The Gut Microbiome from a Biomarker to a Novel Therapeutic Strategy for Immunotherapy Response in Patients with Lung Cancer
Abstract
:1. Introduction
2. Microbiota Profiling of Patients with NSCLC
2.1. Gut Microbiome in NSCLC Patients
2.2. Lung Microbiome in Lung Cancer Patients
2.3. Impact of Concurrent Medications on ICI Responses in Cancer
3. Modulation of the Gut Microbiome to Improve the Efficacy of Anti-PD-1/PD-L1
3.1. Fecal Microbiota Transplantation
3.2. Probiotics
3.3. Diet Evaluation and Prebiotics
3.4. Other Techniques
3.5. Discussion and Conclusions
Author Contributions
Funding
Conflicts of Interest
Abbreviations
ALK | Anaplastic lymphoma kinase |
ATB | Antibiotics |
BAL | Bronchoalveolar lavage |
Bcl-2 | B-cell lymphoma 2 |
Bcl-XL | B-cell lymphoma-extra large |
CagA | Cytotoxin-associated gene |
CD8 | Cluster of differentiation 8 |
CRC | Colorectal cancer |
CTLA-4 | Cytotoxic T-lymphocyte-associated antigen 4 |
DNA | Deoxyribonucleic acid |
DOR | Duration of response |
EGFR | Epidermal growth factor receptor |
EMA | European Medicine Agency |
ERK | Extracellular signal-regulated kinase |
FFQ | Food Frequency Questionnaire |
FMT | Fecal Microbiota Therapy |
FOXP3 | Forkhead box P3 protein |
GF | Germ-free |
GM | Gut microbiome |
IBD | Inflammatory bowel disease |
ICIs | Immune checkpoint inhibitors |
IHC | Immunohistochemistry |
irAEs | Immune-related adverse effects |
mAb | Monoclonal antibody |
MDSC | Myeloid-derived suppressor cells |
MET | Microbial ecosystem therapeutics |
MHC | Major histocompatibility complex |
NGS | Next-generation sequencing |
NK | Natural killer cells |
NSCLC | Non-small cell lung cancer |
ORR | Objective response rate |
OS | Overall survival |
PD-1 | Programmed cell death protein 1 |
PD-L1 | Programmed cell death ligand 1 |
PFS | Progression-free survival |
PI3K | Phosphoinositide 3-kinase |
PPI | Proton pump inhibitor |
QoL | Quality of life |
RCC | Renal cell carcinoma |
rCDI | Refractory Clostridium difficile infection |
rDNA | Ribosomal deoxyribonucleic acid |
RECIST | Response evaluation criteria in solid tumors |
RMT | Oral restorative microbiota therapy |
rRNA | Ribosomal ribonucleic acid |
SCLC | Small cell lung cancer |
SPF | Specific pathogen free |
TLR | Toll-like receptor |
TKI | Tyrosine kinase inhibitor |
TP53 | Tumor protein 53 |
USFDA | United States Food and Drug Administration |
VEGF | Vascular endothelial growth factor |
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References | Sample | Type of Study | ICI | Stage of NSCLC | N | Technique | Responders | Non Responders | Notes | Country |
---|---|---|---|---|---|---|---|---|---|---|
(Routy et al., 2018) [22] | Feces | Retrospective | Anti-PD-1 | All stages | 153 | WGS | Higher: Akkermansia muciniphila, Ruminococcus spp., Allistepes spp. and Eubacterium spp. | Higher: Parabacteroides distasonis, Bacteriodes nordii | ATB uptake negatively impacts OS, but proton pump inhibitor did not. | France |
(Derosa et al., 2022) [32,33] | Feces | Prospective | Anti-PD-1 | All stages | 338 | WGS | Higher: A. muciniphila, Eubacterium hallii, and Bifidobacterium adolescentis | Higher: Clostridium innoccuum | Stools with Akkermansia above the 77th percentile is deleterious. | France, Canada |
(Newsome at al., 2022) [36] | Feces | Prospective | Anti-PD-1/PD-L1 or anti-PD-L1 and anti-CTLA-4 combination | Advanced | 65 | 16S rRNA (V1-V3) | Higher: Ruminococcus, Akkermansia, Blautia, and Faecalibacterium | NA | RNAseq on fecal RNA (N = 10) showed different bacterial transcriptomes within responders and non-responders, such as carbon fixation pathway enriched in prokaryotes in responders while non-responders were enriched in phosphotransferase system. | United States |
(Martini et al., 2022) [38] | Feces | Prospective | Anti-PD-L1 | Advanced | 10 | 16S rRNA (V4) | Higher: Agathobacter M104/1 and Blautia SR1/5 | NA | All patients received ICI as cetuximab + avelumab combination. | Italy |
