Predictable modulation of cancer treatment outcomes by the gut microbiota

Microbiome

Jun Li¹  Ruben Vazquez-Uribe²  Lejla Imamovic²  Scott Quainoo²  Morten O. A. Sommer²  Maria Sørensen²  Billy K. C. Chow³  Aimin Xu⁴  Jin Li⁴  Gianni Panagiotou⁵  Yoshitaro Heshiki⁵  Yueqiong Ni⁶  Glen J. Weiss⁷ 

[1] Department of Infectious Diseases and Public Health, The Jockey Club College of Veterinary Medicine and Life Sciences, City University of Hong Kong;Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark;School of Biological Sciences, Faculty of Sciences, The University of Hong Kong;State Key Laboratory of Pharmaceutical Biotechnology, University of Hong Kong;Systems Biology & Bioinformatics Unit, Leibniz Institute for Natural Product Research and Infection Biology, Hans Knöll Institute;Systems Biology and Bioinformatics Group, School of Biological Sciences, Faculty of Sciences, The University of Hong Kong;University of Arizona College of Medicine-Phoenix;

关键词: Gut microbiota;    Cancer;    Treatment outcome;    Machine learning;   

DOI  :  10.1186/s40168-020-00811-2

来源: DOAJ

【 摘 要 】

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-γ 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.

【 授权许可】

Unknown