Skip to main content

Advertisement

Log in

Microbiota composition in bilateral healthy breast tissue and breast tumors

  • Original Paper
  • Published:
Cancer Causes & Control Aims and scope Submit manuscript

Abstract

Purpose

Previous reports suggest that a complex microbiome exists within the female human breast that might contribute to breast cancer etiology. The purpose of this pilot study was to assess the variation in microbiota composition by breast side (left versus right) within individual women and compare the microbiota of normal and breast tumor tissue between women. We aimed to determine whether microbiota composition differs between these groups and whether certain bacterial taxa may be associated with breast tumors.

Methods

Bilateral normal breast tissue samples (n = 36) were collected from ten women who received routine mammoplasty procedures. Archived breast tumor samples (n = 10) were obtained from a biorepository. DNA was extracted, amplified, and sequenced. Microbiota data were analyzed using QIIME and RStudio.

Results

The most abundant phyla in both tumor and normal tissues were Bacteroidetes, Firmicutes, Proteobacteria, and Actinobacteria. There were statistically significant differences in the relative abundance of various bacterial taxa between groups. Alpha diversity (Simpson’s index) was significantly higher in normal compared to tumor samples (0.968 vs. 0.957, p = 0.022). Based on unweighted UniFrac measures, breast tumor samples clustered distinctly from normal samples (R2 = 0.130; p = 0.01). Microbiota composition in normal samples clustered within women (R2 = 0.394; p = 0.01) and by breast side (left or right) within a woman (R2 = 0.189; p = 0.03).

Conclusion

Significant differences in diversity between tumor and normal tissue and in composition between women and between breasts of the same woman were identified. These results warrant further research to investigate the relationship between microbiota and breast cancer.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. Bray F, Ferlay J, Soerjomataram I et al (2018) Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: Cancer J Clin 68:394–424. https://doi.org/10.3322/caac.21492

    Article  Google Scholar 

  2. Sun Y-S, Zhao Z, Yang Z-N et al (2017) Risk factors and preventions of breast cancer. Int J Biol Sci 13:1387–1397. https://doi.org/10.7150/ijbs.21635

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Tao Z, Shi A, Lu C et al (2015) Breast cancer: epidemiology and etiology. Cell Biochem Biophys 72:333–338. https://doi.org/10.1007/s12013-014-0459-6

    Article  CAS  PubMed  Google Scholar 

  4. Sutton T, Reilly P, Johnson N, Garreau JR (2018) Breast cancer in women under 50: most are not high risk. Am J Surg 215:848–851. https://doi.org/10.1016/j.amjsurg.2018.01.003

    Article  PubMed  Google Scholar 

  5. Ursell LK, Metcalf JL, Parfrey LW, Knight R (2012) Defining the human microbiome. Nutr Rev 70:S38–S44. https://doi.org/10.1111/j.1753-4887.2012.00493.x

    Article  PubMed  Google Scholar 

  6. Dahmus JD, Kotler DL, Kastenberg DM, Kistler CA (2018) The gut microbiome and colorectal cancer: a review of bacterial pathogenesis. J Gastrointest Oncol 9:769–777. https://doi.org/10.21037/jgo.2018.04.07

    Article  PubMed  PubMed Central  Google Scholar 

  7. Meng C, Bai C, Brown TD et al (2018) Human gut microbiota and gastrointestinal cancer. Genomics Proteomics Bioinformatics 16:33–49. https://doi.org/10.1016/j.gpb.2017.06.002

    Article  PubMed  PubMed Central  Google Scholar 

  8. Tilg H, Cani PD, Mayer EA (2016) Gut microbiome and liver diseases. Gut. https://doi.org/10.1136/gutjnl-2016-312729

    Article  PubMed  Google Scholar 

  9. Fernández M, Reina-Pérez I, Astorga J et al (2018) Breast cancer and its relationship with the microbiota. Int J Environ Res Public Health 15:1747. https://doi.org/10.3390/ijerph15081747

    Article  CAS  PubMed Central  Google Scholar 

  10. Shively CA, Register TC, Appt SE et al (2018) Consumption of mediterranean versus western diet leads to distinct mammary gland microbiome populations. Cell Rep 25:47–56.e3. https://doi.org/10.1016/j.celrep.2018.08.078

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Hieken TJ, Chen J, Hoskin TL et al (2016) The microbiome of aseptically collected human breast tissue in benign and malignant disease. Sci Rep 6:30751. https://doi.org/10.1038/srep30751

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Banerjee S, Tian T, Wei Z et al (2018) Distinct microbial signatures associated with different breast cancer types. Front Microbiol. https://doi.org/10.3389/fmicb.2018.00951

    Article  PubMed  PubMed Central  Google Scholar 

  13. Meng S, Chen B, Yang J et al (2018) Study of microbiomes in aseptically collected samples of human breast tissue using needle biopsy and the potential role of in situ tissue microbiomes for promoting malignancy. Front Oncol. https://doi.org/10.3389/fonc.2018.00318

