| Literature DB >> 33816289 |
Chengliang Huang1,2, Meizhang Li3, Ben Liu4, Huanbo Zhu2,5, Qun Dai2, Xianming Fan1, Kathan Mehta2, Chao Huang2, Prakash Neupane2, Fen Wang6, Weijing Sun2, Shahid Umar7,8, Cuncong Zhong4, Jun Zhang2,8.
Abstract
Background: Gut microbiome is proved to affect the activity of immunotherapy in certain tumors. However, little is known if there is universal impact on both the treatment response and adverse effects (AEs) of immune checkpoint inhibitors (ICIs) across multiple solid tumors, and whether such impact can be modulated by common gut microbiome modifiers, such as antibiotics and diet.Entities:
Keywords: antibiotics; cancer immunotherapy; diet; gut microbiome; immunotherapy; microbiota; modulating factors; solid tumors
Year: 2021 PMID: 33816289 PMCID: PMC8012896 DOI: 10.3389/fonc.2021.642110
Source DB: PubMed Journal: Front Oncol ISSN: 2234-943X Impact factor: 6.244
Figure 1The PRISMA flow diagram of selecting publications to study the correlation of gut microbiome with the efficacy and adverse effects of ICIs across various solid tumors. In total 10 studies were included for the analysis of gut microbiome in correlating with the therapeutic efficacy of immunotherapy, and three studies for toxicity (adverse effects).
Figure 2Correlation of gut microbiome to the treatment response of ICIs across various solid tumors. A phylogenetic tree was constructed using the phyloT software (https://phylot.biobyte.de) to capture and categorize all bacterial taxa reported to be associated with the treatment response from ICI in clinical studies across various solid tumors, ranking from phylum to species inside-out. Bacteria correlated to better response were labeled in green, and poor response in red. Those with mixed reports were labeled in gray. The lowercase alphabetical letters next to each bacterium indicate the individual studies from which bacterial taxa information was derived. The asterisks (*) indicate identified bacteria taxa at the genus level.
Figure 3Correlation of gut microbiome to the toxicity of ICIs across various solid tumors. A phylogenetic tree was constructed using the phyloT software (https://phylot.biobyte.de) to capture and categorize all bacterial taxa reported to be associated with the adverse effects from ICI treatment in clinical studies across various solid tumors, ranking from phylum to species inside-out. Bacteria correlated to less toxicity were labeled in green, and more toxicity in red. The lowercase alphabetical letters next to each bacterium indicate the individual studies from which bacterial taxa information was derived. The asterisks (*) indicate identified bacteria taxa at the genus level.
Figure 4The impact of antibiotic exposure on ICI treatment across various solid tumors. (A) A schematic illustration showing studies with either negative or no association between antibiotic use and clinical outcome from ICI treatment. The study name, sample size and retrospective vs. prospective nature are all labeled. (B) Studies (including both retrospective and prospective) that have antibiotic use within 2 months prior to the initiation of ICI treatment were universally associated with poor clinical outcome. (C) Detail timing and duration of antibiotic use for studies shown in (B). n: sample size; P: prospective study; R: retrospective study; *: mixed results based on the timing of antibiotic use.
Figure 5The impact of dietary intervention on gut microbiome. To minimize the confounding factors, only studies on healthy adults were included. (A) The alterations of gut microbiome after dietary intervention are displayed in 3 lines, which represent increase, no change and decrease in each category (red: Firmicutes; purple: α diversity; orange: Proteobacteria; and green: Verrucomicrobia). Solid and hollow circles represent plant- and animal-based diet, respectively. (B) A Fisher's exact test to compare the effect of plant- vs. animal-based diet on the enrichment of “ICI-favoring” vs. “ICI-unfavoring” gut microbiota (P = 0.0476).