Characterization of the Immune Microenvironmental Landscape of Lung Squamous Cell Carcinoma with Immune Cell Infiltration

Abstract

Background. Increasing 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. Method. TCGA 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. Results. We 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. Conclusion. This 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.

Predictive and Prognostic Relevance of Tumor-Infiltrating Immune Cells: Tailoring Personalized Treatments against Different Cancer Types

“…This observation may potentially contribute to the unfavorable prognosis associated with these subtypes. Cohorts of patients undergoing immunotherapy have confirmed that those with higher ICI scores exhibit notable therapeutic benefits and clinical advantages [127]. The findings of this study provide evidence supporting the utility of ICI scores as a reliable prognostic biomarker and predictive indicator for immunotherapy.…”

MMP12 serves as an immune cell–related marker of disease status and prognosis in lung squamous cell carcinoma

“…Lung cancer is one of the deadliest malignancies in the world and is the primary cause of death among cancer patients ( Siegel, Miller & Jemal, 2020 ; Mridha et al., 2022 ; Adams et al., 2023 ). Lung squamous cell carcinoma (LUSC) belongs to non-small-cell lung cancer (NSCLC), and clinical statistics show that the number of patients with LUSC accounts for about 30% of patients with NSCLC ( Li et al., 2022a ; Ding et al., 2022 ; Chen et al., 2022a ; Pan et al., 2022 ). In recent years, conventional platinum-based two-drug therapy and combination chemotherapy have remained the first-line treatments for advanced LUSC.…”

TMBcalc: a computational pipeline for identifying pan-cancer Tumor Mutational Burden gene signatures

“…Additionally, they observed favorable clinical outcomes correlated with TTN mutations. Furthermore, within the 389 gene signature panel, we identified genes such as (i) ACE2, recognized for its involvement in innate immunity; (ii) TRIM51, which exhibits increased expression in patients with high immune cell infiltration (Chen et al, 2022); (iii) SERPINB4, whose high mutation frequency correlates with improved survival postimmunotherapy in melanoma patients (Riaz et al, 2016); and (iv) ADAM2, whose expression enhances the cytotoxicity of CD8 T-cells (Dervovic et al, 2023). These findings underscore the potential of our panel not only for TMB assessment but also for uncovering crucial immunoregulatory mechanisms underlying tumor response to therapy.…”