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Bifidobacterium bifidum strains synergize with immune checkpoint inhibitors to reduce tumour burden in mice

Nature Microbiology volume 6, pages 277–288 (2021)Cite this article

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Abstract

The gut microbiome can influence the development of tumours and the efficacy of cancer therapeutics1,2,3,4,5; however, the multi-omics characteristics of antitumour bacterial strains have not been fully elucidated. In this study, we integrated metagenomics, genomics and transcriptomics of bacteria, and analyses of mouse intestinal transcriptome and serum metabolome data to reveal an additional mechanism by which bacteria determine the efficacy of cancer therapeutics. In gut microbiome analyses of 96 samples from patients with non-small-cell lung cancer, Bifidobacterium bifidum was abundant in patients responsive to therapy. However, when we treated syngeneic mouse tumours with commercial strains of B. bifidum to establish relevance for potential therapeutic uses, only specific B. bifidum strains reduced tumour burden synergistically with PD-1 blockade or oxaliplatin treatment by eliciting an antitumour host immune response. In mice, these strains induced tuning of the immunological background by potentiating the production of interferon-γ, probably through the enhanced biosynthesis of immune-stimulating molecules and metabolites.

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Fig. 1: Marker gene sequence (16S rRNA) profiling of the gut microbiome in patients with NSCLC.
Fig. 2: Synergistic antitumour effect of B. bifidum strains in combination with oxaliplatin or anti-PD-1 in syngeneic mouse tumour models.
Fig. 3: Transcriptomics and metabolomics analysis of syngeneic tumour models treated with B. bifidum strains.
Fig. 4: Comparative genomics and transcriptomics analyses of B. bifidum strains.

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Data availability

16s rRNA and WGS data from human stool samples, the mouse intestinal RNA sequencing data, the bacterial WGS data and the bacterial RNA sequencing data have been deposited in the European Nucleotide Archive (accession number PRJEB26531). Source data are provided with this paper.

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Acknowledgements

This research was supported by the Bio & Medical Technology Development Program of the National Research Foundation (NRF), funded by the Ministry of Science & ICT (NRF-2017M3A9F3046536 to H.P.); a GIST Research Institute (GRI) grant, funded by the GIST in 2020; Genome and Company (grant number GNC GR 17-01 to H.P.). This work was also supported by grants from National Cancer Centre, Korea (NCC-1911267 to H.P); the Post-Genome Technology Development Program (10067758, Business model development driven by clinico-genomic database for precision immuno-oncology to S.-H.L.) funded by the Ministry of Trade, Industry and Energy (MOTIE, Korea). S.-Y.C. is supported by the Bio & Medical Technology Development Program of the NRF, funded by the Ministry of Science & ICT (NRF-2018M3A9F3056902). This research was also supported by a grant from the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (grant number HI17C1076 to K.W.Y). This work was supported by the NRF grant funded by the Korea government (MSIT) (number 2017R1C1B2011196 to K.W.Y). M.H.M. is supported by grant NRF-2018R1A2A1A05019794 from the NRF of Korea. B.-N.J. is supported by the Basic Science Research Program through the NRF of Korea, funded by the Ministry of Education, Science and Technology (MEST) (2018R1C1B6005768). The International Research & Development Program of the NRF of Korea, funded by the Ministry of Science, ICT & Future Planning (grant no. 2015K1A4A3047851) also funded C.L. The Ewha Womans University Professorship is supported in part by the Ewha Womans University research grant of 2017-2019. This study is also supported in part by operational funds from The First Affiliated Hospital of Xi’an Jiaotong University.

Author information

Author notes

  1. These authors contributed equally: Se-Hoon Lee, Sung-Yup Cho, Youngmin Yoon and Changho Park.

  2. These authors jointly supervised this work: Joon-Suk Park, George M. Weinstock and Charles Lee.

Authors and Affiliations

  1. Division of Hematology–Oncology, Department of Internal Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea

    Se-Hoon Lee & Hong Sook Kim

  2. Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University School of Medicine, Seoul, Korea

    Se-Hoon Lee

  3. Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul, Korea

    Sung-Yup Cho

  4. Cancer Research Institute, Seoul National University College of Medicine, Seoul, Korea

    Sung-Yup Cho

  5. Department of Biomedical Science and Engineering, Gwangju Institute of Science and Technology (GIST), Gwangju, Korea

    Youngmin Yoon, Gihyeon Kim, Sujeong Kim, Yunjae Kim, Yeongmin Kim, Hyun Kim & Hansoo Park

