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

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 (TH) 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. 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 TH 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 TH cell subtypes with extracellular matrix (ECM). The breast cancer samples from the datasets in TCGA database and GEO database were classified into TH-activated and TH-silenced clusters, which was verified by the immune cell components and enriched immune-related pathways. The DEGs of TH-activated and TH-silenced clusters were obtained. In addition to TH 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 TH-activated cluster were better, respectively. Fluorescent labeling of TH cell subtypes and staining of the collagen area of surgical specimens further illustrated the relationship between TH cell subtypes and ECM in breast cancer, among which high TH1 infiltration was related to low collagen content (p-value < 0.001), while high TH2 and Treg infiltration contained more abundant collagen (p-value < 0.05) in TNBC. With regard to the relationship of TH cell subtypes, TH2 was positively correlated with Treg (p-value < 0.05), while TH1 was negatively correlated with both of them. The immune and ECM characteristics of breast cancer subtypes based on TH cell characteristics were revealed, and the relationship between different TH cell subsets and ECM and prognosis was explored in this study. The crosstalk between ECM and TH cell subtypes formed a balanced TME influencing the prognosis and treatment response in breast cancer, which suggests that the correlation between TH cells and ECM needs to be further emphasized in future breast cancer studies.

Zhou Y ，Tian Q ，Gao H ，Zhu L ，Zhang Y ，Zhang C ，Yang J ，Wang B ... -  《Frontiers in Immunology》

The regulatory roles of T helper cells in distinct extracellular matrix characterization in breast cancer.

Tumors are characterized by extracellular matrix (ECM) remodeling and stiffening. The ECM has been recognized as an important determinant of breast cancer progression and prognosis. Recent studies have revealed a strong link between ECM remodeling and immune cell infiltration in a variety of tumor types. However, the landscape and specific regulatory mechanisms between ECM and immune microenvironment in breast cancer have not been fully understood. Using genomic data and clinical information of breast cancer patients obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, we conducted an extensive multi-omics analysis to explore the heterogeneity and prognostic significance of the ECM microenvironment. Masson and Sirius red staining were applied to quantify the contents of collagen in the ECM microenvironment. Tissue immunofluorescence (IF) staining was applied to identify T helper (Th) cells. We classified breast cancer patients into two ECM-clusters and three gene-clusters by consensus clustering. Significant heterogeneity in prognosis and immune cell infiltration have been found in these distinct clusters. Specifically, in the ECM-cluster with better prognosis, the expression levels of Th2 and regulatory T (Treg) cells were reduced, while the Th1, Th17 and T follicular helper (Tfh) cells-associated activities were significantly enhanced. The correlations between ECM characteristics and Th cells infiltration were then validated by clinical tissue samples from our hospital. The ECM-associated prognostic model was then constructed by 10 core prognostic genes and stratified breast cancer patients into two risk groups. Kaplan-Meier analysis showed that the overall survival (OS) of breast cancer patients in the high-risk group was significantly worse than that of the low-risk group. The risk scores for breast cancer patients obtained from our prognostic model were further confirmed to be associated with immune cell infiltration, tumor mutation burden (TMB) and stem cell indexes. Finally, the half-maximal inhibitory concentration (IC50) values of antitumor agents for patients in different risk groups were calculated to provide references for therapy targeting distinct ECM characteristics. Our findings identify a novel strategy for breast cancer subtyping based on the ECM characterization and reveal the regulatory roles of Th cells in ECM remodeling. Targeting ECM remodeling and Th cells hold potential to be a therapeutic alternative for breast cancer in the future.

Tian Q ，Gao H ，Ma Y ，Zhu L ，Zhou Y ，Shen Y ，Wang B ... -  《Frontiers in Immunology》

3D Collagen Fiber Concentration Regulates Treg Cell Infiltration in Triple Negative Breast Cancer.

Triple negative breast cancer (TNBC) is characterized by poor prognosis and a lack of effective therapeutic agents owing to the absence of biomarkers. A high abundance of tumor-infiltrating regulatory T cells (Tregs) was associated with worse prognosis in malignant disease. Exploring the association between Treg cell infiltration and TNBC will provide new insights for understanding TNBC immunosuppression and may pave the way for developing novel immune-based treatments. Patients from TCGA were divided into Treg-high (Treg-H) and Treg-low (Treg-L) groups based on the abundance of Tregs according to CIBERSORT analysis. The association between expression level of Tregs and the clinical characteristics as well as prognosis of breast cancer were evaluated. Next, a Treg-related prognostic model was established after survival-dependent univariate Cox and LASSO regression analysis, companied with an external GEO cohort validation. Then, GO, KEGG and GSEA analyses were performed between the Treg-H and Treg-L groups. Masson and Sirius red/Fast Green staining were applied for ECM characterization. Accordingly, Jurkat T cells were encapsulated in 3D collagen to mimic the ECM microenvironment, and the expression levels of CD4, FOXP3 and CD25 were quantified according to immunofluorescence staining. The expression level of Tregs is significantly associated with the clinical characteristics of breast cancer patients, and a high level of Treg cell expression indicates a poor prognosis in TNBC. To further evaluate this, a Treg-related prognostic model was established that accurately predicted outcomes in both TCGA training and GEO validation cohorts of TNBC patients. Subsequently, ECM-associated signaling pathways were identified between the Treg-H and Treg-L groups, indicating the role of ECM in Treg infiltration. Since we found increasing collagen concentrations in TNBC patients with distant migration, we encapsulated Jurkat T cells within a 3D matrix with different collagen concentrations and observed that increasing collagen concentrations promoted the expression of Treg biomarkers, supporting the regulatory role of ECM in Treg infiltration. Our results support the association between Treg expression and breast cancer progression as well as prognosis in the TNBC subtype. Moreover, increasing collagen density may promote Treg infiltration, and thus induce an immunosuppressed TME.

