Chromogenic Multiplex Immunohistochemistry Reveals Modulation of the Immune Microenvironment Associated with Survival in Elderly Patients with Lung Adenocarcinoma.
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ABSTRACT:
SUBMITTER: Ilie M
PROVIDER: S-EPMC6162494 | biostudies-literature | 2018 Sep
REPOSITORIES: biostudies-literature
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Project description:Background The FAST study identified claudin-18 (CLDN18.2) as a promising novel therapeutic target for gastric cancer (GC). However, the tumor immune microenvironment and clinicopathological features of CLDN18.2-positive GC are unclear, making it difficult to develop and optimize CLDN18.2-targeted treatments. Methods This study included 80 GC patients, 60 of whom received anti-PD-1/PD-L1 treatment. CD4/CD8/CD20/CD66b/CD68/CD163/PD-1/PD-L1/TIM-3/LAG-3/FoxP3/CTLA-4/HLA-DR/STING, and CLDN18.2 were labeled using multiplex immunohistochemistry (m-IHC) to decipher the rate and spatial distribution of T cells, B cells, macrophages, and neutrophils in formalin-fixed, paraffin-embedded tumor tissues isolated from these patients. Tumor immune-microenvironmental features and patient survival stratified by CLDN18.2 expression were analyzed using two independent-sample t-tests and log-rank tests, respectively. Results We considered moderate-to-strong CLDN18.2 expression ≥ 40% of tumor cells as the cut-off for positivity. The proportion of CD8+PD-1−, CD8+LAG-3−, and CD8+TIM-3− T cells was significantly higher in CLDN18.2-positive tumors than in negative tumors (0.039 vs. 0.026, P = 0.009; 0.050 vs.0.035, P = 0.024; 0.045 vs. 0.032, P = 0.038, respectively). In addition, the number of neutrophils (CD66b+) was higher in the CLDN18.2-positive group than in the negative group (0.081 vs. 0.055, P = 0.031, respectively), while the rates of M1 (CD68+CD163−HLA-DR+), M2 macrophages (CD68+CD163+HLA-DR−), and B cells (CD20+) were comparable between the CLDN18.2-positive and negative groups. The average numbers of CD8+PD-1−, CD8+LAG-3−, and CD8+TIM-3−T cells surrounding tumor cells within a 20-μm range were higher in CLDN18.2-positive tumors than in the CLDN18.2-negative tumors (0.16 vs. 0.09, P = 0.011; 0.20 vs. 0.12, P = 0.029; 0.18 vs. 0.12, P = 0.047, respectively). In addition, in the CLDN18.2-positive group, tumor cells surrounded by CD8+PD-1−, CD8+LAG-3− T cells, or M1 macrophages within a 20-μm range accounted for a higher proportion of all tumor cells than those in the CLDN18.2-negative group (10.79% vs. 6.60%, P = 0.015; 12.68% vs. 8.70%, P = 0.049; 9.08% vs. 6.56%, P = 0.033, respectively). These findings suggest that CLDN18.2-positive GC harbors complex immune-microenvironmental features. Additionally, CLDN18.2-positive group had shorter OS and irOS than CLDN18.2-negative group (median OS: 23.33 vs.36.6 months, P < 0.001; median irOS: 10.03 vs. 20.13 months, P = 0.044, respectively). Conclusions CLDN18.2-positive GC displayed unique immune-microenvironmental characteristics, which is of great significance for the development of CLDN18.2-targeted therapies. However, the impact of CLDN18.2-related microenvironmental features on prognosis requires further investigation. Supplementary Information The online version contains supplementary material available at 10.1186/s12916-022-02421-1.
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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.
