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A clinic-responder-derived defined microbial consortium enhances anti-PD-1 immunotherapy efficacy in mice

Nature Microbiology (2026)Cite this article

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Abstract

Targeting the gut microbiota is a promising strategy to enhance the efficiency of cancer immunotherapy; however, success has been limited. Here we combined metagenomic analysis and in silico prediction to identify bacterial species associated with immunotherapy response in patients with non-small-cell lung cancer. We constructed a defined consortium (RCom) of 15 bacterial species, most of which were isolated from responder patient faeces, associated with improved clinical response to anti-programmed cell death protein 1 (PD-1) treatment. Metabolic models and in vitro experiments revealed that RCom is a stable and cooperative community, and in vivo experiments showed that RCom engrafts and produces immunomodulatory metabolites. Oral administration of RCom improved the anti-tumour activity of anti-PD-1 by increasing the intratumoural infiltration and cytotoxic function of CD8+ T cells in syngeneic tumour models and across mice with heterogeneity in baseline gut microbiota composition. RCom supplementation also limited anti-PD-1 resistance in mice conferred by faecal microbiota transplantation from individual non-responsive patients. These findings suggest that RCom is a potential adjuvant to improve responsiveness to anti-PD-1 therapy in cancer.

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Fig. 1: A defined bacterial consortium derived from clinic responders to anti-PD-1 treatment.
Fig. 2: RCom is a stable and cooperative community.
Fig. 3: RCom supplementation shapes intestine microbiome functions and immune responses.
Fig. 4: RCom administration enhances anti-PD-1 efficacy in tumour mouse models with a variety of baseline microbiota.
Fig. 5: Therapeutic RCom supplementation circumvents the anti-PD-1 resistance in mice conferred by FMT from non-responder patients.

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Data availability

Whole-genome shotgun sequencing data of human faecal samples (accession number PRJNA1195622), genomic sequences of the 10 isolated strains (PRJNA1312235), 16S rRNA (PRJNA1193840) and whole-genome shotgun sequencing data (PRJNA1196516) of mouse caecal samples have been deposited in the National Institutes of Health Sequence Read Archive. The proteomics data have been deposited in the PRIDE repository (PXD064618). Source data are provided with this paper.

Code availability

The code for metabolic interaction analysis and Cox regression analysis of survival generated in this study is available via GitHub at http://github.com/zhouhaiyan5555/RCom (ref. 69).

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Acknowledgements

We thank X. Xu, S. Wang and W. Hu for technical assistance on metabolomic and proteomic analyses. This work was supported by the National Natural Science Foundation of China grant 82241228 (to Y.W. and C.Y.), 82241227 (to S.L.), 82030045 (to S.L.), 32230060 (to C.Y.), 31925001 (to C.Y.) and 82073152 (to L.X.); National Multi-disciplinary Treatment Project for Major Diseases 2020NMDTP (to S.L.); Shanghai Program for Outstanding Medical Academic Leader 2022LJ016 (to Y.W.); Collaborative Innovation Center for Clinical and Translational Science by Ministry of Education & Shanghai CCTS-202407PT (to S.L.) and CCTS-202508PT (to S.L.); Shanghai Chest Hospital Basic Research Project 2023YNKT-1 (to S.L.); and Shanghai Key Laboratory of Thoracic Tumor Biotherapy 2025SZ1710 (to S.L.).

