| Literature DB >> 35488243 |
Yuting Lu1, Xiangliang Yuan2, Miao Wang1, Zhihao He1, Hongzhong Li3, Ji Wang4, Qin Li5.
Abstract
The gut microbiota have long been recognized to play a key role in human health and disease. Currently, several lines of evidence from preclinical to clinical research have gradually established that the gut microbiota can modulate antitumor immunity and affect the efficacy of cancer immunotherapies, especially immune checkpoint inhibitors (ICIs). Deciphering the underlying mechanisms reveals that the gut microbiota reprogram the immunity of the tumor microenvironment (TME) by engaging innate and/or adaptive immune cells. Notably, one of the primary modes by which the gut microbiota modulate antitumor immunity is by means of metabolites, which are small molecules that could spread from their initial location of the gut and impact local and systemic antitumor immune response to promote ICI efficiency. Mechanistic exploration provides novel insights for developing rational microbiota-based therapeutic strategies by manipulating gut microbiota, such as fecal microbiota transplantation (FMT), probiotics, engineered microbiomes, and specific microbial metabolites, to augment the efficacy of ICI and advance the age utilization of microbiota precision medicine.Entities:
Keywords: Cancer immunotherapy; Gut microbiota; Immune checkpoint inhibitors; Microbiota-derived metabolites; Therapeutic strategies
Mesh:
Year: 2022 PMID: 35488243 PMCID: PMC9052532 DOI: 10.1186/s13045-022-01273-9
Source DB: PubMed Journal: J Hematol Oncol ISSN: 1756-8722 Impact factor: 23.168
Fig. 1Timeline of gut microbiota and ICI efficacy: from discovery to application. From 2007 to 2013, mouse studies showed that the gut microbiota could stimulate antitumor immune responses. In 2015, two preclinical mouse studies first linked the gut microbiota to ICI responses. In 2018, mouse and human studies demonstrated that gut microbiota composition and diversity were predictive of the response to ICI immunotherapy. FMT from ICI responding patients to germ-free or antibiotic-treated mice could improve tumor control and ameliorate responses to ICI. From 2019 to 2020, prospective studies confirmed a significant association between gut microbiota and ICI outcomes in advanced solid tumors. Retrospective studies have implicated that antibiotics are associated with decreased survival and attenuated response to ICI. In 2021, two clinical trials found that FMT from ICI responders combined with anti-PD-1 therapy overcame resistance to PD-1 blockade in melanoma patients
Studies on gut microbiota target innate and adaptive immune cells to promote ICI efficacy
| Year | Cancer types | ICI | Beneficial gut microbiota | Interventions factors and/or biological effects | References |
|---|---|---|---|---|---|
| 2015 | Melanoma | PD-L1 inhibitor | DCs and CD8 + T cells | [ | |
| 2015 | Melanoma | CTLA4 inhibitor | Tumor draining lymph nodes: Th1; TME: DCs | [ | |
| 2018 | NSCLC, RCC, Urothelial carcinoma | PD-1 inhibitor, CTLA4 inhibitor | TME: IL-12 and CCR9 + CXCR3 + CD4 + T lymphocytes | [ | |
| 2018 | Melanoma | PD-1 inhibitor | Mice receiving R-FMT: Increased innate effector cells and decreased suppressive myeloid cells; Mice receiving NR-FMT: Increased RORγT+ T helper 17 cells | [ | |
| 2018 | Melanoma | PD-L1 inhibitor | Mice receiving R-FMT: Augmented T cell responses | [ | |
