Comprehensive analyses of the heterogeneity and prognostic significance of tumor-infiltrating immune cells in non-small-cell lung cancer: Development and validation of an individualized prognostic model

•Heterogeneity and prognostic significance of TIICs in NSCLC were comprehensively analyzed.•The associations between TIICs and immune-related DEGs network were investigated in depth.•A novel index IGRI was developed based on five TIICs-related DEGs.•A model was built and validated to predict the OS...

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Veröffentlicht in:International immunopharmacology 2020-09, Vol.86, p.106744-106744, Article 106744
Hauptverfasser: Pang, Zhaofei, Chen, Xiaowei, Wang, Yu, Wang, Yadong, Yan, Tao, Wan, Jun, Du, Jiajun
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creator Pang, Zhaofei
Chen, Xiaowei
Wang, Yu
Wang, Yadong
Yan, Tao
Wan, Jun
Du, Jiajun
description •Heterogeneity and prognostic significance of TIICs in NSCLC were comprehensively analyzed.•The associations between TIICs and immune-related DEGs network were investigated in depth.•A novel index IGRI was developed based on five TIICs-related DEGs.•A model was built and validated to predict the OS for NSCLC patients. Understanding the role of tumor-infiltrating immune cells (TIICs) in non-small cell lung cancer (NSCLC) is critical to finding new prognostic biomarkers and improving prognostic evaluation. Herein, we aimed to comprehensively analyze tumor-infiltrating pattern of TIICs in NSCLC and build a TIICs-associated, risk-stratification prognostic model for clinical practice. We applied CIBERSORT and ESTIMATE computational methods to analyze RNA-seq samples of 852 NSCLC patients from The Cancer Genome Atlas (TCGA). Prognotic factors were identified by univariate and multivariate Cox regression analyses for overall survival (OS). A novel model was developed to predict the 1-, 3- and 5-year OS of NSCLC based on the TCGA cohort, validated by external validation cohorts (GSE31210, GSE37745), and then evaluated by C-indexes and calibration plots. Significant heterogeneity in the infiltrating patterns of TIICs was shown among various pathological subtypes of NSCLC and between different genders. Further analyses showed that abundances of naive B cells (NBCs), T cells and mast cells (MCs) were positively correlated with prognosis. Tumor samples with high T cells abundances tended to have higher expression levels of immune checkpoint genes (PD-1, PD-L1, CTLA-4). A new immune-gene related index (IGRI) was built by five immune-related differentially expressed genes (DEGs) including BTK, CCR2, CLEC10A, NCR3 and PRKCB, which were closely correlated with TIICs abundances and prognosis. Tumor stage, IGRI, abundances of NBCs, T cells, MCs and NK cells were significant independent prognostic factors and were included in the nomogram as predictors. The internal and external calibration plots of the nomogram were in excellent agreement. This study reveals that TIICs are significantly correlated with clinicopathological features and prognosis in NSCLC and thus can be potential prognostic biomarker or therapeutic target. The remarkable heterogeneity of TIICs suggests that specific infiltrating patterns of TIICs should also be taken into consideration when determining individualized immunotherapy strategies for NSCLC patients.
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Understanding the role of tumor-infiltrating immune cells (TIICs) in non-small cell lung cancer (NSCLC) is critical to finding new prognostic biomarkers and improving prognostic evaluation. Herein, we aimed to comprehensively analyze tumor-infiltrating pattern of TIICs in NSCLC and build a TIICs-associated, risk-stratification prognostic model for clinical practice. We applied CIBERSORT and ESTIMATE computational methods to analyze RNA-seq samples of 852 NSCLC patients from The Cancer Genome Atlas (TCGA). Prognotic factors were identified by univariate and multivariate Cox regression analyses for overall survival (OS). A novel model was developed to predict the 1-, 3- and 5-year OS of NSCLC based on the TCGA cohort, validated by external validation cohorts (GSE31210, GSE37745), and then evaluated by C-indexes and calibration plots. Significant heterogeneity in the infiltrating patterns of TIICs was shown among various pathological subtypes of NSCLC and between different genders. Further analyses showed that abundances of naive B cells (NBCs), T cells and mast cells (MCs) were positively correlated with prognosis. Tumor samples with high T cells abundances tended to have higher expression levels of immune checkpoint genes (PD-1, PD-L1, CTLA-4). A new immune-gene related index (IGRI) was built by five immune-related differentially expressed genes (DEGs) including BTK, CCR2, CLEC10A, NCR3 and PRKCB, which were closely correlated with TIICs abundances and prognosis. Tumor stage, IGRI, abundances of NBCs, T cells, MCs and NK cells were significant independent prognostic factors and were included in the nomogram as predictors. The internal and external calibration plots of the nomogram were in excellent agreement. This study reveals that TIICs are significantly correlated with clinicopathological features and prognosis in NSCLC and thus can be potential prognostic biomarker or therapeutic target. 