Pretreatment gut microbiome predicts chemotherapy-related bloodstream infection.
Pretreatment gut microbiome predicts chemotherapy-related bloodstream infection.
复制标题
DOI：
10.1186/s13073-016-0301-4
复制
发表时间：
2016-04-28
期刊：
影响因子：
12.3
通讯作者：
Knights D
中科院分区：
文献类型：
--
作者：
Montassier E;Al-Ghalith GA;Ward T;Corvec S;Gastinne T;Potel G;Moreau P;de la Cochetiere MF;Batard E;Knights D
菌血症或血流感染（BSI）是某些类型癌症患者死亡的主要原因。先前的一项研究报道，肠道优势（定义为单个细菌分类群占据至少30%的微生物群）与接受allo-HSCT的患者的BSI相关。然而，治疗开始前肠道微生物组对后续BSI风险的影响仍不清楚。我们的目标是描述治疗前收集的粪便微生物组，以确定预测BSI风险的微生物。我们对28例接受异基因造血干细胞移植（HSCT）的非霍奇金淋巴瘤患者在化疗前进行了采样，并使用高通量DNA测序对16 S核糖体RNA基因进行了表征。我们量化了细菌分类群，并使用机器学习技术来识别预测后续BSI的微生物生物标志物。我们发现，发生后续BSI的患者表现出整体多样性下降，包括Barnesiellaceae，Coriobacteriaceae，Faecalibacterium，Christensenella，Dehalobacterium，Desulfovibrio和Sutterella的类群丰度下降。使用机器学习方法，我们开发了一种能够预测BSI发病率的BSI风险指数，其灵敏度为90%，特异性为90%，仅基于治疗前的粪便微生物组。这些结果表明，肠道微生物群可以在HSCT前识别高危患者，并且操纵肠道微生物群以预防高危患者的BSI可能是未来研究的有用方向。这种方法可能会激发在其他疾病中开发类似的基于微生物组的诊断和预后模型。本文的在线版本（doi：10.1186/s13073-016-0301-4）包含补充材料，可供授权用户使用。
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. 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. 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. 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. The online version of this article (doi:10.1186/s13073-016-0301-4) contains supplementary material, which is available to authorized users.