(Katayama et al., 2019) [39] | Feces | Retrospective | Anti-PD-1 | Advanced | 17 | 16S rRNA (V1-V2) | Higher: Lactobacillus, Clostridium, and Syntrophococcus | Higher: Sutterella, Bilophila and Parabacteroides | Patients with higher abundance of Lactobacillus and Clostridium also had longer treatment to TTF. | Japan |
(Hakozaki et al., 2020) [40] | Feces | Prospective | Anti-PD-1/PD-L1 | Advanced | 70 | 16S rRNA (V3-V4) | Higher: Agathobacter and Ruminococcaceae UCG 13 | Higher: Eggerthellaceae and Barnesiella | ATB use was associated with lower α-diversity. Lactobacillaceae and Raoultella were enriched in patients with no significant irAE. | Japan |
(Shoji et al., 2021) [41] | Feces and Saliva | Prospective | Anti-PD-1/PD-L1 | Stage II/III/IV | 28 | 16S rRNA (V3-V4) | Higher: Blautia | Higher: RF32 unclassified | Responders had higher α-diversity but lower β-diversity in feces. No significant signal was found from saliva. | Japan |
(Ouaknine et al., 2018) [42] | Blood | Prospective | Anti-PD-1 | Advanced | 35 | 16S rRNA (V3-V4) | Higher: Peptostreptococcaceae, Lewinella, Paludibaculum, and Holophagae | Higher: Gemmatimonadaceae | Presence of Gemmatimonadaceae at baseline was associated with worse PFS and OS. | France |
(Jin et al., 2019) [43] | Feces | Prospective | Anti-PD-1 | Advanced | 37 | 16S rRNA (V3-V4) | Higher: Alistipes putredinis, Bifidobacterium longum, and Prevotella copri | Higher: Ruminococcus_unclassified | Responders had higher α-diversity. High α-diversity was associated with enhanced memory T cell and NK cell signatures. | China |
(Song et al., 2020) [44] | Feces | Prospective | Anti-PD-1 | Advanced | 63 | WGS | Higher: Parabacteroides and Methanobrevibacter | Higher: Veillonella, Selenomonadales, and Negativicutes | Responders had higher β-diversity. Differences in KEGG functional group and metabolic potential of methanol and methane were also noted. | China |
(He et al., 2021) [45] | Feces | Prospective | Anti-PD-1 | Advanced | 16 | 16S rRNA (V3-V4) | Higher: Escherichia, Shigella, Akkermansia, and Olsenella | Higher: Anaeroglobus | Escherichia-Shigella was positively correlated with IL-12, IFN-γ, and basophils in plasma. Akkermansia was positively correlated with monocytes. | China |
(Zhang et al., 2021) [46] | Feces and Saliva | Prospective | Anti-PD-1 | Advanced | 75 | 16S rRNA (V3-V4) | Higher (in feces): Desulfovibrio, Actinomycetales, Bifidobacterium, Odoribacteraceae, Anaerostipes, Rikenellaceae, Faecalibacterium, and Alistipes | Higher (in feces): Fusobacterales, Fusobacteriia, Fusobacterium, Fusobacteria, and Fusobacteriaceae | Responders had higher α-diversity in feces. The abundance of Streptococcus in saliva was associated with higher CD8+ T cell density. α-diversity between feces and saliva microbiota was uncorrelated. | China |
(Masuhiro et al., 2022) [47] | BAL | Prospective | PD-1 | Advanced | 12 | 16S rRNA (V3-V4) | Higher: Bacteriodetes | Higher: Proteobacteria | Responders had higher α-diversity and CXCL9 levels in BAL. | Japan |
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Duttagupta, S.; Hakozaki, T.; Routy, B.; Messaoudene, M. The Gut Microbiome from a Biomarker to a Novel Therapeutic Strategy for Immunotherapy Response in Patients with Lung Cancer. Curr. Oncol. 2023, 30, 9406-9427. https://doi.org/10.3390/curroncol30110681
Duttagupta S, Hakozaki T, Routy B, Messaoudene M. The Gut Microbiome from a Biomarker to a Novel Therapeutic Strategy for Immunotherapy Response in Patients with Lung Cancer. Current Oncology. 2023; 30(11):9406-9427. https://doi.org/10.3390/curroncol30110681
Chicago/Turabian StyleDuttagupta, Sreya, Taiki Hakozaki, Bertrand Routy, and Meriem Messaoudene. 2023. "The Gut Microbiome from a Biomarker to a Novel Therapeutic Strategy for Immunotherapy Response in Patients with Lung Cancer" Current Oncology 30, no. 11: 9406-9427. https://doi.org/10.3390/curroncol30110681
APA StyleDuttagupta, S., Hakozaki, T., Routy, B., & Messaoudene, M. (2023). The Gut Microbiome from a Biomarker to a Novel Therapeutic Strategy for Immunotherapy Response in Patients with Lung Cancer. Current Oncology, 30(11), 9406-9427. https://doi.org/10.3390/curroncol30110681