Leveraging Existing 16S rRNA Gene Surveys To Identify Reproducible Biomarkers in Individuals with Colorectal Tumors.
Sze MA, Schloss PD
MBio. Jun 2018
COMMENT: Colorectal cancer (CRC) is a growing worldwide health problem in which the microbiota has been hypothesized to have a role in disease progression. Numerous studies using murine models of CRC have shown the importance of both individual microbes and the overall community in tumorigenesis.
Recently, there has been an intense focus on identifying microbiota-based biomarkers, yielding a seemingly endless number of candidate taxa. Some studies point toward mouth-associated genera such as Fusobacterium, Peptostreptococcus, Parvimonas, and Porphyromonas that are enriched in people with carcinomas. Other studies have identified members of Akkermansia, Bacteroides, Enterococcus, Escherichia, Klebsiella, Mogibacterium, Streptococcus, and Providencia.
In this article Sze & Schloss from the University of Michigan analyze previously published 16S rRNA gene sequencing data collected from feces (n = 1,737) and colon tissue (492 samples from 350 individuals) from 14 studies. They 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.
Because individual taxa had limited association with tumor diagnosis, they 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.
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.