4.7 Article

Infectious Complications Are Associated With Alterations in the Gut Microbiome in Pediatric Patients With Acute Lymphoblastic Leukemia

出版社

FRONTIERS MEDIA SA
DOI: 10.3389/fcimb.2019.00028

关键词

microbiome; genomics; cancer; leukemia; clinical; infection; metagenomics

资金

  1. NSERC Discovery Grant
  2. Terry Fox Research Institute (TFRI)
  3. Canadian Institutes of Health Research (CIHR)-CAG-CCC New Investigator Award (2015-2018)
  4. Canada Research Chair Tier 2 in Translational Microbiomics (2018-2023)
  5. Canadian Foundation of Innovation [35235, 36764]
  6. Nova Scotia Health Research Foundation (NSHRF)
  7. IWK Health Centre Research Associateship
  8. CIHR-SPOR-Chronic Diseases grant (Inflammation, Microbiome, and Alimentation: Gastro-Intestinal and Neuropsychiatric Effects: the IMAGINE-SPOR chronic disease network)

向作者/读者索取更多资源

Acute lymphoblastic leukemia is the most common pediatric cancer. Fortunately, survival rates exceed 90%, however, infectious complications remain a significant issue that can cause reductions in the quality of life and prognosis of patients. Recently, numerous studies have linked shifts in the gut microbiome composition to infection events in various hematological malignances including acute lymphoblastic leukemia (ALL). These studies have been limited to observing broad taxonomic changes using 16S rRNA gene profiling, while missing possible differences within microbial functions encoded by individual species. In this study we present the first combined 16S rRNA gene and metagenomic shotgun sequencing study on the gut microbiome of an independent pediatric ALL cohort during treatment. In this study we found distinctive differences in alpha diversity and beta diversity in samples from patients with infectious complications in the first 6 months of therapy. We were also able to find specific species and functional pathways that were significantly different in relative abundance between samples that came from patients with infectious complications. Finally, machine learning models based on patient metadata and bacterial species were able to classify samples with high accuracy (84.09%), with bacterial species being the most important classifying features. This study strengthens our understanding of the association between infection and pediatric acute lymphoblastic leukemia treatment and warrants further investigation in the future.

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