Dysbiosis of lower respiratory tract microbiome are associated with proinflammatory states in non-small cell lung cancer patients.

BACKGROUND: The lung has a sophisticated microbiome, and respiratory illnesses are greatly influenced by the lung microbiota. Despite the fact that numerous studies have shown that lung cancer patients have a dysbiosis as compared to healthy people, more research is needed to explore the association between the microbiota dysbiosis and immune profile within the tumor microenvironment (TME). METHODS: In this study, we performed metagenomic sequencing of tumor and normal tissues from 61 non-small cell lung cancer (NSCLC) patients and six patients with other lung diseases. In order to characterize the impact of the microbes in TME, the cytokine concentrations of 24 lung tumor and normal tissues were detected using a multiple cytokine panel. RESULTS: Our results showed that tumors had lower microbiota diversity than the paired normal tissues, and the microbiota of NSCLC was enriched in Proteobacteria, Firmicutes, and Actinobacteria. In addition, proinflammatory cytokines such as IL-8, MIF, TNF- α, and so on, were significantly upregulated in tumor tissues. CONCLUSION: We discovered a subset of bacteria linked to host inflammatory signaling pathways and, more precisely, to particular immune cells. We determined that lower airway microbiome dysbiosis may be linked to the disruption of the equilibrium of the immune system causing lung inflammation. The spread of lung cancer may be linked to specific bacteria.

Early detection and stratification of lung cancer aided by a cost-effective assay targeting circulating tumor DNA (ctDNA) methylation.

BACKGROUND: Detection of lung cancer at earlier stage can greatly improve patient survival. We aim to develop, validate, and implement a cost-effective ctDNA-methylation-based plasma test to aid lung cancer early detection. METHODS: Case-control studies were designed to select the most relevant markers to lung cancer. Patients with lung cancer or benign lung disease and healthy individuals were recruited from different clinical centers. A multi-locus qPCR assay, LunaCAM, was developed for lung cancer alertness by ctDNA methylation. Two LunaCAM models were built for screening (-S) or diagnostic aid (-D) to favor sensitivity or specificity, respectively. The performance of the models was validated for different intended uses in clinics. RESULTS: Profiling DNA methylation on 429 plasma samples including 209 lung cancer, 123 benign diseases and 97 healthy participants identified the top markers that detected lung cancer from benign diseases and healthy with an AUC of 0.85 and 0.95, respectively. The most effective methylation markers were verified individually in 40 tissues and 169 plasma samples to develop LunaCAM assay. Two models corresponding to different intended uses were trained with 513 plasma samples, and validated with an independent collection of 172 plasma samples. In validation, LunaCAM-S model achieved an AUC of 0.90 (95% CI: 0.88-0.94) between lung cancer and healthy individuals, whereas LunaCAM-D model stratified lung cancer from benign pulmonary diseases with an AUC of 0.81 (95% CI: 0.78-0.86). When implemented sequentially in the validation set, LunaCAM-S enables to identify 58 patients of lung cancer (90.6% sensitivity), followed by LunaCAM-D to remove 20 patients with no evidence of cancer (83.3% specificity). LunaCAM-D significantly outperformed the blood test of carcinoembryonic antigen (CEA), and the combined model can further improve the predictive power for lung cancer to an overall AUC of 0.86. CONCLUSIONS: We developed two different models by ctDNA methylation assay to sensitively detect early-stage lung cancer or specifically classify lung benign diseases. Implemented at different clinical settings, LunaCAM models has a potential to provide a facile and inexpensive avenue for early screening and diagnostic aids for lung cancer.

Single-Cell Transcriptomics of Immune Cells Reveal Diversity and Exhaustion Signatures in Non-Small-Cell Lung Cancer.

Understanding immune cell phenotypes in the tumor microenvironment (TME) is essential for explaining and predicting progression of non-small cell lung cancer (NSCLC) and its response to immunotherapy. Here we describe the single-cell transcriptomics of CD45+ immune cells from tumors, normal tissues and blood of NSCLC patients. We identified three clusters of immune cells exerting immunosuppressive effects: CD8+ T cells with exhausted phenotype, tumor-associated macrophages (TAMs) with a pro-inflammatory M2 phenotype, and regulatory B cells (B regs) with tumor-promoting characteristics. We identified genes that may be mediating T cell phenotypes, including the transcription factors ONECUT2 and ETV4 in exhausted CD8+ T cells, TIGIT and CTL4 high expression in regulatory T cells. Our results highlight the heterogeneity of CD45+ immune cells in the TME and provide testable hypotheses about the cell types and genes that define the TME.