
==== Front
Biomed J
Biomed J
Biomedical Journal
2319-4170
2320-2890
Chang Gung University

S2319-4170(24)00001-5
10.1016/j.bj.2024.100698
100698
Original Article
Gut microbiota and clinical response to immune checkpoint inhibitor therapy in patients with advanced cancer
Chang John Wen-Cheng ab
Hsieh Jia-Juan a
Tsai Chih-Yu a
Chiu Horng-Yih c
Lin Yu-Feng bd
Wu Chiao-En ab
Shen Yung-Chi a
Hou Ming-Mo ab
Chang Chieh-Ying a
Chen Jian-An a
Chen Chyi-Liang ef
Chiu Cheng-Tang gh
Yeh Yuan-Ming ymyeh@cgmh.org.tw
hij∗
Chiu Cheng-Hsun chchiu@cgmh.org.tw
ehk∗∗
a Division of Hematology-Oncology, Department of Internal Medicine, Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Taoyuan, Taiwan
b Immuno-Oncology Center of Excellence, Chang Gung Memorial Hospital, Taoyuan, Taiwan
c School of Medicine, Chang Gung University College of Medicine, Taoyuan, Taiwan
d Department of Nursing, Chang Gung Memorial Hospital, Taoyuan, Taiwan
e Molecular Infectious Disease Research Center, Chang Gung Memorial Hospital, Taoyuan, Taiwan
f Department of Microbiology and Immunology, Chang Gung University College of Medicine, Taoyuan, Taiwan
g Division of Gastroenterology and Hepatology, Department of Internal Medicine, Chang Gung Memorial Hospital, Taoyuan, Taiwan
h Chang Gung Microbiota Therapy Center, Chang Gung Memorial Hospital, Taoyuan, Taiwan
i Graduate Institute of Health Industry Technology, Chang Gung University of Science and Technology, Taoyuan, Taiwan
j Genomic Medicine Core Laboratory, Chang Gung Memorial Hospital, Taoyuan, Taiwan
k Division of Pediatric Infectious Diseases, Department of Pediatrics, Chang Gung Memorial Hospital, Taoyuan, Taiwan
∗ Corresponding author. Genomic Medicine Core Laboratory, Chang Gung Memorial Hospital, No.5, Fuxing St., Guishan Dist., Taoyuan City 333, Taiwan. ymyeh@cgmh.org.tw
∗∗ Corresponding author. Division of Pediatric Infectious Diseases, Department of Pediatrics, Chang Gung Memorial Hospital, No.5, Fuxing St., Guishan Dist., Taoyuan City 333, Taiwan chchiu@cgmh.org.tw
25 1 2024
10 2024
25 1 2024
47 5 1006988 8 2023
7 1 2024
14 1 2024
© 2024 The Authors
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Background

There is currently no well-accepted consensus on the association between gut microbiota and the response to treatment of immune checkpoint inhibitors (ICIs) in patients with advanced cancer.

Methods

Fecal samples were collected before ICI treatment. Gut microbiota was analyzed using 16 S ribosomal RNA sequencing. We investigated the relationship between the α-diversity of fecal microbiota and patients’ clinical outcomes. Microbiota profiles from patients and healthy controls were determined. Pre-treatment serum was examined by cytokine array.

Results

We analyzed 74 patients, including 42 with melanoma, 8 with kidney cancer, 13 with lung cancer, and 11 with other cancers. Combination therapy of anti-PD1 and anti-CTLA-4 was used in 14 patients, and monotherapy in the rest. Clinical benefit was observed in 35 (47.3 %) cases, including 2 complete responses, 16 partial responses, and 17 stable diseases according to RECIST criteria. No significant difference in α-diversity was found between the benefiter and non-benefiter groups. However, patients with α-diversity within the range of our healthy control had a significantly longer median overall survival (18.9 months), compared to the abnormal group (8.2 months) (p = 0.041, hazard ratio = 0.546) for all patients. The microbiota composition of the benefiters was similar to that of healthy individuals. Furthermore, specific bacteria, such as Prevotella copri and Faecalibacterium prausnitzii, were associated with a favorable outcome. We also observed that serum IL-18 before treatment was significantly lower in the benefiters, compared to non-benefiters.

