Predictable modulation of cancer treatment outcomes by the gut microbiota.
Yoshitaro HeshikiRuben Vazquez-UribeJin LiYueqiong NiScott QuainooLejla ImamovicJun LiMaria SørensenBilly K C ChowGlen J WeissAimin XuMorten O A SommerGianni PanagiotouPublished in: Microbiome (2020)
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.
Keyphrases
- papillary thyroid
- immune response
- machine learning
- squamous cell
- cancer therapy
- end stage renal disease
- chronic kidney disease
- endothelial cells
- dendritic cells
- ejection fraction
- lymph node metastasis
- microbial community
- newly diagnosed
- poor prognosis
- drug delivery
- oxidative stress
- big data
- childhood cancer
- toll like receptor
- bone marrow
- high resolution
- mesenchymal stem cells
- epidermal growth factor receptor
- young adults
- long non coding rna
- induced pluripotent stem cells
- artificial intelligence
- diabetic rats
- wastewater treatment
- replacement therapy
- patient reported
- single molecule
- mass spectrometry