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Project description:Long non-coding RNAs (lncRNAs), which are involved in the regulation of RNA methylation, can be used to evaluate tumor prognosis. lncRNAs are closely related to the prognosis of patients with lung adenocarcinoma (LUAD); thus, it is crucial to identify RNA methylation-associated lncRNAs with definitive prognostic value. We used Pearson correlation analysis to construct a 5-Methylcytosine (m5C)-related lncRNAs-mRNAs coexpression network. Univariate and multivariate Cox proportional risk analyses were then used to determine a risk model for m5C-associated lncRNAs with prognostic value. The risk model was verified using Kaplan-Meier analysis, univariate and multivariate Cox regression analysis, and receiver operating characteristic curve analysis. We used principal component analysis and gene set enrichment analysis functional annotation to analyze the risk model. We also verified the expression level of m5C-related lncRNAs in vitro. The association between the risk model and tumor-infiltrating immune cells was assessed using the CIBERSORT tool and the TIMER database. Based on these analyses, a total of 14 m5C-related lncRNAs with prognostic value were selected to build the risk model. Patients were divided into high- and low-risk groups according to the median risk score. The prognosis of the high-risk group was worse than that of the low-risk group, suggesting the good sensitivity and specificity of the constructed risk model. In addition, 5 types of immune cells were significantly different in the high-and low-risk groups, and 6 types of immune cells were negatively correlated with the risk score. These results suggested that the risk model based on 14 m5C-related lncRNAs with prognostic value might be a promising prognostic tool for LUAD and might facilitate the management of patients with LUAD.
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Project description:BackgroundTargeted therapies have improved survival and quality of life for patients with non-small-cell lung cancer with actionable driver mutations. However, epidermal growth factor receptor (EGFR) and human epidermal growth factor receptor 2 gene (HER2, also known as ERBB2) exon 20 insertions (Ex20mut) are characterized by a poor response to currently approved tyrosine kinase inhibitors and immunotherapies. The underlying immune biology is not well understood.Materials and methodsWe carried out messenger RNA expression profiling of lung adenocarcinomas (ADCs) with ERBB2 (n = 19) and EGFR exon 20-insertion mutations (n = 13) and compared these to tumors with classical EGFR mutations (n = 40, affecting EGFR exons 18, 19 or 21) and EGFR/ERBB2 mutation-negative lung ADC (EGFR/ERBB2wt, n = 26) focusing on immunologically relevant transcripts. Tumor-infiltrating immune cells were estimated from gene expression profiles.ResultsCytotoxic cells were significantly lower in EGFR-mutated tumors regardless of the affected exon, while Th1 cells were significantly lower in EGFR-Ex20mut compared to EGFR/ERBB2wt tumors. We assessed the differentially expressed genes of ERBB2-Ex20mut and EGFR-Ex20mut tumors compared to EGFR-Ex18/19/21mut and EGFR/ERBB2wt tumors. Of these, the genes GUSB, HDAC11, IFNGR2, PUM1, RASGRF1 and RBL2 were up-regulated, while a lower expression of CBLC, GBP1, GBP2, GBP4 and MYC was observed in all three comparison groups. The omnibus test revealed 185 significantly (FDR = 5%) differentially expressed genes and we found these four most significant gene expression changes in the study cohort: VHL and JAK1 were overexpressed in ERBB2-Ex20mut and EGFR-Ex20mut tumors compared to both EGFR-Ex18/19/21mut and EGFR/ERBB2wt tumors. RIPK1 and STK11IP showed the highest expression in ERBB2-Ex20mut tumors.ConclusionsTargeted gene expression profiling is a promising tool to read out the characteristics of the tumor microenvironment from routine diagnostic lung cancer biopsies. Significant immune reactivity and specific immunosuppressive characteristics in ERBB2-Ex20mut and EGFR-Ex20mut lung ADC with at least some degree of immune infiltration support further clinical evaluation of immune-modulators as partners of immune checkpoint inhibitors in such tumors.