  6. Genome and Company, Gyeonggi-do, Korea

    Changho Park, Jinyoung Sohn, Jin-Ju Jeong, Bu-Nam Jeon, Mongjoo Jang, Choa An, Suro Lee, Yun Yeon Kim, Eun Ju Lee, Sang Gyun Kim, Kyoung Wan Yoon & Hansoo Park

  7. Department of Chemistry, Yonsei University, Seoul, Korea

    Gwang Bin Lee & Myeong Hee Moon

  8. Department of Biotechnology, Hoseo University, Asan, Korea

    Hyun-Suk Yang, Sarang Kim & Kyoung Wan Yoon

  9. Laboratory Animal Center, Daegu-Gyeongbuk Medical Innovation Foundation (DGMIF), Daegu, Korea

    Seonggon Kim & Joon-Suk Park

  10. Korea Basic Science Institute, Seoul Center, Seoul, Korea

    Hayung Chung & Myung Hee Nam

  11. The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA

    Jee Young Kwon, George M. Weinstock & Charles Lee

  12. Department of Public Health Sciences, Seoul National University, Seoul, Korea

    Sungho Won

  13. Department of Life Science, Ewha Womans University, Seoul, Korea

    Charles Lee

  14. The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, China

    Charles Lee

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  1. Se-Hoon Lee
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Contributions

S.-H.L. and H.S.K. collected and analysed the clinical data. S.-H.L., S.-Y.C., Y.Y., C.P. and K.W.Y. wrote the manuscript. J.Y.K., S.W., J.-S.P., G.M.W., C.L. and H.P. revised the manuscript. S.-H.L., S.-Y.C., C.P. and G.K. analysed the 16S rRNA sequences and transcriptomics data. J.-J.J., B.-N.J., H.-S.Y. and Sarang Kim performed the cytometry analysis. Y.Y., J.S., S.L., Y.Y.K., Sujeong Kim, Yunjae Kim and S.G.K. performed the syngeneic mouse model experiments. Seonggon Kim. and J.-S.P. performed the orthotopic mouse model experiments. C.A., E.J.L., Yeongmin Kim and H.K. cultured the bacteria. H.-S.Y. and Sarang Kim performed the in vitro T-cell assays. M.J., H.C. and M.H.N. analysed metabolomics data. G.B.L. and M.H.M. analysed the lipidomics data. K.W.Y. and H.P. designed and supervised all experiments and analyses.

Corresponding authors

Correspondence to Kyoung Wan Yoon or Hansoo Park.

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Competing interests

The authors declare no competing interests.

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Peer review information Nature Microbiology thanks the anonymous reviewers for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Comparison of gut microbiome composition between non–small cell lung cancer (NSCLC) patients and healthy controls.

a, Scheme for human, syngeneic tumour model, and bacterial study. b, c Box-and-whisker plot illustrating alpha diversity (b) and principal coordinate analysis plot illustrating beta diversity (c) of gut microbiomes in NSCLC patients (red, n = 96) and healthy controls (blue, n = 139). p value of alpha diversity calculated using Kruskal-Wallis test. Box represents first, third quartiles, and median, and whiskers are range up to 1.5×Interquartile Ranges (IQR). d, A plot of linear discriminant analysis (LDA) scores from the linear discriminant analysis effect size (LEfSe) method illustrating the differential abundance of the indicated taxa in the gut microbiomes of NSCLC patients (red) and healthy controls (blue) (LDA score > 4). e, The abundance of B. bifidum in NSCLC patients except epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor (TKI)-treated patients was determined by qPCR (n = 80). Data show means ± SEM. p value calculated using two-sided unpaired Mann-Whitney U test, p = 0.0128. f, Effect of B. bifidum and Leuconostoc strains on in vitro stimulation of CD8+ T cells by monocytes. Secreted IFN-γ was measured by ELISA in co-cultures of human autologous T cells and monocytes treated with B. bifidum and Leuconostoc strains (Supplementary Table 5). 3 technical replicates. Data show means ± SEM. p values calculated using one-way ANOVA with Tukey for multiple comparison. B. bif_M31 versus L. mes_G16, p = 0.0040; B. bif_M31 versus L. mes_K20, p = 0.0245; B. bif_M31 versus L. gar_K21, p = 0.0301. For all graphs, *p < 0.05, and **p < 0.01.

