The Predictive Role of Immune Related Subgroup Classification in Immune Checkpoint Blockade Therapy for Lung Adenocarcinoma.

Xiaozhou Yu,Wengui Xu,Yiwen Chen,Xiaofeng Li,Wei Chen,Guotao Yin,Lei Zhu,Ziyang Wang,Jianjing Liu

doi:10.3389/fgene.2021.771830

Abstract

Background: In lung adenocarcinoma (LUAD), the predictive role of immune-related subgroup classification in immune checkpoint blockade (ICB) therapy remains largely incomplete. Methods: Transcriptomics analysis was performed to evaluate the association between immune landscape and ICB therapy in lung adenocarcinoma and the associated underlying mechanism. First, the least absolute shrinkage and selection operator (LASSO) algorithm and K-means algorithm were used to identify immune related subgroups for LUAD cohort from the Cancer Genome Atlas (TCGA) database (n = 572). Second, the immune associated signatures of the identified subgroups were characterized by evaluating the status of immune checkpoint associated genes and the immune cell infiltration. Then, potential responses to ICB therapy based on the aforementioned immune related subgroup classification were evaluated via tumor immune dysfunction and exclusion (TIDE) algorithm analysis, and survival analysis and further Cox proportional hazards regression analysis were also performed for LUAD. In the end, gene set enrichment analysis (GSEA) was performed to explore the metabolic mechanism potentially responsible for immune related subgroup clustering. Additionally, two LUAD cohorts from the Gene Expression Omnibus (GEO) database were used as validation cohort. Results: A total of three immune related subgroups with different immune-associated signatures were identified for LUAD. Among them, subgroup 1 with higher infiltration scores for effector immune cells and immune checkpoint associated genes exhibited a potential response to IBC therapy and a better survival, whereas subgroup 3 with lower scores for immune checkpoint associated genes but higher infiltration scores for suppressive immune cells tended to be insensitive to ICB therapy and have an unfavorable prognosis. GSEA revealed that the status of glucometabolic reprogramming in LUAD was potentially responsible for the immune-related subgroup classification. Conclusion: In summary, immune related subgroup clustering based on distinct immune associated signatures will enable us to screen potentially responsive LUAD patients for ICB therapy before treatment, and the discovery of metabolism associated mechanism is beneficial to comprehensive therapeutic strategies making involving ICB therapy in combination with metabolism intervention for LUAD.

Highlights

Lung cancer is one of the most common type of malignancies worldwide, and is the leading cause of cancer-related death among men and women globally (Siegel et al, 2021)
The least absolute shrinkage and selection operator (LASSO) algorithm and 10-fold cross-validation were used to extract the optimal subsets of immune associated genes based on Immunome database for immune related subgroup clustering of lung adenocarcinoma (LUAD) cohort from TCGA database
Based on the immune related subgroup clustering, gene set enrichment analyses (GSEA) was performed on LUAD data set from the TCGA database using the gene sets significantly associated with glucose metabolism, including the process of glycolysis (Figure 5A), tricarboxylic acid (TCA) cycle (Figure 5B), gluconeogenesis (Figure 5C), oxidative phosphorylation (OXPHOS) in mitochondria (Figure 5D)

Summary

Introduction

Lung cancer is one of the most common type of malignancies worldwide, and is the leading cause of cancer-related death among men and women globally (Siegel et al, 2021). As an emerging therapeutic approach for tumor, immunotherapy, such as immune checkpoint blockade (ICB) therapy, is increasingly approved to be effective for LUAD (Huang et al, 2020a). Attenuated anti-tumor immune response or induced immunosuppression in local tumor microenvironment (TME) partially result from excessive negative immune response mediated by immune checkpoints (Anichini et al, 2020). ICB therapy aims to enhance anti-tumor immune response by inhibiting detrimental immunosuppression induced by immune checkpoint in TME. In lung adenocarcinoma (LUAD), the predictive role of immune-related subgroup classification in immune checkpoint blockade (ICB) therapy remains largely incomplete

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