The human gut microbiome as a screening tool for colorectal cancer

Abstract

Recent studies have suggested that the gut microbiome may be an important factor in the development of colorectal cancer. Abnormalities in the gut microbiome have been reported in patients with colorectal cancer; however, this microbial community has not been explored as a potential screen for early-stage disease. We characterized the gut microbiome in patients from three clinical groups representing the stages of colorectal cancer development: healthy, adenoma, and carcinoma. Analysis of the gut microbiome from stool samples revealed both an enrichment and depletion of several bacterial populations associated with adenomas and carcinomas. Combined with known clinical risk factors of colorectal cancer (e.g., BMI, age, race), data from the gut microbiome significantly improved the ability to differentiate between healthy, adenoma, and carcinoma clinical groups relative to risk factors alone. Using Bayesian methods, we determined that using gut microbiome data as a screening tool improved the pretest to posttest probability of adenoma more than 50-fold. For example, the pretest probability in a 65-year-old was 0.17% and, after using the microbiome data, this increased to 10.67% (1 in 9 chance of having an adenoma). Taken together, the results of our study demonstrate the feasibility of using the composition of the gut microbiome to detect the presence of precancerous and cancerous lesions. Furthermore, these results support the need for more cross-sectional studies with diverse populations and linkage to other stool markers, dietary data, and personal health information.

Figure 1. Microbial biomarkers improve accuracy of predictive models for healthy and adenoma clinical groups

A. Relative abundance of differentially abundant OTUs for all healthy (n=30; grey) and adenoma (n=30; black) subjects. The mean relative abundance is represented for each clinical group by a vertical black line. B. ROC curves for microbial biomarkers alone, clinical data alone, and microbial biomarkers with clinical data. The straight line represents the null model.

Figure 2. Microbial biomarkers improve accuracy of predictive models for healthy and carcinoma clinical groups

A. Relative abundance of differentially abundant OTUs for all healthy (n=30; grey) and carcinoma (n=30; black) subjects. The mean relative abundance is represented for each clinical group by a vertical black line. B. ROC curves for microbial biomarkers alone, clinical data alone, and microbial biomarkers with clinical data. The straight line represents the null model.

Figure 3. Microbial biomarkers improve accuracy of predictive models for differentiating between healthy subjects and those with colonic lesions

Adenoma and carcinoma subjects were combined into a single clinical group (Lesions; n=60). A. Relative abundance of differentially abundant OTUs for healthy (n=30; grey) subjects and those with lesions (n=60; black). The mean relative abundance is represented for each clinical group by a vertical black line. B. ROC curves for microbial biomarkers alone, clinical data alone, and microbial biomarkers with clinical data. The straight line represents the null model.

Figure 4. Microbial biomarkers improve accuracy of predictive models for adenoma and carcinoma clinical groups

A. Relative abundance of differentially abundant OTUs for adenoma (n=30; grey) and carcinoma (n=30; black) subjects. The mean relative abundance is represented for each clinical group by a vertical black line. B. ROC curves for microbial biomarkers alone, clinical data alone, FOBT alone, microbial biomarkers with clinical data, and microbial biomarkers with FOBT and clinical data. For each comparison, the straight line represents the null model.