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Brief Report

Is There a Microbiological Basis for Increased Breast Cancer Risk in Women with High Mammographic Density?

by
Jack W. Sample
1,
Matteo Redaelli
1,
Jun Chen
2,
Tanya L. Hoskin
2,
Stephen Johnson
2,
Marina Walther-Antonio
3 and
Tina J. Hieken
1,*
1
Division of Breast and Melanoma Surgical Oncology, Department of Surgery, Mayo Clinic, Rochester, MN 55905, USA
2
Department of Quantitative Health Science Research, Mayo Clinic, Rochester, MN 55905, USA
3
Department of Obstetrics and Gynecology, Mayo Clinic, Rochester, MN 55905, USA
*
Author to whom correspondence should be addressed.
Appl. Microbiol. 2026, 6(3), 39; https://doi.org/10.3390/applmicrobiol6030039
Submission received: 31 December 2025 / Revised: 27 February 2026 / Accepted: 28 February 2026 / Published: 3 March 2026
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Abstract

(1) Background: Mammographic breast density (MBD) is a well-established predictor of breast cancer risk, yet the biological mechanisms underlying this association remain incompletely understood. MBD is characterized by alterations in breast stromal architecture, including increased collagen deposition and changes in immune cell composition. Given emerging evidence that the breast harbors a resident microbiome, we investigated whether the breast tissue microbiome correlates with MBD. (2) Methods: Adjacent normal breast tissue was collected under sterile conditions from 33 women undergoing surgery for benign or malignant breast disease. DNA was extracted and subjected to 16S rRNA gene sequencing (Illumina MiSeq). (3) Results: We observed a non-significant trend toward lower α-diversity in high-MBD samples compared to low-MBD samples, p = 0.13. β-Diversity analyses identified a modest association between MBD and microbial community composition (MiRKAT p = 0.049). A random forest-based model incorporating genus-level relative abundances improved prediction of MBD over clinical characteristics alone, identifying Corynebacterium (Actinobacteria) and other genera as key predictors. (4) Conclusions: Breast tissue microbial features vary with mammographic breast density, suggesting a potential association with density-associated breast cancer risk. These exploratory findings warrant validation in larger cohorts to better elucidate biological mechanisms and clinical relevance.

1. Introduction

Mammographic breast density (MBD), the proportion of fibroglandular tissue relative to adipose tissue on mammography, is one of the strongest independent predictors of breast cancer risk [1,2,3,4,5,6,7]. Women with extremely dense breasts have an approximately two-fold higher risk of developing breast cancer compared with women whose breasts are predominantly fatty, even when using modern digital mammography [2]. This association persists across menopausal status and is independent of body mass index and is estimated to contribute to more than one quarter of all breast cancers [8,9]. In addition to its intrinsic biological risk, high MBD substantially reduces the mammographic sensitivity, contributing to both delayed detection and diagnostic uncertainty [2,7].
Women with dense breasts experience higher rates of interval breast cancers, tumors detected as tumors diagnosed within 12 months of a negative screening examination, and are more likely to present with later-stage disease, reflecting the combined challenges of increased cancer susceptibility and reduced detectability [10,11]. Importantly, MBD remains an independent risk factor even after adjusting for masking, indicating that biological mechanisms beyond impaired imaging detection contribute to elevated risk [12].
Despite robust epidemiologic evidence, the biological drivers linking MBD to breast cancer risk remain incompletely understood. High-density breast tissue exhibits distinct stromal characteristics, including increased collagen deposition, extracellular matrix (ECM) remodeling, heightened tissue stiffness, and alterations in immune cell composition [3,5]. These microenvironment features may promote breast cancer development or progression, yet the upstream drivers remain unclear. An emerging area of interest is the potential contribution of the breast tissue microbiome. Using genomic techniques, we previously demonstrated that sterilely collected human breast tissue harbors a distinct microbial signature, compositionally distinct from that of other body niches such as the skin or oral cavity, and varying between benign and malignant conditions [13]. Given the accumulating evidence that tissue-specific microbiomes can influence local immune microenvironments and disease-specific factors, we hypothesize that microbial communities may exert similar effects in breast tissue, including features associated with breast density.
In this exploratory study, our primary aim was to evaluate whether the composition of the breast tissue microbiome varied according to MBD. Given the established association between higher breast density and increased breast cancer risk, understanding biological factors linked to density may provide clinically relevant insights into breast tissue microbial features that influence disease risk and inform future hypotheses evaluating the role of the breast microbiome in breast density-associated biology, and ultimately, breast cancer risk.

