Leveraging Existing 16S rRNA Gene Surveys To Identify Reproducible Biomarkers in Individuals with Colorectal Tumors - PubMed (original) (raw)

Meta-Analysis

Leveraging Existing 16S rRNA Gene Surveys To Identify Reproducible Biomarkers in Individuals with Colorectal Tumors

Marc A Sze et al. mBio. 2018.

Erratum in

Abstract

An increasing body of literature suggests that both individual and collections of bacteria are associated with the progression of colorectal cancer. As the number of studies investigating these associations increases and the number of subjects in each study increases, a meta-analysis to identify the associations that are the most predictive of disease progression is warranted. We analyzed previously published 16S rRNA gene sequencing data collected from feces and colon tissue. We quantified the odds ratios (ORs) for individual bacterial taxa that were associated with an individual having tumors relative to a normal colon. Among the fecal samples, there were no taxa that had significant ORs associated with adenoma and there were 8 taxa with significant ORs associated with carcinoma. Similarly, among the tissue samples, there were no taxa that had a significant OR associated with adenoma and there were 3 taxa with significant ORs associated with carcinoma. Among the significant ORs, the association between individual taxa and tumor diagnosis was equal to or below 7.11. Because individual taxa had limited association with tumor diagnosis, we trained Random Forest classification models using only the taxa that had significant ORs, using the entire collection of taxa found in each study, and using operational taxonomic units defined based on a 97% similarity threshold. All training approaches yielded similar classification success as measured using the area under the curve. The ability to correctly classify individuals with adenomas was poor, and the ability to classify individuals with carcinomas was considerably better using sequences from feces or tissue.IMPORTANCE Colorectal cancer is a significant and growing health problem in which animal models and epidemiological data suggest that the colonic microbiota have a role in tumorigenesis. These observations indicate that the colonic microbiota is a reservoir of biomarkers that may improve our ability to detect colonic tumors using noninvasive approaches. This meta-analysis identifies and validates a set of 8 bacterial taxa that can be used within a Random Forest modeling framework to differentiate individuals as having normal colons or carcinomas. When models trained using one data set were tested on other data sets, the models performed well. These results lend support to the use of fecal biomarkers for the detection of tumors. Furthermore, these biomarkers are plausible candidates for further mechanistic studies into the role of the gut microbiota in tumorigenesis.

Keywords: 16S rRNA; adenoma; biomarkers; carcinoma; colorectal cancer; diagnostic; feces; microbiome.

Copyright © 2018 Sze and Schloss.

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Figures

FIG 1

FIG 1

Comparison of alpha diversity indices that were significant between individuals with normal colons and those with adenomas or carcinomas using data collected from fecal samples. (A) Comparison of evenness between individuals with normal colons and adenomas. (B) Comparison of evenness between individuals with normal colons and carcinomas. (C) Comparison of Shannon diversity values between individuals with normal colons and carcinomas. Blue points represent individuals with normal colons, yellow points represent individuals with adenomas (A), and red points represent individuals with carcinomas (B and C). The black lines represent the median value for each group.

FIG 2

FIG 2

Comparison of odds ratios calculated using alpha diversity community metrics associated with the presence of adenomas (A) or carcinomas (B) relative to those in individuals with normal colons using data collected from stool samples.

FIG 3

FIG 3

AUC values when classifying individuals as having normal colons or carcinomas using taxa with significant ORs when using stool samples (A) and unmatched tissue samples (B). We did not identify any taxa as having a significant OR to differentiate individuals with normal colons and adenomas or using matched tissue samples. The large black circles represent the median AUC of all studies, and the smaller circles represent the individual AUC for a particular study. The dashed line denotes an AUC of 0.5.

FIG 4

FIG 4

Relative importance of taxa with significant ORs in Random Forest models for differentiating between individuals with normal colons and carcinomas using stool samples (A) or unmatched tissue samples (B). The colors indicate the Z-transformed (i.e., mean of 0.0 and standard deviation of 1.0) mean decrease in accuracy values calculated from the model for each study. The taxa are ranked by their mean Z-score-transformed mean decrease in accuracy.

FIG 5

FIG 5

Comparison of Random Forest modeling approaches to classify individuals as having normal colons or adenomas (A) or carcinomas (B) when training the models using the taxa with significant ORs, all taxa in a community, or all OTUs in a community when using stool samples. No taxon had a significant OR associated with the presence of adenomas using stool samples. The black line represents the median AUC for the respective group. The dashed gray line indicates an AUC of 0.5.

FIG 6

FIG 6

Testing of Random Forest models to classify individuals as having normal colons or adenomas (A) or carcinomas (B) when using sequence data obtained from stool samples. Models were trained on data from each study (Fig. 5) and tested on the other studies. The black lines represent the median AUC of all test AUCs for a specific study. The dashed gray line represents the AUC at 0.5.

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