Hepatocellular carcinoma (HCC) is one of the most common neoplasms encountered, and its incidence is increasing worldwide because of the increasing prevalence of hepatitis B and C virus infection.[1–3] The prognosis of patients with HCC at very early or early stages has improved because of advances in diagnosis and treatment modalities. Unfortunately, 70%–80% of patients cannot benefit from such opportunities because they are diagnosed at an advanced stage, and sorafenib has been the only systemic therapeutic agent available. During the last decade, more than ten drugs have failed to meet clinical endpoints in phase III trials.[4–7]
During recent years, new immune-modulatory agents were introduced for oncological treatment, eventually leading to the clinical breakthrough of checkpoint inhibitors (CPIs) targeting programmed death-1 (PD-1), programmed death-ligand 1 (PD-L1), or cytotoxic T lymphocyte antigen-4 (CTLA-4).[8,9] Therapies targeting the programmed death ligand 1 (PD-1)/programmed death ligand 1 (PD-L1) checkpoints have produced impressive responsive rates in patients with metastatic disease refractory to multiples lines of therapy. PD-L1 expression in tumor cells is correlated with poorer survival, with peritumoral hepatocytes, and with circulation levels and could be a good target for the treatment of advanced stages of HCC, even in an adjuvant setting after hepatectomy. Recently, more and more patients received ICIs combined with surgical resection, chemotherapy, radiotherapy, Tyrosine kinase inhibitors (TKIs), Transarterial chemoembolization (TACE) and others cytotoxic agents or ICIs and acquired acceptable outcomes.[10–12]
Recent studies have observed that intestinal bacteria can also play a role in this process. Increased intestinal permeability, which is a hallmark of cirrhosis, exposes the liver to a great amounts of bacteria and bacterial components,[13,14] such as lipopolysaccharide (LPS), derived from the gut. The interaction between LPS and toll-like receptor 4 (TLR4) is crucial in the initiation and promotion of hepatocarcinogenesis through inflammation, chronic liver injury, and fibrosis.[15,16] This complex proinflammatory network triggered by gut microbes involves resident liver cells and bone marrow-derived mononuclear cells that are known to favor HCC progression through modulation of the immune response. Studies have explored the qualitative and/or quantitative differences between the gut microbiota of patients with and without HCC, however, no study focused on the changing of gut microbiota in patients who received immunotherapy which could significantly impact on the type and quantity of microbiota.[17–20]
In this study, we explored the characteristics of gut microbiota in patients with primary hepatocellular carcinoma in advanced stage who received immune checkpoint inhibitors based on a large population with HBV infection. The results from this study would give the evidence for future researches on improving outcome of ICIs in patients with HCC.
2 Subjects and methods
2.1 Patient cohort
An initial cohort of 65 patients with metastatic melanoma were included in this study. All patients were treated with immune checkpoint inhibitors therapy at Fujian provincial geriatric hospital between August 2016 and June 2018 and signed voluntary informed consent for collection and analysis of tumor, blood and microbiome samples under Institutional Review Board (IRB)-approved protocols (IRB GA28493). Patients who were diagnosed with uveal HCC (n = 10), or who received ICIs in combination with targeted agents or with adoptive T cell transfer therapy (n = 7), or in whom response could not be determined (n = 6) were excluded from this analysis. Electronic medical charts were reviewed independently by three investigators to assign clinical response group and document other clinical parameters. The primary outcome of clinical response (responder, R) was defined by radiographic evidence of complete response, partial response or stable disease per RECIST 1.1 criteria for at least 6 months. Lack of a clinical response (non-responder, NR) was defined by disease progression (PD) on serial CT scans or stable disease lasting less than 6 months.
2.2 Specimen collection
Buccal and fecal specimens were collected from each patient at baseline, continued approximately every 96 hours as available, and stopped upon neutrophil recovery. Baseline samples were considered up to 8 days before and 24 hours following ICIs initiation. As per availability of samples, 60 (92.3%) of the patients had oral samples collected before or at the same time as the initiation of immunotherapy, while 55 (84.6%) of the patients had stool samples collected before or at the same time as the initiation of immunotherapy. The buccal mucosa of each individual was swabbed three times on each side using a Catch-AllTM Sample Collection Swab (Epicentre). Patient stool samples were either collected in a stool hat or using a BBLTM CultureSwab (BD Diagnostics). All samples were placed in sterile 2-mL cryovials and stored immediately at −80°C until further processing.
2.3 16S rRNA sequencing and data processing
The 16S rDNA V4 region was amplified by PCR and sequenced on the MiSeq platform (Illumina, Inc, San Diego, CA) using the 2 × 250 bp paired-end protocol yielding paired-end reads with near-complete overlap. The primers used for amplification contain adapters for MiSeq sequencing and single-end barcodes allowing pooling and direct sequencing of PCR products
2.4 Statistical analysis
Statistical calculation was conducted using SPSS 19.0 software (IBM). Intra-patient temporal variability of microbial diversity was defined as the coefficient of variation (CV) of a longitudinal collection of α-diversity values, and was calculated for each patient's set of oral and stool samples. Higher values were indicative of more variable microbial diversity. Paired sample t-test was used for comparing the differences of gene expression between cancerous and paracancerous tissues according to microarray data. For other intergroup comparisons, independent samples t test was performed. P < .05 was considered significant difference. Each experiment was repeated for 3 times. All graphs were plotted with GraphPad Prism v6.01 (GraphPad software, lnc.).
