| Literature DB >> 35277453 |
Nick Lung-Ngai Ting1, Harry Cheuk-Hay Lau1, Jun Yu2.
Abstract
Despite the promising advances in novel cancer therapy such as immune checkpoint inhibitors (ICIs), limitations including therapeutic resistance and toxicity remain. In recent years, the relationship between gut microbiota and cancer has been extensively studied. Accumulating evidence reveals the role of microbiota in defining cancer therapeutic efficacy and toxicity. Unlike host genetics, microbiota can be easily modified via multiple strategies, including faecal microbiota transplantation (FMT), probiotics and antibiotics. Preclinical studies have identified the mechanisms on how microbes influence cancer treatment outcomes. Clinical trials have also demonstrated the potential of microbiota modulation in cancer treatments. Herein, we review the mechanistic insights of gut microbial interactions with chemotherapy and ICIs, particularly focusing on the interplay between gut bacteria and the pharmacokinetics (eg, metabolism, enzymatic degradation) or pharmacodynamics (eg, immunomodulation) of cancer treatment. The translational potential of basic findings in clinical settings is then explored, including using microbes as predictive biomarkers and microbial modulation by antibiotics, probiotics, prebiotics, dietary modulations and FMT. We further discuss the current limitations of gut microbiota modulation in patients with cancer and suggest essential directions for future study. In the era of personalised medicine, it is crucial to understand the microbiota and its interactions with cancer. Manipulating the gut microbiota to augment cancer therapeutic responses can provide new insights into cancer treatment. © Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.Entities:
Keywords: cancer; chemotherapy; enteric bacterial microflora; immunotherapy
Mesh:
Substances:
Year: 2022 PMID: 35277453 PMCID: PMC9185832 DOI: 10.1136/gutjnl-2021-326264
Source DB: PubMed Journal: Gut ISSN: 0017-5749 Impact factor: 31.793
Summary of modulation of chemotherapy efficacy and toxicity by microbiota
| Chemotherapy | Involved microbes | Mechanisms | Translational potential |
| Irinotecan (CPT-11) |
β-glucuronidase-expressing bacteria, especially |
Bacterial β-glucuronidase reactivated SN-38G to SN-38 in the gut, inducing significant intestinal toxicity and diarrhoea. |
Probiotics could reduce the activity of β-glucuronidase to decrease incidence of irinotecan-induced diarrhoea. |
| 5-Fluorouracil |
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Bacterial vitamins B6 and B9, ribonucleotide metabolism, and deoxynucleotide imbalance increased the efficacy of 5-FU. |
N/A. |
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| Floxuridine |
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N/A. |
| Camptothecin |
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N/A. |
| Gemcitabine |
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Bacterial long isoform cytidine deaminase metabolised gemcitabine into its inactive form. |
Intratumoral LPS, a surrogate marker for Gram-negative bacteria, could be used as a negative predictor of gemcitabine efficacy in PDAC. Antibiotic use was associated with improved gemcitabine response in patients with pancreatic cancer. |
| Cyclophosphamide |
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Patients receiving anti-Gram-positive antibiotics and cyclophosphamide/cisplatin concurrently had significantly lower PFS and OS. |
| Oxaliplatin |
Unspecified. |
Modulation of MYD88-dependent signalling pathway primed intratumoral myeloid cells for ROS production. |
N/A. |
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Immunogenic commensals (non-enterotoxigenic |
Epithelial cell apoptosis induced by oxaliplatin plus immunogenic commensals stimulated TFH cells to interact with B cells for IgG2b response and enhanced anticancer effector/memory CD8+ T cells. | ||
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Butyrate-producing bacteria. |
Butyrate activated CD8+ T cells via ID2-dependent IL-12 signalling to promote anticancer immune response. | ||
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Gram-negative bacteria with LPS component. |
Microbial LPS interacted with TLR4 on macrophages causing hyperalgesia. | ||
| Cisplatin |
Unspecified. |
Modulation of MYD88-dependent signalling pathway primed intratumoral myeloid cells for ROS production. |
Patients receiving anti-Gram-positive antibiotics and cyclophosphamide/cisplatin concurrently had significantly lower PFS and OS. |
AJCC, American Joint Committee on Cancer; CPT-11, irinotecan; CPT, camptothecin; CRC, colorectal cancer; CTX, cyclophosphamide; 5-FU, 5-fluorouracil; FUDR, floxuridine; ID2, inhibitor of DNA binding 2; IFN-γ, interferon-γ; IL, interleukin; LPS, lipopolysaccharides; MYD88, myeloid differentiation primary response 88; N/A, not applicable; OS, overall survival; PDAC, pancreatic ductal adenocarcinoma; PFS, progression-free survival; ROS, reactive oxygen species; SN-38, 7-ethyl-10-hydroxycamptothecin; TFH, follicular T helper; Th, T helper; TLR4, toll-like receptor-4; Treg, T regulatory cells.
