Unravelling novel microbial players in the breast tissue of TNBC patients: a meta-analytic perspective

Abstract

Triple-negative breast cancer (TNBC) is the most aggressive subtype of breast cancer (BC), accounting for nearly 40% of BC-related deaths. Emerging evidence suggests that the breast tissue microbiome harbors distinct microbial communities; however, the microbiome specific to TNBC remains largely unexplored. This study presents the first comprehensive meta-analysis of the TNBC tissue microbiome, consolidating 16S rRNA amplicon sequencing data from 200 BC samples across four independent cohorts. Our analysis highlights the enrichment of Azospirillum genus as well as butyrate-producing species, namely Gemmiger formicilis and Anaerobutyricum soehngenii, potentially influencing TNBC aggressiveness and clinical outcomes. Additionally, our functional analyses reveal the involvement of the TNBC microbiome in several pathways associated with chronic inflammation, increased cellular proliferation, invasion, and metastasis. This study uncovers novel microbial players in TNBC that could explain its aggressiveness and poor prognosis, and warrants further investigation into microbiome-driven interventions.

Fig. 1. PRISMA chart for meta-analysis.

The chart illustrates the selection process for studies included in the meta-analysis, where four homogenous studies were finally retained.

Fig. 2. Comparative analysis of microbial diversity and composition in TNBC and non-TNBC samples.

a Observed Diversity Index, quantifies species diversity across classes and within each study. b Shannon’s Diversity Index illustrates the estimated richness of microbial species across classes and within each study. c Beta diversity compares microbial community composition between TNBC and non-TNBC across all studies and individually per study. d Bar plots illustrate the relative abundance of microbial phyla in TNBC and non-TNBC samples.

Fig. 3. Detailed visualization of bacterial significance and predictive modelling in TNBC and non-TNBC.

a A cladogram that highlights significant bacteria at various taxonomic levels, provides a hierarchical view of microbial differences. b A heatmap displays the significant bacterial species differentially abundant between TNBC and non-TNBC. c Results from STAMP, identify key functional pathways impacted in TNBC. d Confusion matrices and Receiver Operating Characteristic (ROC) curves for machine learning models, demonstrate the predictive accuracy and validity of these models in distinguishing between TNBC and non-TNBC samples.

Fig. 4. Dose-dependent anti-tumorigenic and pro-tumorigenic hypothesized roles of butyrate in TNBC.

The diagram illustrates butyrate’s hypothesized dual role in TNBC: high doses (left) exert anti-tumour effects through epigenetic modulation and immune activation, while low doses (right) may promote tumour growth, metastasis, and immune evasion in specific microbial environment contexts. HDAC: Histone Deacetylase, P21: Cyclin-dependent kinase inhibitor 1 A, BAK: Bcl-2 homologous antagonist/killer, MMP15: Matrix Metalloproteinase 15, ROS: Reactive Oxygen Species, lncRNA H19: Long non-coding RNA H19, G6PDH: Glucose-6-phosphate dehydrogenase.