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. 2020 Mar 5;8(1):28.
doi: 10.1186/s40168-020-00811-2.

Predictable Modulation of Cancer Treatment Outcomes by the Gut Microbiota

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Free PMC article

Predictable Modulation of Cancer Treatment Outcomes by the Gut Microbiota

Yoshitaro Heshiki et al. Microbiome. .
Free PMC article

Abstract

The gut microbiota has the potential to influence the efficacy of cancer therapy. Here, we investigated the contribution of the intestinal microbiome on treatment outcomes in a heterogeneous cohort that included multiple cancer types to identify microbes with a global impact on immune response. Human gut metagenomic analysis revealed that responder patients had significantly higher microbial diversity and different microbiota compositions compared to non-responders. A machine-learning model was developed and validated in an independent cohort to predict treatment outcomes based on gut microbiota composition and functional repertoires of responders and non-responders. Specific species, Bacteroides ovatus and Bacteroides xylanisolvens, were positively correlated with treatment outcomes. Oral gavage of these responder bacteria significantly increased the efficacy of erlotinib and induced the expression of CXCL9 and IFN-γ in a murine lung cancer model. These data suggest a predictable impact of specific constituents of the microbiota on tumor growth and cancer treatment outcomes with implications for both prognosis and therapy.

Keywords: Cancer; Gut microbiota; Machine learning; Treatment outcome.

Conflict of interest statement

All authors have approved the final version of the manuscript. The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
Taxonomic analysis of intestinal microbiota of cancer patients. a Sample collection scheme and dendrogram based on Bray-Curtis dissimilarity. b Alpha diversity (Shannon index) of the gut microbiota in responders (R) and non-responders (NR). c Non-metric multidimensional scaling (NMDS) plot of R and NR in human cancer samples based on the gut microbial compositions using Bray-Curtis dissimilarities (ANOSIM p = 0.0001). Intrapatient samples are linked to each other. d NMDS plot of R, NR, and HMP samples based on the gut microbial compositions at the species level using Bray-Curtis dissimilarities (ANOSIM p = 0.0001). e Phylogenetic composition of cancer samples at the phylum level. fFirmicutes/Bacteroidetes (F/B) ratio of cancer samples. g Heatmap of differentially abundant species detected in the comparison of R and NR (FDR p < 0.05, Wilcoxon rank-sum test). R-associated and NR-associated bacteria validated in mouse model are shown in red and cyan asterisks, respectively
Fig. 2
Fig. 2
Bacterial species co-abundance networks. a Network in responders. b Network in non-responders. Each node represents a species and edges correspond to significant species-species associations as inferred by BAnOCC [26]. The size of each node is proportional to the mean relative abundance. The 95% credible interval criteria were used to assess significance, and estimated correlations were then filtered with the correlation coefficient ≥ 0.4. The shown subnetworks were made by extracting the edges that are connected with B. ovatus, B. xylanisolvens, C. symbiosum, and R. gnavus, which are further highlighted
Fig. 3
Fig. 3
Functional profiles of intestinal microbiota of cancer patients. a NMDS plot of cancer samples based on KEGG pathway abundances using Bray-Curtis dissimilarities (ANOSIM p = 0.0299). b Differentially abundant KEGG pathways (FDR p < 0.1, Wilcoxon rank-sum test) detected in the comparison of responders (R) and non-responders (NR). c CAZy class comparison between R and NR. *p < 0.1, **p < 0.05. d Performance of the C5.0 decision tree models in classifying R and NR
Fig. 4
Fig. 4
Increased anti-tumor efficacy of chemotherapy in the presence of B. ovatus and B. xylanisolvens.a Experimental design: male 6-week C57BL6/N mice (n = 5–8) were treated with antibiotic cocktail in drinking water for 1 week before bacterial oral gavage. Control PBS, B. ovatus and B. xylanisolvens, and C. symbiosum and R. gnavus were orally gavaged into mice 1 week prior to tumor cell inoculation. A total of 107 Lewis lung cancer cells in 200 μl PBS were subcutaneously injected into the mice to induce tumor formation. Mice were treated with erlotinib (60 mg/kg body weight) once the tumor size reached approximately 250–500 mm3. Time in days is relative to tumor cells injection. b Tumor size measurement at day 14. c Tumor growth curve after Lewis lung carcinoma cell inoculation. Dark dots indicate the application of erlotinib. d, e CRL5883 bronchoalveolar carcinoma cell line was cultured for 72 h in the presence of erlotinib (d) or drug-free (e) supernatants from R (B. xylanisolvens and B. ovatus) or NR (R. gnavus and C. symbiosum) bacteria species. d Non-linear regression curves showing cell viability as percentage of cell control viability. Bacterial supernatants had n = 4, GAM control had n = 2, and cell control had n = 10. e Cell viability is presented as percentage of cell control viability. Colored circles show individual data points. Outliers were identified and removed by the ROUT method (Q = 0.1%). Supernatants had n = 3–4 and cell control had n = 16. All data are mean ± SEM. Significant differences were identified via unpaired t test (*p < 0.05, **p < 0.005). f, g Tumor expressions of chemokines involved in the recruitment of T cells (f), myeloid cells, and cytotoxic T cells (g) by real-time PCR (normalized against GAPDH). Data are presented as mean ± SEM. *p < 0.05, **p < 0.01, ***p < 0.001

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