Innate Immune Landscape in Early Lung Adenocarcinoma by Paired Single-Cell Analyses.
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ABSTRACT:
SUBMITTER: Lavin Y
PROVIDER: S-EPMC5737939 | biostudies-literature | 2017 May
REPOSITORIES: biostudies-literature
Publications
Innate Immune Landscape in Early Lung Adenocarcinoma by Paired Single-Cell Analyses.
Lavin Yonit Y Kobayashi Soma S Leader Andrew A Amir El-Ad David ED Elefant Naama N Bigenwald Camille C Remark Romain R Sweeney Robert R Becker Christian D CD Levine Jacob H JH Meinhof Klaus K Chow Andrew A Kim-Shulze Seunghee S Wolf Andrea A Medaglia Chiara C Li Hanjie H Rytlewski Julie A JA Emerson Ryan O RO Solovyov Alexander A Greenbaum Benjamin D BD Sanders Catherine C Vignali Marissa M Beasley Mary Beth MB Flores Raja R Gnjatic Sacha S Pe'er Dana D Rahman Adeeb A Amit Ido I Merad Miriam M
Cell 20170501 4
To guide the design of immunotherapy strategies for patients with early stage lung tumors, we developed a multiscale immune profiling strategy to map the immune landscape of early lung adenocarcinoma lesions to search for tumor-driven immune changes. Utilizing a barcoding method that allows a simultaneous single-cell analysis of the tumor, non-involved lung, and blood cells, we provide a detailed immune cell atlas of early lung tumors. We show that stage I lung adenocarcinoma lesions already har ...[more]
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Project description:We perform massively parallel single cell RNA-seq (MARS-Seq) on non-lymphocytic immune cells sorted from a human stage IA lung adenocarcinoma lesion and from the adjacent non-involved lung. With an unbiased single cell transcriptomic analysis of non-lymphocyte cells accumulating in these tissues, we sought to capture the heterogeneity of the tumor-infiltrating myeloid (TIM) compartment. Using a previously described unbiased expectation-maximization algorith (Paul et al., 2015), we identified clusters that were then named according to literature-reported marker profiles. This revealed a CD141+ DC subets, a CD1c+ DC subset, and a macrophage subset that clustered separately and was found to be enriched in the lesion as compared to other macrophage subsets.
2017-05-04 | GSE97168 | GEO
Project description:BackgroundLung adenocarcinoma (LUAD) patients with different American Joint Committee on Cancer stages have different overall 5-year survival rates. The tumor microenvironment (TME) and intra-tumor heterogeneity (ITH) have been shown to play a crucial role in the occurrence and development of tumors. However, the TME and ITH in different lesions of LUAD have not been extensively explored.MethodsWe present a 204,157-cell catalog of the TME transcriptome in 29 lung samples to systematically explore the TME and ITH in the different stages of LUAD. Traditional RNA sequencing data and complete clinical information were downloaded from publicly available databases.ResultsBased on these high-quality cells, we constructed a single-cell network underlying cellular and molecular features of normal lung, early LUAD, and advanced LUAD cells. In contrast with early malignant cells, we noticed that advanced malignant cells had a remarkably more complex TME and higher ITH level. We also found that compared with other immune cells, more differences in CD8+/CTL T cells, regulatory T cells, and follicular B cells were evident between early and advanced LUAD. Additionally, cell-cell communication analyses, revealed great diversity between different lesions of LUAD at the single-cell level. Flow cytometry and qRT-PCR were used to validate our results.ConclusionOur results revealed the cellular diversity and molecular complexity of cell lineages in different stages of LUAD. We believe our research, which serves as a basic framework and valuable resource, can facilitate exploration of the pathogenesis of LUAD and identify novel therapeutic targets in the future.