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Author notes

  1. These authors contributed equally: Haiyan Zhou, Ruiming Sun, Xiaoqun Nie, Liliang Xia.

Authors and Affiliations

  1. State Key Laboratory of Microbial Metabolism, and School of Life Sciences & Biotechnology, Shanghai Jiao Tong University, Shanghai, China

    Haiyan Zhou, Xiaoqun Nie, Yujie Liu, Wenyue Dong & Chen Yang

  2. Shanghai Institute of Immunology, Department of Immunology and Microbiology, Key Laboratory of Cell Differentiation and Apoptosis of Chinese Ministry of Education, Shanghai Jiao Tong University School of Medicine, Shanghai, China

    Ruiming Sun & Ying Wang

  3. Shanghai Lung Cancer Center, Shanghai Key Laboratory of Thoracic Tumor Biotherapy, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China

    Ruiming Sun, Liliang Xia, Xiaokuan Zhu, Yaxian Yao & Shun Lu

  4. Department of Gastroenterology, Shanghai Key Laboratory of Pancreatic Diseases, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China

    Hui Dong

  5. CAS-Key Laboratory of Synthetic Biology, CAS Center for Excellence in Molecular Plant Sciences, Shanghai Institute of Plant Physiology and Ecology, Chinese Academy of Sciences, Shanghai, China

    Shurui Hou & Guo-Ping Zhao

  6. Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai, China

    Guo-Ping Zhao

  7. Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China

    Chen Yang

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Contributions

C.Y., Y.W. and S.L. conceived the project. H.Z. performed most of the experiments, analysed data and wrote the paper. R.S. performed immunological analyses and some animal experiments. X.N. performed bioinformatic analysis and bacterial community design. L.X. collected and analysed clinical data. H.D. performed metagenomic sequencing on human faecal samples. Y.L. performed proteomic and metabolomic experiements and analysed the data. S.H. and W.D. provided technical assistance and essential materials. X.Z. and Y.Y. collected human samples. G.-P.Z. contributed to the experimental design and discussion. Y.W. and S.L. supervised tumour models and analysed data. C.Y. designed experiments, analysed data and wrote the paper.

Corresponding authors

Correspondence to Shun Lu, Ying Wang or Chen Yang.

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Competing interests

C.Y., R.S., H.Z., Y.W. and S.L. have a patent pending related to this work (Chinese patent application number 2025110199804). The other authors declare no competing interests.

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Extended data

Extended Data Fig. 1 Design of the defined bacterial consortium RCom.

a, Schematic of the design process. The key criteria for selecting the 15 bacterial species for the RCom are shown in colored boxes. Based on metagenomic shotgun sequencing of faecal samples from anti-PD-1-treated NSCLC patients, the bacterial species enriched (LDA score ≥ 3.0) and prevalent (prevalence = 1.0) in the responder group were identified. For safety, the species associated with ≥grade 3 irAEs or harboring putative toxins or multiple drug-resistant genes were excluded. Lastly, a metabolically cooperative community was predicted based on metabolic interaction simulations. b, Selection of bacterial species based on enrichment and prevalence in responders and safety rule. The bacterial species that meet the criteria are shown in green boxes, whereas those excluded are denoted by bold X. c, Metabolic model-based prediction of interspecies interactions in bacterial communities. Calculated competition (MRO) and cooperation (SMETANA) scores for different sizes of bacterial communities are shown on the right. The SMETANA score was normalized by community size. Arrow indicates the RCom. d, Number of predicted intra- and inter-phylum cross-feeding interactions within the 11 core members and the RCom. e, Metabolite classes predicted to be exchanged between the RCom members. Compound classification is based on the Human Metabolome Database (HMDB). f, Top 20 potential cross-fed compounds in the RCom. Amino acids are indicated in brown.

Source data

Extended Data Fig. 2 Construction of the bacterial community RCom.

a, Schematic showing the isolation of RCom members from faecal samples from anti-PD-1-responsive patients. b, A neighbor-joining phylogenetic tree of the 15 bacterial strains. Genome sequencing was conducted for the isolated 10 strains. Their closest reference strain was identified by comparing to the NCBI 16S ribosomal RNA sequence database. The RCom subsets (5-mix, 10-mix-I, 10-mix-II, and 10-mix-III) tested in vivo are indicated by colored boxes. c, For comparison, 13 bacterial strains were isolated from faecal samples from non-responder patients. Most of them are enriched in non-responders. Heatmap shows their relative abundances in responders versus non-responders among anti-PD-1-treated NSCLC patients as determined by metagenomic shotgun sequencing.

