Distinct microbes, metabolites, and ecologies define the microbiome in deficient and proficient mismatch repair colorectal cancers

Abstract

Background: Links between colorectal cancer (CRC) and the gut microbiome have been established, but the specific microbial species and their role in carcinogenesis remain an active area of inquiry. Our understanding would be enhanced by better accounting for tumor subtype, microbial community interactions, metabolism, and ecology.

Methods: We collected paired colon tumor and normal-adjacent tissue and mucosa samples from 83 individuals who underwent partial or total colectomies for CRC. Mismatch repair (MMR) status was determined in each tumor sample and classified as either deficient MMR (dMMR) or proficient MMR (pMMR) tumor subtypes. Samples underwent 16S rRNA gene sequencing and a subset of samples from 50 individuals were submitted for targeted metabolomic analysis to quantify amino acids and short-chain fatty acids. A PERMANOVA was used to identify the biological variables that explained variance within the microbial communities. dMMR and pMMR microbial communities were then analyzed separately using a generalized linear mixed effects model that accounted for MMR status, sample location, intra-subject variability, and read depth. Genome-scale metabolic models were then used to generate microbial interaction networks for dMMR and pMMR microbial communities. We assessed global network properties as well as the metabolic influence of each microbe within the dMMR and pMMR networks.

Results: We demonstrate distinct roles for microbes in dMMR and pMMR CRC. Bacteroides fragilis and sulfidogenic Fusobacterium nucleatum were significantly enriched in dMMR CRC, but not pMMR CRC. These findings were further supported by metabolic modeling and metabolomics indicating suppression of B. fragilis in pMMR CRC and increased production of amino acid proxies for hydrogen sulfide in dMMR CRC.

Conclusions: Integrating tumor biology and microbial ecology highlighted distinct microbial, metabolic, and ecological properties unique to dMMR and pMMR CRC. This approach could critically improve our ability to define, predict, prevent, and treat colorectal cancers.

Fig. 1

Top 3 microbes significantly enriched in tumor as compared to normal samples (colon tissue and mucosa) in individuals with (a, b, c) dMMR or (d, e, f) pMMR CRC. For full results, please see Additional file 1: Tables S2-S4. Notably, the top 3 microbes enriched in dMMR CRC tumor samples were not enriched at all in pMMR CRC and vice versa. Y-axis is square root transformed. See Additional file 1: Figure S3 for stratification of these results by tumor location

Fig. 2

a Hydrogen sulfide flux predicted based on community metabolic modeling. Flux was predicted in millimol per gram dry weight of bacteria per hour. b Amino acid proxies for hydrogen sulfide were quantified using UPLC-MS on dMMR and pMMR tumor and normal-adjacent colon tissue samples (Kruskal-Wallis followed by Dunn’s Test for posthoc comparisons: *p < 0.05; **p < 0.0005, ***p < 0.0005, ****p < 0.00005)

Fig. 3

Microbial influence networks for a dMMR and b pMMR microbial communities. Node size indicates a microbe’s metabolic influence over other microbes. Edges, which are directional and weighted according to the magnitude of their influence, indicate how one microbe affects the growth rate of another. Grey edges indicate a positive interaction, i.e., predicted increase in growth when paired, while red edges indicate a negative interaction, i.e., predicted suppression in growth when paired

Fig. 4

Predicted influence of other microbes (weighted influence score) on B. fragilis growth, stratified by MMR status. A negative influence score indicates microbial community suppression of B. fragilis growth. B. fragilis is significantly more suppressed in pMMR microbial communities (tumor and normal) as compared to dMMR microbial communities (Wilcoxon rank sum test p < 0.001)