    Article  PubMed  PubMed Central  Google Scholar 

  14. Thompson KJ, Ingle JN, Tang X et al (2017) A comprehensive analysis of breast cancer microbiota and host gene expression. PLoS ONE 12:e0188873. https://doi.org/10.1371/journal.pone.0188873

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Urbaniak C, Cummins J, Brackstone M et al (2014) Microbiota of human breast tissue. Appl Environ Microbiol 80:3007–3014. https://doi.org/10.1128/AEM.00242-14

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Urbaniak C, Gloor GB, Brackstone M et al (2016) The microbiota of breast tissue and its association with breast cancer. Appl Environ Microbiol 82:5039–5048. https://doi.org/10.1128/AEM.01235-16

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Wang H, Altemus J, Niazi F et al (2017) Breast tissue, oral and urinary microbiomes in breast cancer. Oncotarget 8:88122–88138. https://doi.org/10.18632/oncotarget.21490

    Article  PubMed  PubMed Central  Google Scholar 

  18. Xuan C, Shamonki JM, Chung A et al (2014) Microbial dysbiosis is associated with human breast cancer. PLoS ONE 9:e83744. https://doi.org/10.1371/journal.pone.0083744

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Smith A, Pierre JF, Makowski L et al (2019) Distinct microbial communities that differ by race, stage, or breast-tumor subtype in breast tissues of non-Hispanic Black and non-Hispanic White women. Sci Rep 9:1–10. https://doi.org/10.1038/s41598-019-48348-1

    Article  CAS  Google Scholar 

  20. Costantini L, Magno S, Albanese D et al (2018) Characterization of human breast tissue microbiota from core needle biopsies through the analysis of multi hypervariable 16S-rRNA gene regions. Sci Rep 8:1–9. https://doi.org/10.1038/s41598-018-35329-z

    Article  CAS  Google Scholar 

  21. Caporaso JG, Kuczynski J, Stombaugh J et al (2010) QIIME allows analysis of high-throughput community sequencing data. Nat Methods 7:335–336. https://doi.org/10.1038/nmeth.f.303

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Pylro VS, Roesch LFW, Ortega JM et al (2014) Brazilian microbiome project: revealing the unexplored microbial diversity—challenges and prospects. Microb Ecol 67:237–241. https://doi.org/10.1007/s00248-013-0302-4

    Article  PubMed  Google Scholar 

  23. Edgar RC (2010) Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26:2460–2461. https://doi.org/10.1093/bioinformatics/btq461

    Article  CAS  PubMed  Google Scholar 

  24. Wang Q, Garrity GM, Tiedje JM, Cole JR (2007) Naïve Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl Environ Microbiol 73:5261–5267. https://doi.org/10.1128/AEM.00062-07

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. DeSantis TZ, Hugenholtz P, Larsen N et al (2006) Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB. Appl Environ Microbiol 72:5069–5072. https://doi.org/10.1128/AEM.03006-05

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Price MN, Dehal PS, Arkin AP (2010) FastTree 2—approximately maximum-likelihood trees for large alignments. PLoS ONE 5:e9490. https://doi.org/10.1371/journal.pone.0009490

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. McMurdie PJ, Holmes S (2013) phyloseq: An R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE 8:e61217. https://doi.org/10.1371/journal.pone.0061217

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Love MI, Huber W, Anders S (2014) Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. https://doi.org/10.1186/s13059-014-0550-8

    Article  PubMed  PubMed Central  Google Scholar 

  29. Iwai S, Weinmaier T, Schmidt BL et al (2016) Piphillin: improved prediction of metagenomic content by direct inference from human microbiomes. PLoS ONE 11:e0166104. https://doi.org/10.1371/journal.pone.0166104

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. RStudio Team (2018) RStudio: integrated development for R. RStudio Inc, Boston, MA

    Google Scholar 

  31. Oksanen J, Guillaume FB, Friendly M et al (2019) vegan: Community Ecology Package. R package

  32. Mallick H, McIver LJ, Rahnavard A et al (2020) Multivariable association in population-scale meta-omics studies

  33. Morris EK, Caruso T, Buscot F et al (2014) Choosing and using diversity indices: insights for ecological applications from the German Biodiversity Exploratories. Ecol Evol 4:3514–3524. https://doi.org/10.1002/ece3.1155

    Article  PubMed  PubMed Central  Google Scholar 

  34. Pellegrini S, Sordi V, Bolla AM et al (2017) Duodenal mucosa of patients with type 1 diabetes shows distinctive inflammatory profile and microbiota. J Clin Endocrinol Metab 102:1468–1477. https://doi.org/10.1210/jc.2016-3222

    Article  PubMed  Google Scholar 

  35. Zhou J, Yao Y, Jiao K et al (2017) Relationship between gingival crevicular fluid microbiota and cytokine profile in periodontal host homeostasis. Front Microbiol. https://doi.org/10.3389/fmicb.2017.02144

    Article  PubMed  PubMed Central  Google Scholar 

  36. Tobias DK, Akinkuolie AO, Chandler PD et al (2018) Markers of inflammation and incident breast cancer risk in the women’s health study. Am J Epidemiol 187:705–716. https://doi.org/10.1093/aje/kwx250

