skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

Explore Research Products in the PAR
It may take a few hours for recently added research products to appear in PAR search results.


Title: Composition and Functional Potential of the Human Mammary Microbiota Prior to and Following Breast Tumor Diagnosis
ABSTRACT Microbiota studies have reported changes in the microbial composition of the breast upon cancer development. However, results are inconsistent and limited to the later phases of cancer development (after diagnosis). We analyzed and compared the resident bacterial taxa of histologically normal breast tissue (healthy, H, n  = 49) with those of tissues donated prior to (prediagnostic, PD, n  = 15) and after (adjacent normal, AN, n  = 49, and tumor, T, n  = 46) breast cancer diagnosis ( n total = 159). DNA was isolated from tissue samples and submitted for Illumina MiSeq paired-end sequencing of the V3-V4 region of the 16S gene. To infer bacterial function in breast cancer, we predicted the functional bacteriome from the 16S sequencing data using PICRUSt2. Bacterial compositional analysis revealed an intermediary taxonomic signature in the PD tissue relative to that of the H tissue, represented by shifts in Bacillaceae , Burkholderiaceae , Corynebacteriaceae , Streptococcaceae , and Staphylococcaceae . This compositional signature was enhanced in the AN and T tissues. We also identified significant metabolic reprogramming of the microbiota of the PD, AN, and T tissue compared with the H tissue. Further, preliminary correlation analysis between host transcriptome profiling and microbial taxa and genes in H and PD tissues identified altered associations between the human host and mammary microbiota in PD tissue compared with H tissue. These findings suggest that compositional shifts in bacterial abundance and metabolic reprogramming of the breast tissue microbiota are early events in breast cancer development that are potentially linked with cancer susceptibility. IMPORTANCE The goal of this study was to determine the role of resident breast tissue bacteria in breast cancer development. We analyzed breast tissue bacteria in healthy breast tissue and breast tissue donated prior to (precancerous) and after (postcancerous) breast cancer diagnosis. Compared to healthy tissue, the precancerous and postcancerous breast tissues demonstrated differences in the amounts of breast tissue bacteria. In addition, breast tissue bacteria exhibit different functions in pre-cancerous and post-cancerous breast tissues relative to healthy tissue. These differences in function are further emphasized by altered associations of the breast tissue bacteria with gene expression in the human host prior to cancer development. Collectively, these analyses identified shifts in bacterial abundance and metabolic function (dysbiosis) prior to breast tumor diagnosis. This dysbiosis may serve as a therapeutic target in breast cancer prevention.  more » « less
Award ID(s):
1950350
PAR ID:
10399634
Author(s) / Creator(s):
; ; ; ; ; ; ;
Editor(s):
Gibbons, Sean M.
Date Published:
Journal Name:
mSystems
Volume:
7
Issue:
3
ISSN:
2379-5077
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. ; ; (, Frontiers in Oncology)
    The mammary microbiome is a newly characterized bacterial niche that might offer biological insight into the development of breast cancer. Together with in-depth analysis of the gut microbiome in breast cancer, current evidence using next-generation sequencing and metabolic profiling suggests compositional and functional shifts in microbial consortia are associated with breast cancer. In this review, we discuss the fundamental studies that have progressed this important area of research, focusing on the roles of both the mammary tissue microbiome and the gut microbiome. From the literature, we identified the following major conclusions, (I) There are unique breast and gut microbial signatures (both compositional and functional) that are associated with breast cancer, (II) breast and gut microbiome compositional and breast functional dysbiosis represent potential early events of breast tumor development, (III) specific breast and gut microbes confer host immune responses that can combat breast tumor development and progression, and (IV) chemotherapies alter the microbiome and thus maintenance of a eubiotic microbiome may be key in breast cancer treatment. As the field expectantly advances, it is necessary for the role of the microbiome to continue to be elucidated using multi-omic approaches and translational animal models in order to improve predictive, preventive, and therapeutic strategies for breast cancer. 
    more » « less
  2. ; ; ; ; ; ; ; ; ; ; et al (, Nature Biotechnology)
    Abstract Spatially resolved gene expression profiling provides insight into tissue organization and cell–cell crosstalk; however, sequencing-based spatial transcriptomics (ST) lacks single-cell resolution. Current ST analysis methods require single-cell RNA sequencing data as a reference for rigorous interpretation of cell states, mostly do not use associated histology images and are not capable of inferring shared neighborhoods across multiple tissues. Here we present Starfysh, a computational toolbox using a deep generative model that incorporates archetypal analysis and any known cell type markers to characterize known or new tissue-specific cell states without a single-cell reference. Starfysh improves the characterization of spatial dynamics in complex tissues using histology images and enables the comparison of niches as spatial hubs across tissues. Integrative analysis of primary estrogen receptor (ER)-positive breast cancer, triple-negative breast cancer (TNBC) and metaplastic breast cancer (MBC) tissues led to the identification of spatial hubs with patient- and disease-specific cell type compositions and revealed metabolic reprogramming shaping immunosuppressive hubs in aggressive MBC. 