| S-EPMC7943914 | biostudies-literature
Project description:Tumor-infiltrating B and plasma cells (TIB) are prevalent in lung adenocarcinoma (LUAD); however, they are poorly characterized. We performed paired single-cell RNA and B-cell receptor (BCR) sequencing of 16 early-stage LUADs and 47 matching multiregion normal tissues. By integrative analysis of ∼50,000 TIBs, we define 12 TIB subsets in the LUAD and adjacent normal ecosystems and demonstrate extensive remodeling of TIBs in LUADs. Memory B cells and plasma cells (PC) were highly enriched in tumor tissues with more differentiated states and increased frequencies of somatic hypermutation. Smokers exhibited markedly elevated PCs and PCs with distinct differentiation trajectories. BCR clonotype diversity increased but clonality decreased in LUADs, smokers, and with increasing pathologic stage. TIBs were mostly localized within CXCL13+ lymphoid aggregates, and immune cell sources of CXCL13 production evolved with LUAD progression and included elevated fractions of CD4 regulatory T cells. This study provides a spatial landscape of TIBs in early-stage LUAD.SignificanceWhile TIBs are highly enriched in LUADs, they are poorly characterized. This study provides a much-needed understanding of the transcriptional, clonotypic states and phenotypes of TIBs, unraveling their potential roles in the immunopathology of early-stage LUADs and constituting a road map for the development of TIB-targeted immunotherapies for the treatment of this morbid malignancy. This article is highlighted in the In This Issue feature, p. 2483.
| S-EPMC9633381 | biostudies-literature
Project description:A thorough interrogation of the immune landscape is crucial for immunotherapy strategy selection and prediction of clinical responses in non-small-cell lung cancer (NSCLC) patients. Single-cell RNA sequencing (scRNA-seq) techniques have prompted the opportunity to dissect the distinct immune signatures between lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC), the two major subtypes of NSCLC. Here, we performed scRNA-seq on 72,475 immune cells from 40 samples of tumor and matched adjacent normal tissues spanning 19 NSCLC patients, and drew a systematic immune cell transcriptome atlas. Joint analyses of the distinct cellular compositions, differentially expressed genes (DEGs), cell-cell interactions, pseudotime trajectory, transcriptomic factors and prognostic factors based on The Cancer Genome Atlas (TCGA), revealed the central roles of cytotoxic and effector T and NK cells and the distinct functional macrophages (Mφ) subtypes in the immune microenvironment heterogeneity between LUAD and LUSC. The dominant subtype of Mφ was FABP4-Mφ in LUAD and SPP1-Mφ in LUSC. Importantly, we identified a novel lymphocyte-related Mφ cluster, which we named SELENOP-Mφ, and further established its antitumor role in both types, especially in LUAD. Our comprehensive depiction of the immune heterogeneity and definition of Mφ clusters could help design personalized treatment for lung cancer patients in clinical practice.
| S-EPMC9411197 | biostudies-literature
Project description:BACKGROUND:Understanding the genomic landscape and immune microenvironment features of preinvasive and early invasive lung adenocarcinoma may provide critical insight and facilitate development of novel strategies for early detection and intervention. METHODS:A total of 80 tumor tissue samples and 30 paired histologically normal lung tissue samples from 30 patients with adenocarcinoma in situ (AIS) (n = 8), minimally invasive adenocarcinoma (MIA) (n = 8), and invasive adenocarcinoma (IAC) (n = 14) were subjected to multiregion whole exome sequencing and immunohistochemistry staining for CD8 and programmed death ligand 1 (PD-L1). RESULTS:All tumors, including AIS, exhibited evidence of genomic intratumor heterogeneity. Canonical cancer gene mutations in EGFR, erb-b2 receptor tyrosine kinase 2 gene (ERBB2), NRAS, and BRAF were exclusively trunk mutations detected in all regions within each tumor, whereas genes associated with cell mobility, gap junction, and metastasis were all subclonal mutations. EGFR mutation represented the most common driver alterations across AIS, MIA, and IAC, whereas tumor protein p53 gene (TP53) was identified in MIA and IAC but not in AIS. There was no difference in PD-L1 expression among AIS, MIA, and IAC, but the CD8 positivity rate was higher in IAC. Tumors positive for both PD-L1 and CD8 had a larger proportion of subclonal mutations. CONCLUSIONS:Mutations in EGFR, ERBB2, NRAS, and BRAF are early clonal genomic events during carcinogenesis of lung adenocarcinoma, whereas TP53 and cell mobility, gap junction, and metastasis-related genes may be late events associated with subclonal diversification and neoplastic progression. Genomic intratumor heterogeneity and immunoediting are common and early phenomena that may have occurred before the acquisition of invasion.