Source data

Extended Data Fig. 2 Synergistic anti-tumor effect of B. bifidum strains in combination with oxaliplatin or anti-PD-1 in syngeneic mouse tumor models.

a, Abundance of B. bifidum in the mouse intestine after administration. Mice were administered a onetime dose of 1 × 109 CFU of B. bif_K57 and stool samples were collected at 4, 8, 12, and 24 hr after administration. B. bifidum abundance was determined using a qPCR assay with B. bifidum specific primers and normalized to 16S rRNA. n = 5 independent biological replicates. Data show means ± SEM. b, Flow cytometry analyses were performed on mouse spleen and tumor tissue. Upper: Heatmap represents the p values of differences in immune cell profile between oxaliplatin treatment and combined treatment with oxaliplatin and B. bifidum strains. p values calculated using two-sided unpaired t test. The exact p values are provided in Supplementary Table 14. Lower: Ratio of CD8+ T/Treg and effector CD8+ T/Treg in spleen and tumor. Ctrl, n = 9; Oxp or Oxp + B. bif_K57, n = 7; Oxp + B. bif_B06, n = 8 mice per group. Data show means ± SEM. p values calculated using one-way ANOVA with Tukey for multiple comparison. Oxp versus Oxp + B. bif_K57, p = 0.0002 (Ratio of CD8+ T/Treg and effector CD8+ T/Treg, tumor); Oxp versus Oxp + B. bif_B06, p = 0.0124 (Ratio of effector CD8+ T/Treg, tumor); Oxp versus Oxp + B. bif_K57, p = 0.0011 (Ratio of CD8+ T/Treg, spleen); Oxp versus Oxp + B. bif_K57, p = 0.0039 (Ratio of effector CD8+ T/Treg, spleen). c, Cytokine expression profiles in tumours from mice treated with oxaliplatin or both oxaliplatin and B. bifidum were measured by qPCR. Ctrl or Oxp groups, n = 6 independent biological replicates/group; oxaliplatin + B. bif_K57 or B. bif_ B06 groups, n = 8 independent biological replicates/group. Data show means ± SEM. p values calculated using one-way ANOVA with Tukey for multiple comparison within oxaliplatin treated groups. IFN-γ of Oxp versus Oxp + B. bif_K57, p = 0.0478; IL-2 of Oxp versus oxp + B. bif_K57, p < 0.0001; IL-2 of Oxp versus Oxp + B. bif_B06, p = 0.0006; TNF-α of Oxp versus Oxp + B. bif_K57, p = 0.0004; TNF-α of Oxp versus Oxp + B. bif_B06, p = 0.0040; IL-10 of Oxp versus Oxp + B. bif_K57, p < 0.0001; IL-10 of Oxp versus Oxp + B. bif_B06, p = 0.0063. d, Representative tumor growth curves after administration of B. bif_M31, with or without anti-PD-1 treatment. IgG, n = 8; anti-PD-1, n = 5; B. bif_M31, n = 10; anti-PD-1 + B. bif_M31, n = 9 mice per group. Data show means ± SEM. p value calculated using two-way ANOVA with Tukey for multiple comparison. anti-PD-1 versus anti-PD-1 + B. bif_M31, p < 0.0001. e, Representative tumor growth curves after administration of B. bif_C01, with or without anti-PD-1 treatment. IgG, n = 6; anti-PD-1 or B. bif_C01 or and anti-PD-1 + B. bif_C01, n = 7 mice per group. Data show means ± SEM. p value calculated using two-way ANOVA with Tukey for multiple comparison. f, Flow cytometry analysis of CD4+ T, NK and Treg cells in lamina propria of the intestine. n = 7 mice per group. Data show means ± SEM. p values calculated using one-way ANOVA with Tukey for multiple comparison. CD4+ T cells of anti-PD-1 versus anti-PD-1 + B. bif_K57, p = 0.0015; NK cells of anti-PD-1 versus anti-PD-1 + B. bif_K57, p = 0.0037. For all graphs, *p < 0.05, **p < 0.01, ***p < 0.001, and ns = not significant.

