Characterization of the Immune Cell Infiltration Landscape in Head and Neck Squamous Cell Carcinoma to Aid Immunotherapy

Abstract

The tumor microenvironment (TME) chiefly consists of tumor cells and tumor-infiltrating immune cells admixed with the stromal component. A recent clinical trial has shown that the tumor immune cell infiltration (ICI) is correlated with the sensitivity to immunotherapy and the head and neck squamous cell carcinoma (HNSC) prognosis. However, to date, the immune infiltrative landscape of HNSC has not yet been elucidated. Herein, we proposed two computational algorithms to unravel the ICI landscape of 1,029 HNSC patients. Three ICI patterns were defined, and the ICI scores were determined by using principal-component analysis. A high ICI score was characterized by an increased tumor mutation burden (TMB) and the immune-activating signaling pathways. Activation of transforming growth factor-β (TGF-β) and WNT signaling pathways were observed in low ICI score subtypes, indicating T cell suppression, and may be responsible for poor prognosis. Two immunotherapy cohorts confirmed patients with higher ICI scores demonstrated significant therapeutic advantages and clinical benefits. This study demonstrated that the ICI scores serve as an effective prognostic biomarker and predictive indicator for immunotherapy. Evaluating the ICI patterns of a larger cohort of samples will extend our understanding of TME, and it may provide directions to the current research investigations on immunotherapeutic strategies for HNSC.

Tumor Immune Microenvironment Characterization Identifies Prognosis and Immunotherapy-Related Gene Signatures in Melanoma

“…We compared the predictive ability of the signature genes in our study and other models in terms of the immunotherapy benefit. We selected signature genes identified by the INF model and the HNSC model ( 15 , 24 ). The Kaplan-Meier curves and the ROC curves of the ICI score of the other two models are shown in Fig S4A-F. For the other two models, there were no statistically significant differences in the overall survival between those with high and low ICI scores, and the prognosis differed among the cohorts.…”

“…We compared the predictive ability of the signature genes in our study and other models in terms of the immunotherapy benefit. We selected signature genes identified by the INF model and the HNSC model (15,24). The Kaplan-Meier curves and the ROC curves of the ICI score of the other two models are shown in Fig S4A-F.…”

“…One study proposed the degree of lymphocyte infiltration as an independent prognostic factor of disease-free survival (DFS) in melanoma patients, with a higher TIL grade associated with a lower risk of death ( 14 ). The characteristics of ICI can serve as an effective prognostic biomarker and predictive indicator of response to immune therapy ( 15 ). Therefore, it is necessary to gain a comprehensive understanding of the immune infiltrate characteristics in melanoma.…”

Comprehensive Analysis of Myeloid Signature Genes in Head and Neck Squamous Cell Carcinoma to Predict the Prognosis and Immune Infiltration

“…Consistent with our results, CD8 + T cell status correlated with the composition of myeloid populations. Recently, accumulating evidence suggested that immune cell infiltration was closely tied with levels of immune activity in the TIME of HNSCC ( 15 , 73 ), and immune infiltration could be evaluated using various models constructed by different methods, such as cancer-associated AS events (CASEs) and m 6 A methylation regulators ( 74 , 75 ). Some of the myeloid genes in our model have been shown to regulate multiple immune cells by previous studies.…”

Ubiquitin Modification Patterns of Clear Cell Renal Cell Carcinoma and the Ubiquitin Score to Aid Immunotherapy and Targeted Therapy

“…Thus we obtained the signature genes A and signature genes B. Finally, the principal components analysis (PCA) was used to calculate the first principal components of signature genes A and B in each sample ( Zhang X. et al, 2020 ). The ubiquitin scores of each sample were extracted as:…”