Source data

Extended Data Fig. 3 Community growth and substrate utilization by the RCom members in vitro.

a, The community reaches a stable composition quickly. The relative abundance of each strain was analyzed by qPCR. The two-letter abbreviations of the RCom members are indicated. b, Heat map showing carbohydrate utilization by each of the RCom members. The OD600 values are normalized to the maximum values within each strain and for the same substrate. Data shown in a and b are mean values from three biological replicates.

Source data

Extended Data Fig. 4 Metabolic cross-feeding in the RCom as revealed by 13C-proteomic and exometabolomic analyses.

a, Five Bacteroidetes species were grown in a chemically defined medium containing 13C-labelled or unlabelled glucose, and the spent medium was added to the culture of the 10-Firmicutes-species community. Extracellular concentrations of 13C-labelled amino acids during cultivation of the Firmicutes community were measured by LC–MS and normalized to the values at the start of the culture (0 h). Data shown are mean ± s.d. (n = 3 independent experiments). b, Data shown are from three independent biological replicates from 13C-labelled (orange) and unlabelled (black) spent medium experiments. The box plots depict the median, the upper and lower quartiles, and the rest of the distribution. Points that are 1.5 times the inter-quartile range beyond the upper and lower quartiles are considered outliers and shown individually. c, Heat map showing the changes in extracellular concentrations of metabolites after 6 h cultivation of the 10-Firmicutes-species community. The concentration values at 6 h are averaged from three independent experiments and normalized to the values at the start of the culture (0 h). The resulting fold changes (log10 transformed) are shown.

Source data

Extended Data Fig. 5 Engraftment of the RCom in SPF mice.

a, SPF mice were supplemented with the RCom (n = 5) or its subsets (PBS, n = 4; 5-mix, n = 4; 10mix-I, n = 5) daily by oral gavage for 7 days. The abundance of each of the 15 species in caecal samples was determined by qPCR and normalized to 16S rRNA level. Each species was denoted by two-letter abbreviation, and the color represents the phyla of the species (Bacteroidetes, blue; Firmicutes, green). b, Heat map showing the changed metabolites in the caecal content of mice supplemented with the indicated bacterial mixtures. Elevated metabolites in RCom- versus subset-supplemented mice are highlighted in red. Exact P values are provided in Source Data. Data in a are mean and s.e.m. Two-tailed unpaired t-test. NS, not significant.

Source data

Extended Data Fig. 6 RCom species act together to modulate colonic immune responses.

a, SPF C57BL/6 mice were supplemented with bacterial mixtures or PBS daily by oral gavage for 7 days. The colonic lamina propria cells were analyzed by flow cytometry. Heat map showing the top 15 differentially abundant immune features between RCom- and PBS-supplemented mice. b, Representative flow cytometry plots show the expression of GzmB and CD107a by colonic CD8+ T cells from RCom- or PBS-fed mice. c–e, Percentages of colonic GzmB+ and CD107a+ cells among CD8+ T cells (c), splenic IFNγ+CD8+ and TNF+CD8+ T cells (d), or colonic IFNγ+CD4+ and TNF+CD4+ T cells (e) from mice supplemented with RCom (n = 5), its subsets 5-mix (n = 4) or 10-mix-I (n = 5), or PBS (n = 4). f,g, Percentages of IFNγ+, TNF+ of colonic CD8+ T cells from mice supplemented with RCom subsets 10-mix-II or 10-mix-III (f) (n = 5 mice per group) or heat-killed (HK) RCom (g) (PBS, n = 6; HK, n = 5; RCom, n = 5). h, Percentages of CD80+, MHC class I+, and IL-12+ of colonic DCs from mice supplemented with RCom or its subsets. PBS, n = 4 or 5; 5-mix, n = 4; 10-mix-I, 10-mix-II, 10-mix-III, RCom, n = 5 mice per group. Data in c‒h are mean and s.e.m. Two-tailed unpaired t-test. NS, not significant.

