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      An intact gut microbiome protects genetically predisposed mice against leukemia

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          Abstract

          The majority of childhood leukemias are precursor B-cell acute lymphoblastic leukemias (pB-ALLs) caused by a combination of prenatal genetic predispositions and oncogenic events occurring after birth. Although genetic predispositions are frequent in children (>1% to 5%), fewer than 1% of genetically predisposed carriers will develop pB-ALL. Although infectious stimuli are believed to play a major role in leukemogenesis, the critical determinants are not well defined. Here, by using murine models of pB-ALL, we show that microbiome disturbances incurred by antibiotic treatment early in life were sufficient to induce leukemia in genetically predisposed mice, even in the absence of infectious stimuli and independent of T cells. By using V4 and full-length 16S ribosomal RNA sequencing of a series of fecal samples, we found that genetic predisposition to pB-ALL (Pax5 heterozygosity or ETV6-RUNX1 fusion) shaped a distinct gut microbiome. Machine learning accurately (96.8%) predicted genetic predisposition using 40 of 3983 amplicon sequence variants as proxies for bacterial species. Transplantation of either wild-type (WT) or Pax5+/– hematopoietic bone marrow cells into WT recipient mice revealed that the microbiome is shaped and determined in a donor genotype–specific manner. Gas chromatography-mass spectrometry (GC-MS) analyses of sera from WT and Pax5+/– mice demonstrated the presence of a genotype-specific distinct metabolomic profile. Taken together, our data indicate that it is a lack of commensal microbiota rather than the presence of specific bacteria that promotes leukemia in genetically predisposed mice. Future large-scale longitudinal studies are required to determine whether targeted microbiome modification in children predisposed to pB-ALL could become a successful prevention strategy.

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          Most cited references65

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          Gene Expression Omnibus: NCBI gene expression and hybridization array data repository.

          R. Edgar (2002)
          The Gene Expression Omnibus (GEO) project was initiated in response to the growing demand for a public repository for high-throughput gene expression data. GEO provides a flexible and open design that facilitates submission, storage and retrieval of heterogeneous data sets from high-throughput gene expression and genomic hybridization experiments. GEO is not intended to replace in house gene expression databases that benefit from coherent data sets, and which are constructed to facilitate a particular analytic method, but rather complement these by acting as a tertiary, central data distribution hub. The three central data entities of GEO are platforms, samples and series, and were designed with gene expression and genomic hybridization experiments in mind. A platform is, essentially, a list of probes that define what set of molecules may be detected. A sample describes the set of molecules that are being probed and references a single platform used to generate its molecular abundance data. A series organizes samples into the meaningful data sets which make up an experiment. The GEO repository is publicly accessible through the World Wide Web at http://www.ncbi.nlm.nih.gov/geo.
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            Gut microbiome influences efficacy of PD-1–based immunotherapy against epithelial tumors

            Immune checkpoint inhibitors (ICI) targeting the PD-1/PD-L1 axis induce sustained clinical responses in a sizeable minority of cancer patients. Here, we show that primary resistance to ICI can be due to abnormal gut microbiome composition. Antibiotics (ATB) inhibited the clinical benefit of ICI in patients with advanced cancer. Fecal microbiota transplantation (FMT) from cancer patients who responded to ICI (but not from non-responding patients) into germ-free or ATB-treated mice ameliorated the antitumor effects of PD-1 blockade. Metagenomics of patient stools at diagnosis revealed correlations between clinical responses to ICI and the relative abundance of Akkermansia muciniphila. Oral supplementation with A. muciniphila post-FMT with non-responder feces restored the efficacy of PD-1 blockade in an IL-12-dependent manner, by increasing the recruitment of CCR9+CXCR3+CD4+ T lymphocytes into tumor beds.
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              Gut microbiome modulates response to anti–PD-1 immunotherapy in melanoma patients

              Pre-clinical mouse models suggest that the gut microbiome modulates tumor response to checkpoint blockade immunotherapy; however, this has not been well-characterized in human cancer patients. Here we examined the oral and gut microbiome of melanoma patients undergoing anti-PD-1 immunotherapy (n=112). Significant differences were observed in the diversity and composition of the patient gut microbiome of responders (R) versus non-responders (NR). Analysis of patient fecal microbiome samples (n=43, 30R, 13NR) showed significantly higher alpha diversity (p<0.01) and relative abundance of Ruminococcaceae bacteria (p<0.01) in responding patients. Metagenomic studies revealed functional differences in gut bacteria in R including enrichment of anabolic pathways. Immune profiling suggested enhanced systemic and anti-tumor immunity in responding patients with a favorable gut microbiome, as well as in germ-free mice receiving fecal transplants from responding patients. Together, these data have important implications for the treatment of melanoma patients with immune checkpoint inhibitors.
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                Author and article information

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                Journal
                Blood
                American Society of Hematology
                0006-4971
                1528-0020
                October 29 2020
                October 29 2020
                : 136
                : 18
                : 2003-2017
                Affiliations
                [1 ]Institute for Biomedical Research of Salamanca (IBSAL), Salamanca, Spain;
                [2 ]Pediatric Oncology, Hematology and Clinical Immunology, Medical Faculty, Heinrich Heine University, Düsseldorf, Germany;
                [3 ]Algorithmic Bioinformatics, Justus Liebig University, Giessen, Germany;
                [4 ]German Cancer Research Center (DKFZ), Heidelberg, Germany;
                [5 ]Experimental Therapeutics and Translational Oncology Program, Instituto de Biología Molecular y Celular del Cáncer, Consejo Superior de Investigaciones Científicas (CSIC)/Universidad de Salamanca, Salamanca, Spain;
                [6 ]Institute of Plant Biochemistry, Cluster of Excellence on Plant Science, Heinrich Heine University, Düsseldorf, Germany;
                [7 ]Bioinformatics Unit, and
                [8 ]Bioinformatics and Functional Genomics Research Group, Cancer Research Center (CSIC-USAL), Salamanca, Spain;
                [9 ]Departamento de Cirugía, Universidad de Salamanca, Salamanca, Spain;
                [10 ]Biological and Medical Research Center, Genomics and Transcriptomics Laboratory, Heinrich Heine University Düsseldorf, Düsseldorf, Germany;
                [11 ]National Center for Tumor Diseases (NCT), Dresden, Germany: and
                [12 ]Department of Pediatrics, Pediatric Hematology and Oncology, University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
                Article
                10.1182/blood.2019004381
                7694022
                32911536
                66018e8e-7dc9-43b8-9c15-3007d5fa38c5
                © 2020
                History

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