2. Materials and Methods

Following the study’s Institutional Review Board (IRB) approval, women at least 18 years of age undergoing non-mastectomy breast surgery for benign or malignant disease were enrolled after providing written consent to participate in the research. Patients were excluded if they received systemic antibiotics or systemic steroids within 14 days of surgery; had evidence of active infection, current pregnancy or lactation; had a history of prior breast or skin cancer; or had a history of solid organ or bone marrow transplantation.
Mammographic breast density was assessed using preoperative mammography reports and categorized according to the Breast Imaging Reporting and Data Systems (Bi-RADS, 5th edition) [14].
In the operating room, adjacent normal breast tissue samples of at least 1 cm2 were obtained under sterile conditions following confirmation of negative surgical margins by frozen section pathology. Adjacent normal breast tissue was defined as grossly normal appearing tissue within the surgical lumpectomy cavity following confirmation of negative surgical margins by frozen section pathology. Specimens were then immediately placed in labeled sterile specimen containers, snap-frozen at −80 °C, and then stored at −20 °C prior to processing and analysis.
Whole-tissue DNA was extracted from each sample using standard isolation protocols. The bacterial 16S rRNA gene encompassing the V3–V5 hypervariable regions was amplified using polymerase chain reaction (PCR). Resulting amplicons were sequenced using the Illumina MiSeq platform to characterize the tissue-associated microbiota. Empty sterile specimen collection containers and swabs were processed for DNA extraction, amplification, and sequencing using the same reagents and methodology as tissue samples to serve as negative controls.
Sequence reads were processed using the IM-TORNADO bioinformatics pipeline, which merges paired-end reads into a single multiple sequence alignment and generates taxonomic classifications (Figure 1). We filtered out all taxa that were either prevalent in under 10% of samples or had a maximum relative abundance below 0.2% across all samples.
Downstream analysis, including data visualization and statistical testing, was performed using the QIIME platform. Random forest modeling was done such that p-values were derived from a Friedman test comparing classification errors across 100 bootstrap iterations, in which the random forest was trained on bootstrap-resampled data and evaluated on the held-out (out-of-bag) samples in each iteration. This bootstrap out-of-sample validation approach guards against overfitting, as model performance was never evaluated on training data.
To compare differences between groups based on MBD, after correction for age and disease state, α-diversity and β-diversity measures were used to compare microbial communities between groups. Two α-diversity metrics were used: the Shannon index and observed operational taxonomic units (OTU) number. β-Diversity was assessed by both weighted and unweighted UniFrac distance metrics, and the MiRKAT test was used to assess associations between β-diversity measures [15]. Statistical comparisons were performed as described in the results section, and significance was defined as p < 0.05.

3. Results

A total of 33 women undergoing surgery for benign (n = 16) or malignant (n = 17) breast disease were included in the analysis. The median age at the time of surgery was 60 years (range 33–84), and most women were post-menopausal (66.7%). Patient characteristics overall and by disease status are summarized in Table 1.
Principal coordinate analysis demonstrated that the breast tissue microbiome is distinct from microbiota found in other body sites, including skin tissue, skin swabs, and buccal swabs (Figure 2a).

3.1. Alpha Diversity

Analysis of α-diversity, which reflects species richness and evenness within samples, revealed lower diversity in samples with high mammographic breast density (MBD), although this did not reach statistical significance (linear regression for OTU number, p = 0.13) (Figure 2b).

