Epidemiologic studies of the human microbiome and cancer

Abstract

The human microbiome, which includes the collective genome of all bacteria, archaea, fungi, protists, and viruses found in and on the human body, is altered in many diseases and may substantially affect cancer risk. Previously detected associations of individual bacteria (e.g., Helicobacter pylori), periodontal disease, and inflammation with specific cancers have motivated studies considering the association between the human microbiome and cancer risk. This short review summarises microbiome research, focusing on published epidemiological associations with gastric, oesophageal, hepatobiliary, pancreatic, lung, colorectal, and other cancers. Large, prospective studies of the microbiome that employ multidisciplinary laboratory and analysis methods, as well as rigorous validation of case status, are likely to yield translational opportunities to reduce cancer morbidity and mortality by improving prevention, screening, and treatment.

Figure 1

Schema for a human microbiome study. To conduct a human microbiome study, it is important to develop a well-formulated hypothesis, apply a valid study design, and collect requisite covariate data. Relevant specimens (e.g., oral, faecal, tissue, or other applicable samples) should be collected and promptly frozen or chemically stabilised. Once the DNA is extracted, currently there are two typical methods for sequencing: 16S rRNA gene sequencing (in blue) or shotgun metagenomic sequencing (in red). To date, most epidemiologic-scale studies profile the microbiome by amplifying and sequencing only the prokaryote 16S rRNA gene. Once the 16S rRNA sequencing is completed, the data are often processed using various publically available tools that are used to cluster the sequences into operational taxonomic units (OTUs) and to assign taxonomy using public sequence databases. For shotgun metagenomic sequencing, the DNA is sheared and then all the fragments are sequenced. From this type of data a variety of bioinformatic processing can be conducted, but often the short reads are used to cluster OTUs and assign taxonomy, similar to 16S, but also to determine the functional capabilities of the genes present by mapping the reads to public gene databases. For both 16S rRNA and shotgun metagenomic sequencing, study participants can be compared by alpha diversity (i.e., within participant diversity) and beta diversity (i.e., between participant diversity) metrics. For alpha diversity, conventional statistical methods are often used. Typically, random sampling for a standardised number of OTUs (i.e., rarefaction) is conducted to minimise bias due to amplification or sampling efficiency and then analyses include adjustment for taxa abundances in order to minimise influence of rare taxa. For associations with the entire microbial community, beta diversity analyses are based on a matrix of distances between all pairs of specimens, followed by principle coordinate analysis and higher level statistics.