    more » « less
  3. ; ; ; ; ; (, Advanced Science)
    Abstract A mutualistic co‐evolution exists between the host and its associated microbiota in the human body. Bacteria establish ecological niches in various tissues of the body, locally influencing their physiology and functions, but also contributing to the well‐being of the whole organism through systemic communication with other distant niches (axis). Emerging evidence indicates that when the composition of the microbiota inhabiting the niche changes toward a pathogenic state (dysbiosis) and interactions with the host become unbalanced, diseases may present. In addition, imbalances within a single niche can cause dysbiosis in distant organs. Current research efforts are focused on elucidating the mechanisms leading to dysbiosis, with the goal of restoring tissue homeostasis. In vitro models can provide critical experimental platforms to address this need, by reproducing the niche cyto‐architecture and physiology with high fidelity. This review surveys current in in vitro host–microbiota research strategies and provides a roadmap that can guide the field in further developing physiologically relevant in vitro models of ecological niches, thus enabling investigation of the role of the microbiota in human health and diseases. Lastly, given the Food and Drug Administration Modernization Act 2.0, this review highlights emerging in vitro strategies to support the development and validation of new therapies on the market. 
    more » « less
  4. ; ; ; ; ; ; ; ; (, Biological Procedures Online)
    Abstract BackgroundBreast cancer poses a significant health risk to women worldwide, with approximately 30% being diagnosed annually in the United States. The identification of cancerous mammary tissues from non-cancerous ones during surgery is crucial for the complete removal of tumors. ResultsOur study innovatively utilized machine learning techniques (Random Forest (RF), Support Vector Machine (SVM), and Convolutional Neural Network (CNN)) alongside Raman spectroscopy to streamline and hasten the differentiation of normal and late-stage cancerous mammary tissues in mice. The classification accuracy rates achieved by these models were 94.47% for RF, 96.76% for SVM, and 97.58% for CNN, respectively. To our best knowledge, this study was the first effort in comparing the effectiveness of these three machine-learning techniques in classifying breast cancer tissues based on their Raman spectra. Moreover, we innovatively identified specific spectral peaks that contribute to the molecular characteristics of the murine cancerous and non-cancerous tissues. ConclusionsConsequently, our integrated approach of machine learning and Raman spectroscopy presents a non-invasive, swift diagnostic tool for breast cancer, offering promising applications in intraoperative settings. 
    more » « less
  5. ; ; ; ; ; ; ; ; ; ; et al (, International Journal of Molecular Sciences)
    There is currently no gene expression assay that can assess if premalignant lesions will develop into invasive breast cancer. This study sought to identify biomarkers for selecting patients with a high potential for developing invasive carcinoma in the breast with normal histology, benign lesions, or premalignant lesions. A set of 26-gene mRNA expression profiles were used to identify invasive ductal carcinomas from histologically normal tissue and benign lesions and to select those with a higher potential for future cancer development (ADHC) in the breast associated with atypical ductal hyperplasia (ADH). The expression-defined model achieved an overall accuracy of 94.05% (AUC = 0.96) in classifying invasive ductal carcinomas from histologically normal tissue and benign lesions (n = 185). This gene signature classified cancer development in ADH tissues with an overall accuracy of 100% (n = 8). The mRNA expression patterns of these 26 genes were validated using RT-PCR analyses of independent tissue samples (n = 77) and blood samples (n = 48). The protein expression of PBX2 and RAD52 assessed with immunohistochemistry were prognostic of breast cancer survival outcomes. This signature provided significant prognostic stratification in The Cancer Genome Atlas breast cancer patients (n = 1100), as well as basal-like and luminal A subtypes, and was associated with distinct immune infiltration and activities. The mRNA and protein expression of the 26 genes was associated with sensitivity or resistance to 18 NCCN-recommended drugs for treating breast cancer. Eleven genes had significant proliferative potential in CRISPR-Cas9/RNAi screening. Based on this gene expression signature, the VEGFR inhibitor ZM-306416 was discovered as a new drug for treating breast cancer. 
    more » « less
  • Citation Formats
  • MLA
  • APA
  • Chicago
  • BibTeX
  • Save / Share this Record
  • Send to Email