3.2. Beta Diversity

β-Diversity analysis, assessing differences in microbial community composition between samples, showed a significant association with MBD using weighted UniFrac distances (p = 0.049, MiRKAT), but not unweighted UniFrac distances (p = 0.31), suggesting that MBD is associated with differences in microbial abundance rather than presence or absence of specific taxa (Figure 2c).
A random forest classification model that incorporated genus-level microbial abundance data demonstrated significant performance in predicting MBD compared to an otherwise identical model without microbial information (Friedman test, p < 0.001). This finding indicated that the inclusion of microbial features substantially improved the model’s predictive accuracy (Figure 3a).
To further characterize the microbial taxa that may contribute to model performance, feature selection was performed using the Boruta algorithm. This analysis identified Corynebacterium (phylum Actinobacteria) as the most strongly predictive genus associated with MBD. In addition, several other genera were highlighted as potentially important contributors, including Actinomyces (Actinobacteria), Sutterella and Pseudomonas (Proteobacteria), and Anaerotruncus (Firmicutes), suggesting that these microbial signatures may hold potential biological relevance or diagnostic value (Figure 3b).

4. Discussion

In this exploratory study, we identified distinct alterations in the breast tissue microbiome associated with mammographic breast density. We observed a trend toward lower α-diversity in high-density breast tissue and significant differences in β-diversity, indicating that MBD is associated with shifts in microbial community structure. These differences in β-diversity reflect changes in the relative abundance of taxa rather than changes in the presence or absence of specific microbial lineages. Notably, Corynebacterium (Actinobacteria) emerged as the genus most predictive of MBD. Taken together, these findings contribute to the growing body of evidence demonstrating that the breast tissue harbors a resident microbiome and variation may be associated with density and breast cancer biology.
High MBD reflects a complex interplay among stromal architecture, hormonal signaling, genetic predisposition, and local immune regulation, all of which may contribute to the increased risk of breast cancer observed in women with dense breast tissue. Dense breast stroma is enriched in collagen, with both its abundance and organization markedly altered compared to non-dense tissue [16]. Additionally, changes in extracellular matrix (ECM) organization and stiffness can profoundly influence cell signaling, mechanotransduction, and immune-related interactions. One proposed mechanism linking MBD to breast cancer risk involves integrin-mediated signaling, which is activated in response to increased matrix stiffness, and may promote pro-tumorigenic microenvironments [16,17]. Although these processes are increasingly recognized, the upstream factors that shape these microenvironment features of breast tissue density remain incompletely understood. Our findings raise the possibility that the breast resident microbiome may interact with factors associated with breast density, influencing the biology of breast disease.
The specific taxa enriched in high-density breast tissue may offer mechanistic insight. For example, we identified several abundant genera predictive of MBD, including Corynebacterium, Actinomyces, and Anaerotruncus, which are Gram-positive organisms capable of producing lipoteichoic acid (LTA), a toll-like receptor 2 (TLR2) ligand [18]. Activation of TLR2 can drive downstream fibroinflammatory changes to the ECM [19]. In colorectal cancer, Actinomyces has been implicated in dysbiosis and reduced tumoral T-cell infiltration [20]. Although speculative, these parallels raise the possibility that enriched Gram-positive taxa in high-density breast tissue may participate in microenvironmental processes relevant to density-associated breast cancer risk. Importantly, such effects are likely context-dependent and do not imply direct causality.
Our findings align with prior work characterizing the breast tissue native microbiome. Multiple studies have demonstrated that breast tissue contains a distinct microbial community that differs from that of the overlying skin. Urbaniak et al. first reported that breast tissue harbors diverse bacterial populations dominated by the phylum Proteobacteria [21,22]. Our group similarly identified unique microbial signatures in aseptically collected healthy breast tissue specimens and observed differences between benign and malignant disease states [13]. This work established that the breast microbiome is distinct from skin and oral microbiomes. Additional studies have begun to explore other potential microbiome-associated breast phenotypes. For example, Yaghjyan et al. found that gut microbiome diversity and the prevalence of specific taxa may vary with MBD in post-menopausal women [23]. Together, these studies support the emerging concept that microbial communities may contribute to breast tissue biology. Currently, studies evaluating the interplay between the breast microbiome and MBD are lacking, and the present study adds a novel dimension by demonstrating that microbial diversity and composition vary with MBD, an established breast cancer risk factor.
There are several limitations that should be considered when interpreting the findings of this study. The relatively small sample size and low microbial biomass of the tissue samples constrain statistical power. We did not apply false discovery rate controls to the differential taxa analyses in order to have sufficient power to identify true positives at the cost of possible detection of false positive findings. As a result, these findings should be regarded as preliminary and hypothesis-generating. Additionally, potentially meaningful microbial signals may not have been detected in our analysis, and the reported associations should be interpreted with appropriate caution. In addition, samples were collected from operations performed at a single institution, which may limit the generalizability of these findings. Institutional practices, patient demographics, and regional factors may influence breast tissue microbiome composition and reduce the applicability of these findings to the broader population of women with breast disease. Future studies involving larger sample sizes and more diverse, population-based cohorts will be helpful to validate these findings and provide a more comprehensive characterization of associations between breast density and the breast tissue microbiome.
These findings suggest an underrecognized association between the breast tissue microbiome and mammographic breast density. While our results do not establish causality, they raise the possibility that microbial composition may reflect microenvironment features that characterize high-density breast tissue. This work highlights an emerging area for investigation in which the breast tissue microbiome could contribute to biological pathways associated with density-associated breast cancer risk. Specifically, the breast tissue microbiome may function as a novel biomarker for high mammographic density, potentially enabling the development of tools to better risk-stratify women according to their future breast cancer risk. Taken together, these findings help provide a foundation supporting further research into tissue microbial contributions to breast tissue biology and breast cancer development.

