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Human breast microbiome correlates with prognostic features and immunological signatures in breast cancer
BACKGROUND: Currently, over half of breast cancer cases are unrelated to known risk factors, highlighting the importance of discovering other cancer-promoting factors. Since crosstalk between gut microbes and host immunity contributes to many diseases, we hypothesized that similar interactions could...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8052771/ https://www.ncbi.nlm.nih.gov/pubmed/33863341 http://dx.doi.org/10.1186/s13073-021-00874-2 |
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author | Tzeng, Alice Sangwan, Naseer Jia, Margaret Liu, Chin-Chih Keslar, Karen S. Downs-Kelly, Erinn Fairchild, Robert L. Al-Hilli, Zahraa Grobmyer, Stephen R. Eng, Charis |
author_facet | Tzeng, Alice Sangwan, Naseer Jia, Margaret Liu, Chin-Chih Keslar, Karen S. Downs-Kelly, Erinn Fairchild, Robert L. Al-Hilli, Zahraa Grobmyer, Stephen R. Eng, Charis |
author_sort | Tzeng, Alice |
collection | PubMed |
description | BACKGROUND: Currently, over half of breast cancer cases are unrelated to known risk factors, highlighting the importance of discovering other cancer-promoting factors. Since crosstalk between gut microbes and host immunity contributes to many diseases, we hypothesized that similar interactions could occur between the recently described breast microbiome and local immune responses to influence breast cancer pathogenesis. METHODS: Using 16S rRNA gene sequencing, we characterized the microbiome of human breast tissue in a total of 221 patients with breast cancer, 18 individuals predisposed to breast cancer, and 69 controls. We performed bioinformatic analyses using a DADA2-based pipeline and applied linear models with White’s t or Kruskal–Wallis H-tests with Benjamini–Hochberg multiple testing correction to identify taxonomic groups associated with prognostic clinicopathologic features. We then used network analysis based on Spearman coefficients to correlate specific bacterial taxa with immunological data from NanoString gene expression and 65-plex cytokine assays. RESULTS: Multiple bacterial genera exhibited significant differences in relative abundance when stratifying by breast tissue type (tumor, tumor adjacent normal, high-risk, healthy control), cancer stage, grade, histologic subtype, receptor status, lymphovascular invasion, or node-positive status, even after adjusting for confounding variables. Microbiome–immune networks within the breast tended to be bacteria-centric, with sparse structure in tumors and more interconnected structure in benign tissues. Notably, Anaerococcus, Caulobacter, and Streptococcus, which were major bacterial hubs in benign tissue networks, were absent from cancer-associated tissue networks. In addition, Propionibacterium and Staphylococcus, which were depleted in tumors, showed negative associations with oncogenic immune features; Streptococcus and Propionibacterium also correlated positively with T-cell activation-related genes. CONCLUSIONS: This study, the largest to date comparing healthy versus cancer-associated breast microbiomes using fresh-frozen surgical specimens and immune correlates, provides insight into microbial profiles that correspond with prognostic clinicopathologic features in breast cancer. It additionally presents evidence for local microbial–immune interplay in breast cancer that merits further investigation and has preventative, diagnostic, and therapeutic potential. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13073-021-00874-2. |
format | Online Article Text |
id | pubmed-8052771 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-80527712021-04-19 Human breast microbiome correlates with prognostic features and immunological signatures in breast cancer Tzeng, Alice Sangwan, Naseer Jia, Margaret Liu, Chin-Chih Keslar, Karen S. Downs-Kelly, Erinn Fairchild, Robert L. Al-Hilli, Zahraa Grobmyer, Stephen R. Eng, Charis Genome Med Research BACKGROUND: Currently, over half of breast cancer cases are unrelated to known risk factors, highlighting the importance of discovering other cancer-promoting factors. Since crosstalk between gut microbes and host immunity contributes to many diseases, we hypothesized that similar interactions could occur between the recently described breast microbiome and local immune responses to influence breast cancer pathogenesis. METHODS: Using 16S rRNA gene sequencing, we characterized the microbiome of human breast tissue in a total of 221 patients with breast cancer, 18 individuals predisposed to breast cancer, and 69 controls. We performed bioinformatic analyses using a DADA2-based pipeline and applied linear models with White’s t or Kruskal–Wallis H-tests with Benjamini–Hochberg multiple testing correction to identify taxonomic groups associated with prognostic clinicopathologic features. We then used network analysis based on Spearman coefficients to correlate specific bacterial taxa with immunological data from NanoString gene expression and 65-plex cytokine assays. RESULTS: Multiple bacterial genera exhibited significant differences in relative abundance when stratifying by breast tissue type (tumor, tumor adjacent normal, high-risk, healthy control), cancer stage, grade, histologic subtype, receptor status, lymphovascular invasion, or node-positive status, even after adjusting for confounding variables. Microbiome–immune networks within the breast tended to be bacteria-centric, with sparse structure in tumors and more interconnected structure in benign tissues. Notably, Anaerococcus, Caulobacter, and Streptococcus, which were major bacterial hubs in benign tissue networks, were absent from cancer-associated tissue networks. In addition, Propionibacterium and Staphylococcus, which were depleted in tumors, showed negative associations with oncogenic immune features; Streptococcus and Propionibacterium also correlated positively with T-cell activation-related genes. CONCLUSIONS: This study, the largest to date comparing healthy versus cancer-associated breast microbiomes using fresh-frozen surgical specimens and immune correlates, provides insight into microbial profiles that correspond with prognostic clinicopathologic features in breast cancer. It additionally presents evidence for local microbial–immune interplay in breast cancer that merits further investigation and has preventative, diagnostic, and therapeutic potential. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13073-021-00874-2. BioMed Central 2021-04-16 /pmc/articles/PMC8052771/ /pubmed/33863341 http://dx.doi.org/10.1186/s13073-021-00874-2 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Tzeng, Alice Sangwan, Naseer Jia, Margaret Liu, Chin-Chih Keslar, Karen S. Downs-Kelly, Erinn Fairchild, Robert L. Al-Hilli, Zahraa Grobmyer, Stephen R. Eng, Charis Human breast microbiome correlates with prognostic features and immunological signatures in breast cancer |
title | Human breast microbiome correlates with prognostic features and immunological signatures in breast cancer |
title_full | Human breast microbiome correlates with prognostic features and immunological signatures in breast cancer |
title_fullStr | Human breast microbiome correlates with prognostic features and immunological signatures in breast cancer |
title_full_unstemmed | Human breast microbiome correlates with prognostic features and immunological signatures in breast cancer |
title_short | Human breast microbiome correlates with prognostic features and immunological signatures in breast cancer |
title_sort | human breast microbiome correlates with prognostic features and immunological signatures in breast cancer |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8052771/ https://www.ncbi.nlm.nih.gov/pubmed/33863341 http://dx.doi.org/10.1186/s13073-021-00874-2 |
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