Conclusions

The α-diversity of gut microbiota is positively correlated with more prolonged overall survival in cancer patients following ICI therapy.

Highlights

• α-diversity of gut microbiota positively correlated with more prolonged survival.

• α-diversity appeared to be a prognostic factor rather a predictive factor.

• Prevotella copri and Faecalibacterium prausnitzii were linked to a favorable outcome.

Keywords

Immune checkpoint inhibitors
Clinical response
Gut microbiota
==== Body
pmc1 At a glance of commentary

1.1 Scientific background on the subject

The relationship between gut microbiota and cancer therapy response has been previously studied; however, no well-accepted consensus on the association between gut microbiota and response to immune checkpoint inhibitors in advanced cancer patients has been reported in Asia.

1.2 What this study adds to the field

The α-diversity of gut microbiota is positively correlated with prolonged overall survival in cancer patients following therapy of immune checkpoint inhibitors (ICI), suggesting that α-diversity could potentially function as a prognostic factor rather than a predictive factor. Two specific bacterial species, Prevotella copri and Faecalibacterium prausnitzii, were associated with a favorable outcome. The level of serum IL-18 before the ICI treatment was lower in benefiters than that in non-benefiters.

2 Background

The human microbiota is estimated to consist of approximately 1013–1014 microbial cells, roughly a 1:1 ratio of microbial cells to human cells. The gut microbiota, in particular, is an intricate ecosystem composed of numerous bacterial species, with an estimated count of 3.8 × 1013 bacteria [1]. Recent studies suggested that human gut microbiota composition is closely linked to host health and can even contribute to diseases such as obesity and cancer [2,3]. As a result, manipulation of gut microbiota may hold potential as a promising approach to cancer treatment [3].

Gut microbiota may be influenced by antibiotics, chemotherapy, and immune checkpoint inhibitor (ICI). A previous study showed that applying antibiotics before ICI therapy negatively influenced the outcome, even after adjusting for prognostic risk factors [4]. The gut microbiota shapes systemic immune responses [5,6]. In the context of cancer, the role of intestinal microbiota in mediating immune activation in response to chemotherapeutic agents has been demonstrated [7,8]. Sivan et al. [9] reported that oral administration of Bifidobacterium alone improved tumor control to the same degree as PD-L1-specific antibody therapy, and that combination treatment nearly abolished tumor outgrowth. Gut microbiota is involved in immune regulation to promote the efficacy of cancer therapy [10]. Lam et al. [11] have shown that intestinal bacteria such as A. muciniphila produce metabolites that directly interact with monocytes to modulate the tumor microenvironment.

Gopalakrishnan et al. identified significant differences in the diversity and composition of gut microbiota between responders and non-responders to immunotherapy [12]. They found a higher α-diversity and relative abundance of Ruminococcaceae family bacteria in responding patients compared to non-responders by analyzing fecal samples from patients [12]. These findings suggested that gut microbiota may have a modulatory effect on anti-PD-1 immunotherapy response in melanoma patients [12]. Matson et al. recently reported a significant association between clinical response in melanoma patients undergoing immunotherapy and the composition of their commensal microbiota [13]. They analyzed baseline stool samples for selected bacterial species before initiating immunotherapy treatment. Responders possessed higher levels of Bifidobacterium longum, Collinsella aerofaciens, and Enterococcus faecium [13]. Routy et al. also demonstrated that fecal microbiota transplantation (FMT) transplant from cancer patients who have responded to ICIs into germ-free or antibiotic-treated mice enhances the antitumor effects of PD-1 blockade [14]. In contrast, FMT from non-responding patients had no effect [14].