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Extended Data Fig. 3 Transcriptomic and Lipidomic analysis of syngeneic tumor model treated with B. bifidum strains.

a, Network representation of enriched Gene Ontology (GO) biological pathways among differentially up-regulated genes in mice treated with anti-PD-1 + B. bif_K57 mice, determined using ClueGO. b, Principal component analysis of syngeneic mouse serum lipid profiles, based on lipids differential abundance among mice treated with anti-PD-1, anti-PD-1 + B. bif_B06, and anti-PD-1 + B. bif_K57 (>1.5-fold & p < 0.01). c, Heatmap of lipids showing significant differences (>1.5-fold & p < 0.01) in serum of syngeneic mice, as revealed by the lipidomic analysis. PE, phosphatidylethanolamine; PI, phosphoinositol; PC, phosphatidylcholine; LPG, lysophosphatidylglycerol; DG, diacylglycerol; TG, triglycerol.

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Extended Data Fig. 4 Synergistic anti-tumor effect of B. bifidum strains in combination with anti-PD-1 in various syngeneic mouse tumor models.

a, Effect of B. bifidum strains on in vitro stimulation of CD8 T+ cells by monocytes. Secreted IFN-γ was measured by ELISA in co-cultures of human autologous T cells and monocytes treated with B. bif_K57 or B. bif_B06. 2 technical replicates. Data show means ± SEM. b, Upper: Experimental timeline: Animals were treated with the antibiotic cocktail (ABX) for 14 days. B. bif_K57 was then orally administered for 14 days before inoculation with MC38 colon cancer cells, followed by administration of anti-PD-1 via intraperitoneal injection twice a week for 21 days. Lower: MC38 tumor growth curves are shown for animals subjected to the indicated experimental treatments. IgG after PBS or anti-PD-1 after PBS, n = 5; anti-PD-1 + B. bif_K57 after PBS, n = 6; anti-PD-1 after ABX, n = 5; anti-PD-1 + B. bif_K57 after ABX, n = 7 mice per group. Data show means ± SEM. p values calculated using two-way ANOVA with Tukey for multiple comparison. anti-PD-1 after PBS versus anti-PD-1 + B. bif_K57 after PBS, p = 0.0053; anti-PD-1 after PBS versus anti-PD-1 + B. bif_K57 after ABX, p = 0.0004. c, Effect of ABX treatment on the abundance of gut microbiome. Stool 16S rRNA genes were analyzed by qPCR. n = 5 mice per group. Data show means ± SEM. p value calculated using two-sided unpaired t test, p < 0.0001. d, Upper: Experimental timeline: 14 days after initial oral administration of B. bif_K57, mice were inoculated with LLC1. anti-PD-1 was administered via intraperitoneal injection eight times, twice a week for 28 days. Lower: LLC1 tumor growth curves were generated for animals subjected to the indicated experimental treatments. n = 6 mice per group. Data show means ± SEM. p values calculated using two-way ANOVA with Tukey for multiple comparison. IgG versus anti-PD-1 + B. bif_K57, p < 0.0001. e, Macroscopic findings of tumor-inoculated lungs at day 23. The implanted tumors are indicated by yellow arrows. Scale bar, 50 mm. f, Upper: Experimental timeline: 14 days after initial oral administration of B. bif_K57, mice were inoculated with 4T1 breast cancer cells. anti-PD-1 was administered via intraperitoneal injection six times, twice a week for 21 days. Lower: 4T1 tumor growth curves were generated for animals subjected to the indicated experimental treatments. n = 10 mice per group. Data show means ± SEM. IgG versus anti-PD-1 + B. bif_K57, p < 0.0001. For all graphs, **p < 0.01, ***p < 0.001, and ns = not significant.

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Extended Data Fig. 5 The abundance of peptidoglycan in B. bifidum strains.

The abundance of peptidoglycan of B. bif_K57 and B. bif_B06 was estimated using ELISA. Data show means ± SEM. 3 biological replicates. p value calculated using two-sided unpaired t test, p < 0.0006. For all graphs, ***p < 0.001.

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Lee, SH., Cho, SY., Yoon, Y. et al. Bifidobacterium bifidum strains synergize with immune checkpoint inhibitors to reduce tumour burden in mice. Nat Microbiol 6, 277–288 (2021). https://doi.org/10.1038/s41564-020-00831-6

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