Abstract
The crucial role of gut microbiota in shaping immunotherapy outcomes has prompted investigations into potential modulators. Here we show that oral administration of acarbose significantly increases the anti-tumour response to anti-PD-1 therapy in female tumour-bearing mice. Acarbose modulates the gut microbiota composition and tryptophan metabolism, thereby contributing to changes in chemokine expression and increased T cell infiltration within tumours. We identify CD8+ T cells as pivotal components determining the efficacy of the combined therapy. Further experiments reveal that acarbose promotes CD8+ T cell recruitment through the CXCL10–CXCR3 pathway. Faecal microbiota transplantation and gut microbiota depletion assays indicate that the effects of acarbose are dependent on the gut microbiota. Specifically, acarbose enhances the efficacy of anti-PD-1 therapy via the tryptophan catabolite indoleacetate, which promotes CXCL10 expression and thus facilitates CD8+ T cell recruitment, sensitizing tumours to anti-PD-1 therapy. The bacterial species Bifidobacterium infantis, which is enriched by acarbose, also improves response to anti-PD-1 therapy. Together, our study endorses the potential combination of acarbose and anti-PD-1 for cancer immunotherapy.
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Data availability
The data that support the findings of this study have been deposited in public databases. The TCGA dataset was accessed using the UCSC Xena Browser (https://xenabrowser.net/) or TIMER database(http://timer.cistrome.org/). All 16S rRNA gene sequences were provided and are available at the National Center for Biotechnology Information Sequence Read Archive database (https://www.ncbi.nlm.nih.gov/sra) with accession no. PRJNA1124223. The accession no. for the entire RNA-seq dataset reported in this paper is PRJNA1123668. Source data are provided with this paper.
Code availability
No custom codes were used in this study.
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Acknowledgements
The present study was supported by grants from the National Natural Science Foundation of China (grant no. 82203048) and Shanghai Sailing Program (grant no. 22YF1407100) to S.L.Z. and the Fujian Province Medical Innovation Project (grant no. 2021CXB024) to Z.M.W.
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S.L.Z. conceived and supervised this study. Z.M.W. and Y.W. jointly supervised this study. S.L.Z., Z.Y.Z., Q.Q.C. and Y.Y.X. performed the experiments, analysed the data and wrote the paper. X.W. and M.X.Y. participated in most of the experiments. S.L.Z. conducted bioinformatics analysis. Q.A.J. and C.C. provided suggestions in project design. S.L.Z. and Y.Y.X. wrote the paper with input from all authors. All authors read and approved the final paper.
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Extended data
Extended Data Fig. 1 The changes in gut microbiota and blood glucose in the CRC model treated with different doses of acarbose.
a-c, Changes in the α-diversity between each group was determined using the Observed species (a), and Shannon (b), and Chao index (c) (control: n = 3; 250 mg/kg acarbose: n = 3; 500 mg/kg acarbose: n = 4; 1000 mg/kg acarbose: n = 3). d, PCoA of β-diversity using the Bray–Curtis dissimilarity metric among samples of individual groups of mice in each group (control: n = 4; 250 mg/kg acarbose: n = 3; 500 mg/kg acarbose: n = 3; 1000 mg/kg acarbose: n = 3). e-g. Relative abundance of immune-regulating microbiota, including Bifidobacterium (e), Lactobacillus (f) and Akkermansia (g) between each group (control: n = 4; 250 mg/kg acarbose: n = 3; 500 mg/kg acarbose: n = 3; 1000 mg/kg acarbose: n = 3). h. Blood glucose levels between each group (control: n = 3; 250 mg/kg acarbose: n = 3; 500 mg/kg acarbose: n = 4; 1000 mg/kg acarbose: n = 3). i, Blood glucose levels between isotype IgG control, or anti-PD-1 alone or acarbose alone, and anti-PD-1+acarbose (n = 5 per group). j, Body weight surveillance of each group during the experiment (isotype IgG control: n = 6; anti-PD-1:n = 7; acarbose: n = 7; anti-PD-1+ acarbose:n = 8). Data were presented as the mean ± s.e.m. Each dot represented a biological replicate. Statistical analysis was performed using unpaired two-tailed Student’s t-test (a,c).
