STTT (IF=38) Sichuan University Li Weimin/Zhang Li/Wang Chengdi reveal the heterogeneous immune landscape between lung adenocarcinoma and squamous cell carcinoma

The selection of immunotherapy strategies and prediction of clinical response in patients with non-small cell lung cancer (NSCLC) are critical

On August 26, 2022, Li Weimin, Zhang Li and Wang Chengdi of Sichuan University jointly published an online publication entitled "The heterogeneous immune landscape between lung adenocarcinoma and squamous carcinoma revealed by single-cell RNA sequencing" in Signal Transduction and Targeted Therapy (IF=38).

Importantly, this study identified a novel lymphocyte-associated Mφ cluster, named SELENOP-Mφ, and further identified its antitumor effect in both types, especially in LUAD

Immunotherapy has continued clinical significance in non-small cell lung cancer (NSCLC)
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However, due to inherent genomic and immunogenic heterogeneity, it can be difficult to predict whether a particular form of immunotherapy will be effective in different patients
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Lung cancer is the leading cause of cancer-related death, accounting for 1.
796 million (18%) deaths worldwide each year
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NSCLC accounts for more than 85% of lung cancer cases, of which lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) are the two major subtypes of NSCLC, accounting for about 80% of NSCLC cases
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To uncover the role of different genes for lung cancer subtypes in patients, new research opportunities have emerged that may aid in different treatment decisions
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There are multiple predictors of treatment response in the tumor microenvironment (TME), including genomic signatures, transcriptomic signatures, and epigenetic modifications, which also contribute to the heterogeneity of clinical responses across cancer subtypes and coordinate tumor A favorable or unfavorable outcome of progress
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As an essential component of the TME, the immune microenvironment includes comprehensive immune cells including macrophages (Mφ), lymphocytes, monocytes, and dendritic cells (DCs), as well as programmed cell death-1 (PD) -1), programmed cell death ligand 1 (PD-L1) and immune checkpoint molecules including cytotoxic T lymphocyte antigen 4 (CTLA-4), many of which are considered putative biomarkers in clinical practice thing
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Considering the complexity of the tumor microenvironment, single-cell RNA sequencing (scRNA-seq) enables comprehensive transcriptome profiling at single-cell resolution, unbiased classification of cellular diversity, and is an important tool for the extensive study of immune heterogeneity in the tumor microenvironment.
Qualitative a promising tool
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Compared to batch sequencing of averaged signals, scRNA-seq has the unique ability to interrogate specific cell subsets and states
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However, existing studies have mainly focused on stromal cells and cancer cells in the TME, e.
g.
Lambrechts and colleagues elucidated the heterogeneity of stromal cells in the TME of lung cancer
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However, Guo and colleagues have characterized T cells through single-cell sequencing
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However, the dynamics and molecular characterization of the lung cancer immune landscape at single-cell resolution remain largely unknown, let alone the details of the distinct immune profiles between LUAD and LUSC
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Different gene expression patterns of immune cells in LUAD and LUSC heterogeneity (Figure from Signal Transduction and Targeted Therapy) To bridge this gap, in this study, we first attempted to provide an immune cell map of lung cancer and compared LUAD and LUSC
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The authors comprehensively characterized the transcriptomic signature of immune cells from 19 patients with NSCLC (LUAD: n = 10; LUSC: n = 9), decoded the dynamic changes in cell percentage, and identified cellular subtypes between LUAD and LUSC.
Type heterogeneity and cell-cell interactions
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This study, through our understanding of cellular composition, status, and dynamics in the immune microenvironment, helps to explain the differences between the two major NSCLC subtypes and lays the groundwork for the development of TME therapeutic targets in future lung cancer clinical workflows
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Original link: —END—The content is [iNature]