Patterns of Immune Infiltration in HNC and Their Clinical Implications: A Gene Expression-Based Study

Background: Immune infiltration of head and neck cancer (HNC) highly correlated with the patient's prognosis. However, previous studies failed to explain the diversity of different cell types that make up the function of the immune response system. The aim of the study was to uncover the differences in immune phenotypes of the tumor microenvironment (TME) between HNC adjacent tumor tissues and tumor tissues using CIBERSORT method and explore their therapeutic implications. Method: In current work, we employed the CIBERSORT method to evaluate the relative proportions of immune cell profiling in 11 paired HNC and adjacent samples, and analyzed the correlation between immune cell infiltration and clinical information. The tumor-infiltrating immune cells of TCGA HNC cohort was analyzed for the first time. The fractions of LM22 immune cells were imputed to determine the correlation between each immune cell subpopulation and survival and response to chemotherapy. Three types of molecular classification were identified via "CancerSubtypes" R-package. The functional enrichment was analyzed in each subtype. Results: The profiles of immune infiltration in TCGA HNC cohort significantly vary between paired cancer and para-cancerous tissue and the variation could reflect the individual difference. Total Macrophage, Macrophages M0 and NK cells resting were elevated in HNC tissues, while total T cells, total B cells, T cells CD8, B cell navie, T cell follicular helper, NK cells activated, Monocyte and Mast cells resting were decreased when compared to paracancerous tissues. Among each cell immune subtype, T cells regulatory Tregs, B cells naïve, T cells follicular helper, and T cells CD4 memory activated was significantly associated with HNC survival. Three clusters were observed via Cancer Subtypes R-package. Each cancer subtype has a specific molecular classification and subtype-specific immune cell characterization. Conclusions: Our data suggest a difference in immune response may be an important driver of HNC progression and response to treatment. The deconvolution algorithm of gene expression microarray data by CIBERSOFT provides useful information about the immune cell composition of HNC patients.