Extended Data Fig. 2 Pathological changes in the tumour tissues from different groups.
a-c, Representative histological analysis of tumour tissues stained by H&E (a), Ki67 (b), TUNEL assay (c). Scale bars, 100 μm (a-b); 200 μm (c). d, Quantification of proliferation index of the tumour tissues in the Ki67 staining assay (n = 3 per group). e, Quantification of apoptosis index of the tumour tissues in the TUNEL staining assay (n = 3 per group). Data were presented as the mean ± s.e.m. Each dot represented a biological replicate. Statistical analysis was performed using unpaired two-tailed Student’s t-test (d-e).
Extended Data Fig. 3 The effects of acarbose in male tumor-bearing mice and B16 melanoma tumor-bearing mice.
a, Acarbose did not enhanced the anti-PD-1 efficacy in the male tumor-bearing mice (n = 5 per group). b, Intraperitoneal administration of acarbose did not influence the response to anti-PD-1 in the tumor-bearing mice (n = 5 per group). c, Tumor growth curves after treatment with isotype IgG control, or anti-PD-1 alone or acarbose alone, and anti-PD-1+acarbose (n = 6 per group). d, Representative images of tumor tissues at the end of experiment. Data were presented as the mean ± s.e.m. Each dot represented a biological replicate. Statistical analysis was performed using two-way ANOVA with Tukey’s multiple comparison test (a-c).
Extended Data Fig. 4 The gut microbiota profile between the anti-PD-1 group and the anti-PD-1+acarbose group.
a, Dominant phyla and their contribution to each group: a visual circle diagram describing the correspondence between samples and phylum (n = 5 per group). b, A Manhattan plot showing the distribution of differential OTUs between anti-PD-1 group and acarbose+anti-PD-1 group (n = 5 per group). OTUs were arranged at the phylum level and coloured according to the phylum. c, Ranking of differentially abundant taxa at the genus level by importance to the gut microbial community, as determined by their MDA using the unsupervised RandomForest analysis. MDA: mean decrease in accuracy, higher MDA indicated greater importance. Differential taxa were determined using R package “limma”. P values were adjusted using the Beniamini and Hochberg’s approach for controlling the false discovery rate (b,c).
Extended Data Fig. 5 Significantly different KO functional categories that were significantly enriched in the anti-PD-1 group or anti-PD-1+acarbose group were separately analysed for KEGG pathway enrichment.
KO functional categories and pathways that were significantly enriched in the anti-PD-1 group were shown in red, and those significantly enriched in the anti-PD-1+ acarbose group were shown in green.
Extended Data Fig. 6 Acarbose reshaped immune responses mainly through influencing chemokine profile within tumours.
a, Significant enrichment in gene ontology (GO) functional terms (Top 20). Statistical significance was assessed using a two-sided hypergeometric test, with P values adjusted for multiple comparisons using the Benjamini-Hochberg method. b-c, Gene Set Enrichment Analysis (GSEA) plot and gene sets for T CELL ACTIVATION (b) and POSITIVE REGULATION OF CHEMOKINE PRODUCTION (c). d, Based on gut microbiota and crucial metabolites, network of associations was established based on the correlation between gut microbes and 122 immunomodulators (chemokines, receptors, MHC molecules, and immunostimulators). Correlation coefficients larger than 0.70 with adjusted P value < 0.05 were considered significant links. Statistical significance was determined by Fisher’s exact test.
Extended Data Fig. 7 CXCL10-associated CD8 + T cell infiltration and correlation with response to anti-PD-1 therapy in cancer patient cohorts.
