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. 2021 Sep 4;12(9):831.
doi: 10.1038/s41419-021-04092-x.

Prognostic correlations with the microbiome of breast cancer subtypes

Sagarika Banerjee  1 , Zhi Wei  2 , Tian Tian  2 , Dipayan Bose  1 , Natalie N C Shih  3 , Michael D Feldman  3 , Thaer Khoury  4 , Angela De Michele  5 , Erle S Robertson  6
Affiliations

Prognostic correlations with the microbiome of breast cancer subtypes

Sagarika Banerjee et al. Cell Death Dis. .

Abstract

Alterations to the natural microbiome are linked to different diseases, and the presence or absence of specific microbes is directly related to disease outcomes. We performed a comprehensive analysis with unique cohorts of the four subtypes of breast cancer (BC) characterized by their microbial signatures, using a pan-pathogen microarray strategy. The signature (includes viruses, bacteria, fungi, and parasites) of each tumor subtype was correlated with clinical data to identify microbes with prognostic potential. The subtypes of BC had specific viromes and microbiomes, with ER+ and TN tumors showing the most and least diverse microbiome, respectively. The specific microbial signatures allowed discrimination between different BC subtypes. Furthermore, we demonstrated correlations between the presence and absence of specific microbes in BC subtypes with the clinical outcomes. This study provides a comprehensive map of the oncobiome of BC subtypes, with insights into disease prognosis that can be critical for precision therapeutic intervention strategies.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1
Fig. 1. Oncobiome diversity in different breast cancer subtypes.
A The four types of breast cancers have distinct oncobiome compositions. PCA plot using NBClust shows that TN breast cancer oncobiome is strikingly different from the other three breast cancer subtypes studied. The unique aspects of the oncobiomes of each breast cancer subtype are represented as violin plots showing the full distribution of the data. ER+ BC shows the most diversity in oncobiome. B Using topological data analysis, we further show the similarity in the oncobiomes of triple positive and ER positive BCs, while both Her2+ and triple-negative breast cancer have oncobiome characteristics very different from other BCs. C Bar graphs showing different types and phyla of oncobiome in the four breast cancer subtypes. D Venn diagrams show the viral and microbial signatures that are shared and unique to the four breast cancer subtypes.
Fig. 2
Fig. 2. Viral and bacterial microbial signatures detected in the four types of breast cancers.
A, C The bars represent the average hybridization signal for each virus and bacterial signatures respectively, while the percent prevalence of those virus and bacterial signatures in the sample set is indicated by the red dots. B The average hybridization signals for specific viruses were summed and represented as heatmap to show low to high detections of specific viral signatures in the four types of breast cancers.
Fig. 3
Fig. 3. Fungal and parasitic signatures detected in the breast cancers.
A, B The bars represent the average hybridization signal for each fungal or parasitic signatures, while the percent prevalence of those fungal or parasitic signatures in the sample set is indicated by the orange dots.
Fig. 4
Fig. 4. Oncobiome signatures in triple-negative breast cancer where higher hybridization signals correlated with better disease outcome.
A The graphs show disease-free rate and/or survival relative to high or low hybridization signals for the specific microorganisms in the TN sample set. In the cases shown, higher hybridization signal correlates with increased disease-free time and/or survival. B TN BC samples were clustered based on high and low hybridization signals for those organisms where high hybridization signal correlated with higher disease-free time and survival (better outcomes). The high (cluster 1) and low (cluster 2) hybridization clusters were then correlated with clinical data shown as horizontal (a–g) and vertical (h) cluster barplot.
Fig. 5
Fig. 5. Oncobiome signatures in ER+ breast cancer where higher hybridization signals correlate with better disease outcome.
A The graphs show disease-free rate relative to high or low hybridization signals for the specific microorganisms in the tumor sample set. In the cases shown, higher hybridization signal correlated with increased disease-free time or survival. The tumor samples were then clustered based on the hybridization levels for these microorganisms. The high (cluster 1) and the low (cluster 2) hybridization detection clusters were correlated with other clinical data shown as horizontal (a–g) and vertical (h) cluster barplot. B Box plot showing average hybridization signal of microorganism detection in different tumor grades. ND not diagnosed. χ2 p values showing significant (p ≤ 0.05) differences in the hybridization signal for detection in different grades provided.
Fig. 6
Fig. 6. Oncobiome signatures in ER+ BCs where high hybridization signals correlated with worse disease outcome.
A The graphs show survival time relative to high or low hybridization signals for the specific microorganisms shown. In the cases shown, higher hybridization signal correlated with decreased survival. B. ER+ BC samples were then clustered based on the hybridization levels for these microorganisms. The low (cluster 1) and the high (cluster 2) hybridization detection clusters correlated with other clinical data shown as horizontal (a–g) and vertical (h) cluster barplot.
Fig. 7
Fig. 7. Oncobiome signatures in HER2+ BCs where high hybridization signals correlated with worse disease outcome.
A The graphs show disease-free rate relative to high or low hybridization signals for the specific microorganisms in the tumor sample set. In the cases shown, higher hybridization signal correlates with decreased disease-free time. These microorganisms detected in patients with lower disease-free rate or survival were not significantly associated with higher cancer staging. B The tumor samples were then clustered based on the hybridization levels for these microorganisms. The low (cluster 1) and the high (cluster 2) hybridization detection clusters correlated with other clinical data shown as horizontal (a–g) and vertical (h) cluster barplot.
Fig. 8
Fig. 8. Oncobiome signatures in TP+ BCs where higher hybridization signals correlated with worse outcome.
A The graphs show disease-free rate relative to high or low hybridization signals for specific microorganisms in the tumor sample set. In the cases shown, higher hybridization signal correlated with decreased disease-free time. B The tumor samples were then clustered based on the hybridization levels for these microorganisms. The high (cluster 1) and the low (cluster 2) hybridization detection clusters correlated with other clinical data shown as horizontal (a–g) and vertical (h) cluster barplot.
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References

    1. Xuan C, Shamonki JM, Chung A, Dinome ML, Chung M, Sieling PA, et al. Microbial dysbiosis is associated with human breast cancer. PLoS One. 2014;9:e83744. doi: 10.1371/journal.pone.0083744. - DOI - PMC - PubMed
    1. Castano-Rodriguez N, Goh KL, Fock KM, Mitchell HM, Kaakoush NO. Dysbiosis of the microbiome in gastric carcinogenesis. Sci Rep. 2017;7:15957. doi: 10.1038/s41598-017-16289-2. - DOI - PMC - PubMed
    1. Banerjee S, Tian T, Wei Z, Shih N, Feldman MD, Peck KN, et al. Distinct microbial signatures associated with different breast cancer types. Front Microbiol. 2018;9:951. doi: 10.3389/fmicb.2018.00951. - DOI - PMC - PubMed
    1. Sheflin AM, Whitney AK, Weir TL. Cancer-promoting effects of microbial dysbiosis. Curr Oncol Rep. 2014;16:406. doi: 10.1007/s11912-014-0406-0. - DOI - PMC - PubMed
    1. Turnbaugh PJ, Ley RE, Mahowald MA, Magrini V, Mardis ER, Gordon JI. An obesity-associated gut microbiome with increased capacity for energy harvest. Nature. 2006;444:1027–31. doi: 10.1038/nature05414. - DOI - PubMed

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