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Understanding the role of tumor-infiltrating immune cells (TIICs) in non-small cell lung cancer (NSCLC) is critical to finding new prognostic biomarkers and improving prognostic evaluation. Herein, we aimed to comprehensively analyze tumor-infiltrating pattern of TIICs in NSCLC and build a TIICs-associated, risk-stratification prognostic model for clinical practice. We applied CIBERSORT and ESTIMATE computational methods to analyze RNA-seq samples of 852 NSCLC patients from The Cancer Genome Atlas (TCGA). Prognotic factors were identified by univariate and multivariate Cox regression analyses for overall survival (OS). A novel model was developed to predict the 1-, 3- and 5-year OS of NSCLC based on the TCGA cohort, validated by external validation cohorts (GSE31210, GSE37745), and then evaluated by C-indexes and calibration plots. Significant heterogeneity in the infiltrating patterns of TIICs was shown among various pathological subtypes of NSCLC and between different genders. Further analyses showed that abundances of naive B cells (NBCs), T cells and mast cells (MCs) were positively correlated with prognosis. Tumor samples with high T cells abundances tended to have higher expression levels of immune checkpoint genes (PD-1, PD-L1, CTLA-4). A new immune-gene related index (IGRI) was built by five immune-related differentially expressed genes (DEGs) including BTK, CCR2, CLEC10A, NCR3 and PRKCB, which were closely correlated with TIICs abundances and prognosis. Tumor stage, IGRI, abundances of NBCs, T cells, MCs and NK cells were significant independent prognostic factors and were included in the nomogram as predictors. The internal and external calibration plots of the nomogram were in excellent agreement. This study reveals that TIICs are significantly correlated with clinicopathological features and prognosis in NSCLC and thus can be potential prognostic biomarker or therapeutic target. 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Understanding the role of tumor-infiltrating immune cells (TIICs) in non-small cell lung cancer (NSCLC) is critical to finding new prognostic biomarkers and improving prognostic evaluation. Herein, we aimed to comprehensively analyze tumor-infiltrating pattern of TIICs in NSCLC and build a TIICs-associated, risk-stratification prognostic model for clinical practice. We applied CIBERSORT and ESTIMATE computational methods to analyze RNA-seq samples of 852 NSCLC patients from The Cancer Genome Atlas (TCGA). Prognotic factors were identified by univariate and multivariate Cox regression analyses for overall survival (OS). A novel model was developed to predict the 1-, 3- and 5-year OS of NSCLC based on the TCGA cohort, validated by external validation cohorts (GSE31210, GSE37745), and then evaluated by C-indexes and calibration plots. Significant heterogeneity in the infiltrating patterns of TIICs was shown among various pathological subtypes of NSCLC and between different genders. Further analyses showed that abundances of naive B cells (NBCs), T cells and mast cells (MCs) were positively correlated with prognosis. Tumor samples with high T cells abundances tended to have higher expression levels of immune checkpoint genes (PD-1, PD-L1, CTLA-4). A new immune-gene related index (IGRI) was built by five immune-related differentially expressed genes (DEGs) including BTK, CCR2, CLEC10A, NCR3 and PRKCB, which were closely correlated with TIICs abundances and prognosis. Tumor stage, IGRI, abundances of NBCs, T cells, MCs and NK cells were significant independent prognostic factors and were included in the nomogram as predictors. The internal and external calibration plots of the nomogram were in excellent agreement. This study reveals that TIICs are significantly correlated with clinicopathological features and prognosis in NSCLC and thus can be potential prognostic biomarker or therapeutic target. The remarkable heterogeneity of TIICs suggests that specific infiltrating patterns of TIICs should also be taken into consideration when determining individualized immunotherapy strategies for NSCLC patients.</abstract><cop>Amsterdam</cop><pub>Elsevier B.V</pub><doi>10.1016/j.intimp.2020.106744</doi><tpages>1</tpages></addata></record>
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source ScienceDirect Journals (5 years ago - present)
subjects Biomarkers
Calibration
CCR2 protein
Computer applications
Correlation analysis
CTLA-4 protein
Genes
Genomes
Heterogeneity
Immune checkpoint
Immune system
Immune-related differentially expressed genes
Immunotherapy
Lung cancer
Lymphocytes
Lymphocytes B
Lymphocytes T
Mast cells
Medical prognosis
Monocyte chemoattractant protein 1
Nomogram
Nomograms
Non-small cell lung carcinoma
Non-small-cell lung cancer
Pattern analysis
PD-1 protein
PD-L1 protein
Prognosis
Regression analysis
Ribonucleic acid
RNA
Small cell lung carcinoma
Tumor-infiltrating immune cells
Tumor-infiltrating lymphocytes
Tumors
title Comprehensive analyses of the heterogeneity and prognostic significance of tumor-infiltrating immune cells in non-small-cell lung cancer: Development and validation of an individualized prognostic model
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