Source data

Extended Data Fig. 7 RCom supplementation in mice harboring a variety of baseline gut microbiota.

a, Four cohorts of SPF C57BL/6 mice were obtained from different commercial suppliers or the same supplier but in separate shipment batches. The baseline caecal microbiota were analysed by 16S rRNA sequencing. Phylogenetic composition of common bacterial taxa at the phylum level is shown. b, Alpha diversity of baseline caecal microbiota composition. c, Principal coordinate analysis (PCoA) of baseline microbiota composition using weighted UniFrac distances. d, Four cohorts of SPF C57BL/6 mice with different baseline gut microbiota were orally supplemented with RCom or PBS for 7 days. The abundance of each of the 15 species in caecal samples was determined by qPCR and normalized to 16S rRNA level. e, PCoA plot of caecal microbiota composition in RCom- and PBS-fed mice from the four cohorts. f, Metabolic modules and their encoding MAGs enriched in RCom- versus PBS-fed mice from two cohorts (baselines 1 and 2) as determined by metagenomic shotgun sequencing. MAGs were taxonomically classified according to NCBI RefSeq. g, Butyrate synthetic pathway in gut bacteria. Each circle in b–e represents an individual animal. Baseline 1: PBS, RCom, n = 6 mice per group; Baseline 2: PBS, RCom, n = 5 mice per group; Baseline 3: PBS, RCom, n = 5 mice per group; Baseline 4: PBS, RCom, n = 4 mice per group. Data shown in b and d are mean and s.e.m. Two-tailed unpaired t-test.

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Extended Data Fig. 8 RCom species act together to enhance anti-tumour immunity and anti-PD-1 efficacy.

a,b, SPF C57BL/6 mice were i.p. injected with an anti-PD-1 antibody. Supplementation with RCom, heat-kiled (HK) RCom, or PBS daily by oral gavage started one week before anti-PD-1 treatment. Schematic of the experimental setup is shown (a). Percentages of IFNγ+ and TNF+ in CD8+ T cells and DCs in CD45+ cells in spleens from the mice were determined by flow cytometry (b) (n = 5 mice per group). c‒g, SPF C57BL/6 mice were injected s.c. with LLC tumour followed by anti-PD-1 treatment. Supplementation of the indicated bacterial mixtures daily by oral gavage started one week before tumor inoculation. Schematic of the experimental setup is shown (c). Tumour weight at termination in PBS- (n = 4) or RCom- (n = 5) or 13-mix-fed (n = 5) mice (d) and tumor growth in mice supplemented with RCom- (n = 5) or 5-mix (n = 5) or 10-mix-I (n = 4) or PBS (n = 5) (e) were measured. Percentages of CD8+ T cells in CD45+ cells, CD44+ cells in CD8+ T cells, Foxp3+ (Treg) cells in CD4+ T cells, NK cells in CD45+ cells, macrophage (Mac) in CD45+ cells in the tumours (f) (n = 4 mice per group), IFNγ+, TNF+, GzmB+, CD107a+ among CD8+ T cells in the tumour (g) (10-mix-I, n = 4; RCom, 5-mix, PBS, n = 5 mice per group) from mice supplemented with RCom or its subsets were determined by flow cytometry. h, Effect of DC depletion on proportions of IFNγ+ and TNF+ among CD8+ T cells in the tumour from mice supplemented with RCom or PBS. DCs were depleted by i.p. injection of diphtheria toxin (DT). n = 5 mice per group. i,j, SPF C57BL/6 or BALB/c mice were injected s.c. with CMT-167 or ASB-XIV tumours followed by anti-PD-1 treatment. Supplementation of RCom or PBS daily by oral gavage started one week before tumour inoculation. Schematic of the experimental setup is shown (i) (n = 5 mice per group). Tumour weight of CMT-167 and ASB-XIV at termination was measured (j) (αPD-1, n = 5; RCom+αPD-1, n = 6). Data are mean and s.e.m. Two-way ANOVA (e) or two-tailed unpaired t-test (b, d, f‒h, j). NS, not significant.

