Use of antigen-primed dendritic cells for inducing antitumor immune responses in vitro in patients with non-small cell lung cancer

Modified Dendritic cell-based T-cell expansion protocol and single-cell multi-omics allow for the selection of the most expanded and in vitro-effective clonotype via profiling of thousands of MAGE-A3-specific T-cells

Introduction

Adoptive cell therapy using TCR-engineered T-cells is one of the most effective strategies against tumor cells. The TCR T-cell approach has been well tested against a variety of blood neoplasms but is yet to be deeply tested against solid tumors. Among solid tumors, cancer-testis antigens are the most prominent targets for tumor-specific therapy, as they are usually found on cells that lie behind blood-tissue barriers.

Methods

We have employed a novel efficient protocol for MAGE-A3-specific T-cell clonal expansion, performed single-cell multi-omic analysis of the expanded T-cells via BD Rhapsody, engineered a selected T-cell receptor into a lentiviral construct, and tested it in an in vitro LDH-cytotoxicity test.

Results and discussion

We have observed a 191-fold increase in the MAGE-A3-specific T-cell abundance, obtained a dominant T-cell receptor via single-cell multi-omic BD Rhapsody data analysis in the TCRscape bioinformatics tool, and observed potent cytotoxicity of the dominant-clonotype transduced TCR T-cells against a MAGE-A3-positive tumor. We have demonstrated the efficiency of our T-cell enrichment protocol in obtaining potent anti-tumor T-cells and their T-cell receptors, especially when paired with the modern single-cell analysis methods.

Nitrogen Metabolism Disorder Accelerates Occurrence and Development of Lung Adenocarcinoma: A Bioinformatic Analysis and In Vitro Experiments

Background

Nitrogen metabolism (NM) plays a pivotal role in immune regulation and the occurrence and development of cancers. The aim of this study was to construct a prognostic model and nomogram using NM-related genes for the evaluation of patients with lung adenocarcinoma (LUAD).

Methods

The differentially expressed genes (DEGs) related to NM were acquired from The Cancer Genome Atlas (TCGA) database. Consistent clustering analysis was used to divide them into different modules, and differentially expressed genes and survival analysis were performed. The survival information of patients was combined with the expressing levels of NM-related genes that extracted from TCGA and Gene Expression Omnibus (GEO) databases. Subsequently, univariate Cox analysis and the least absolute shrinkage and selection operator (LASSO) regression were used to build a prognostic model. GO and KEGG analysis were elaborated in relation with the mechanisms of NM disorder (NMD). Meanwhile, immune cells and immune functions related to NMD were discussed. A nomogram was built according to the univariate and multivariate Cox analysis to identify independent risk factors. Finally, real-time fluorescent quantitative PCR (RT-PCR) and Western bolt (WB) were used to verify the expression level of hub genes.

Results

There were 138 differential NM-related genes that were divided into two gene modules. Sixteen NM-related genes were used to build a prognostic model and the receiver operating characteristic curve (ROC) showed that the efficiency was reliable. GO and KEGG analysis suggested that NMD accelerated development of LUAD through the Wnt signaling pathway. The level of activated dendritic cells (aDCs) and type II interferon response in the low-risk group was higher than that of the high-risk group. A nomogram was constructed based on ABCC2, HMGA2, and TN stages, which was identified as four independent risk factors. Finally, RT-PCR and WB showed that CDH17, IGF2BP1, IGFBP1, ABCC2, and HMGA2 were differently expressed between human lung fibroblast (HLF) cells and cancer cells.

Conclusions

High NM levels were revealed as a poor prognosis of LUAD. NMD regulates immune system through affecting aDCs and type II interferon response. The prognostic model with NM-related genes could be used to effectively evaluate the outcomes of patients.

Prognostic Ability of Enhancer RNAs in Metastasis of Non-Small Cell Lung Cancer

(1) Background: Non-small cell lung cancer (NSCLC) is the most common lung cancer. Enhancer RNA (eRNA) has potential utility in the diagnosis, prognosis and treatment of cancer, but the role of eRNAs in NSCLC metastasis is not clear; (2) Methods: Differentially expressed transcription factors (DETFs), enhancer RNAs (DEEs), and target genes (DETGs) between primary NSCLC and metastatic NSCLC were identified. Prognostic DEEs (PDEEs) were screened by Cox regression analyses and a predicting model for metastatic NSCLC was constructed. We identified DEE interactions with DETFs, DETGs, reverse phase protein arrays (RPPA) protein chips, immunocytes, and pathways to construct a regulation network using Pearson correlation. Finally, the mechanisms and clinical significance were explained using multi-dimensional validation unambiguously; (3) Results: A total of 255 DEEs were identified, and 24 PDEEs were selected into the multivariate Cox regression model (AUC = 0.699). Additionally, the NSCLC metastasis-specific regulation network was constructed, and six key PDEEs were defined (ANXA8L1, CASTOR2, CYP4B1, GTF2H2C, PSMF1 and TNS4); (4) Conclusions: This study focused on the exploration of the prognostic value of eRNAs in the metastasis of NSCLC. Finally, six eRNAs were identified as potential markers for the prediction of metastasis of NSCLC.

Impaired dendritic cell functions in lung cancer: a review of recent advances and future perspectives

Lung cancer is the leading cause of cancer mortality worldwide. Dendritic cells (DCs) are the key factors providing protective immunity against lung tumors and clinical trials have proven that DC function is reduced in lung cancer patients. It is evident that the immunoregulatory network may play a key role in the failure of the immune response to terminate tumors. Lung tumors likely employ numerous strategies to suppress DC-based anti-tumor immunity. Here, we summarize the recent advances in our understanding on lung tumor-induced immunosuppression in DCs, which affects the initiation and development of T-cell responses. We also describe which existing measures to restore DC function may be useful for clinical treatment of lung tumors. Furthering our knowledge of how lung cancer cells alter DC function to generate a tumor-supportive environment will be essential in order to guide the design of new immunotherapy strategies for clinical use.