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Frontiers of Medicine

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Front. Med.    2022, Vol. 16 Issue (3) : 307-321    https://doi.org/10.1007/s11684-022-0927-0
REVIEW
Analysis of interactions of immune checkpoint inhibitors with antibiotics in cancer therapy
Yingying Li, Shiyuan Wang, Mengmeng Lin, Chunying Hou, Chunyu Li(), Guohui Li()
Pharmacy Department, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
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Abstract

The discovery of immune checkpoint inhibitors, such as PD-1/PD-L1 and CTLA-4, has played an important role in the development of cancer immunotherapy. However, immune-related adverse events often occur because of the enhanced immune response enabled by these agents. Antibiotics are widely applied in clinical treatment, and they are inevitably used in combination with immune checkpoint inhibitors. Clinical practice has revealed that antibiotics can weaken the therapeutic response to immune checkpoint inhibitors. Studies have shown that the gut microbiota is essential for the interaction between immune checkpoint inhibitors and antibiotics, although the exact mechanisms remain unclear. This review focuses on the interactions between immune checkpoint inhibitors and antibiotics, with an in-depth discussion about the mechanisms and therapeutic potential of modulating gut microbiota, as well as other new combination strategies.
Keywords tumor immunotherapy      immune checkpoint inhibitor      antibiotics      gut microbiota      drug–drug interaction     
Corresponding Author(s): Chunyu Li,Guohui Li   
Just Accepted Date: 22 April 2022   Online First Date: 06 June 2022    Issue Date: 18 July 2022
 Cite this article:   
Yingying Li,Shiyuan Wang,Mengmeng Lin, et al. Analysis of interactions of immune checkpoint inhibitors with antibiotics in cancer therapy[J]. Front. Med., 2022, 16(3): 307-321.
 URL:  
https://academic.hep.com.cn/fmd/EN/10.1007/s11684-022-0927-0
https://academic.hep.com.cn/fmd/EN/Y2022/V16/I3/307
Fig.1  ICI mechanisms. CTLA-4 on T cells binds with CD80/CD86 on antigen-presenting cells (APCs), preventing T cell activation. Blocking CTLA-4 with CTLA-4 inhibitor restores T cell function. PD-1 on T cells binds with PD-L1 on tumor cells, inhibiting T cell function and leading to tumor cell immune escape. PD-1 or PD-L1 blockade by PD-1 inhibitor or PD-L1 inhibitor releases T cell inhibition, enabling them to kill tumor cells.
irAEs Antibiotic use
Skin irAEs Grade 3 or 4; tetracycline antibiotics have been reported to be effective replacement drugs [42]
Gastrointestinal (GI) irAEs Grade 2, 3, or 4; use of antibiotics should be considered to prevent opportunistic infections [25]
Pneumonia irAEs Grade 2; prophylactic use of antibiotics should be considered Grade 3 or 4; antibiotics should be added to prevent opportunistic infections [25], and broad-spectrum antibiotics are recommended [42]
Renal irAEs Grade 2 or 3; antibiotics should be used when necessary to prevent opportunistic infections Grade 4; antibiotics are added to prevent opportunistic infections [25]
Tab.1  Examples of ICIs combined with antibiotics
Cancer type/patients Antibiotic type Antibiotic exposure Median PFS Median OS
mo vs. mo HR P mo vs. mo HR P
NSCLC/50 [41] β-lactams/quinolones Antibiotics vs. no antibiotics 4.1 vs. 12.4 − 0.004 11.3 vs. 15.3 − −
