Anti-ELF3 monoclonal antibody, clone ZL7-4

Lung adenocarcinoma (LUAD) harboring EGFR mutations prevails in Asian population. However, the inter-patient and intra-tumor heterogeneity has not been addressed at single-cell resolution. Here we performed single-cell RNA sequencing (scRNA-seq) of total 125,674 cells from seven stage-I/II LUAD samples harboring EGFR mutations and five tumor-adjacent lung tissues. We identified diverse cell types within the tumor microenvironment (TME) in which myeloid cells and T cells were the most abundant stromal cell types in tumors and adjacent lung tissues. Within tumors, accompanied by an increase in CD1C(+) dendritic cells, the tumor-associated macrophages (TAMs) showed pro-tumoral functions without signature gene expression of defined M1 or M2 polarization. Tumor-infiltrating T cells mainly displayed exhausted and regulatory T-cell features. The adenocarcinoma cells can be categorized into different subtypes based on their gene expression signatures in distinct pathways such as hypoxia, glycolysis, cell metabolism, translation initiation, cell cycle, and antigen presentation. By performing pseudotime trajectory, we found that ELF3 was among the most upregulated genes in more advanced tumor cells. In response to secretion of inflammatory cytokines (e.g., IL1B) from immune infiltrates, ELF3 in tumor cells was upregulated to trigger the activation of PI3K/Akt/NF-kappa B pathway and elevated expression of proliferation and anti-apoptosis genes such as BCL2L1 and CCND1. Taken together, our study revealed substantial heterogeneity within early-stage LUAD harboring EGFR mutations, implicating complex interactions among tumor cells, stromal cells and immune infiltrates in the TME.

BackgroundThe current prognosis of thymic epithelial tumors (TETs) is according to the World Health Organization (WHO) histologic classification and the Masaoka staging system. These methods of prognosis have certain limitations in clinical application and there is a need to seek new method for determining the prognosis of patients with TETs. To date, there have been no studies done on the use of DNA methylation biomarkers for prognosis of TETs. The present study was therefore carried out to identify DNA methylation biomarkers that can determine the overall survival in patients with TETs.MethodsBioinformatic analysis of TCGA 450K methylation array data, transcriptome sequencing data, WHO histologic classification and Masaoka staging system was performed to identify differentially expressed methylation sites between thymoma and thymic carcinoma as well as the different DNA methylation sites associated with the overall survival in patients with TETs. Using pyrosequencing, 4 different methylation sites (cg05784862, cg07154254, cg02543462, and cg06288355) were sequenced from tumor tissues of 100 Chinese patients with TETs. A prognostic model for TETs was constructed using these four methylation sites.ResultsThe TCGA dataset showed 5155 and 6967 hyper- and hypomethylated CpG sites in type A-B3 group and type C group, respectively, of which 3600 were located within the gene promoter regions. One hundred thirty-four genes were silenced by promoter hypermethylation and 174 mRNAs were upregulated. Analysis of univariate and multivariate Cox regression showed significant association between the methylation levels of 187 sites and the overall survival in patients with TETs. cg05784862(KSR1), cg07154254(ELF3), cg02543462(ILRN), and cg06288355(RAG1) were identified as independent prognostic factors for overall survival in patients with TETs after adjusting for Masaoka staging in 100 Chinese patients. The prognostic model which consists of the four abovementioned genes had higher accuracy for predicting the 5-year overall survival in patients with TETs as compared to the Masaoka clinical staging. (Time-dependent ROC analysis AUC 1.000 vs 0.742, P=2.7x10(-6)).ConclusionsThe methylation levels of cg05784862(KSR1), cg07154254(ELF3), cg02543462(ILRN), and cg06288355(RAG1) sites are associated with the progression of TETs and may serve as new biomarkers for predicting the overall survival in patients with TETs.