    Article  PubMed  Google Scholar 

  37. Sanapareddy N, Legge RM, Jovov B et al (2012) Increased rectal microbial richness is associated with the presence of colorectal adenomas in humans. ISME J 6:1858–1868. https://doi.org/10.1038/ismej.2012.43

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Chng KR, Chan SH, Ng AHQ et al (2016) Tissue microbiome profiling identifies an enrichment of specific enteric bacteria in Opisthorchis viverrini associated Cholangiocarcinoma. EBioMedicine 8:195–202. https://doi.org/10.1016/j.ebiom.2016.04.034

    Article  PubMed  PubMed Central  Google Scholar 

  39. Yang J, McDowell A, Kim EK et al (2019) Development of a colorectal cancer diagnostic model and dietary risk assessment through gut microbiome analysis. Exp Mol Med 51:1–15. https://doi.org/10.1038/s12276-019-0313-4

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Sun T, Liu S, Zhou Y et al (2016) Evolutionary biologic changes of gut microbiota in an ‘adenoma-carcinoma sequence’ mouse colorectal cancer model induced by 1,2-dimethylhydrazine. Oncotarget 8:444–457. https://doi.org/10.18632/oncotarget.13443

    Article  PubMed Central  Google Scholar 

  41. Peters BA, Wu J, Pei Z et al (2017) Oral microbiome composition reflects prospective risk for esophageal cancers. Cancer Res 77:6777–6787. https://doi.org/10.1158/0008-5472.CAN-17-1296

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Shaw KA, Bertha M, Hofmekler T et al (2016) Dysbiosis, inflammation, and response to treatment: a longitudinal study of pediatric subjects with newly diagnosed inflammatory bowel disease. Genome Med. https://doi.org/10.1186/s13073-016-0331-y

    Article  PubMed  PubMed Central  Google Scholar 

  43. Liu G, Tang CM, Exley RM (2015) Non-pathogenic Neisseria: members of an abundant, multi-habitat, diverse genus. Microbiology 161:1297–1312. https://doi.org/10.1099/mic.0.000086

    Article  CAS  PubMed  Google Scholar 

  44. Iglewski BH (1996) Pseudomonas. In: Baron S (ed) Medical microbiology, 4th ed. University of Texas Medical Branch at Galveston, Galveston (TX)

  45. Cavarretta I, Ferrarese R, Cazzaniga W et al (2017) The microbiome of the prostate tumor microenvironment. Eur Urol 72:625–631. https://doi.org/10.1016/j.eururo.2017.03.029

    Article  CAS  PubMed  Google Scholar 

  46. Wang Y, Xue J, Zhou X et al (2014) Oral microbiota distinguishes acute lymphoblastic leukemia pediatric hosts from healthy populations. PLoS ONE. https://doi.org/10.1371/journal.pone.0102116

    Article  PubMed  PubMed Central  Google Scholar 

  47. Biagi E, Quercia S, Aceti A et al (2017) The bacterial ecosystem of mother’s milk and infant’s mouth and gut. Front Microbiol. https://doi.org/10.3389/fmicb.2017.01214

    Article  PubMed  PubMed Central  Google Scholar 

  48. Ruiz L, Bacigalupe R, García-Carral C et al (2019) Microbiota of human precolostrum and its potential role as a source of bacteria to the infant mouth. Sci Rep 9:1–13. https://doi.org/10.1038/s41598-019-42514-1

    Article  CAS  Google Scholar 

  49. Moreno I, Codoñer FM, Vilella F et al (2016) Evidence that the endometrial microbiota has an effect on implantation success or failure. Am J Obstet Gynecol 215:684–703. https://doi.org/10.1016/j.ajog.2016.09.075

    Article  PubMed  Google Scholar 

  50. McGovern E, Waters SM, Blackshields G, McCabe MS (2018) Evaluating established methods for Rumen 16S rRNA amplicon sequencing with mock microbial populations. Front Microbiol. https://doi.org/10.3389/fmicb.2018.01365

    Article  PubMed  PubMed Central  Google Scholar 

  51. Siegwald L, Touzet H, Lemoine Y et al (2017) Assessment of common and emerging bioinformatics pipelines for targeted metagenomics. PLoS ONE. https://doi.org/10.1371/journal.pone.0169563

    Article  PubMed  PubMed Central  Google Scholar 

  52. Female Breast Cancer Subtypes - Cancer Stat Facts. In: SEER. https://seer.cancer.gov/statfacts/html/breast-subtypes.html. Accessed 8 Oct 2019

Download references

Funding

The study was funded by the University of Florida Health Cancer Center and National Science Foundation.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Volker Mai.

Ethics declarations

Conflict of interest

The authors declare that they have no competing interests.

Ethical approval

This study was reviewed and approved as exempt by the UF Institutional Review Board (IRB) (Protocol Number IRB201600709).

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Klann, E., Williamson, J.M., Tagliamonte, M.S. et al. Microbiota composition in bilateral healthy breast tissue and breast tumors. Cancer Causes Control 31, 1027–1038 (2020). https://doi.org/10.1007/s10552-020-01338-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10552-020-01338-5

Keywords

Navigation