| 2017 | Melanoma | CTLA4 inhibitor | CD4 + T cells and CD25 | [ | |
| 2019 | Adenocarcinoma, melanoma | PD-1 inhibitor | Eleven strains | IFNγ + CD8 T cell, CD103 + DC, and MHC Ia | [ |
| 2020 | CRC, Intestinal cancer, Bladder cancer, melanoma | PD-L1 inhibitor, CTLA4 inhibitor | DCs and Th1; Inosine: A2AR on Th1 | [ | |
| 2020 | Colon cancer, T cell lymphoma | CD47 inhibitor | STING signaling and DCs | [ | |
| 2021 | Melanoma | PD-1 inhibitor | FMT-R patients: TME: CD8 + T cell; Gut: APC | [ | |
| 2021 | Melanoma | PD-1 inhibitor | FMT-R patients: PBMCs: CD8 + T cells and MAIT cells; TME: CD8 + T cells, HLA II, CD74 and GZMK FMT-NR patients: Increased myeloid cells and CD4 + regulatory T cells | [ | |
| 2021 | Lymphoma, Colon carcinoma, Melanoma, Breast carcinoma | ICI | A high-fiber diet, | Monocytes, Macrophages, NK cells, DCs, Type I IFN, and STING | [ |
APC: antigen presenting cell; A2AR: adenosine 2A receptor; CRC: colorectal cancer; CTLA4: cytotoxic T lymphocyte-associated antigen 4; DC: dendritic cell; FMT: fecal microbiota transplant; FMT-R: responders to fecal microbiota transplant treatment; FMT-NR: non-responders to fecal microbiota transplant treatment; GZMK: granzyme K; ICI: immune checkpoint inhibitor; IFN: interferon; MAIT: mucosal-associated invariant T; MHC: major histocompatibility; NK: natural killer; NSCLC: non-small cell lung cancer; NR-FMT: fecal microbiota transplants from non-responders to immune checkpoint inhibitor; PBMCs: peripheral blood mononuclear cells; PD-1: programmed cell death 1; PD-L1: programmed cell death ligand 1; RCC: renal cell carcinoma; R-FMT: fecal microbiota transplants from responders to immune checkpoint inhibitor; STING: stimulator of interferon gene; Th1: T helper 1; and TME: tumor microenvironment
Fig. 2The gut microbiota modulate innate immunity, adaptive immunity, and tumor antigens to improve ICI responses. A Innate immunity. DCs: Bifidobacterium, eleven strains and their metabolites, and Bacteroides fragilis promote DC maturation or activation and subsequent activation of CD8+ T cells and Th1 cells. NK cells: Lactobacillus plantarum increases NK cell activation; a high-salt diet increases intestinal permeability and localization of intratumoral Bifidobacterium and enhances NK cell activation to induce antitumor immunity. Monocyte: Feeding a high-fiber diet, monocolonization with cdAMP-producing A. muciniphila or transferring fecal microbiota from ICI responders can trigger the monocyte-IFN-I-NK-cell-DC cascade; Bifidobacterium facilitates CD47-based immunotherapy in a STING signaling and IFN-I-dependent fashion; Bacteroides fragilis induces macrophage phenotype polarization to M1. B Adaptive immunity. CD8+ T cells: Bifidobacterium, Enterococcus, Faecalibacterium, Ruminococcus, and Clostridiales promote CD8+ T cell infiltrates in tumor tissues; Phyla Firmicutes and Actinobacteria improve the activation of CD56+CD8+ T cells in the peripheral blood of ICI responders; and eleven strains increase the proportion of effector IFNγ+CD8+ T cells in the systemic circulation. CD4+ T cells: B. pseudolongum and Bacteroides fragilis stimulate Th1 immune responses; A. muciniphila triggers CCR9+CXCR3+CD4+ T lymphocyte recruitment into tumor beds; and Faecalibacterium increases the CD4+ T cell proportion. C Tumor cross-antigen. The gut microbiota increase the immunogenicity of tumor cells by providing tumor cross-antigens to ameliorate the efficacy of ICIs, including the antigen epitope TMP1 and the antigen epitope SVY