3.1 Clinical characteristics and pyrosequencing data summary
To better understand the role of the microbiome in response to immune checkpoint blockade, we prospectively collected microbiome samples from patients with metastatic HCC starting treatment with ICIs therapy (n = 65 patients). Oral (buccal) and gut (fecal) microbiome samples were collected at treatment initiation, and tumor biopsies and blood samples were collected at matched pre-treatment time points when possible to assess for genomic alterations. We first assessed the landscape of the oral and gut microbiome in all available samples in all patients with metastatic HCC via 16S sequencing, with a high abundance of d Bacteroidales in the fecal microbiome (Fig. 1A). To further explore these findings, we performed high dimensional class comparisons via linear discriminant analysis of effect size (LEfSe), which again demonstrated differentially abundant bacteria in the fecal microbiome of response group (R) versus non-response group(NR) to ICIs therapy, with Clostridiales/Ruminococcaceae enriched in R and Bacteroidales enriched in NR (Fig. 1B).
3.2 HCC patients undergoing ICI exhibit temporal instability of the stool and oral microbiome diversity
In order to understand the intra-patient temporal variability of the microbiome among hospitalized patients with HCC, we performed sequencing of the V4 region of the 16S rRNA gene via the MiSeq platform (Illumina) using the 2 × 250-bp protocol on a total of 901 longitudinal samples collected twice weekly from initiation of ICIs until neutrophil recovery for all patients. After the sequencing, we obtained a total of 20,212,234 reads for all the samples from patients. We first sought to determine intra-patient temporal variability of α-diversity by calculating the Shannon Diversity Index (SDI) for both the oral and the stool samples for each patient. The coefficient of variation is defined as the ratio of the standard deviation to the mean; thus, a low CV would mean an individual had relatively stable species diversity over time whereas a high CV would reflect more variation. We found considerable heterogeneity in the temporal stability values of both stool and oral samples among HCC patients during IC (Fig. 2A). There was statistically significant difference in CV values between the two sites (P = .003). This finding is consistent with previous studies performed in healthy individuals where the microbiota of oral samples had been shown to be less variable compared to stool. Assessment of the temporal variability of α-diversity also revealed that the SDI CV of oral and stool samples from the same patients were statistically moderately correlated (P = .001, r = 0.45; Fig. 2B). The relationship between the two sites leads to the postulation that factors influencing temporal variability of microbial diversity in treated cancer patients may be acting on both sites concurrently. The mean CVs of SDI for the cohort were significant different between the oral samples and the stool samples, respectively (Fig. 2C).
3.3 Specific bacterial taxa related with treatment response
To explore how specific bacterial taxa impact patient treatment response, we compared PFS following ICIs therapy as it related to the “top hits” consistently observed across our analyses. From the Ruminococcaceae family of the Clostridiales order, we focused on the Faecalibacterium genus in R, and Bacteroidales order in NR, and stratified patients into high versus low categories based on the median relative abundance of these taxa in the gut microbiome. Patients with high Faecalibacterium abundance had a significantly prolonged PFS versus those with a low abundance (P = .006, Fig. 3A). Conversely, patients with a high abundance of Bacteroidales had a shortened PFS compared to those with a low abundance (P = .002, Fig. 3B).
HCC is associated with a high mortality rate and has shown increased incidence in recent years. Many risk factors, including primary hepatitis C virus, hepatitis B virus, smoking, cirrhosis and obesity, are involved in HCC.[21,22] The gut microbiota is involved in protection against or promotion of different diseases, including various cancers. The gut microbiota composition differs in several diseases, including obesity, inflammatory bowel disease, and hepatocellular carcinoma (HCC), suggesting a distinct intestinal metabolism and immune response in each patient. Ponziani et al. reported the features of the gut microbiota association with HCC in patients with cirrhosis with nonalcoholic fatty liver disease. Moreover, correlations between the gut microbiota and liver cirrhosis (LC) were also characterized by metagenomics analysis.[23,24]
Tremendous advances have been made in the treatment of melanoma and other cancers using immune checkpoint inhibitors targeting the cytotoxic T lymphocyte-associated antigen (CTLA-4) and the programmed death 1 (PD-1) protein, however responses to these therapies are often heterogeneous and not durable. It has recently emerged that factors beyond tumor genomics influence cancer development and therapeutic responses, including host factors such as the gastrointestinal (gut) microbiome.[25–27] A number of studies have shown that the gut microbiome may influence anti-tumor immune responses via innate and adaptive immunity, and that therapeutic responses may be improved via its modulation, however this has not been extensively studied in cancer patients.[28,29]
In this study, we found that the diversities of the gut microbiota in HCC who received ICIS were obviously increased. Negative feedback, which is controlled by interplay between microbial metabolic activities and host pathways, is thought to promote high bacterial diversity. we focused on the Faecalibacterium genus in R, and Bacteroidales order in NR, and stratified patients into high versus low categories based on the median relative abundance of these taxa in the gut microbiome. Patients with high Faecalibacterium abundance had a significantly prolonged PFS versus those with a low abundance. Conversely, patients with a high abundance of Bacteroidales had a shortened PFS compared to those with a low abundance.
Several limitations existed in this study. Firstly, this study is a retrospective cohort study and the sample size is relatively small; secondly, the exact mechanisms of characteristics of gut microbiota involved in the immune phenotypes of T cells are still not elucidated. Thus, further studies are needed to solve these problems.
In summary, the present study examined the oral and gut microbiome of HCC patients undergoing anti-programmed cell death 1 protein (PD-1) immunotherapy. Significant differences were observed in the diversity and composition of the patient gut microbiome of responders versus non-responders.
Lili LI and Jiajian Ye designed the research; Lili LI and Jiajian Yeconducted acquisition of data; Lili LI performed research and statistical analysis; Lili LI and Jiajian Ye wrote the paper; Jiajian Ye conducted a critical revision of the manuscript.
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