Author Contributions

Conceptualization, T.J.H.; methodology, T.J.H., J.C., M.W.-A., S.J. and T.L.H.; software, S.J. and J.C.; validation, T.J.H., S.J. and J.C.; formal analysis, T.J.H., J.C. and S.J.; investigation, T.J.H.; resources, T.J.H.; data curation; S.J., J.C., T.L.H. and T.J.H.; writing—original draft preparation, J.W.S. and M.R.; writing—review and editing, T.J.H., J.W.S., T.L.H. and M.W.-A.; visualization, J.W.S. and T.J.H.; supervision, T.J.H.; project administration, T.J.H.; funding acquisition, T.J.H. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the Arnold and Kit Palmer Career Development Award in Cancer Research, the Mayo Clinic CTSA through grant number UL1TR002377 from the National Center for Advancing Translational Sciences (NCATS), a component of the National Institutes of Health (NIH), grants from the American Society of Breast Surgeons Foundation, and Fraternal Order of Eagles Cancer Research Fund, the Mayo Clinic Department of Surgery Research Unit and Mayo Clinic Center for Individualized Medicine.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Board of Mayo Clinic (IRB approval number: 14-000815).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

Deidentified data that support the findings in this study are available from the corresponding author, T.J.H., on reasonable request.

Acknowledgments

We are grateful to the patients who participated in this research study.

Conflicts of Interest

The authors have no relevant conflicts of interest.

Abbreviations

The following abbreviations are used in this manuscript:
MBDMammographic breast density
ECMExtracellular matrix
BiRADsBreast Imaging Reporting and Data Systems
OTUOperational taxonomic unit
MiSeqIllumina MiSeq Platform
QIIME Quantitative Insights Into Microbial Ecology
MiRKATMicrobiome Regression-Based Kernel Association Test
BMIBody mass index
LTALipoteichoic acid
TLR2Toll-like receptor 2

