Gut microbiota diversity and composition in predicting immunotherapy response and immunotherapy-related colitis in melanoma patients: A systematic review

Table 1 Risk of bias assessment with Risk of Bias In Non-randomised Studies - of Interventions

Ref.	Confounding	Selection	Intervention classification	Deviation from intervention	Missing data	Measurement of outcome	Selection of reported result	Overall
Dubin et al[33], 2016	Moderate	Low	Low	Low	Low	Low	Low	Moderate
Chaput et al[27], 2017	Moderate	Low	Low	Moderate	Low	Low	Moderate	Moderate
Gopalakrishnan et al[12], 2018	Moderate	Low	No information	Low	Low	Low	Low	Moderate
Matson et al[29], 2018	Moderate	No information	Serious	No information	No information	Low	Moderate	Serious
Peters et al[30], 2019	Moderate	Low	No information	No information	Low	Low	Low	Moderate
Baruch et al[26], 2020	Moderate	Low	Low	Low	Moderate	Low	Low	Moderate
Wind et al[31], 2020	Moderate	No information	No information	No information	No information	Low	Low	Moderate
Davar et al[28], 2021	Moderate	Moderate	Low	Low	No information	Low	Low	Moderate
Andrews et al[36], 2021	Moderate	Low	Low	Low	Low	Low	Low	Moderate

Table 2 Characteristics of included studies exploring link between gut microbiome composition and diversity and response to immune-checkpoint blockade therapy in metastatic melanoma patients treated with immune-checkpoint blockade therapy

Ref.	Year	Therapy	Method	Sample size/ time point	Dominant microbes	Microbial diversity
Chaput et al[27], 2017	2017	Anti-CTLA-4	16S rRNA gene sequencing of fecal samples	26 before tx	Responders: Faecalibacterium and Firmicutes	N/A
Matson et al[29], 2018	2018	Anti-PD-1 or anti-CTLA-4	16S rRNA gene and shotgun metagenome sequencing of fecal samples; qPCR on selected bacteria	42 before tx	Responders: Bifidobacterium longum, Collinsella aerofaciens, and Enterococcus faecium Non-responders: Ruminococcus obeum and Roseburia intestinalis	N/A
Gopalakrishnan et al[12], 2018	2018	Anti-PD-1	16S rRNA gene and shotgun metagenome sequencing of fecal samples	43 before tx	Responders: Clostridiales, in particular Faecalibacterium Non-responders: Bacteroidales, in particular Bacteroides thetaiotaomicron; as well as Escherichia coli, and Anaerotruncus colihominis	Higher alpha diversity in patients with longer PFS
Peters et al[30], 2019	2019	Anti-PD-1 or anti-CTLA-4	16S rRNA gene and shotgun metagenome sequencing of fecal samples	27 before tx	Responders: Faecalibacterium, Parabacteroides, and Faecalibacterium prausnitzii Non-responders: Bacteroides and Biophilia	Higher microbial community richness and diversity was associated with longer PFS
Wind et al[31], 2020	2020	Anti-PD-1 or anti-CTLA-4	Shotgun metagenome sequencing of fecal samples	25 before tx	Responders: Ruminococcus gnavus, Streptococcus parasanguinis, and Bacteroides massiliensis. Non-responders: Bifidobacterium longum and Peptostreptococcaceae	No significant difference in alpha-diversity between responder and non responders
Baruch et al[26], 2020	2020	Anti-PD-1 refractory	16S rRNA gene and shotgun metagenome sequencing of fecal samples	10 anti-PD-1 refractory patients	FMT donors (responders): Lachnospiraceae, Veillonellaceae, and Ruminococcaceae Post FMT Responders: Enterococcaceae, Enterococcus, and Streptococcus australis Non-responders: Veillonella atypica	No significant difference in GM composition prior to FMT, but significant difference post-FMT between responders and non-responders Lower microbial richness in the donor of responding recipients
Davar et al[28].2021	2021	Anti-PD-1 refractory	Shotgun metagenomic sequencing of fecal samples	15 anti-PD-1 refractory patients, before FMT	Responders: Firmicutes (Lachnospiraceae and Ruminococcaceae families) and Actinobacteria (Bifidobacteriaceae and Coriobacteriaceae families)	Higher GM diversity of donors who were complete responders compared to donors who were partial responders No significant difference in GM diversity between donors and recipients prior to FMT
Andrews et al[36], 2021	2021	Combined ICB - Anti-PD-1 and anti-CTLA-4	16S rRNA gene and shotgun metagenome sequencing of fecal samples	38	Responders: Bacteroides stercoris, Parabacteroides distasonis, Fournierella massiliensis. Non-responders: Klebsiella aerogenes and Lactobacillus rogosae	No significant difference in GM diversity between responders and non-responders

Anti-CTLA-4: Anti-cytotoxic T lymphocyte-associated antigen-4; N/A: Not applicable; Anti-PD-1: Anti-programmed death-1; qPCR: Quantitative real-time polymerase chain reaction; FMT: Fecal microbiota transplant; GM: Gut microbiome; ICB: Immune checkpoint blockade therapy.

Table 3 Characteristics studies exploring link between gut microbiome composition and diversity and immune-checkpoint blockade therapy-related colitis in metastatic melanoma patients treated with immune-checkpoint blockade therapy

Ref.	Year	Therapy	Method	Sample size/ timepoint	Dominant microbes	Microbial diversity
Dubin et al[33], 2015	2015	Anti-CTLA-4 immunotherapy	16S rRNA gene and shotgun metagenome sequencing of fecal samples	34	Colitis-resistant: Bacteroidetes (Bacteroidaceae, Rikenellaceae and Barnesiellaceae)	No significant difference in microbial richness and diversity
Chaput et al[27], 2017	2017	Anti-CTLA-4 immunotherapy	16S rRNA gene sequencing of fecal samples	26	Colitis-resistant: Bacteroidetes; Colitis-prone: Firmicutes	Decreased bacterial diversity was associated with colitis
Andrews et al[36], 2021		Combined ICB - Anti-PD-1 and anti-CTLA-4	16S rRNA gene and shotgun metagenome sequencing of fecal samples	38	Colitis resistant: Firmicutes; Colitis prone: Bacteriodetes	No significant difference in alpha diversity

Anti-CTLA-4: Anti-cytotoxic T lymphocyte-associated antigen-4; N/A: Not applicable; Anti-PD-1: Anti-programmed death-1; qPCR: Quantitative real-time polymerase chain reaction; ICB: Immune checkpoint blockade therapy.