Microbial dysbiosis is associated with human breast cancer

Abstract

Breast cancer affects one in eight women in their lifetime. Though diet, age and genetic predisposition are established risk factors, the majority of breast cancers have unknown etiology. The human microbiota refers to the collection of microbes inhabiting the human body. Imbalance in microbial communities, or microbial dysbiosis, has been implicated in various human diseases including obesity, diabetes, and colon cancer. Therefore, we investigated the potential role of microbiota in breast cancer by next-generation sequencing using breast tumor tissue and paired normal adjacent tissue from the same patient. In a qualitative survey of the breast microbiota DNA, we found that the bacterium Methylobacterium radiotolerans is relatively enriched in tumor tissue, while the bacterium Sphingomonas yanoikuyae is relatively enriched in paired normal tissue. The relative abundances of these two bacterial species were inversely correlated in paired normal breast tissue but not in tumor tissue, indicating that dysbiosis is associated with breast cancer. Furthermore, the total bacterial DNA load was reduced in tumor versus paired normal and healthy breast tissue as determined by quantitative PCR. Interestingly, bacterial DNA load correlated inversely with advanced disease, a finding that could have broad implications in diagnosis and staging of breast cancer. Lastly, we observed lower basal levels of antibacterial response gene expression in tumor versus healthy breast tissue. Taken together, these data indicate that microbial DNA is present in the breast and that bacteria or their components may influence the local immune microenvironment. Our findings suggest a previously unrecognized link between dysbiosis and breast cancer which has potential diagnostic and therapeutic implications.

Figure 1. Survey of microbial communities residing in breast tissue from breast cancer patients.

A) Phylum level distribution of microbial communities comparing paired normal adjacent (“paired normal”) and breast cancer tissue from 20 patients with ER-positive breast cancer (n = 20). Each bar represents 100% of the bacteria detected in a given sample. B) Combined distribution at the phylum level in paired normal and breast tumor tissue (n = 20). C) Number of OTUs found in each community (n = 20). D) Analysis of OTUs with differential abundance between paired normal and tumor tissue (n = 20). E) Correlation of relative abundances of M. radiotolerans and S. yanoikuyae (n = 20). F) Relative abundances of commonly found skin bacteria (n = 20). p-values from Student’s paired t-test are shown, with P<0.05 considered significant. Error bars represent mean ± s.e.m.

Figure 2. Quantification of bacterial load in tissue from healthy and breast cancer patients.

A) Copy numbers of the bacterial 16S gene were compared among healthy (age-matched) (n = 23), paired normal (n = 39) and tumor tissue (n = 39). Healthy specimens were obtained from patients undergoing reduction mammoplasty, with no evidence of breast cancer. Statistical analysis was performed using Kruskal-Wallis nonparametric ANOVA with Dunn’s Multiple Comparison post-test. B) Bacterial load in tissue according to clinical staging of the tumor specimen. Statistical analysis was performed using Cuzick’s Trend test. All statistical analyses were considered significant when P<0.05. Data represent the average of duplicate values. Error bars represent mean ± s.e.m.

Figure 3. Expression profiles of antibacterial response genes in healthy and breast cancer tissue (n = 9).

Healthy specimens were obtained from patients undergoing reduction mammoplasty, with no evidence of breast cancer. A) Heatmap of gene expression values generated using non-supervised hierarchical clustering B) Expression profiles of antimicrobial response genes. p-values from Student’s paired t-test are shown, with p<0.05 considered significant. Error bars represent mean ± s.e.m.