Pretreatment gut microbiome predicts chemotherapy-related bloodstream infection. Issue 1 (December 2016)

• Record Type: Journal Article
• Authors: Montassier, Emmanuel; Al-Ghalith, Gabriel; Ward, Tonya; Corvec, Stephane; Gastinne, Thomas; Potel, Gilles; Moreau, Philippe; de la Cochetiere, Marie; Batard, Eric; Knights, Dan
• Is Part Of: Genome medicine. Volume 8:Issue 1(2016)
• Page Start: 1
• Page End: 11
• Publication Date: 2016-12
• Subjects: Bloodstream infection -- Chemotherapy -- Intestinal microbiome -- Prediction; Genomics -- Periodicals; Medical genetics -- Periodicals; 616.042
• DOI: 10.1186/s13073-016-0301-4
• Languages: English
• ISSNs: 1756-994X

Abstract

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, andSutterella . 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.