Pathobionts in the tumour microbiota predict survival following resection for colorectal cancer

Abstract

Background and aims: The gut microbiota is implicated in the pathogenesis of colorectal cancer (CRC). We aimed to map the CRC mucosal microbiota and metabolome and define the influence of the tumoral microbiota on oncological outcomes.

Methods: A multicentre, prospective observational study was conducted of CRC patients undergoing primary surgical resection in the UK (n = 74) and Czech Republic (n = 61). Analysis was performed using metataxonomics, ultra-performance liquid chromatography-mass spectrometry (UPLC-MS), targeted bacterial qPCR and tumour exome sequencing. Hierarchical clustering accounting for clinical and oncological covariates was performed to identify clusters of bacteria and metabolites linked to CRC. Cox proportional hazards regression was used to ascertain clusters associated with disease-free survival over median follow-up of 50 months.

Results: Thirteen mucosal microbiota clusters were identified, of which five were significantly different between tumour and paired normal mucosa. Cluster 7, containing the pathobionts Fusobacterium nucleatum and Granulicatella adiacens, was strongly associated with CRC (P_FDR = 0.0002). Additionally, tumoral dominance of cluster 7 independently predicted favourable disease-free survival (adjusted p = 0.031). Cluster 1, containing Faecalibacterium prausnitzii and Ruminococcus gnavus, was negatively associated with cancer (P_FDR = 0.0009), and abundance was independently predictive of worse disease-free survival (adjusted p = 0.0009). UPLC-MS analysis revealed two major metabolic (Met) clusters. Met 1, composed of medium chain (MCFA), long-chain (LCFA) and very long-chain (VLCFA) fatty acid species, ceramides and lysophospholipids, was negatively associated with CRC (P_FDR = 2.61 × 10^-11); Met 2, composed of phosphatidylcholine species, nucleosides and amino acids, was strongly associated with CRC (P_FDR = 1.30 × 10^-12), but metabolite clusters were not associated with disease-free survival (p = 0.358). An association was identified between Met 1 and DNA mismatch-repair deficiency (p = 0.005). FBXW7 mutations were only found in cancers predominant in microbiota cluster 7.

Conclusions: Networks of pathobionts in the tumour mucosal niche are associated with tumour mutation and metabolic subtypes and predict favourable outcome following CRC resection. Video Abstract.

Keywords: Colorectal cancer; Gut microbiota; Metabolome; Metataxonomics.

Fig. 1

Microbiota analysis of the colorectal cancer mucosa a Chao richness paired comparison between tumour and paired normal mucosa (Wilcoxon matched-pairs signed-rank test p = 0.41). b Shannon diversity paired comparison between tumour and paired normal mucosa (Wilcoxon matched-pairs signed-rank test p = 0.99). c Hierarchical clustering of microbiota. Y-axis labels are species or higher taxonomic rank if species data is not known; x-axis labels show the order. d Beta diversity displayed as a nonmetric dimensional scaling (NMDS) plot of weighted UniFrac distances for normal mucosa (blue) and tumour (red). Ellipses drawn to indicate 95% confidence intervals. R² = 0.027; p = 0.014 (adonis PERMANOVA). e Paired comparison between tumour and tumour-paired normal samples for each identified microbiota cluster

Fig. 2

Survival analysis demonstrates prognostic utility of colorectal cancer mucosal microbiota clustering. Kaplan–Meier curves illustrating the difference in disease-free survival in groups stratified by CRC mucosal abundance of microbiota (a cluster 1 microbiota; b cluster 7 microbiota) and by established prognostic factors (c AJCC stage; d tumour differentiation). Time is measured in months since primary tumour resection. For microbiota clusters, individuals were split at the proportional median and classified as “low” (red) and “high” (blue) expressors of each cluster of microbiota. Log-rank test used to generate p-values

Fig. 3

Metabolomic analysis of the colorectal cancer mucosa. a Principal components analysis of metabolomic data for tumour (blue) and paired normal tissue (red). b Cross-validated scores plot of the repeated measures partial least squares discriminant analysis model (goodness-of-fit R²Y = 0.95, goodness of prediction Q²Y = 0.89). Tumour represented in blue and paired normal tissue in red. c Skyline plot indicating metabolites which are significantly higher in tumour (upward blue arrows) or higher in paired normal mucosa (downward red arrows). The dotted horizontal lines indicate the cut-off for the P_FDR at 5%. d Hierarchical clustering of metabolites. Fatty acids are grouped in SCFA, MCFA, LCFA and VLCFAs. e Paired comparison between tumour and paired normal mucosa samples for each identified metabolite cluster

Fig. 4

Integration of the colorectal cancer mucosal microbiota, metabolome and tumour driver mutations. a Correlation between individual microbiota and metabolites. Positive correlations shown in shades of red; negative correlations in shades of blue. Only statistically significant correlations are shown. Microbiota and metabolites are ordered by the clustering from the individual dataset-specific analyses. Microbiota clusters are labelled along the right side of the figure and metabolite clusters along the top with dotted lines indicating division of clusters. b Box and whisker plots showing median and 95% confidence intervals for cluster proportions in patients with ( +) and without ( −) target mutations of interest. **p-value < 0.01