| Literature DB >> 32010123 |
Lin Shui1, Xi Yang1, Jian Li2, Cheng Yi1, Qin Sun3, Hong Zhu1.
Abstract
Gut microbiota refers to the diverse community of more than 100 trillion microorganisms residing in our intestines. It is now known that any shift in the composition of gut microbiota from that present during the healthy state in an individual is associated with predisposition to multiple pathological conditions, such as diabetes, autoimmunity, and even cancer. Currently, therapies targeting programmed cell death protein 1/programmed cell death 1 ligand 1 or cytotoxic T-lymphocyte antigen-4 are the focus of cancer immunotherapy and are widely applied in clinical treatment of various tumors. Owing to relatively low overall response rate, however, it has been an ongoing research endeavor to identify the mechanisms or factors for improving the therapeutic efficacy of these immunotherapies. Other than causing mutations that affect gene expression, some gut bacteria may also activate or repress the host's response to immune checkpoint inhibitors. In this review, we have described recent advancements made in understanding the regulatory relationship between gut microbiome and cancer immunotherapy. We have also summarized the potential molecular mechanisms behind this interaction, which can serve as a basis for utilizing different kinds of gut bacteria as promising tools for reversing immunotherapy resistance in cancer.Entities:
Keywords: CTLA-4; PD-1/PD-L1; cancer immunotherapy; gut microbiome; resistance
Year: 2020 PMID: 32010123 PMCID: PMC6978681 DOI: 10.3389/fimmu.2019.02989
Source DB: PubMed Journal: Front Immunol ISSN: 1664-3224 Impact factor: 7.561
Figure 1Specific species of bacteria have proven to affect immune response to four different immunotherapies and possible mechanisms in recent studies. PD-1 programmed death receptor-1, PD-L1 programmed death-ligand 1, CTLA-4 cytotoxic T lymphocyte-associated protein 4, αPD-1, anti-PD-1 therapy; αPD-L1, anti-PD-L1 therapy; αCTLA-4, anti-CTLA-4 therapy; CpG+αIL10R, TLR9 ligand CpG plus anti-IL10R antibody.
Figure 2Compositional differences in the gut microbiome are associated with responses to anti-PD-1 immunotherapy. Pairwise comparisons by MW test of abundances of metagenomic species (MGS) identified by metagenomic WGS in fecal samples (n = 25): Responder (R) (n = 14, blue), Non-responder (NR) (n = 11, red). *p < 0.05, **p < 0.01. Colors reflect gene abundances visualized using “barcodes” with the following order of intensity: white (0)
Figure 3Baseline gut microbiota as a predictor of response to ipilimumab. Boxplot of the percentages of four dominant (>1% of total reads) genera differentially represented between both groups, i.e., Bacteroides, Faecalibacterium, Clostridium XIVa, and Gemmiger; LT_Benefit, long-term benefit vs. Poor Benefit; *p < 0.05; **p < 0.001 [The figure is reprinted with permission from Chaput et al. (68)].
Recent studies about the regulation of immunotherapy response or related toxicity by targeting gut microbiota.
| Frankel et al. ( | metastatic melanoma patients | ICI (including ipilimumab, nivolumab, ipilimumab plus nivolumab, and pembrolizumab) | High levels of anacardic acid, as microbial metabolites, could stimulate neutrophils and macrophages, and enhance T-cell recruitment to tumor metastases. | |
| Vetizou et al. ( | metastatic melanoma patients; mice model with sarcomas | CTLA-4 blockade | Affect IL-12-dependent TH1 immune responses. | |
| Chaput et al. ( | metastatic melanoma patients | CTLA-4 blockade (ipilimumab) | Faecalibacterium benefit were related to lower percentage of circulating α4+β7+ T cells and CD4+ Tregs. | |
| Dubin et al. ( | metastatic melanoma patients | CTLA-4 blockade (ipilimumab) | Decreased polyamine transport and B vitamin biosynthesis were associated with an increased risk of colitis. | |
| Sivan et al. ( | mice model with melanoma | PD-L1 blockade | Augmented DC function, enhanced CD8+ T cell priming and accumulation in the tumor microenvironment. | |
| Routy et al. ( | GF or ATB-treated mice; patients with advanced cancer | PD-1 blockade | Increasing the recruitment of CCR9+CXCR3+CD4+ T lymphocvtes into tumor beds. | |
| Gopalakrishnan et al. ( | melanoma patients | PD-1 blockade | High abundance of | |
| Higher diversity in the fecal microbiome: significantly prolonged PFS | ||||
| Zheng et al. ( | Patients with hepatocellular carcinoma (HCC). | PD-1 blockade | Responder-enriched species: | Not mentioned. |
| Non-responders-increased species: | ||||
| Jin et al. ( | Advanced NSCLC patients | PD-1 blockade (nivolumab) | Higher diversity of gut microbiome: prolonged PFS; | Responders had a greater frequency of unique memory CD8 T cell and NK cell subsets. |
| Derosa et al. ( | advanced RCC and NSCLC patients | PD-L1 blockade | ATB treatment: decreased PFS and OS | Not mentioned. |
| Tanoue et al. ( | CRC mice model | ICIs | GF mice with | |
| Cremonesi et al. ( | CRC mice model | ACT | Abundance of microbiota were correlated with high chemokine expression and enhanced T cell infiltration. | |
| Uribe-Herranz et al. ( | HPV E6/7-expressing cervical cancer mice model | ACT | Vancomycin treatment: increased ACT efficacy; | Vancomycin-treated mice had increased systemic CD8α+ DCs and IL-12p70 levels and more effective expansion ACT cells. |
| Iida et al. ( | antibiotics-treated or GF mice | CpG-oligonucleotide | Antibiotic treatment induced lower cytokine production (TNF), diminished expression of pro-inflammatory gene ( |
The promising strategies to reverse the immunotherapy resistance by manipulating gut microbiota.
| FMT from responders | Effective in previous trials; ameliorate other immunotherapy- related symptoms | Unidentified composition and pathogenicity; | Select and limit transplanted organisms from a healthy donor |
| Prebiotic supplement | Abundance of supposedly beneficial bacteria | Display inter-individual variation; | Use patient-specific metadata and artificial intelligence to personalize dietary interventions |
| Microbiome-based metabolite therapy | Promising results in preliminary SCFA or flavonoids using | Unexpected interactions between metabolites and members of the microbiome to produce inactive or toxic form; | Require reproducible, stable and easy administered production; |
| Metagenome sequencing as a tool to predict immunotherapy response | Avail in stratify responders from non-responders; | Complicated analysis process; | Need more reasonable standards |
| Proper oral antibiotics to deplete the unfavorable bacterial taxa | Apply easily and conveniently; | Misuse and overuse lead to dysbiosis | Specific and accurate targeting to an individual species of bacteria |