The role of the microbiome in cancer development and therapy

Abstract

Answer questions and earn CME/CNE The human body harbors enormous numbers of microbiota that influence cancer susceptibility, in part through their prodigious metabolic capacity and their profound influence on immune cell function. Microbial pathogens drive tumorigenesis in 15% to 20% of cancer cases. Even larger numbers of malignancies are associated with an altered composition of commensal microbiota (dysbiosis) based on microbiome studies using metagenomic sequencing. Although association studies cannot distinguish whether changes in microbiota are causes or effects of cancer, a causative role is supported by rigorously controlled preclinical studies using gnotobiotic mouse models colonized with one or more specific bacteria. These studies demonstrate that microbiota can alter cancer susceptibility and progression by diverse mechanisms, such as modulating inflammation, inducing DNA damage, and producing metabolites involved in oncogenesis or tumor suppression. Evidence is emerging that microbiota can be manipulated for improving cancer treatment. By incorporating probiotics as adjuvants for checkpoint immunotherapy or by designing small molecules that target microbial enzymes, microbiota can be harnessed to improve cancer care. CA Cancer J Clin 2017;67:326-344. © 2017 American Cancer Society.

Keywords: cancer; dysbiosis; microbiome; prebiotics; probiotics.

Conflict of interest statement

Conflict of Interest: One author (MRR) has inventions licensed to and equity ownership in Symberix, Inc. a pharmaceutical company creating microbiome-targeted therapeutics.

© 2017 American Cancer Society.

Figures

Figure 1. Microbiome research strategy

(A) Flow chart of metagenomic sequence analysis. Biological material (buccal swabs, fecal samples, tissue biopsies, saliva) are procured from disease cases and healthy controls (panel 1); DNA is prepared from each sample (panel 2); Next-generation DNA sequencing (NGS) is performed to obtain targeted (16S rRNA hypervariable regions) or whole-genome shotgun (WGS) sequence reads (panel 3); Computational assembly and analysis of microbial sequence reads allows the microbial community structure to be assessed for each sample (panel 4); Principal Component Analysis (PCA) is a statistical procedure that compares the degree of relatedness of sequence reads between samples and illustrates the relationship between cases (red circles) and controls (blue circles), which often form distinct clusters with minimal overlap (panel 5 upper). Other computational methods allow the abundance of different microbial taxa to be quantified when compared to databases (panel 5 lower). Analysis of 16S data yields the relative abundance of Operational Taxanomic Units (OTUs) and their phylogenetic relationships. Analysis of WGS data provides greater taxonomic resolution, down to the abundance of specific strains within a single species that vary with respect to gene content including virulence factors and single nucleotide polymorphisms (SNPs), and provides more insight into pathways. WGS provides much more information but is more expensive and computationally intensive with less complete database resources, in part, due to a limited number of reference genomes. Further details can be found in other reviews (e.g., , ). (B) Because a microbiome change between cases and controls can be either a cause or consequence of disease, gnotobiotic mouse models are utilized to evaluate the function of specific microbiota in the host. Germfree mouse models, which were originally obtained via C-section delivery but are now obtained by embryo transfer into germfree surrogate females (panel 1), are colonized by oral gavage (panel 2) with one bacterial strain (monoassociated), a consortium of specific bacteria (polyassociated), or complex microbial communities (e.g., fecal microbiota transplants) while maintained in gnotobiotic isolators (panel 3).

Figure 2. Gut microbiota have differential effects on tumorigenesis in the GI tract and at distant sites

The colon is depicted with a single layer of intestinal epithelial cells (yellow) separating commensal bacteria (black shapes) in the lumen above from immune cells (4 different colors) in the underlying lamina propria. The bacteria can have local effects that are either oncogenic (left box) or tumor suppressive (center box) for colorectal cancer, or they can have distal effects mediated by the circulation that are oncogenic or tumor suppressive for cancer at other anatomical sites (right box). Some of the general effects that gut microbiota can have on tumorigenesis are numbered. Left box: 1, Production of putative oncometabolites such as hydrogen sulfide; 2, Impairment of barrier function, which increases the exposure of immune cells to bacterial endotoxins (e.g., LPS) and antigens; 3, Direct effects of bacterial metabolites and antigens on immune cells to stimulate inflammation by altering immune cell subsets (e.g., the effect of segmented filamentous bacteria or SFB on TH17 cells) and hyperactivating immune cell responses via pro-inflammatory cytokines (e.g., IL-6); 4, The presence of virulence factors including pathogenicity islands, which distinguish pathogens from commensals such as E. coli pks, can exert multiple effects including the induction of DNA damage and aberrant Wnt signaling. Center box: 5, Production of putative tumor-suppressive metabolites such as butyrate, which functions via multiple mechanisms; 6, Maintenance of barrier function; 7, Direct effects on immune cells to prevent inflammation by altering immune cells subsets (e.g. the ability of butyrate to induce TReg cells) and dampening the immune cell response via immunosuppressive cytokines (e.g., IL-10); 8, Competitive exclusion of pathogenic bacteria similar to the prevention of lethal C. difficile infections. Right box: Gut microbiota can also have oncogenic or tumor-suppressive effects at distal sites in the body via circulation of microbiota, microbial metabolites, activated or suppressed immune cells, and cytokines.

Figure 3. Microbial mechanisms of oncogenesis and tumor suppression

Microbiota can contribute to oncogenesis (top, black) or tumor suppression (bottom, white) by a variety of molecular mechanisms that are listed at the end of each line. The mechanisms are listed from left to right in a symmetrical manner (top-bottom) to make it easier to appreciate that some are diametrically opposed. The mechanisms are carried out by a variety of microbial gene products, metabolites, and immune modulators, some of which are indicated in smaller font along each arrow. See text for details. Question marks indicate speculative mechanisms that have not yet been characterized.