Dataset Information

Human breast microbiome correlates with prognostic features and immunological signatures in breast cancer.

ABSTRACT:

Background

Currently, over half of breast cancer cases are unrelated to known risk factors, highlighting the importance of discovering other cancer-promoting factors. Since crosstalk between gut microbes and host immunity contributes to many diseases, we hypothesized that similar interactions could occur between the recently described breast microbiome and local immune responses to influence breast cancer pathogenesis.

Methods

Using 16S rRNA gene sequencing, we characterized the microbiome of human breast tissue in a total of 221 patients with breast cancer, 18 individuals predisposed to breast cancer, and 69 controls. We performed bioinformatic analyses using a DADA2-based pipeline and applied linear models with White's t or Kruskal-Wallis H-tests with Benjamini-Hochberg multiple testing correction to identify taxonomic groups associated with prognostic clinicopathologic features. We then used network analysis based on Spearman coefficients to correlate specific bacterial taxa with immunological data from NanoString gene expression and 65-plex cytokine assays.

Results

Multiple bacterial genera exhibited significant differences in relative abundance when stratifying by breast tissue type (tumor, tumor adjacent normal, high-risk, healthy control), cancer stage, grade, histologic subtype, receptor status, lymphovascular invasion, or node-positive status, even after adjusting for confounding variables. Microbiome-immune networks within the breast tended to be bacteria-centric, with sparse structure in tumors and more interconnected structure in benign tissues. Notably, Anaerococcus, Caulobacter, and Streptococcus, which were major bacterial hubs in benign tissue networks, were absent from cancer-associated tissue networks. In addition, Propionibacterium and Staphylococcus, which were depleted in tumors, showed negative associations with oncogenic immune features; Streptococcus and Propionibacterium also correlated positively with T-cell activation-related genes.

Conclusions

This study, the largest to date comparing healthy versus cancer-associated breast microbiomes using fresh-frozen surgical specimens and immune correlates, provides insight into microbial profiles that correspond with prognostic clinicopathologic features in breast cancer. It additionally presents evidence for local microbial-immune interplay in breast cancer that merits further investigation and has preventative, diagnostic, and therapeutic potential.

SUBMITTER: Tzeng A

PROVIDER: S-EPMC8052771 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:BackgroundImmunotherapy has changed the therapeutic landscape of cervical cancer (CC), but has durable anti-tumor activity only in a subset of patients. This study aims to comprehensively analyze the tumor immune microenvironment (TIME) of CC and to mine biomarkers related to immunotherapy and prognosis.MethodsThe Cancer Genome Atlas (TCGA) data was utilized to identify heterogeneous immune subtypes based on survival-related immune cell signatures (ICSs). ICSs prognostic model was constructed by Cox regression analyses, and immunohistochemistry was conducted to verify the gene with the largest weight coefficient in the model. Meanwhile, the tumor immune infiltration landscape was comprehensively characterized by ESTIMATE, CIBERSORT and MCPcounter algorithms. In addition, we also analyzed the differences in immunotherapy-related biomarkers between high and low-risk groups. IMvigor210 and two gynecologic tumor cohorts were used to validate the reliability and scalability of the Risk score.ResultsA total of 291 TCGA-CC samples were divided into two ICSs clusters with significant differences in immune infiltration landscape and prognosis. ICSs prognostic model was constructed based on eight immune-related genes (IRGs), which showed higher overall survival (OS) rate in the low-risk group (P< 0.001). In the total population, time-dependent receiver operating characteristic (ROC) curves displayed area under the curve (AUC) of 0.870, 0.785 and 0.774 at 1-, 3- and 5-years. Immunohistochemical results showed that the expression of the oncogene (FKBP10) was negatively correlated with the degree of differentiation and positively correlated with tumor stage, while the expression of tumor suppressor genes (S1PR4) was the opposite. In addition, the low-risk group had more favorable immune activation phenotype and higher enrichment of immunotherapy-related biomarkers. The Imvigor210 and two gynecologic tumor cohorts validated a better survival advantage and immune efficacy in the low-risk group.ConclusionThis study comprehensively assessed the TIME of CC and constructed an ICSs prognostic model, which provides an effective tool for predicting patient's prognosis and accurate immunotherapy.

Project description:Breast cancer is one of the most common female malignancies worldwide. Due to its early metastases formation and a high degree of malignancy, the 10 year-survival rate of metastatic breast cancer does not exceed 30%. Thus, more precise biomarkers are urgently needed. In our study, we first estimated the tumor microenvironment (TME) infiltration using the xCell algorithm. Based on TME infiltration, the three main TME clusters were identified using consensus clustering. Our results showed that the three main TME clusters cause significant differences in survival rates and TME infiltration patterns (log-rank test, p = 0.006). Then, multiple machine learning algorithms were used to develop a nine-pathway-based TME-related risk model to predict the prognosis of breast cancer (BRCA) patients (the immune-related pathway-based risk score, defined as IPRS). Based on the IPRS, BRCA patients were divided into two subgroups, and patients in the IPRS-low group presented significantly better overall survival (OS) rates than the IPRS-high group (log-rank test, p < 0.0001). Correlation analysis revealed that the IPRS-low group was characterized by increases in immune-related scores (cytolytic activity (CYT), major histocompatibility complex (MHC), T cell-inflamed immune gene expression profile (GEP), ESTIMATE, immune, and stromal scores) while exhibiting decreases in tumor purity, suggesting IPRS-low patients may have a strong immune response. Additionally, the gene-set enrichment analysis (GSEA) result confirmed that the IPRS-low patients were significantly enriched in several immune-associated signaling pathways. Furthermore, multivariate Cox analysis revealed that the IPRS was an independent prognostic biomarker after adjustment by clinicopathologic characteristics. The prognostic value of the IPRS model was further validated in three external validation cohorts. Altogether, our findings demonstrated that the IPRS was a powerful predictor to screen out certain populations with better prognosis in breast cancer and may serve as a potential biomarker guiding clinical treatment decisions.