Gao H ，Tian Q ，Zhou Y ，Zhu L ，Lu Y ，Ma Y ，Feng J ，Jiang Y ，Wang B ... -  《Frontiers in Immunology》

Identification of a five genes prognosis signature for triple-negative breast cancer using multi-omics methods and bioinformatics analysis.

Triple-negative breast cancer (TNBC) has a high degree of malignancy, lack of effective diagnosis and treatment, and poor prognosis. Bioinformatics methods are used to screen the hub genes and signal pathways involved in the progress of TNBC to provide reliable biomarkers for the diagnosis and treatment of TNBC. Download the raw data of four TNBC-related datasets from the Gene Expression Omnibus (GEO) database and use them for bioinformatics analysis. GEO2R tool was used to analyze and identify differentially expressed (DE) mRNAs. DAVID database was used to carry out gene ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genome Pathways (KEGG) signal pathway enrichment analysis for DE mRNAs. STRING database and Cytoscape were used to build DE mRNAs protein-protein interaction (PPI) network diagram and visualize PPI network, respectively. Through cytoHubba, cBioPortal database, Kaplan-Meier mapper database, Gene Expression Profiling Interactive Analysis (GEPIA) Database, UALCAN Database, The Cancer Genome Atlas (TCGA) database, Tumor Immunity Estimation Resource identify hub genes. Perform qRT-PCR, Human Protein Atlas analysis, mutation analysis, survival analysis, clinical-pathological characteristics, and infiltrating immune cell analysis. 22 DE mRNAs were identified from the four datasets, including 16 upregulated DE mRNAs and six downregulated DE mRNAs. Enrichment analysis of the KEGG showed that DE mRNAs were principally enriched in pathways in cancer, mismatch repair, cell cycle, platinum drug resistance, breast cancer. Six hub genes were screened based on the PPI network diagram of DE mRNAs. Survival analysis found that TOP2A, CCNA2, PCNA, MSH2, CDK6 are related to the prognosis of TNBC. In addition, mutations, clinical indicators, and immune infiltration analysis show that these five hub genes play an important role in the progress of TNBC and immune monitoring. Compared with MCF-10A, MCF-7, and SKBR-3 cells, TOP2A, PCNA, MSH2, and CDK6 were significantly upregulated in MDA-MB-321 cells. Compared with normal, luminal, and Her-2 positive tissues, CCNA2, MSH2, and CDK6 were significantly upregulated in TNBC. Through comparative analysis of GEO datasets related to colorectal cancer and lung adenocarcinoma, it was determined that these five hub genes were unique differentially expressed genes of TNBC. At last, the hub genes related to the progression, prognosis, and immunity of TNBC have been successfully screened. They are indeed specific to TNBC as prognostic features. They can be used as potential markers for the prognosis of TNBC and provide potential therapeutic targets.

Ma J ，Chen C ，Liu S ，Ji J ，Wu D ，Huang P ，Wei D ，Fan Z ，Ren L ... -  《-》

A novel model associated with tumor microenvironment on predicting prognosis and immunotherapy in triple negative breast cancer.

Triple negative breast cancer (TNBC) is the most aggressive and malignant subtype in breast cancer. Immunotherapy is a currently promising and effective treatment for TNBC, while not all patients are responsive. Therefore, it is necessary to explore novel biomarkers to screen sensitive populations for immunotherapy. All mRNA expression profiles of TNBC from The Cancer Genome Atlas (TCGA) database were clustered into two subgroups by analyzing tumor immune microenvironment (TIME) with single sample gene set enrichment analysis (ssGSEA). A risk score model was constructed based on differently expressed genes (DEGs) identified from two subgroups using Cox and Least Absolute Shrinkage and Selector Operation (LASSO) regression model. And it was validated by Kaplan-Meier analysis and Receiver Operating Characteristic (ROC) analysis in Gene Expression Omnibus (GEO) and the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) databases. Multiplex immunofluorescence (mIF) and Immunohistochemical (IHC) staining were performed on clinical TNBC tissue samples. The relationship between risk score and immune checkpoint blockades (ICB) related signatures was further investigated, as well as the biological processes were performed by gene set enrichment analysis (GSEA). We obtained three DEGs positively related to prognosis and infiltrating immune cells in TNBC. Our risk score model could be an independent prognostic factor and the low risk group exhibited a prolonged overall survival (OS). Patients in low risk group were more likely to present a higher immune infiltration and stronger response to immunotherapy. GSEA revealed the model was associated with immune-related pathways. We constructed and validated a novel model based on three prognostic genes related to TIME in TNBC. The model contributed a robust signature that could predict the prognosis in TNBC, especially for the efficacy of immunotherapy.

Zhang J ，Zhang M ，Tian Q ，Yang J ... -  《-》