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. 2021 Sep 2;22(17):9549.
doi: 10.3390/ijms22179549.

Relationships of Gut Microbiota Composition, Short-Chain Fatty Acids and Polyamines with the Pathological Response to Neoadjuvant Radiochemotherapy in Colorectal Cancer Patients

Lidia Sánchez-Alcoholado  1 , Aurora Laborda-Illanes  1 , Ana Otero  2 , Rafael Ordóñez  2 , Alicia González-González  1 , Isaac Plaza-Andrades  1 , Bruno Ramos-Molina  3 , Jaime Gómez-Millán  2 , María Isabel Queipo-Ortuño  1
Affiliations

Relationships of Gut Microbiota Composition, Short-Chain Fatty Acids and Polyamines with the Pathological Response to Neoadjuvant Radiochemotherapy in Colorectal Cancer Patients

Lidia Sánchez-Alcoholado et al. Int J Mol Sci. .

Abstract

Emerging evidence has suggested that dysbiosis of the gut microbiota may influence the drug efficacy of colorectal cancer (CRC) patients during cancer treatment by modulating drug metabolism and the host immune response. Moreover, gut microbiota can produce metabolites that may influence tumor proliferation and therapy responsiveness. In this study we have investigated the potential contribution of the gut microbiota and microbial-derived metabolites such as short chain fatty acids and polyamines to neoadjuvant radiochemotherapy (RCT) outcome in CRC patients. First, we established a profile for healthy gut microbiota by comparing the microbial diversity and composition between CRC patients and healthy controls. Second, our metagenomic analysis revealed that the gut microbiota composition of CRC patients was relatively stable over treatment time with neoadjuvant RCT. Nevertheless, treated patients who achieved clinical benefits from RTC (responders, R) had significantly higher microbial diversity and richness compared to non-responder patients (NR). Importantly, the fecal microbiota of the R was enriched in butyrate-producing bacteria and had significantly higher levels of acetic, butyric, isobutyric, and hexanoic acids than NR. In addition, NR patients exhibited higher serum levels of spermine and acetyl polyamines (oncometabolites related to CRC) as well as zonulin (gut permeability marker), and their gut microbiota was abundant in pro-inflammatory species. Finally, we identified a baseline consortium of five bacterial species that could potentially predict CRC treatment outcome. Overall, our results suggest that the gut microbiota may have an important role in the response to cancer therapies in CRC patients.

Keywords: SCFAs; colorectal cancer; gut microbiota; gut permeability; radiochemotherapy; treatment outcome.

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Conflict of interest statement

The authors declare no conflict of interests.

Figures

Figure 1
Figure 1
Relative abundance at phylum (A) and genera (B) levels of differentially abundant bacteria in the stool samples of CRC patients at baseline (CRC-T0) and healthy controls (HC). * p < 0.05, ** p < 0.001.
Figure 2
Figure 2
Gut microbiota diversity and richness at baseline and during RTC treatment and post-treatment points in CRC patients. (A) Shannon index (p = 0.75); (B) Chao1 index (p = 0.61); (C) principal component analysis representation based on Bray–Curtis distance matrix of patient distribution based on bacterial genera composition at baseline and during RTC treatment and at post-treatment points (p = 0.716). The first two coordinates are plotted with the percentage of variability, which is explained and indicated on the axis.
Figure 3
Figure 3
Heatmap diagram of the gut microbiota composition at different taxa levels for baseline (CRC-T0), treatment points with neoadjuvant RCT (CRC-T1, CRC-T2 and CRC-T3), and the healthy control subjects (HC). The 29 phylum and genera that were shared by all of the tested samples (core microbiome) are displayed.
Figure 4
Figure 4
Comparison of alpha and beta diversity in CRC patients according to their response to therapy. (A) Shannon index; (B) Chao1index; (C) principal component plot based on the Bray–Curtis distance matrix and the Jaccard indices from the responder (R) and non-responder (NR) patients at genus-level. The first two coordinates are plotted with the percentage of variability, which is explained and indicated on the axis.
Figure 5
Figure 5
Heatmap of the fecal microbiota composition at the phylum and family levels in the responder (R) and non-responder (NR) patients (A). Relative abundance at phylum (B) and family (C) levels of differentially abundant OTUs in the stool samples of N patients compared to the NR patients. * p < 0.05.
Figure 5
Figure 5
Heatmap of the fecal microbiota composition at the phylum and family levels in the responder (R) and non-responder (NR) patients (A). Relative abundance at phylum (B) and family (C) levels of differentially abundant OTUs in the stool samples of N patients compared to the NR patients. * p < 0.05.
Figure 6
Figure 6
Heatmap of the fecal microbiota composition at genera level in the responder (R) and non-responder (NR) patients (A). Relative abundance at genera level of differentially abundant OTUs in the stool samples of the N patients compared to the NR patients. * p < 0.05 (B).
Figure 7
Figure 7
Receiver operating characteristic (ROC) curve based on the random forest classifier constructed using microbial variables (Ruminococcus albus, Bifidobacterium bifidum, Faecalibacterium prausnitzii, Fusobacterium nucleatum, and Bacteroides fragilis). (A) Training cohort. The area under the ROC curve (AUC) was 0.95, and the 95% confidence interval (CI) was 0.901–1 for the R patients (green), and the AUG was 0.92 and 95% the IC was 0.827–1 for the NR patients (red). (B) Validation cohort. The AUG was 0.93 and the 95% IC was 0.877–0.987 for the R patients (green), and the AUG was 0.91 and 95% the IC was 0.835–0.984 for the NR patients (red).
Figure 8
Figure 8
Heatmap of bacterial gene functional predictions using the PICRUSt algorithm from the fecal samples from the responder (R) patients and the non-responder (NR) patients.
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