Fig. 3Potential mechanisms by which the gut microbial metabolite inosine facilitates the efficacy of ICI. Inosine, purine metabolite of gut microbiota A. muciniphila and B. pseudolongum, combined with in ICI therapy exert synergistic antitumor effects. A Inosine increases the immunogenicity of tumor cells. Inosine can improve the ability of tumor cells to present tumor antigens so that cytotoxic immune cells can easily recognize and kill tumor cells. B Inosine promotes immune cell activation. Inosine could enhance ICI efficacy by acting on A2AR on T lymphocytes. It stimulates the phosphorylation of cAMP response element-binding protein (pCREB) through the inosine-A2AR-cAMP-PKA signaling pathway, which can upregulate IL12Rβ2 and IFNγ transcription and promote Th1-cell differentiation and accumulation in the TME. C Inosine provides an alternative carbon source for CD8+ T cells. Inosine can be used as an alternative carbon source for CD8+ T cells when glucose is limited and relieves the restrictions on CD8+ T cells energy metabolism in tumor cells
Fig. 4Potential mechanisms by which the gut microbial metabolite SCFAs augment the efficacy of ICI. A SCFAs inhibit the proliferation and induce the apoptosis of cancer cells. The butyric acid of SCFAs, a metabolite of Faecalibaculum rodentium PB1 and H. biformis, inhibits the activity of HDAC and the calcineurin-mediated activation of NFATc3 transcription factor, thereby blocking the proliferation of tumor cells. Propionic acid produced by A. muciniphila activates the cell cycle inhibitor p21 through GPR43 and downregulates the IAP inhibitor, which inhibits cancer cell proliferation, induces apoptosis, and improves the antitumor effect of ICI. B SCFAs improve the antitumor immune response. Butyrate can directly enhance CD8+ T cell antitumor cytotoxicity by inducing ID2 expression in CD8+ T cells through IL-12 signaling. Valeric acid and butyric acid of SCFAs promote expression of effector molecules such as IFNγ and TNFα and enhance the antitumor effects of CTLs. C SCFAs provide energy for immune cells. SCFAs provide energy to B cells, memory T cells, and effector T cells by regulating metabolic pathways such as glycolysis, the TCA cycle, and β-oxidation to enhance ICI efficacy
Fig. 5Microbiota-derived metabolites and other gut microbial signature modulation of antitumor immune responses to improve ICI efficacy. A–E Microbiota-derived metabolites target immune cells and tumor cells to modulate antitumor immunity. Inosine, SCFA, and anacardic acid promote antitumor immunity and ICI efficacy; bile acid and tryptophan attenuate antitumor immune responses. F–H Other gut microbial signature modulation of antitumor immune responses. PG, PSA, and OMV promote anti-tumor immune responses by regulating immune cells, tumor cells, and cytokines
Fig. 6Therapeutic strategies utilizing the gut microbiome combined with ICI. Therapeutic strategies for manipulating gut microbiota include FMT, probiotics, engineered microbiome, and other strategies to increase ICI responses
Clinical trials of FMT modulate the efficacy and AEs of ICI www.clinicaltrials.gov
| NCT number | Cancer types | Intervention | Outcome(s) | Stage | References | |
|---|---|---|---|---|---|---|
| NCT04056026 | Mesothelioma | 1 | FMT + Pembrolizumab | PFS | Phase 1 | – |
| NCT04521075 | Melanoma, NSCLC | 50 | FMT + Nivolumab | FMT-related AEs, ORR | Phase 1–2 | – |