| S-EPMC6986039 | biostudies-literature
Project description:PurposeTP53 mutation, one of the most frequent mutations in early-stage lung adenocarcinoma (LUAD), triggers a series of alterations in the immune landscape, progression, and clinical outcome of early-stage LUAD. Our study was designed to unravel the effects of TP53 mutation on the immunophenotype of early-stage LUAD and formulate a TP53-associated immune prognostic model (IPM) that can estimate prognosis in early-stage LUAD patients.Materials and methodsImmune-associated differentially expressed genes (DEGs) between TP53 mutated (TP53MUT ) and TP53 wild-type (TP53WT ) early-stage LUAD were comprehensively analyzed. Univariate Cox analysis and least absolute shrinkage and selection operator (LASSO) analysis identified the prognostic immune-associated DEGs. We constructed and validated an IPM based on the TCGA and a meta-GEO composed of GSE72094, GSE42127, and GSE31210, respectively. The CIBERSORT algorithm was analyzed for assessing the percentage of immune cell types. A nomogram model was established for clinical application.ResultsTP53 mutation occurred in approximately 50.00% of LUAD patients, stimulating a weakened immune response in early-stage LUAD. Sixty-seven immune-associated DEGs were determined between TP53WT and TP53MUT cohort. An IPM consisting of two prognostic immune-associated DEGs (risk score = 0.098 * ENTPD2 expression + 0.168 * MIF expression) was developed through 397 cases in the TCGA and further validated based on 623 patients in a meta-GEO. The IPM stratified patients into low or high risk of undesirable survival and was identified as an independent prognostic indicator in multivariate analysis (HR = 2.09, 95% CI: 1.43-3.06, p < 0.001). Increased expressions of PD-L1, CTLA-4, and TIGIT were revealed in the high-risk group. Prognostic nomogram incorporating the IPM and other clinicopathological parameters (TNM stage and age) achieved optimal predictive accuracy and clinical utility.ConclusionThe IPM based on TP53 status is a reliable and robust immune signature to identify early-stage LUAD patients with high risk of unfavorable survival.
| S-EPMC7897963 | biostudies-literature
Project description:Dipeptidase 2 (DPEP2) is a dipeptidyl peptidase that plays an important role in the hydrolysis of leukotriene D4 (LTD4) to leukotriene E4 (LTE4). Previous studies have suggested that LTD4 promotes tumor progression and survival in non-small cell lung cancer (NSCLC). Therefore, we hypothesized that DPEP2 may play a pivotal role in this tumor. Given that lung adenocarcinoma (LUAD) is the most common subtype of NSCLC, our study aimed to examine the expression and function of DPEP2 in LUAD. Based on bioinformatics and the analysis of clinical samples, our findings revealed that DPEP2 is highly expressed in normal lung tissues, but downregulated in LUAD tissues, and its expression levels were significantly associated with clinical indicators of tumor grade and prognosis. Pathway enrichment analysis showed that DPEP2 is involved in biological processes such as chemokine signaling pathways, leukocyte trans-endothelial migration, and humoral immune responses in LUAD. In addition, DPEP2 expression was significantly associated with various immune cells, especially monocytes-macrophages. Single-cell transcriptome data further confirmed the expression of DPEP2 dominantly in macrophages from normal lung tissues. Analysis of the TCIA database revealed that high DPEP2 expression is associated with a stronger response to immune checkpoint inhibitors such as CTLA4 and PD1, and determines sensitivity to LUAD therapeutic agents. Furthermore, we found that DPEP2 inhibits the migration and invasion of LUAD cells. Therefore, DPEP2 may serve as a potential immune biomarker and therapeutic target for LUAD, providing novel therapeutic approaches for this disease.
