate Cox regression analysis on DEGs to obtain genes that are apparently bound up with LUAD survival (SurGenes). Functional annotation and protein-protein interaction (PPI) was also conducted on SurGenes. By validating the SurGenes with data sets of lung cancer from the Gene Expression Omnibus (GEO), 106 TME related SurGenes were generated. Further, intersection analysis was executed between the 106 TME related SurGenes and hub genes from PPI network, PTPRC and CD19 were obtained. Gene Set Enrichment Analysis and CIBERSORT analysis were performed on PTPRC and CD19. Based on the TCGA LUAD dataset, we conducted factor analysis and Step-wise multivariate Cox regression analysis for 106 TME related SurGenes to construct the prognostic model for LUAD survival prediction. The LUAD dataset in GEO (GSE68465) was used as the testing dataset to confirm the prognostic model. Multivariate Cox regression analysis was used between risk score from the prognostic model and clinical parameters.ResultA total of 106 TME related genes were collected in our research totally, which were markedly correlated with the overall survival (OS) of LUAD patient. Bioinformatics analysis suggest them mainly concentrated on immune response, cell adhesion, and extracellular matrix. More importantly, among 106 TME related SurGenes, PTPRC and CD19 were highly interconnected nodes among PPI network and correlated with immune activity, exhibiting significant prognostic potential. The prognostic model was a weighted linear combination of the 106 genes, by which the low-OS LUAD samples could be separated from the high-OS samples with success. This model was also able to rebustly predict the situation of survival (training set: p-value < 0.0001, area under the curve (AUC) = 0.649; testing set: p-value = 0.0009, AUC = 0.617). By combining with clinical parameters, the prognostic model was optimized. The AUC achieved 0.716 for 3 year and 0.699 for 5 year.ConclusionA series of TME-related prognostic genes were acquired in this research, which could reflect immune disorders within TME, and PTPRC and CD19 show the potential to be an indicator for LUAD prognosis and tumor microenvironment modulation. The prognostic model constructed base on those prognostic genes presented a high predictive ability, and may have clinical implications in the overall survival prediction of LUAD.

Project description:Background: Teashirt homolog 2 (TSHZ2) is essential for maintaining cellular homeostasis and regulating transcription on neoplasia development. However, the regulation of TSHZ2 in lung tumorigenesis and the underlying mechanisms remain unclear. Objective: To evaluate the relationship between TSHZ2 expression in patients' tumor tissue and prognosis in lung adenocarcinoma. Methods: TSHZ2 expression in different lung adenocarcinoma cell lines and human tissue were detected by Western blotting. The effect of TSHZ2 on cell proliferation, apoptosis and migration in lung adenocarcinoma cells was measured by CCK8, colony formation, flowcytometric analyses and wound-healing, respectively. TSHZ2 expression in different lung adenocarcinoma cell lines and human tissue from patients was detected using Western blotting and immunohistochemical staining. We also retrospectively analyzed 226 lung adenocarcinoma patients after surgical resection using immunohistochemical staining, and the association of TSHZ2 expression with the patient survival was evaluated. Results: TSHZ2 was lowly expressed in certain lung adenocarcinoma cell lines (PC9 and B203L), but other cells showed a high expression. Low expression of TSHZ2 was also observed in most lung adenocarcinoma tissues compared with adjacent tissues. Furthermore, we found that the overexpression of TSHZ2 plasmids led to the dramatic inhibition of cell proliferation, colony formation ability, migration and apoptosis induction in PC9 lung adenocarcinoma cells. In contrast, no obvious effect was found when the TSHZ2 expression was down-regulated by si-TSHZ2. An elevated TSHZ2 expression was observed in 155(68.6%) tumor tissues samples of lung adenocarcinoma patients. Notably, the lung adenocarcinoma patients with a high TSHZ2 expression tended to have EGFR mutations less frequently and a preferable prognosis to those with a lower expression. Conclusion: A high TSHZ2 expression inhabited cell proliferation and predicted a better prognosis of lung adenocarcinoma, possibly representing a useful therapeutic target for lung adenocarcinoma.