Figure 1Mechanisms of microbiota modulation on chemotherapy response. (A) Irinotecan (CPT-11) is converted to SN-38 to elicit its cytotoxic effect after injection into the body. SN-38 is then detoxified by UGT in the liver to become SN-38G and excreted into the GI tract. The gut bacteria can reactivate and convert SN-38G back to SN-38, causing toxicity to intestinal cells. (B) Bacterial ribonucleotide metabolism activates fluoropyrimidine prodrugs into activated forms for cytotoxic effects. Vitamin B6 and B9 production is required for the metabolism. (C) Intratumoral Gammaproteobacteria with long isoform of cytidine deaminase can inactivate gemcitabine, leading to chemoresistance. (D) CTX increases intestinal permeability to promote Enterococcus hirae translocation into the spleen to increase pathogenic Th17 cells and intratumoral CD8+/CD4+ T cells ratio. (E) Gut microbes can prime tumour-infiltrating myeloid cells via MYD88-dependent pathway for ROS production in response to chemotherapeutic drugs. (F) Antigenicity from oxaliplatin-induced apoptosis of epithelial cells together with immunogenic bacteria, including non-enterotoxigenic Bacteroides fragilis and Erysipelotrichaceae, can stimulate the differentiation of migratory DCs to TFH cells for B cell activation. (G) Microbial metabolites such as butyrate can activate cytotoxic CD8+ T cells to enhance the efficacy of oxaliplatin. (H) Fusobacterium nucleatum can activate TLR4/MYD88-dependent pathway to inhibit certain miRNAs and switch tumour cells from apoptosis to autophagy, leading to chemoresistance. Figure created with BioRender.com. CPT-11, irinotecan; CTL, cytotoxic T lymphocyte; CTX, cyclophosphamide; DCs, dendritic cells; FdUMP, 5-fluorodeoxyuridine 5′-monophosphate; FUMP, 5-fluorouridine 5′-monophosphate; FUTP, 5-fluorouridine 5′-triphosphate; GzmB, Granzyme B; IFN-γ, interferon-γ; IL, interleukin; miRNA, microRNA; MYD88, myeloid differentiation primary response 88; NK, natural killer; PFN, perforin; pTH17 cells, pathogenic T helper 17 cells; ROS, reactive oxygen species; SN-38, 7-ethyl-10-hydroxycamptothecin; TFH, follicular T helper; TLR4, toll-like receptor-4; TNF-α, tumour necrosis factor alpha; Treg, T regulatory cells; UGT, uridine diphosphate glucuronosyltransferase.
Figure 2Mechanisms of microbiota modulation on immunotherapy response. Microbes–immunotherapy interactions could be categorised by the ‘TIME’ mechanistic framework: T cell mediation, Innate immunity, Metabolites, molecular mimicry, and Epithelial injury. (A) Bacteria such as Bacteroides, Burkholderiales and Bifidobacterium could enhance anticancer T cell immunity mediated by DCs for immunotherapy potentiation. (B) NK cells and proinflammatory M1 macrophages are the main contributors of innate immunity against cancer. Bifidobacterium could activate NK cells to combat cancers, while intratumoral microbiota ablation in PDAC could reprogramme M2 macrophages to M1 macrophages and reduce myeloid-derived suppressor cells. Altogether they increase the sensitivity of tumours to immunotherapy. (C) Bifidobacterium pseudolongum and Akkermansia muciniphila could secrete metabolite inosine. Inosine activates Th1 cells via adenosine 2A receptor costimulated by DCs. Other bacteria could improve ICI anticancer response via molecular mimicry. Bifidobacterium breve and Enterococcus hirae-infecting bacteriophage have SVY and TMP antigens, respectively, which are highly similar to tumour neoantigens. This leads to cross-reactivity of cytotoxic T cells against tumour cells. (D) Epithelial injury and immunogenic bacteria stimulate DCs for anticancer immunity. Figure created with BioRender.com. DCs, dendritic cells; ICI, immune checkpoint inhibitor; IL, interleukin; MDSC, myeloid-derived suppressor cells; NK, natural killer; PD-1, programmed cell death protein-1; PDAC, pancreatic ductal adenocarcinoma; SVY, SVYRYYGL; TFH, follicular T helper; Th, T helper; TMP, tape measure protein.
