Immunity and Extracellular Matrix Characteristics of Breast Cancer Subtypes Based on Identification by T Helper Cells Profiling

BACKGROUND: The therapeutic effect of immune checkpoint inhibitors on tumors is not only related to CD8+ effector T cells but also sufficiently related to CD4+ helper T (T(H)) cells. The immune characteristics of breast cancer, including gene characteristics and tumor-infiltrating lymphocytes, have become significant biomarkers for predicting prognosis and immunotherapy response in recent years. METHODS: Breast cancer samples from The Cancer Genome Atlas (TCGA) database and triple-negative breast cancer (TNBC) samples from GSE31519 in the Gene Expression Omnibus (GEO) database were extracted and clustered based on gene sets representing T(H) cell signatures. CIBERSORT simulations of immune cell components in the tumor microenvironment and gene set enrichment analyses (GSEAs) were performed in the different clusters to verify the classification of the subtypes. The acquisition of differentially expressed genes (DEGs) in the different clusters was further used for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. The clinical information from different clusters was used for survival analysis. Finally, the surgical tissues of TNBC samples were stained by immunofluorescence staining and Masson’s trichrome staining to explore the correlation of T(H) cell subtypes with extracellular matrix (ECM). RESULTS: The breast cancer samples from the datasets in TCGA database and GEO database were classified into T(H)-activated and T(H)-silenced clusters, which was verified by the immune cell components and enriched immune-related pathways. The DEGs of T(H)-activated and T(H)-silenced clusters were obtained. In addition to T(H) cells and other immune-related pathways, ECM-related pathways were found to be enriched by DEGs. Furthermore, the survival data of TCGA samples and GSE31519 samples showed that the 10-year overall survival (p-value < 0.001) and 10-year event-free survival (p-value = 0.162) of the T(H)-activated cluster were better, respectively. Fluorescent labeling of T(H) cell subtypes and staining of the collagen area of surgical specimens further illustrated the relationship between T(H) cell subtypes and ECM in breast cancer, among which high T(H)1 infiltration was related to low collagen content (p-value < 0.001), while high T(H)2 and T(reg) infiltration contained more abundant collagen (p-value < 0.05) in TNBC. With regard to the relationship of T(H) cell subtypes, T(H)2 was positively correlated with T(reg) (p-value < 0.05), while T(H)1 was negatively correlated with both of them. CONCLUSIONS: The immune and ECM characteristics of breast cancer subtypes based on T(H) cell characteristics were revealed, and the relationship between different T(H) cell subsets and ECM and prognosis was explored in this study. The crosstalk between ECM and T(H) cell subtypes formed a balanced TME influencing the prognosis and treatment response in breast cancer, which suggests that the correlation between T(H) cells and ECM needs to be further emphasized in future breast cancer studies.