Prior-30 vs. post-antibiotics Similarly − − Similarly − −
NSCLC/RCC/UC/2740 [44] β-lactams Antibiotics vs. no antibiotics − 1.84 < 0.001 − 2.37 < 0.001
NSCLC/119 β-lactams Prior-antibiotics vs. no antibiotics − − − 2.5 vs. 26 9.3 < 0.001
Melanoma/38 Prior-antibiotics vs. no antibiotics − − − 3.9 vs. 14 7.5 < 0.001
Others/39 [45] Prior-antibiotics vs. no antibiotics − − − 1.1 vs. 11 7.8 < 0.001
NSCLC/157 [46] β-lactams/quinolones/macrolides Prior-30 vs. no antibiotics 2.2 vs. 3.3 − − 5.9 vs. 11.9 − −
High vs. low AIER (during ICIs) 1.9 vs. 3.5 1.053 0.0029 5.1 vs. 13.2 1.069 0.0001
NSCLC/218 [58] β-lactams/macrolides/quinolones Prior-60 vs. no antibiotics 1.4 vs. 5.5 2.22 < 0.01 1.8 vs. 15.4 2.61 < 0.05
c-antibiotics vs. no antibiotics 7.0 vs. 3.6 0.86 0.01 11.7 vs. 11.7 1.10 0.62
Post-30 vs. no antibiotics 3.6 vs. 4.5 1.15 0.59 17.5 vs. 11.5 0.86 0.62
NSCLC/90 [48] β-lactams Prior-30 vs. no antibiotics 1.2 vs. 4.4 − − 8.8 vs. < 8 2.02 0.19
NSCLC/60 [54] Tetracyclines/macrolides Prior-14 and/or post-14 vs. no antibiotics − 1.6 0.048 6.0 vs. 22.3 1.6 0.003
Fluoroquinolones Broad- vs. narrow-spectrum antibiotics − 1.895 − − − −
RCC/121 [57] β-lactams/quinolones Prior-30 vs. no antibiotics 1.9 vs. 7.4 3.1 < 0.01 17.3 vs. 30.6 3.5 0.03
Prior-60 vs. no antibiotics 3.1 vs. 7.4 2.3 < 0.01 23.4 vs. 30 1.9 0.15
NSCLC/239 [57] Prior-30 vs. no antibiotics 1.9 vs. 3.8 1.5 0.03 7.9 vs. 24.6 4.4 < 0.01
Prior-60 vs. no antibiotics − − − 9.8 vs. 21.9 2.0 < 0.01
NSCLC/melanoma/102 [62] β-lactams Antibiotics 30+ vs. antibiotics 30− 4.3 vs. 5.8 1.43 0.1 11.7 vs. 14.5 1.53 0.1
Fluoroquinolones Antibiotics + vs. antibiotics − 5.8 vs. 4.4 0.69 0.1 13.3 vs. 13.8 0.98 0.9
Tab.2  Narrative comparison of median PFS and OS between different antibiotic exposure groups based on univariable analysis
Fig.2  Proposed antibiotic immunomodulatory mechanisms influencing ICI anticancer efficacy via the gut microbiota in animal models and patients. Akkermansia muciniphila, Enterococcus hirae, Clostridiales, Ruminococcaceae, and Faecalibacterium can upregulate CD4+ and CD8+ T cell expression in peripheral blood (PB). A. muciniphila and E. hirae can upregulate central memory CD4+ T cell expression in mouse mesenteric lymph nodes (MLNs) and tumor-draining lymph nodes (TDLNs) and can upregulate the CD4+/Foxp3 ratio in mouse tumors, thus enhancing antitumor efficacy. Clostridiales, Ruminococcaceae, and Faecalibacterium can increase CD8+ T cell quantity in mouse tumors, thus enhancing antitumor efficacy. Bifidobacterium spp. and Bacteroides fragilis can induce dendritic cell (DC) maturation in TDLNs and further enhance IFN-γ secreting helper T cell 1 (TH1) immune responses. Bifidobacterium spp. also upregulate CD8+ T cell expression and class I and class II major histocompatibility complex (MHC) in TME. Bacteroidales can induce CD4+ T cell differentiation into regulatory T cells (Tregs) that secrete numerous anti-inflammatory cytokines (such as IL-10), leading to tumor immune escape and TH17 secretion of IL-17, which play important roles in promoting inflammatory responses. Immunosuppressive myeloid-derived suppressor cell (MDSC) differentiation is also increased in mice rich in B. fragilis and Bacteroidales, which promotes colon tumor occurrence.
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