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Extended Data Fig. 9 Enhancement of anti-PD-1 efficacy by RCom was not influenced by baseline microbial variations.

a, Schematic of the experimental setup. Three cohorts of SPF mice with different baseline gut microbiota (L1, L2, L3, and B1, B2, B3, respectively) were subject to inoculation of LLC or B16-F10 tumours followed by anti-PD-1 treatment. Daily administration of the RCom started one week before tumour inoculation. b, Shared and unique OTUs between the three baseline microbiota. c,d, LLC tumour growth curve (c) and tumour weight at termination (d) in the three cohorts of anti-PD-1 treated mice supplemented with RCom or PBS. e,f, B16-F10 tumour growth curve (e) and tumour weight at termination (f) in the three cohorts of anti-PD-1 treated mice supplemented with RCom or PBS. g, Percentages of IFNγ+, TNF+, and GzmB+ cells among CD8+ T cells in the LLC tumour and spleen of the three cohorts of mice, as determined by flow cytometry. L1: n = 4 mice per group; L2: n = 5 mice per group; L3: αPD-1, n = 5; RCom+αPD-1, n = 4. B1: n = 5 mice per group; B2: n = 5 mice per group; B3: n = 4 mice per group. Data are mean and s.e.m. Two-way ANOVA (c, e) or two-tailed unpaired t-test (d, f, g).

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Extended Data Fig. 10 RCom administration restores the anti-tumour activity of anti-PD-1 in mice with FMT from non-responder patients.

a, Schematic of the experimental setup in the RCom prophylactic approach. FMT of faecal samples from three non-responder (NR) patients with NSCLC were individually performed in SPF C57BL/6 mice pretreated with ATB. Mice were inoculated with LLC tumour followed by injection of an anti-PD-1 antibody. Supplementation of the RCom or PBS daily by oral gavage started one week before tumour inoculation. b, Faecal microbial composition of NR donors analyzed by 16S rRNA sequencing. Phylogenetic composition of common bacterial taxa at the phylum level is shown. c, Shared and unique OTUs between the three NR patients (NR3, NR4, and NR5). d,e, LLC tumour growth curve (d) and tumour weight at termination (e) in RCom-supplemented and anti-PD-1-treated mice with FMT from individual NR donors. NR3: n = 5 mice per group; NR4: n = 4 mice per group; NR5: PBS, αPD-1, n = 4 mice per group, RCom+αPD-1, n = 5. f, IHC and quantification of CD8+ T cells in LLC tumour from anti-PD-1 treated mice supplemented with RCom or PBS. Arrow indicates CD8 + T cells. n = 4 mice per group. g, Percentages of IFNγ+CD8+, TNF+CD8+, and GzmB+CD8+ T cells in the tumour of anti-PD-1 treated mice fed with RCom or PBS. n = 5 mice per group. Data are mean and s.e.m. Two-way ANOVA (d) or two-tailed unpaired t-test (e‒g). ns, not significant. h, Despite a profound heterogeneity of the baseline gut microbiota, interspecies interactions within the RCom enabled its optimal engraftment and production of immunomodulatory metabolites including butyrate and inosine. Supplementation of the RCom increased intratumoral infiltration and cytotoxic activity of CD8+ T cells, probably mediated by activation of DCs. Collectively, oral administration of the RCom robustly enhances the efficacy of anti-PD-1 cancer immunotherapy in preclinic models.

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Zhou, H., Sun, R., Nie, X. et al. A clinic-responder-derived defined microbial consortium enhances anti-PD-1 immunotherapy efficacy in mice. Nat Microbiol (2026). https://doi.org/10.1038/s41564-026-02279-6

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