a-c, Positive regulation of leucocyte chemotaxis (a), positive regulation of leucocyte migration (b), and chemokine activity (c) were enriched in the responsive tumours versus non-responsive tumours from a melanoma patient cohort treated with anti-PD-1, using the GSEA analysis (n = 118). d, Heatmap for the core enrichment genes in the pathway of positive regulation of leucocyte chemotaxis (n = 118). e, Distribution of CD8 + T cell fraction in the tumours from nonresponders and responders treated with anti-PD-1 in the Liu and the Riaz et al. cohorts (n = 141). f, Multivariate Cox proportional hazard analysis of the correlation of tumor-infiltrating immune cells with overall survival in the TCGA-COAD cohort, and the results were visualized as a forest plot. Red squares represented hazard ratio (n = 444). g, Partial Spearman’s correlation of CXCL10 mRNA expression and relative abundance of CD8 + T cell in the CIBERSORT analysis in the TCGA-COAD cohort. The correlation was adjusted by tumor purity, since most immune cell types were negatively correlated with tumor purity. The shade represented the confidence intervals of the regression estimate. h-i, Spearman’s correlation of CXCL10 expression and immune (h) and ESTIMATE (i) scores using the ESTIMATE algorithm in the TCGA-COAD cohort (n = 457). The shade represented the 95% confidence intervals of the regression estimate. Data were presented as the mean ± s.e.m. Each dot represented a biological replicate. Statistical analysis was performed using unpaired two-tailed Student’s t-test (e), log-rank (Mantel–Cox) tests (f), or Fisher’s exact test (g-i).
Extended Data Fig. 8 Plasma metabolomics for quantification of metabolites in the anti-PD-1 and anti-PD-1+acarbose groups.
a, Principal components analysis (PCoA) score plot showing the separation of metabolite profiles between anti-PD-1 and anti-PD-1+acarbose groups (n = 6 per group). b, Orthogonal partial least squares discriminant analysis (OPLS-DA) plot showing the separation of metabolites from anti-PD-1 and anti-PD-1+acarbose groups. c, Heatmap of differential metabolites between anti-PD-1 group and anti-PD-1+acarbose groups (n = 6 per group). Differential metabolites were determined using R package “limma”. P values were adjusted using the Beniamini and Hochberg’s approach for controlling the false discovery rate.
Extended Data Fig. 9 Strategy and model performance of the integrative modelling on multi-omics associated with anti-PD-1+acarbose combination therapy.
a, Swim lane plots for classification analysis (n = 5 per group). Each swim lane presented predictions for one mice sample. The predictions of class probabilities were colour coded by class and presented per repetition (smaller dots) and averaged over all repetitions (larger dots). And the misclassified predictions were represented as circled dots. b, Multi-omics correlation plot indicating the correlations between the first principal component from each omics data and the power to separate sample from distinct groups. c,The circus plot and relevance network demonstrated the either positive (red lines) or negative (blue lines) correlations between the selected multi-omics features (n = 5 per group). The additional line plots on the outer ring visualized the relative abundance of each feature per group. The size of each node in the relevance network represents the betweenness centrality. d, The clustered heatmap (Euclidean distance, complete linkage) represented the scaled expression of the selected multi-omics signatures (n = 5 per group). Samples were represented in rows, selected features on the first component in columns. The red and the blue coloured blocks represented a positive or a negative association respectively, while the yellow colour represented weak levels of association.
Extended Data Fig. 10 Changes in the gut microbiota composition in the recipient mice after FMT from the acarbose-treated donors.
a, Relative abundance of gut microbiota at the family level, including Helicobacteraceae, Lactobacillaceae, Erysipelotrichaceae and Bacteroidaceae, between the acarbose-treated donors and recipient mice (n = 5 per group). b, Hierarchical clustering associated with a heatmap showing the variant genera of the OTUs between the acarbose-treated donors and recipient mice (control: n = 4; acarbose-treated donors:n = 5; recipient mice: n = 5). c, Relative abundance of gut microbiota at the genus level, including Alistipes, Marvinbryantia, Parabacteroides and Lactobacillus between the acarbose-treated donors and recipient mice (n = 5 per group). Data were presented as the mean ± s.e.m. Each dot represented a biological replicate. Statistical analysis was performed using unpaired two-tailed Student’s t-test (a,c).
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Zhang, SL., Wang, X., Cai, QQ. et al. Acarbose enhances the efficacy of immunotherapy against solid tumours by modulating the gut microbiota. Nat Metab 6, 1991–2009 (2024). https://doi.org/10.1038/s42255-024-01137-1
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DOI: https://doi.org/10.1038/s42255-024-01137-1
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