Similar studies have been conducted in Asian countries. In Japan, Nishijima et al. found that the Japanese gut microbiome composition was more abundant in Actinobacteria at the phylum level, particularly the genus Bifidobacterium [15]. However, the study also demonstrated that Japanese gut microbiome significantly differed from those of other populations [15]. In Korea, Nam et al. has reported that human gut microbiota differed between countries: Korea, USA, Japan and China, but tended to vary less between individual Koreans [16]. In China, Cheng et al. has demonstrated that the gut microbiota was associated with the clinical response to immunotherapy in cancer patients. The gut microbiota of Archaea, Lentisphaerae, and Victivallaceae, were significantly enriched in the response group before immunotherapy [17]. The differences in gut microbial community structure among Asian countries is attributed to host genetics and diverse ethnic diets and lifestyle practices, perhaps leading to gut microbiota differences among Asian countries. In this study, we investigated the overall composition of the gut microbiota and the association of gut microbiota of cancer patients undergoing ICI therapy in Taiwan. We prospectively examined fecal microbiota from cancer patients before initiating ICI treatment and correlated the results with patients’ outcomes.

3 Methods

3.1 Patient cohort

We recruited Taiwanese patients with metastatic non-alimentary tract cancer who lived in Taiwan and were undergoing ICI therapy, had no acute or chronic infections, did not receive antibiotic treatment within one week before recruitment, did not have an autoimmune disease, were not on chronic steroid therapy, and had an expected life expectancy of over 3 months. Informed consent was obtained from 79 patients who agreed to provide blood and fecal samples for analysis under an approved protocol by the Institutional Review Board of Chang Gung Memorial Hospital (approval number 201801261B0 and 202101242B0). Two patients who did not receive immunotherapy and three who had incomplete data were excluded. Our study eventually included 74 patients who received anti-CTLA-4, anti-PD-1, and anti-PD-L1 immune checkpoint blockade therapy at CGMH from September 2018 to June 2021. We used the RECIST 1.1 criteria to evaluate the treatment response to ICIs every three months, including radiological evaluation. Patients are categorized as benefiters and non-benefiters. Benefiters are patients whose disease showed complete response, partial response, or stable disease, while lack of clinical benefit is determined by disease progression (PD) as non-benefiters. For microbiota comparison, we selected a cohort of 103 healthy controls in Taiwan, matched for age, sex, and body mass index. Gut microbiota profile of the controls was examined with the same method.

3.2 Fecal sample collection and DNA extraction

Stool samples were collected from patients using the Longsee Fecalpro kit (Longsee Medical Technology Co., China) within 2 days prior to treatment, 3-month and 6-month after ICI treatment. Samples were immediately transferred to the laboratory, where microbiome genomic DNA was extracted using the QIAamp PowerFecal DNA Kit (Qiagen, Germany) following the manufacturer's instructions. The extracted DNA was then stored at −80 °C until further processing.

3.3 Cytokine array

Blood samples were collected from the patients within 2 days prior to treatment, 3-month and 6-month after ICI treatment and stored for research purposes. We used the Proteome Profiler Human Cytokine Array Kit (ARY005B, R&D) to investigate cytokine production in the sera. The array was conducted according to the manufacturer's instructions, with a modified protocol for detection, and then photographed using the UVP Chemstudio (Analytic Jena, Jena, Germany). Cytokine expression was calculated as the fold change of the positive reference spots for each array.

3.4 16 S rRNA gene sequencing and analysis

Genomic DNA extracted from patient stool samples was used for amplicon sequencing targeting the 16 S rRNA V3–V4 region, following a previously described method [18]. The multiplex amplified libraries were sequenced on a MiSeq System with MiSeq Reagent Kit v3 (600 cycles) (Illumina, USA). Samples were demultiplexed with MiSeq Reporter v2.6 to assign paired-end reads to each sample barcode. Bioinformatics analysis was performed using USEARCH (v11.0.667_i86linux64) (https://drive5.com/usearch) to merge paired-end reads, quality filtering, denoise, and cluster zero-radius operational taxonomic units (ZOTUs) [19]. The RDP training set (version 16) was used as a reference species database [20], and taxonomic assignments were made using the SINTAX algorithm [21]. The SINTAX algorithm utilizes bootstrapping to generate the estimated confidence. If the confidence is 0.9, the prediction error rate is around 10%. Additionally, we used ZymoBIOMICS® Microbial Community DNA Standard (Zymo Research, CA, USA) as the positive control to verify the sensitivity of our analytical process. QIIME [22] was used to calculate α-diversity (e.g., Shannon and Chao1 index). Additionally, to identify bacteria with significantly different abundance between the benefiter and non-benefiter groups, Linear discriminant analysis effect size (LEfSe) was applied [23]. LDA scores were determined using the Kruskal-Wallis sum-rank test, and a significance threshold of greater than 3 was set.