Project description:Background:Lung cancer has the highest morbidity and mortality worldwide, and lung adenocarcinoma (LADC) is the most common pathological subtype. Accumulating evidence suggests the tumor microenvironment (TME) is correlated with the tumor progress and the patient's outcome. As the major components of TME, the tumor-infiltrated immune cells and stromal cells have attracted more and more attention. In this study, differentially expressed immune and stromal signature genes were used to construct a TME-related prognostic model for predicting the outcomes of LADC patients. Methods:The expression profiles of LADC samples with clinical information were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). The differentially expressed genes (DEGs) related to the TME of LADC were identified using TCGA dataset by Wilcoxon rank sum test. The prognostic effects of TME-related DEGs were analyzed using univariate Cox regression. Then, the least absolute shrinkage and selection operator (LASSO) regression was performed to reduce the overfit and the number of genes for further analysis. Next, the prognostic model was constructed by step multivariate Cox regression and risk score of each sample was calculated. Then, survival and Receiver Operating Characteristic (ROC) analyses were conducted to validate the model using TCGA and GEO datasets, respectively. The Kyoto Encyclopedia of Genes and Genomes analysis of gene signature was performed using Gene Set Enrichment Analysis (GSEA). Finally, the overall immune status, tumor purity and the expression profiles of HLA genes of high- and low-risk samples was further analyzed to reveal the potential mechanisms of prognostic effects of the model. Results:A total of 93 TME-related DEGs were identified, of which 23 DEGs were up-regulated and 70 DEGs were down-regulated. The univariate cox analysis indicated that 23 DEGs has the prognostic effects, the hazard ratio ranged from 0.65 to 1.25 (p < 0.05). Then, seven genes were screened out from the 23 DEGs by LASSO regression method and were further analyzed by step multivariate Cox regression. Finally, a three-gene (ADAM12, Bruton Tyrosine Kinase (BTK), ERG) signature was constructed, and ADAM12, BTK can be used as independent prognostic factors. The three-gene signature well stratified the LADC patients in both training (TCGA) and testing (GEO) datasets as high-risk and low-risk groups, the 3-year area under curve (AUC) of ROC curves of three GEO sets were 0.718 (GSE3141), 0.646 (GSE30219) and 0.643 (GSE50081). The GSEA analysis indicated that highly expressed ADAM12, BTK, ERG mainly correlated with the activation of pathways involving in focal adhesion, immune regulation. The immune analysis indicated that the low-risk group has more immune activities and higher expression of HLA genes than that of the high-risk group. In sum, we identified and constructed a three TME-related DEGs signature, which could be used to predict the prognosis of LADC patients.

Project description:BackgroundDue to the relatively insidious early symptoms of lung adenocarcinoma (LUAD), most LUAD patients are at an advanced stage at the time of diagnosis and lose the best chance of surgical resection. Mounting evidence suggested that the tumor microenvironment (TME) was highly correlated with tumor occurrence, progress, and prognosis. However, TME in advanced LUAD remained to be studied and reliable prognostic signatures based on TME in advanced LUAD also had not been well-established. This study aimed to understand the cell composition and function of TME and construct a gene signature associated with TME in advanced LUAD.MethodsThe immune, stromal, and ESTIMATE scores of each sample from The Cancer Genome Atlas (TCGA) database were, respectively, calculated using an ESTIMATE algorithm. The LASSO and Cox regression model were applied to select prognostic genes and to construct a gene signature associated with TME. Two independent datasets from the Gene Expression Omnibus (GEO) were used for external validation. Twenty-two subsets of tumor-infiltrating immune cells (Tiics) were analyzed using the CIBERSORT algorithm.ResultsFavorable overall survival (OS) and progression-free survival (PFS) were found in patients with high immune score (p = 0.048 and p = 0.028; respectively) and stromal score (p = 0.024 and p = 0.025; respectively). Based on the immune and stromal scores, 453 differentially expressed genes (DEGs) were identified. Using the LASSO and Cox regression model, a seven-gene signature containing AFAP1L2, CAMK1D, LOXL2, PIK3CG, PLEKHG1, RARRES2, and SPP1 was identified to construct a risk stratification model. The OS and PFS of the high-risk group were significantly worse than that of the low-risk group (p < 0.001 and p < 0.001; respectively). The receiver operating characteristic (ROC) curve analysis confirmed the good potency of the seven-gene signature. Similar findings were validated in two independent cohorts. In addition, the proportion of macrophages M2 and Tregs was higher in high-risk patients (p = 0.041 and p = 0.022, respectively).ConclusionOur study established and validated a seven-gene signature associated with TME, which might serve as a prognosis stratification tool to predict survival outcomes of advanced LUAD patients. In addition, macrophages M2 polarization may lead to worse prognosis in patients with advanced LUAD.

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An immunogenic NSCLC microenvironment is associated with favorable survival in lung adenocarcinoma.

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An immunogenic NSCLC microenvironment is associated with favorable survival in lung adenocarcinoma.

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