Mechanistic studies of modulation of immunotherapy effect by microbiota
| Immunotherapy | Involved microbes | Mechanisms |
| Anti-CTLA-4 mAbs |
| Stimulation of CD11b+ DCs improved IL-12–dependent Th1 immune response for enhanced antitumour immune response. |
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| Bacterial-derived inosine acted on adenosine 2A receptors to stimulate Th1 response in the presence of costimulations from DCs and enhanced tumour shrinkage. | |
| Anti-PD-1/PD-L1 mAbs |
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| LGG activated DCs via cGAS-STING-TBK1-IRF7-IFN-β cascade to enhance CD8+ T cell activity against tumour cells. | |
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| Molecular mimicry between SVY antigen of | |
| Bacteriophage-infecting | TMP of bacteriophage-infecting |
cGAS, cyclic GMP-AMP synthase; CTLA-4, cytotoxic T lymphocyte-associated antigen-4; DCs, dendritic cells; IFN-β, interferon-β; IL, interleukin; IRF7, interferon regulatory factor 7; LGG, Lactobacillus rhamnosus GG; mAbs, monoclonal antibodies; NK, natural killer; PD-1, programmed cell death protein-1; PD-L1, programmed death-ligand 1; PSMB4, proteasome subunit beta type-4; SIY, SIYRYYGL; STING, stimulator of IFN genes; SVY, SVYRYYGL; TBK1, TANK binding kinase 1; Th, T helper; TMP, tape measure protein; Treg, T regulatory cells.
Summary of gut microbes associated with immunotherapy response
| Immunotherapy | Patient cohort | Key findings |
| Anti-PD-1/PD-L1 mAbs |
43 patients with metastatic melanoma. |
R-enriched: Ruminococcaceae family and NR-enriched: Bacteroidales order ( Elevated abundance of CD8+ T cells in TME. |
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42 patients with metastatic melanoma. |
R-enriched: NR-enriched: FMT from R to germ-free mice enhances anti-PD-L1 mAbs response with T cell enrichment. | |
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249 patients (140 NSCLC, 67 RCC and 42 urothelial carcinoma). 338 patients with NSCLC. |
R-enriched: Associated with shorter PFS and OS: antibiotic use before or after first injection of ICIs. Associated with longer OS and ORR: Akk+ enriched: Ruminococcaceae family, Lachnospiraceae family and others. | |
| Monotherapy or combined immunotherapy |
27 patients with metastatic melanoma. |
Higher microbial diversity was associated with longer PFS. Associated with longer PFS: Associated with shorter PFS: Risk-associated pathways: L-rhamnose degradation, guanosine nucleotide biosynthesis and B vitamin biosynthesis. |
| Anti-CTLA-4 mAbs±anti-PD-1 |
39 patients with metastatic melanoma. |
R-enriched (combined anti-CTLA-4/anti-PD-1): R-enriched (anti-PD-1): |
| Anti-CTLA-4 |
26 patients with metastatic melanoma. |
R-enriched: R-depleted: |
Akk+, patients with detectable faecal Akkermansia muciniphila; CTLA-4, cytotoxic T lymphocyte-associated antigen-4; FMT, faecal microbiota transplantation; ICIs, immune checkpoint inhibitors; mAbs, monoclonal antibodies; NR, non-responders; NSCLC, non-small cell lung carcinoma; ORR, objective response rate; OS, overall survival; PD-1, programmed cell death protein-1; PD-L1, programmed death-ligand 1; PFS, progression-free survival; PFS, progression-free survival; R, responders; RCC, renal cell carcinoma; TME, tumour microenvironment.
Current challenges and future directions of microbiota application in clinical settings
| Aspects | Challenges | Future directions/potential solutions |
| Discovery of biomarkers | ||
| Developing predictive biomarkers for treatment response |
Suboptimal sensitivity and specificity. |
Combination of microbial features with other potential biomarkers such as tumour mutational loads. |
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Lack of consensus on the microbial features as predictive biomarkers. |
Standardisation of sample collection, processing and bioinformatics pipelines. Large clinical cohorts with different ethnicity. | |
| Modulation of microbiota to optimise cancer treatment outcomes | ||
| Antibiotic use and cancer therapy |
Concurrent use of prophylactic antibiotics and cancer therapy may lead to poor treatment outcomes. |
Use of autologous FMT may restore the dysbiosis induced by antibiotics. |
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Targeting cancer-promoting microbes with antibiotics is non-specific. |
Use of narrow-spectrum antibiotics. Development of highly specific approach such as use of phage and engineered microbes. | |
| Probiotics use and cancer therapy |
Lack of efficacy of using probiotics to reduce cancer treatment side effects. |
Consider probiotics as an adjuvant therapy to more efficacious treatments. Further investigations in multicentre, large-scale, phase III clinical trials with longer duration to confirm the effects of probiotics. |
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Potential risks of infection with probiotics use. |
Avoid the use of probiotics in immunocompromised or critically ill patients. | |
| FMT and cancer therapy |
Potential risk of transferring pathogens from donors to recipients. |
Use of capsules consisting of purified bacterial spores may be safer and have higher consistency than FMT. Rigorous and comprehensive donor screening. Characterise the baseline microbiota composition that is more likely to respond to FMT. |
FMT, faecal microbiota transplantation.