3.5 Statistical analysis

We compared paired samples using the Kruskal-Wallis test for α-diversity and taxonomic levels. We used the Mann-Whitney U test to evaluate significant differences in IL-18 levels between the two groups. Statistical significance was determined at p < 0.05 (∗), p < 0.01 (∗∗), or p < 0.001 (∗∗∗). Additionally, we identified variables associated with survival rate using the Cox proportional hazards regression model. All statistical analyses were conducted using R (version 4.2.0) and SPSS V23.0 (SPSS Inc., Chicago, IL, USA).

4 Results

We used progression-free survival (PFS) and overall survival (OS) to divide the benefiter and non-benefiter patient groups after ICI therapy. Median PFS was 15 months in the benefiter group and 2.5 months in the non-benefiter group (p < 0.001, hazard ratio = 0.045) [Fig. 1A]. The median overall survival of the benefiter group was over 40 months, which was significantly longer than the 3 months in non-benefiter group (p < 0.001, hazard ratio = 0.106) [Fig. 1B].Fig. 1 Patients' progression-free survival (PFS) and overall survival (OS). Median PFS and OS were improved significantly in the benefiter group as compared to non-benefiter group.

Fig. 1

To characterize the gut microbiota in benefiter and non-benefiter patient groups undergoing ICI therapy, fecal samples were collected during routine clinic visits before treatment from the 74 patients who did not receive antibiotic treatment within one week. The patient characteristics are summarized in [Table 1]. Patient population consisted of 42 with melanoma, 8 with kidney cancer, 13 with lung cancer, 3 with hepatoma, 1 with cervical cancer, 1 with cholangiocarcinoma, 1 with gastric cancer, 1 with low gum cancer, 1 with sarcoma, 1 with thymic cancer, 1 with urothelial carcinoma, and 1 with skin squamous cell carcinoma. Monotherapy was administered to most patients, with 40 receiving nivolumab, 7 pembrolizumab, 3 atezolizumab, and 1 durvalumab. A combination of anti-PD1 and anti-CTLA-4 was used in 14 patients. Immunotherapy was administered as first-line treatment in 28 cases, second-line in 34, and third-line or later in 12 cases. Clinical benefit was observed in 35 (47.3 %) cases, including 2 complete responses, 16 partial responses, and 17 stable disease.Table 1 Clinical baseline information of the 74 patients.

Table 1	Benefiter	Non-benefiter	
Disease control rate	35 (47.3 %)	39 (52.7 %)	
Age, median (year)	62 (42–94)	61 (24–90)	
 >65	11 (31.4 %)	12 (30.8 %)	
 <65	24 (68.6 %)	27 (69.2 %)	
Sex	
 Male	20 (57.1 %)	23 (59.0 %)	
 Female	15 (42.9 %)	16 (41.0 %)	
Diagnosis	
 Mel	20 (57.1 %)	22 (56.4 %)	
 RCC	3 (8.6 %)	5 (12.8 %)	
 Lung cancer	6 (17.1 %)	7 (17.9 %)	
 Others	6 (17.1 %)	5 (12.8 %)	
IO treatment	
 Nivolumab	16 (45.7 %)	24 (61.5 %)	
 Pembrolizumab	2 (5.7 %)	5 (12.8 %)	
 Atezolizumab	0 (0 %)	3 (7.7 %)	
 Durvaluma	0 (0 %)	1 (2.6 %)	
 Double IO	10 (28.6 %)	4 (10.3 %)	
 Others	7 (20.0 %)	2 (5.1 %)	
Immunotherapy	
 First-line (n = 28)	16 (57.1 %)	12 (42.9 %)	
 Second-line (n = 34)	17 (50.0 %)	17 (50.0 %)	
 Later-line (n = 12)	2 (16.7 %)	10 (83.3 %)	
Response	
 CR	2	NA	
 PR	16	NA	
 SD	17	NA	
 PD	NA	39	
irAEs grade	
 0	15 (42.9 %)	29 (74.4 %)	
 1-2	15 (42.9 %)	7 (17.9 %)	
 3-5	5 (14.2 %)	3 (7.7 %)	
Abbreviations:Mel:melanoma;RCC:renal cell carcinoma;IO:immune oncology;CR:complete remission;PR:partial remission;SD:stable disease;PD:progression of disease; irAE:immune related adverse event.

