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Genome Med. 2016 Apr 28;8(1):49. doi: 10.1186/s13073-016-0301-4.

Pretreatment gut microbiome predicts chemotherapy-related bloodstream infection.

Author information

1
Université de Nantes, EA 3826 Thérapeutiques cliniques et expérimentales des infections. Faculté de médecine, 1 Rue G Veil, Nantes, 44000, France.
2
Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN, 55455, USA.
3
Biomedical Informatics and Computational Biology, University of Minnesota, Minneapolis, MN, 55455, USA.
4
Biotechnology Institute, University of Minnesota, St. Paul, MN, 55108, USA.
5
Nantes University Hospital, Microbiology Laboratory, Nantes, France.
6
Hematology Department, Nantes University Hospital, Nantes, France.
7
Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN, 55455, USA. dknights@umn.edu.
8
Biotechnology Institute, University of Minnesota, St. Paul, MN, 55108, USA. dknights@umn.edu.

Abstract

BACKGROUND:

Bacteremia, or bloodstream infection (BSI), is a leading cause of death among patients with certain types of cancer. A previous study reported that intestinal domination, defined as occupation of at least 30 % of the microbiota by a single bacterial taxon, is associated with BSI in patients undergoing allo-HSCT. However, the impact of the intestinal microbiome before treatment initiation on the risk of subsequent BSI remains unclear. Our objective was to characterize the fecal microbiome collected before treatment to identify microbes that predict the risk of BSI.

METHODS:

We sampled 28 patients with non-Hodgkin lymphoma undergoing allogeneic hematopoietic stem cell transplantation (HSCT) prior to administration of chemotherapy and characterized 16S ribosomal RNA genes using high-throughput DNA sequencing. We quantified bacterial taxa and used techniques from machine learning to identify microbial biomarkers that predicted subsequent BSI.

RESULTS:

We found that patients who developed subsequent BSI exhibited decreased overall diversity and decreased abundance of taxa including Barnesiellaceae, Coriobacteriaceae, Faecalibacterium, Christensenella, Dehalobacterium, Desulfovibrio, and Sutterella. Using machine-learning methods, we developed a BSI risk index capable of predicting BSI incidence with a sensitivity of 90 % at a specificity of 90 % based only on the pretreatment fecal microbiome.

CONCLUSIONS:

These results suggest that the gut microbiota can identify high-risk patients before HSCT and that manipulation of the gut microbiota for prevention of BSI in high-risk patients may be a useful direction for future research. This approach may inspire the development of similar microbiome-based diagnostic and prognostic models in other diseases.

KEYWORDS:

Bloodstream infection; Chemotherapy; Intestinal microbiome; Prediction

PMID:
27121964
PMCID:
PMC4848771
DOI:
10.1186/s13073-016-0301-4
[Indexed for MEDLINE]
Free PMC Article

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