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Project description:BackgroundImmune cells, vital components of tumor microenvironment, regulate tumor survival and progression. Lung adenocarcinoma (LUAD), the tumor with the highest mortality rate worldwide, reconstitutes tumor immune microenvironment (TIME) to avoid immune destruction. Data have shown that TIME influences LUAD prognosis and predicts immunotherapeutic efficacy. The related information about the role of TIME's characteristics in LUAD is limited.MethodsWe performed unsupervised consensus clustering via machine-learning techniques to identify TIME clusters among 1906 patients and gathered survival data. The characteristics of TIME clusters of LUAD were visualized by multi-omics analysis, pseudo-time dynamic analysis, and enrichment analysis. TIME score model was constructed by principal component analysis. Comprehensive analysis and validation were conducted to test the prognostic efficacy and immunotherapeutic response of TIME score.ResultsTIME clusters (A, B and C) were constructed and exhibited different immune infiltration states. Multi-omics analyses included significant mutated genes (SMG), copy number variation (CNV) and cancer stemness that were significantly different among the three clusters. TIME cluster A had a lower SMG, lower CNV, and lower stemness but a higher immune infiltration level compared to TIME clusters B and C. TIME score showed that patients in low TIME score group had higher overall survival rates, higher immune infiltration level and high expression of immune checkpoints. In validation cohorts, low TIME score subgroup had better drug sensitivity and favorable immunotherapeutic response.ConclusionWe constructed a stable model of LUAD immune microenvironment characteristics that may improve the prognostic accuracy of patients, provide improved explanations of LUAD responses to immunotherapy, and provide new strategies for LUAD treatment.
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Project description:We sought to determine the clinicopathological significance of PD-1, LAG3, and TIM3 in gastric cancer (GC) by examining their expression and immune context. Immunohistochemistry (IHC) for PD-1, TIM3, LAG3, and tumor-infiltrating immune cell (TIIC) markers was performed in 385 stage II/III GCs. Epstein-Barr virus (EBV) and microsatellite stability (MSI) testing were performed for molecular classification. Chromogenic multiplex IHC (mIHC) for PD1, TIM3, LAG3, CD3, CD8, FOXP3, CD68, and cytokeratin was performed in 58 of the total samples. PD-1, LAG3, and TIM3 expression in TIICs was observed in 91 (23.6%), 193 (50.1%), and 257 (66.8%) GCs by single IHC, respectively. The expression was associated with EBV+ and MSI-H molecular subtypes (p ≤ 0.001). A positive expression of LAG3 in the invasive margin of the tumor was associated with better prognosis in univariate (p = .020) and multivariate (p = .026) survival analyses. The expression of different immune checkpoint receptors (ICRs) was significantly positively correlated. Dual or triple ICR expression was more frequent in high PD-1 and TIM3 density groups than in low-density groups by mIHC (all p ≤ 0.05). ICRs were mainly expressed in CD3+CD8+ and CD3+CD8- T cells. Fifty-eight GCs were classified into three groups by clustering analysis based on mIHC, and the group with the highest ICR expression in TIICs showed significantly better outcomes in progression-free survival (p = .020). In GC, PD-1, LAG3, and TIM3 expression is positively correlated and associated with better prognosis. Our study provides information for the application of effective immune checkpoint inhibitors against GC.
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Project description:This study attempted to profile the tumor immune microenvironment (TIME) of non-small cell lung cancer (NSCLC) by multiplex immunofluorescence of 681 NSCLC cases. The number, density, and proportion of 26 types of immune cells in tumor nest and tumor stroma were evaluated, revealing some close interactions particularly between intrastromal neutrophils and intratumoral regulatory T cells (Treg) (r 2 = 0.439, P < 0.001), intrastromal CD4+CD38+ T cells and CD20-positive B cells (r 2 = 0.539, P < 0.001), and intratumoral CD8-positive T cells and M2 macrophages expressing PD-L1 (r 2 = 0.339, P < 0.001). Three immune subtypes correlated with distinct immune characteristics were identified using the unsupervised consensus clustering approach. The immune-activated subtype had the longest disease-free survival (DFS) and demonstrated the highest infiltration of CD4-positive T cells, CD8-positive T cells, and CD20-positive B cells. The immune-defected subtype was rich in cancer stem cells and macrophages, and these patients had the worst prognosis. The immune-exempted subtype had the highest levels of neutrophils and Tregs. Intratumoral CD68-positive macrophages, M1 macrophages, and intrastromal CD4+ cells, CD4+FOXP3- cells, CD8+ cells, and PD-L1+ cells were further found to be the most robust prognostic biomarkers for DFS, which were used to construct and validate the immune-related risk score for risk stratification (high vs. median vs. low) and the prediction of 5-year DFS rates (23.2% vs. 37.9% vs. 43.1%, P < 0.001). In conclusion, the intricate and intrinsic structure of TIME in NSCLC was demonstrated, showing potency in subtyping and prognostication.
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