The α-diversity with Chao1 richness did not significantly differ amongst healthy controls, benefiters, and non-benefiters based on clinical response ([Fig. 2A], p = 0.82), but significantly differed between non-benefiters and healthy controls ([Fig. 2B], p = 0.025). To define the “normal” and “abnormal” microbiota groups for patients undergoing ICI therapy by using α-diversity of the gut microbiota, the Chao1 richness value from the healthy controls was analyzed to define the interquartile range (IQR). We defined the values of α-diversity in the first quartile (325) to the third quartile (475) as belonging to the “normal” group, and those under the first quartile and over the third quartile as the “abnormal” group. There was no difference in PFS between the normal and abnormal groups in α-diversity (Chao1 richness) in all patients [Fig. 2C]. The median PFS was 4.9 months in the normal group and 2.5 months in the abnormal group (p = 0.201, hazard ratio = 0.714). However, a significant improvement in OS was observed in patients with normal α-diversity compared to abnormal patients [Fig. 2D]. The normal group achieved a significantly longer median OS of 18.9 months, compared to the abnormal group's 8.2 months (p = 0.041, hazard ratio = 0.546).Fig. 2 Microbial community variations between healthy controls, benefiters, and non-benefiters. Box plot (The normal group was defined by the interquartile range of the healthy control) of changes in Chao1 richness (A) and in Shannon diversity (B) are demonstrated. The progression-free survival (PFS) (C) and overall survival (OS) (D) between normal and abnormal groups in the 74 patients are shown. The definition of normal and abnormal groups is described in the text.

Fig. 2

Gut microbiota distribution of the benefiters was less different from that of healthy controls than the non-benefiters [Fig. 3A] in terms of the abundance of genus Prevotella (p = 0.097) and Faecalibacterium (p = 0.0039). The non-benefiters exhibited a significant reduction in the abundance of genus Fusicatenibacter ([Fig. 3B], p = 2.4e-13) and genus Lachnospiracea incertae sedis ([Fig. 3C], p = 7.3e-09), and a significant increase in the abundance of genus Escherichia/Shigella ([Fig. 3D], p = 0.00033). We utilized the SINTAX algorithm to improve confidence in estimating species-level taxa from 16 S rRNA gene analysis. Below are the bacterial species for which a confidence level exceeding 0.99 was achieved. Regarding the species level, gut microbiota distribution of benefiters was less different from that of healthy controls than the non-benefiters [Fig. 3E]. The non-benefiters showed a decrease in Prevotella copri (p = 0.54) and Faecalibacterium prausnitzii (p = 0.0041). Furthermore, Fusicatenibacter saccharivorans was significantly reduced in both benefiters and non-benefiters compared to healthy controls. In addition, non-benefiters had a significantly lower level of Fusicatenibacter saccharivorans than benefiters ([Fig. 3F], p = 2.4e-13).Fig. 3 Phylogenetic profiles of gut microbes among healthy controls and all patients. The relative abundance of gut microbiome at the genus level is demonstrated (A); genus Fusicatenibacter (B) and Lachnospiracea_incertae_sedis (C) were significantly reduced, but genus Escherichia/Shigella (D) was significantly increased in the non-benefiters. The relative abundance of gut microbiome at the species level is demonstrated (E). Species Fusicatenibacter_saccharivorans (F) was significantly decreased in both benefiters and non-benefiters, as compared to healthy controls, and also significantly decreased in the non-benefiters, as compared to the benefiters.

