Gut microbiome identifies risk for colorectal polyps. Issue 1 (27th May 2019)

• Record Type: Journal Article
• Authors: Dadkhah, Ezzat; Sikaroodi, Masoumeh; Korman, Louis; Hardi, Robert; Baybick, Jeffrey; Hanzel, David; Kuehn, Gregory; Kuehn, Thomas; Gillevet, Patrick M
• Is Part Of: BMJ open gastroenterology. Volume 6:Issue 1(2019)
• Publication Date: 2019-05-27
• Subjects: microbiome -- colorectal cancer -- polyp -- biopsy -- stool -- sequencing -- machine learning -- classification -- risk assessment; Gastroenterology -- Periodicals; 616.33005
• DOI: 10.1136/bmjgast-2019-000297
• Languages: English
• ISSNs: 2054-4774

Abstract

Abstract : Objective: To characterise the gut microbiome in subjects with and without polyps and evaluate the potential of the microbiome as a non-invasive biomarker to screen for risk of colorectal cancer (CRC). Design: Presurgery rectal swab, home collected stool, and sigmoid biopsy samples were obtained from 231 subjects undergoing screening or surveillance colonoscopy. 16S rRNA analysis was performed on 552 samples (231 rectal swab, 183 stool, 138 biopsy) and operational taxonomic units (OTU) were identified using UPARSE. Non-parametric statistical methods were used to identify OTUs that were significantly different between subjects with and without polyps. These informative OTUs were then used to build classifiers to predict the presence of polyps using advanced machine learning models. Results: We obtained clinical data on 218 subjects (87 females, 131 males) of which 193 were White, 21 African-American, and 4 Asian-American. Colonoscopy detected polyps in 56% of subjects. Modelling of the non-invasive home stool samples resulted in a classification accuracy >75% for Naïve Bayes and Neural Network models using informative OTUs. A naïve holdout analysis performed on home stool samples resulted in an average false negative rate of 11.5% for the Naïve Bayes and Neural Network models, which was reduced to 5% when the two models were combined. Conclusion: Gut microbiome analysis combined with advanced machine learning represents a promising approach to screen patients for the presence of polyps, with the potential to optimise the use of colonoscopy, reduce morbidity and mortality associated with CRC, and reduce associated healthcare costs.