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Identification of a novel cancer microbiome signature for predicting prognosis of human breast cancer patients

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Abstract

Background

Prognosis of breast cancer (BC) patients differs considerably and identifying reliable prognostic biomarker(s) is imperative. With evidence that the microbiome plays a critical role in the response to cancer therapies, we aimed to identify a cancer microbiome signature for predicting the prognosis of BC patients.

Methods

The TCGA BC microbiome data (TCGA-BRCA-microbiome) was downloaded from cBioPortal. Univariate and multivariate Cox regression analyses were used to examine association of microbial abundance with overall survival (OS) and to identify a microbial signature for creating a prognostic scoring model. The performance of the scoring model was assessed by the area under the ROC curve (AUC). Nomograms using the microbial signature, clinical factors, and molecular subtypes were established to predict OS and progression-free survival (PFS).

Results

Among 1406 genera, the abundances of 94 genera were significantly associated with BC patient OS in TCGA-BRCA-microbiome dataset. From that set we identified a 15-microbe prognostic signature and developed a 15-microbial abundance prognostic scoring (MAPS) model. Patients in low-risk group significantly prolong OS and PFS as compared to those in high-risk group. The time-dependent ROC curves with MAPS showed good predictive efficacy both in OS and PFS. Moreover, MAPS is an independent prognostic factor for OS and PFS over clinical factors and PAM50-based molecular subtypes and superior to the previously published 12-gene signature. The integration of MAPS into nomograms significantly improved prognosis prediction.

Conclusion

MAPS was successfully established to have independent prognostic value, and our study provides a new avenue for developing prognostic biomarkers by microbiome profiling.

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Data availability

All data used in the study were downloaded from a publicly available source (cBioPortal).

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Acknowledgements

We thank the people in our financial office for the financial management of the DOD grant. Lawrence Berkeley National Laboratory (LBNL) is operated by the University of California for the DOE under contract DE AC02-05CH11231.

Funding

This research and APC were funded by DOD BCRP, grant number BC190820.

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Author notes

  1. A. W. Mao, H. Barck, J. Young, A. Paley have contributed equally.

Authors and Affiliations

  1. Berkeley Biomedical Data Science Center, Lawrence Berkeley National Laboratory, Berkeley, CA, USA

    A. W. Mao, H. Barck, J. Young, A. Paley, J. -H. Mao & H. Chang

  2. Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA

    J. -H. Mao & H. Chang

Authors
  1. A. W. Mao
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  2. H. Barck
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Contributions

Conception and design: JHM and HC; collection and assembly of data: JHM and HC; data analysis and interpretation: AWM, HB, JY and AP; manuscript writing: JHM and HC; manuscript editing AWM, HB, JY and AP; final approval of manuscript: all authors.

Corresponding authors

Correspondence to J. -H. Mao or H. Chang.

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Conflict of interest

The authors declare no conflict of interest.

Ethical statement

There was no requirement for ethical approval since all data used in this study were downloaded from public databases. The authors are responsible for the accuracy or integrity of any aspects of this study.

Reporting checklist

The authors used the TRIPOD reporting checklist in this study.

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Mao, A.W., Barck, H., Young, J. et al. Identification of a novel cancer microbiome signature for predicting prognosis of human breast cancer patients. Clin Transl Oncol 24, 597–604 (2022). https://doi.org/10.1007/s12094-021-02725-3

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  • DOI: https://doi.org/10.1007/s12094-021-02725-3

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