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

• A. W. Mao ^[1] ; H. Barck ^[1] ; J. Young ^[1] ; A. Paley ^[1] ; J. H. Mao ^[2] ; H. Chang ^[2]
  1. [1] Lawrence Berkeley National Laboratory

     Lawrence Berkeley National Laboratory

     Estados Unidos

     
  2. [2] Berkeley Biomedical Data Science Center, Lawrence Berkeley National Laboratory, Berkeley, CA, USA Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
• Localización: Clinical & translational oncology, ISSN 1699-048X, Vol. 24, Nº. 3 (Marzo), 2022, págs. 597-604
• Idioma: inglés
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• Resumen

  • 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.