Fig. 3

To elucidate the microbial signature associated with response, Linear Discriminant analysis was used. The species Fusicatenibacter saccharivorans, genus Lachnospiracea incertae sedis, and genus Fusicatenibacter were found to be enriched in benefiters, whereas the fecal samples of patients in the non-benefiter group showed a significant overrepresentation [log10 (LDA score) > 3] of the genus Escherichia/Shigella [Fig. 4].Fig. 4 The differentially enriched bacteria in the benefiters, compared to the non-benefiters. LDA analysis demonstrated species Fusicatenibacter saccharivorans, genus Lachnospiracea incertae sedis and genus Escherichia/Shigella were significantly different between the benefiters and non-benefiters. The non-benefiter group is shown in wheat color, while the benefiter group in brown color.

Fig. 4

Cytokine analysis showed no detectable IL-18 in 5 out of 12 benefiters, whereas 21 out of 22 non-benefiters showed positive results. The fold change relative to the normal controls revealed a significant decrease (p = 0.0013) in the level of IL-18 in the sera of benefiters (0.085 ± 0.127) prior to ICT treatment, compared to non-benefiters (0.351 ± 0.266) [Fig. 5]. Regarding other cytokines tested by the array, there were no significant differences among the groups [Table S1].Fig. 5 The fold change in the level of IL-18. IL-18 is significantly decreased in the pre-treatment sera of the benefiters, compared to the non-benefiters.

Fig. 5

5 Discussion

In this study, we explored the association between gut microbiota in patients with solid tumors and their response to ICI. Lau et al. previously examined the crucial roles of commensal microbes in cancer progression, highlighting the significant influence of gut microbiota on three primary immunotherapy approaches: adoptive cell transfer, immune checkpoint blockade, and CpG-oligodeoxynucleotide therapy [24]. Alterations in the diversity and composition of the gut microbiome have been observed in various human epithelial tumors, including lung cancer [25], cervical cancer [26], and melanoma [27], all of which can influence the efficacy of PD-1-based immunotherapy [14]. Responders were found to have higher abundance of certain bacterial species, such as Bifidobacterium longum, Collinsella aerofaciens, and Enterococcus faecium in metastatic melanoma [12,13], and Bifidobacterium longum, Akkermansia muciniphila, and Prevotella corpri in NSCLC and RCC [14,28]. Additionally, specific bacteria were associated with efficacy of ICI in colon [29], head and neck [30], and melanoma [27] cancers. In animal models, oral administration of Bifidobacterium alone improved tumor control to the same extent as PD-L1-specific antibody therapy (checkpoint blockade), and notably, combinational treatment nearly eliminated tumor growth [9].

The presence of fecal Akkermansia muciniphila (Akk) has been linked to the clinical benefits of ICI in patients with non-small cell lung cancer (NSCLC) or kidney cancer in western countries [31]. In patients’ gut with A. muciniphila, there was a greater prevalence of commensal bacteria such as Eubacterium hallii and Bifidobacterium adolescentis and a more inflamed tumor microenvironment in some cases [31]. However, it is important to note that gut microbiota can vary based on race, geography, and diet. A recent study from Taiwan investigated the relationship between gut microbiota and ICI response in hepatocellular carcinoma [32]. The study found that three taxa, Bifidobacterium, Coprococcus, and Acidaminococcus were more abundant in patients with disease control. However, the baseline levels of the three taxa did not predict overall survival benefits. Our study mainly focused on non-gastrointestinal tract solid tumor cancers. Patients with such tumors may better preserve the gut environment for microbes to thrive, as such the microbiota may regulate the immune system to avoid direct interference from cancer. Our findings indicated that gut microbiota of patients who benefited from ICI therapy was similar to that of healthy controls. On the other hand, non-benefiters had significantly reduced levels of genus Lachnospiracea incertae sedis and significantly increased levels of genus Escherichia/Shigella. These bacteria are known to cause diarrheal disease and can interfere with the host immune system in a complex manner, potentially leading to reduced treatment response to ICI in cancer patients [33]. The role of Lachnospiracea incertae sedis in cancer patients remains poorly understood. Patients with Pemphigus vulgaris (PV), an autoimmune disease, exhibited gut dysbiosis, including reduced Lachnospiracea incertae sedis, which may contribute to immune disturbance associated with the development of PV [34]. However, in our cancer patients, this enhancement of autoimmunity in PV patients did not result in an excellent antitumor response to ICI treatment. This discrepancy highlights the complexity of the interaction between the immune system and different diseases. Interestingly, low serum level of IL-18 was associated with good treatment response to ICI. IL-18 was initially described as an interferon (IFN)γ‐inducing factor that could activate Th1, NK cells, Th2, IL‐17‐producing γδ T cells, and macrophages [35,36]. IL-18 induces immunosuppression by increasing PD-L1 expression [37]. Our data demonstrated that serum IL-18 levels were negatively associated with treatment response. However, it remains unclear whether the serum IL-18 was secreted by host immune cells or tumors in our cases. Future studies should explore host- and tumor-derived IL-18 to better understand its role in treatment response.

