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The Immune Subtypes and Landscape of Squamous Cell Carcinoma.

ABSTRACT: PURPOSE:To identify immune subtypes and investigate the immune landscape of squamous cell carcinomas (SCC), which share common etiology and histologic features. EXPERIMENTAL DESIGN:Based on the immune gene expression profiles of 1,368 patients with SCC in the Cancer Genome Atlas (TCGA), we used consensus clustering to identify robust clusters of patients and assessed their reproducibility in an independent pan-SCC cohort of 938 patients. We further applied graph structure learning-based dimensionality reduction to the immune profiles to visualize the distribution of individual patients. RESULTS:We identified and independently validated six reproducible immune subtypes associated with distinct molecular characteristics and clinical outcomes. An immune-cold subtype had the least amount of lymphocyte infiltration and a high level of aneuploidy, and these patients had the worst prognosis. By contrast, an immune-hot subtype demonstrated the highest infiltration of CD8+ T cells, activated NK cells, and elevated IFN? response. Accordingly, these patients had the best prognosis. A third subtype was dominated by M2-polarized macrophages with potent immune-suppressive factors such as TGF? signaling and reactive stroma, and these patients had relatively inferior prognosis. Other subtypes showed more diverse immunologic features with intermediate prognoses. Finally, our analysis revealed a complex immune landscape consisting of both discrete clusters and continuous spectrum. CONCLUSIONS:This study provides a conceptual framework to understand the tumor immune microenvironment of SCCs. Future work is needed to evaluate its relevance in the design of combination treatment strategies and guiding optimal selection of patients for immunotherapy.

SUBMITTER: Li B

PROVIDER: S-EPMC6571041 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature

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The Immune Subtypes and Landscape of Squamous Cell Carcinoma.

Li Bailiang B Cui Yi Y Nambiar Dhanya K DK Sunwoo John B JB Li Ruijiang R

Clinical cancer research : an official journal of the American Association for Cancer Research 20190304 12

<h4>Purpose</h4>To identify immune subtypes and investigate the immune landscape of squamous cell carcinomas (SCC), which share common etiology and histologic features.<h4>Experimental design</h4>Based on the immune gene expression profiles of 1,368 patients with SCC in the Cancer Genome Atlas (TCGA), we used consensus clustering to identify robust clusters of patients and assessed their reproducibility in an independent pan-SCC cohort of 938 patients. We further applied graph structure learning ...[more]

PMID: 30833271

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Project description:BackgroundImmunotherapy has achieved remarkable efficacy in treating oesophageal squamous cell carcinoma (ESCC). However, this treatment has limited efficacy in some patients. An increasing number of evidence suggested that immune cells within the tumour microenvironment (TME) are strongly related to immunotherapy response and patient prognosis. Thus, the landscape of immune cell infiltration (ICI) in ESCC needs to be mapped.MethodsIn the study, the ICI pattern in 206 cases of ESCC was characterised by two algorithms, namely, CIBERSORT and single-sample gene set enrichment analysis (ssGSEA). The ICI score of each specimen was calculated by principal component analysis (PCA) according to ICI signature genes A (ICISGA) and B (ICISGB). The prognostic difference was evaluated by using the Kaplan-Meier method. The related pathways of ICI score were investigated by applying gene set enrichment analysis (GSEA). The R packages of 'regplot', 'timeROC' and 'rms' were applied for the construction of nomogram model.ResultThree TME subtypes were identified with no prognostic implication. A total of 333 differentially expressed genes (DEGs) among immune subtypes were determined, among which ICISGA and ICISGB were identified. Finally, ICI scores were constructed, and the patients were grouped into high or low ICI score group. Compared with the low ICI score group, the high ICI score group had better prognosis. GSEA revealed that the high ICI score group referred to multiple signalling pathways, including B cell receptor, Fc gamma R-mediated phagocytosis, NOD-like receptor and TGF-β signalling pathways. In addition, the nomogram model was constructed to evaluate 1-, 3- and 5-year probability of death in an ESCC patient. The ROC and calibration curves indicated that the model has a good discrimination ability.ConclusionWe depicted a comprehensive ICI landscape in ESCC. ICI score may be used as a predictor of survival rate, which may be helpful for guiding immunotherapy in the future.

Project description:BackgroundIncreasing evidence supports that immune cell infiltration (ICI) patterns play a key role in the tumor progression of lung squamous cell carcinoma (LUSC). However, to date, the immune infiltration picture of LUSC has not been elucidated.MethodTCGA was used to download multiomics data from LUSC samples. At the same time, we included two datasets on lung squamous cell carcinoma, GSE17710 and GSE157010. To reveal the landscape of tumor immune microenvironment (TIME), the ESTIMATE algorithm, ssGSEA approach, and CIBERSORT analysis are used. To quantify the ICI pattern in a single tumor, consistent clustering is used to determine the LUSC subtype based on the ICI pattern, and principal component analysis (PCA) is used to obtain the ICI score. The prognostic value of the Kaplan-Meier curves is confirmed. GSEA (Gene Set Enrichment Analysis) was used to perform functional annotation. To investigate the immunotherapeutic effects of the ICI score, the immunophenotyping score (IPS) is used. Finally, analyze the mutation data with the "maftools" R package.ResultsWe identified four different immune infiltration patterns with different prognosis and biological characteristics in 792 LUSC samples. The identification of ICI patterns in individual tumors developed under ICI-related characteristic genes based on the ICI score helps to analyze the biological process, clinical results, immune cell infiltration, immunotherapy effects, and genetic variation. Immune failure is indicated by a high ICI score subtype marked by immunosuppression. Patients with low ICI scores have an abundance of efficient immune cells, which corresponds to the immunological activation phenotype and may have therapeutic benefits. The immunophenotypic score was used as a surrogate indicator of immunotherapy results, and samples with low ICI scores obtained significantly higher immunophenotypic scores. Finally, the relationship between the ICI score and tumor mutation burden (TMB) was proven.ConclusionThis study fully clarified the indispensable role of the ICI model in the complexity and diversity of TIME. The quantitative identification of ICI patterns in a single tumor will help draw the picture of TIME and further optimize precision immunotherapy.

Dataset Information

The Immune Subtypes and Landscape of Squamous Cell Carcinoma.

Publications

The Immune Subtypes and Landscape of Squamous Cell Carcinoma.

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