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Applmicrobiol 06 00039 g001
Figure 1. Schematic representation of the custom multiple alignment tool (IM-TORNADO), which merges paired-end reads into a single multiple alignment and obtains taxa calls.
Figure 1. Schematic representation of the custom multiple alignment tool (IM-TORNADO), which merges paired-end reads into a single multiple alignment and obtains taxa calls.
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Figure 2. (a) Schematic representation highlighting the distinct composition of the breast microbiome compared to other body sites; (b) α-diversity of breast tissue microbiota in samples stratified by high and low mammographic breast density; (c) β-diversity analysis of the breast microbiome in high- vs. low-mammographic-density samples, visualized using weighted UniFrac distances.
Figure 2. (a) Schematic representation highlighting the distinct composition of the breast microbiome compared to other body sites; (b) α-diversity of breast tissue microbiota in samples stratified by high and low mammographic breast density; (c) β-diversity analysis of the breast microbiome in high- vs. low-mammographic-density samples, visualized using weighted UniFrac distances.
Applmicrobiol 06 00039 g002
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Figure 3. (a) RF-M: Random forest-based predictor incorporating genus-level abundances; Guess: Classifier based on the majority class in the training data. (b) Relative abundance of Boruta selected bacterial genera; blue indicates patients with high MBD whilst red indicates patients with low MBD.
Figure 3. (a) RF-M: Random forest-based predictor incorporating genus-level abundances; Guess: Classifier based on the majority class in the training data. (b) Relative abundance of Boruta selected bacterial genera; blue indicates patients with high MBD whilst red indicates patients with low MBD.
Applmicrobiol 06 00039 g003
Table 1. Patient and lesion characteristics.
Table 1. Patient and lesion characteristics.
Variable Total (n = 33)Benign (n = 16)Malignant (n = 17)p-Value
Age, years 0.002
Median (Range)60 (33–84)50 (33–70)66 (44–84)
Menopausal status, n (%) 0.02
Pre-menopause 9 (27.3%)7 (43.8%)2 (11.8%)
Peri-menopause 2 (6.1%)2 (12.5%)0
Post-menopause22 (66.7%)7 (43.8%)15 (88.2%)
BMI category, n (%) 0.74
18.5–24.97 (21.2%)3 (18.8%)4 (23.5%)
25–29.914 (42.4%)6 (37.5%)8 (47.1%)
≥3012 (36.4%)7 (43.8%)5 (29.4%)
Smoking status, n (%) 0.48
Current smoker 000
Former smoker 12 (36.4%)7 (43.8%)5 (29.4%)
Never smoker21 (63.6%)9 (56.3%)12 (70.6%)
Diabetes mellitus, n (%) >0.99
No29 (87.9%)14 (87.5%)15 (88.2%)
Yes, NIDDM 4 (12.1%)2 (12.5%)2 (11.8%)
Prior ipsilateral breast surgery, n (%) >0.99
No32 (97.0%)16 (100%)16 (94.1%)
Yes 1 (3.0%)01 (5.9%)
Parity, n (%) >0.99
0 6 (18.2%)3 (18.8%)3 (17.6%)
1–322 (66.7%)11 (68.8%)11 (64.7%)
>35 (15.2%)2 (12.5%)3 (17.6%)
Breastfed ever, n (%) >0.99
No 14 (56.0%)5 (55.6%)9 (56.3%)
Yes 11 (44.0%)4 (44.4%)7 (43.8%)
Missing 871
Family history of breast cancer, n (%) >0.99
No 21 (63.6%)10 (62.5%)11 (64.7%)
Yes 12 (36.4%)6 (37.5%)6 (35.3%)
Distance of specimen from nipple, n (%) 0.90
≤2 cm 5 (15.2%)3 (18.8%)2 (11.8%)
>2 cm and ≤5 cm 12 (36.4%)6 (37.5%)6 (35.3%)
>5 cm 16 (48.5%)7 (43.8%)9 (52.9%)
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MDPI and ACS Style

Sample, J.W.; Redaelli, M.; Chen, J.; Hoskin, T.L.; Johnson, S.; Walther-Antonio, M.; Hieken, T.J. Is There a Microbiological Basis for Increased Breast Cancer Risk in Women with High Mammographic Density? Appl. Microbiol. 2026, 6, 39. https://doi.org/10.3390/applmicrobiol6030039

AMA Style

Sample JW, Redaelli M, Chen J, Hoskin TL, Johnson S, Walther-Antonio M, Hieken TJ. Is There a Microbiological Basis for Increased Breast Cancer Risk in Women with High Mammographic Density? Applied Microbiology. 2026; 6(3):39. https://doi.org/10.3390/applmicrobiol6030039

Chicago/Turabian Style

Sample, Jack W., Matteo Redaelli, Jun Chen, Tanya L. Hoskin, Stephen Johnson, Marina Walther-Antonio, and Tina J. Hieken. 2026. "Is There a Microbiological Basis for Increased Breast Cancer Risk in Women with High Mammographic Density?" Applied Microbiology 6, no. 3: 39. https://doi.org/10.3390/applmicrobiol6030039

APA Style

Sample, J. W., Redaelli, M., Chen, J., Hoskin, T. L., Johnson, S., Walther-Antonio, M., & Hieken, T. J. (2026). Is There a Microbiological Basis for Increased Breast Cancer Risk in Women with High Mammographic Density? Applied Microbiology, 6(3), 39. https://doi.org/10.3390/applmicrobiol6030039

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