The microbiome's diversity is crucial in regulating the immune system [38]. For example, studies have shown that abundance of the phylum in Firmicutes was significantly reduced in the cancer group. Additionally, the phyla of Bacteroidetes and Firmicutes in the cancer group were significantly down-regulated and up-regulated, respectively, compared to the normal group. Specifically, Faecalibacterium prausnitzii, Bacteroides vulgatus, and Fusicatenibacter saccharivorans were considerably lower in the cancer group than in the normal group. Conversely, Prevotella copri, Mansonia uniformis, and Escherichia coli were significantly higher in the cancer group than in the normal group. These findings suggest that beneficial bacteria were reduced considerably in colorectal cancer (CRC) patients, while harmful bacteria were significantly increased in CRC patients [39].

Gut microbiome diversity was identified as an independent predictor of survival in cervical cancer patients who received chemoradiation [18]. However, this study did not observe a significant association between α-diversity and treatment response. Moreover, neither benefiters nor non-benefiters showed a significant difference in α-diversity, compared to healthy controls. Nevertheless, all patients with α-diversity within the normal range showed good overall survival, although PFS did not differ significantly. These findings suggest that α-diversity may serve as a prognostic factor for patients rather than a predictive factor for ICI response.

6 Conclusions

Some studies from Western countries have focused on targeting microbiota to improve therapeutic effectiveness, and preliminary data were encouraging [40,41]. The latest research underscores the complex interplay between gut microbiota and response to ICI therapy in cancer patients. In this study, the α-diversity of the gut microbiota showed a positive correlation with extended overall survival in cancer patients undergoing immune checkpoint inhibitor (ICI) therapy, indicating its potential as a prognostic factor rather than a predictive factor. Notably, two specific bacteria, Prevotella copri and Faecalibacterium prausnitzii, have been linked to favorable outcomes. These findings shed light on the intricate relationship between gut microbiota, immune response, and the effectiveness of ICI therapy in cancer patients. Further research in this area may help enhance treatment strategies and patient outcomes.

Declaration of competing interest

The authors have no financial or ethical conflicts of interest to report.

Appendix A Supplementary data

The following is/are the supplementary data to this article.Multimedia component 1

Multimedia component 1

Acknowledgements

The study and data collection processes were funded by the grants from 10.13039/100012553 Chang Gung Memorial Hospital (CIRPG3H0061‒2 , CORPG3J0151‒2 , CMRPG3M2071 and CMRPG3L0462 ), CGMH-NTHU Joint Research Program-2022-CORPG3M0041 , and 10.13039/100020595 National Science and Technology Council, Taiwan (109-2314-B-182A-103-MY3 and 110-2221-E-182A-003-MY3 ). The authors thank all the Genomic Medicine Core Laboratory members, Immune-Oncology Center of Excellence, Chang Gung Memorial Hospital, and Prof. Alex Chang for their invaluable help. The authors also thank Yu-Bin Pan, 10.13039/100007404 Clinical Trial Center , 10.13039/100012553 Chang Gung Memorial Hospital , for the statistical support with a grant (MOHW112-TDU-B-212-144005 ) from the 10.13039/100008903 Ministry of Health and Welfare of Taiwan .

Peer review under responsibility of Chang Gung University.

Appendix A Supplementary data to this article can be found online at https://doi.org/10.1016/j.bj.2024.100698.
==== Refs
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