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Predictable modulation of cancer treatment outcomes by the gut microbiota.

ABSTRACT: The gut microbiota has the potential to influence the efficacy of cancer therapy. Here, we investigated the contribution of the intestinal microbiome on treatment outcomes in a heterogeneous cohort that included multiple cancer types to identify microbes with a global impact on immune response. Human gut metagenomic analysis revealed that responder patients had significantly higher microbial diversity and different microbiota compositions compared to non-responders. A machine-learning model was developed and validated in an independent cohort to predict treatment outcomes based on gut microbiota composition and functional repertoires of responders and non-responders. Specific species, Bacteroides ovatus and Bacteroides xylanisolvens, were positively correlated with treatment outcomes. Oral gavage of these responder bacteria significantly increased the efficacy of erlotinib and induced the expression of CXCL9 and IFN-? in a murine lung cancer model. These data suggest a predictable impact of specific constituents of the microbiota on tumor growth and cancer treatment outcomes with implications for both prognosis and therapy.

SUBMITTER: Heshiki Y

PROVIDER: S-EPMC7059390 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature

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Predictable modulation of cancer treatment outcomes by the gut microbiota.

Heshiki Yoshitaro Y Vazquez-Uribe Ruben R Li Jin J Ni Yueqiong Y Quainoo Scott S Imamovic Lejla L Li Jun J Sørensen Maria M Chow Billy K C BKC Weiss Glen J GJ Xu Aimin A Sommer Morten O A MOA Panagiotou Gianni G

Microbiome 20200305 1

The gut microbiota has the potential to influence the efficacy of cancer therapy. Here, we investigated the contribution of the intestinal microbiome on treatment outcomes in a heterogeneous cohort that included multiple cancer types to identify microbes with a global impact on immune response. Human gut metagenomic analysis revealed that responder patients had significantly higher microbial diversity and different microbiota compositions compared to non-responders. A machine-learning model was ...[more]

PMID: 32138779

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Project description:Understanding the mechanisms of microbiome assembly during host development is crucial for successful modulation of the gut microbiome to improve host health and growth. However, results from previous microbial intervention studies to improve swine growth have largely been inconsistent due to the constantly changing nature of the gut microbiota and limited longitudinal sampling. Detailed characterization of the swine gut microbiota through meta-analysis allows us to understand the dynamics of microbial community succession, as well as the transient and natural variations between time points and animals. A total of 3,313 fecal microbial communities from over 349 pigs covering 60 time points (from birth to market age) from 14 publications were included in this meta-analysis. Despite differences in animal breeds and management, generalizable patterns of community assembly were identified. Alpha diversity continuously increased during early stages of animal growth and more slowly in the later stages. Beta regression analysis revealed that more microbial taxa were enriched, while fewer were excluded by the gut microbiota. The microbial community structure also changed significantly between days at early ages and became more similar as pigs aged, as revealed by dissimilarity and distance metrics. Dirichlet multinomial mixtures analysis supported gradient microbial clustering in longitudinal pig fecal samples, and we found that the early samples spread to more clusters than those from older pigs. Random forest regression identified 30 operational taxonomic units as potential microbial biomarkers for modeling swine gut microbiota development. External validation showed this model could be generalized to future microbiome studies conducted in suckling and weaning pigs and adds to the toolkit to quantify community succession. IMPORTANCE The swine gut microbiome undergoes an age-dependent assembly pattern with a developmental phase at early ages and a stabilization phase at later ages. Shorter time intervals and a wider range of data sources provided a clearer understanding of the gut microbiota colonization and succession and their associations with pig growth and development. The rapidly changing microbiota of suckling and weaning pigs implies potential time targets for growth and health regulation through gut microbiota manipulation. Since swine gut microbiota development is predictable, swine microbiota age can be calculated and compared between animal treatment groups rather than relying only on static time-matched comparisons.

Project description:BackgroundA bidirectional kidney-gut axis was described in patients with chronic kidney disease (CKD). On the one hand, gut dysbiosis could promote CKD progression, but on the other hand, studies reported specific gut microbiota alterations linked to CKD. Therefore, we aimed to systematically review the literature on gut microbiota composition in CKD patients, including those with advanced CKD stages and end-stage kidney disease (ESKD), possibilities to shift gut microbiota, and its impact on clinical outcomes.Materials and methodsWe performed a literature search in MEDLINE, Embase, Scopus, and Cochrane databases to find eligible studies using pre-specified keywords. Additionally, key inclusion and exclusion criteria were pre-defined to guide the eligibility assessment.ResultsWe retrieved 69 eligible studies which met all inclusion criteria and were analyzed in the present systematic review. Microbiota diversity was decreased in CKD patients as compared to healthy individuals. Ruminococcus and Roseburia had good power to discriminate between CKD patients and healthy controls (AUC = 0.771 and AUC = 0.803, respectively). Roseburia abundance was consistently decreased in CKD patients, especially in those with ESKD (p < 0.001). A model based on 25 microbiota dissimilarities had an excellent predictive power for diabetic nephropathy (AUC = 0.972). Several microbiota patterns were observed in deceased ESKD patients as compared to the survivor group (increased Lactobacillus, Yersinia, and decreased Bacteroides and Phascolarctobacterium levels). Additionally, gut dysbiosis was associated with peritonitis and enhanced inflammatory activity. In addition, some studies documented a beneficial effect on gut flora composition attributed to synbiotic and probiotic therapies. Large randomized clinical trials are required to investigate the impact of different microbiota modulation strategies on gut microflora composition and subsequent clinical outcomes.ConclusionsPatients with CKD had an altered gut microbiome profile, even at early disease stages. Different abundance at genera and species levels could be used in clinical models to discriminate between healthy individuals and patients with CKD. ESKD patients with an increased mortality risk could be identified through gut microbiota analysis. Modulation therapy studies are warranted.

Dataset Information

Predictable modulation of cancer treatment outcomes by the gut microbiota.

Publications

Predictable modulation of cancer treatment outcomes by the gut microbiota.

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