| NCT04130763 | Gastrointestinal | 10 | FMT + Anti-PD-1 | FMT-related AEs, ORR | Phase 1 | – |
| NCT04116775 | Prostate | 32 | FMT + Enzalutamide + Pembrolizumab | Anticancer effect | Phase 2 | – |
| NCT04729322 | dMMR CRC | 15 | FMT + Pembrolizumab–Nivolumab | ORR | Phase 1 | – |
| NCT04577729 | Melanoma | 60 | Allogenic FMT + ICI versus Autologous FMT + ICI | PFS | NA | – |
| NCT04758507 | RCC | 50 | Donor FMT + ICI versus Placebo FMT + ICI | PFS | Phase 1–2 | – |
| NCT04924374 | NSCLC | 20 | Anti-PD-1 + FMT versus Anti-PD-1 | Treatment safety and responses | NA | – |
| NCT04988841 | Melanoma | 60 | MaaT013 + Ipilimumab + Nivolumab versus placebo + Ipilimumab + Nivolumab | AE, ORR | Phase 2 | – |
| NCT03772899 | Melanoma | 20 | FMT + Pembrolizumab/Nivolumab | Safety, ORR | Phase 1 | – |
| NCT03341143 | Melanoma | 20 | FMT + Pembrolizumab | ORR | Phase 2 | [ |
| NCT03353402 | Melanoma | 40 | FMT + ICI | FMT-related AEs, | Phase 1 | [ |
| NCT04038619 | RCC | 40 | Loperamide + FMT + ICI | FMT-related AEs, ICI-related diarrhea/colitis | Phase 1 | – |
| NCT04163289 | Renal cancer | 20 | FMT + Nivolumab/Ipilimumab | Immune-related colitis | Phase 1 | – |
| NCT03819296 | Solid tumors | 800 | FMT + Infliximab/Vedolizumab | FMT-related AEs, ICI-related colitis | Phase 1–2 | – |
| NCT04883762 | Solid tumors | 10 | FMT + ICI | FMT-related AEs, ICI-related diarrhea | Phase 1 | – |
AEs: adverse events; CRC: colorectal cancer; dMMR: mismatch-repair deficiency; FMT: fecal microbiota transplant; ICI: immune checkpoint inhibitor; n: number of patients; NA: not applicable; NSCLC: non-small cell lung cancer; ORR: objective responses rate; PFS: progression-free survival; and RCC: renal cell carcinoma
Clinical trials of probiotics and engineered microbiomes in combination with ICIs in cancer treatment www.clinicaltrials.gov
| NCT number | Cancer types | Microbiome-based therapy | Intervention | Stage | References | |
|---|---|---|---|---|---|---|
| NCT03775850 | Solid tumors | 120 | EDP1503 ( | EDP1503 (capsules) + Pembrolizumab | Phase 1–2 | [ |
| NCT03637803 | Solid tumors | 132 | MRx0518 ( | MRx0518 (capsules) + Pembrolizumab | Phase 1–2 | – |
| NCT05107427 | Urothelial carcinoma | 30 | MRx0518 ( | MRx0518 (capsules) + Avelumab | Phase 2 | – |
| NCT03686202 | Solid tumors | 65 | MET-4 (Mixture of pure live cultures of intestinal bacteria) | MET (capsules) + ICI | Phase 1 | – |
| NCT04601402 | Solid tumors | 93 | GEN-001 ( | GEN-001 (capsules) + Avelumab | Phase 1 | – |
| NCT04187404 | Adrenal tumors | 60 | EO2401 (Vaccines of microbial-derived peptide homologous to tumor-associated antigens) | EO2401 + Nivolumab | Phase 1–2 | – |
| NCT04116658 | Glioblastoma | 52 | EO2401 (Vaccines of microbial-derived peptide homologous to tumor-associated antigens) | EO2401 + Nivolumab | Phase 1–2 | – |
| NCT03829111 | Renal cell carcinoma | 30 | CBM588 ( | Nivolumab + Ipilimumab versus CBM588 (capsules) + Nivolumab + Ipilimumab | Phase 1 | [ |
| NCT04167137 | Solid neoplasm, lymphoma | 70 | SYNB1891 (Engineered | SYNB1891 (intratumoral injection) + Atezolizumab | Phase 1 | – |
| NCT04208958 | Solid tumors | 54 | VE800 (11 commensal bacterial strains) | VE800 + Nivolumab | Phase 1–2 | – |
| NCT03817125 | Melanoma | 14 | SER-401 (Defined bacterial consortia) | SER-401 (capsules) + Nivolumab versus Placebo + Nivolumab | Phase 1 | – |
ICI: immune checkpoint inhibitor; n: number of patients