| S-EPMC10302781 | biostudies-literature
Project description:The impacts of the tumor microenvironment (TME) on tumor evolvability remain unclear. A challenge for nearly all cancer types is spatial heterogeneity, providing substrates for the emergence and evolvability of drug resistance and leading to unfavorable prognosis. Understanding TME heterogeneity among different tumor sites would provide deeper insights into personalized therapy. We found 9,992 cell profiles of the TME in human lung adenocarcinoma (LUAD) samples at a single-cell resolution. By comparing different tumor sites, we discovered high TME heterogeneity. Single-sample gene set enrichment analysis (ssGSEA) was utilized to explore functional differences between cell subpopulations and between the core, middle and edge of tumors. We identified 8 main cell types and 27 cell subtypes of T cells, B cells, fibroblasts and myeloid cells. We revealed CD4+ naive T cells in the tumor core that express high levels of immune checkpoint molecules and have a higher activity of immune-exhaustion signaling. CD8+ T cell subpopulations in the tumor core correlate with the upregulated activity of transforming growth factor-β (TGF-β) and fibroblast growth factor receptor (FGFR) signaling and downregulated T cell activity. B cell subtypes in the tumor core downregulate cytokine production. In this study, we revealed that there was immunological heterogeneity in the TME of patients with LUAD that have different ratios of immune cells and stromal cells, different functions, and various degrees of activation of immune-related pathways in different tumor parts. Therefore, clarifying the spatial heterogeneity of the tumor in the immune microenvironment can help clinicians design personalized treatments.
| S-EPMC8424094 | biostudies-literature
Project description:The immune cell infiltration in TME has been reported to be associated with prognosis and immunotherapy efficiency of lung cancers. However, to date, the immune infiltrative landscape of lung adenocarcinoma (LUAD) has not been elucidated yet. Therefore, this study aimed to identify a new transcriptomic-based TME classification and develop a risk scoring system to predict the clinical outcomes of patients with LUAD. We applied "CIBERSORT" algorithm to analyze the transcriptomic data of LUAD samples and classified LUAD into four discrete subtypes according to the distinct immune cell infiltration patterns. Furthermore, we established a novel predictive tool (TMEscore) to quantify the immune infiltration patterns for each LUAD patient by principal component analysis. The TMEscore displayed as a reliable and independent prognostic biomarker for LUAD, with worse survival in TMEscrore-high patients and better survival in TMEscrore-low patients in both TCGA and other five GEO cohorts. In addition, enriched pathways and genomic alterations were also analyzed and compared in different TMEscore subgroups, and we observed that high TMEscore was significantly correlated with more aggressive molecular changes, while the low TMEscore subgroup enriched in immune active-related pathways. The TMEscore-low subtype showed overexpression of PD-1, CTLA4, and associations of other markers of sensitivity to immunotherapy, including TMB, immunophenoscore (IPS) analysis, and tumor immune dysfunction and exclusion (TIDE) algorithm. Conclusively, TMEscore is a promising and reliable biomarker to distinguish the prognosis, the molecular and immune characteristics, and the benefit from ICIs treatments in LUAD.
| S-EPMC9168373 | biostudies-literature
Project description:Patients with early-stage lung adenocarcinoma (LUAD) exhibit different overall survival (OS) rates and immunotherapy responses. Understanding the immune landscape facilitates the personalized treatment of LUAD. The immune cell populations in tumour tissues were quantified to depict the immune landscape in early-stage LUAD patients in The Cancer Genome Atlas (TCGA). Early-stage LUAD patients in three immune clusters identified by the immune landscape exhibited different survival potentials. A prognostic immune-related gene signature was built to predict the survival of early-stage LUAD patients. Several machine learning methods (support vector machine, naive Bayes, random forest, and neural network-based deep learning) were applied to train the classifiers to identify the immune clusters in early-stage LUAD based on the gene signature. The four classifiers exhibited a robust effect in identifying the immune clusters. A random forest regression model identified that TP53 was the most important gene mutation associated with the immune-related signature. Furthermore, a decision tree and a nomogram were constructed based on the immune-related gene signature and clinicopathological traits to improve risk stratification and quantify risk assessment for individual patients. Five external test cohorts were applied to validate the accuracy of the immune-related signature. Our study might contribute to the development of immunotherapy and the personalized treatment of early-stage LUAD. KEY MESSAGES: Immune landscape correlates with the clinical outcome of early-stage adenocarcinoma (LUAD). Machine learning methods identifies a prognostic gene signature to predict the survival and prognosis of early-stage LUAD. TP53 gene mutation status correlates with the immune landscape in early-stage LUAD.
| S-EPMC7297823 | biostudies-literature