Microbiome analysis reveals universal diagnostic biomarkers for colorectal cancer across populations and technologies

Differences in diversity and composition of mucosa-associated colonic microbiota in colorectal cancer and non-colorectal cancer in Indonesia

BACKGROUND

Colorectal cancer is the third most common malignancy and the fourth leading cause of cancer-related deaths worldwide. Several studies have shown an association between gut microbiota and colorectal cancer. Gut microbiota is unique and can be influenced by geographic factors and habits. This study aimed to determine the diversity and composition of colonic mucosal microbiota in patients with and without colorectal cancer.

AIM

To determine the diversity and composition of colonic mucosal microbiota in patients with and without colorectal cancer in Indonesia.

METHODS

This case-control study included 59 subjects (35 colorectal cancer patients and 24 non-colorectal cancer patients indicated for colonoscopy at Dr. Cipto Mangunkusumo Gastrointestinal Endoscopy Center and Fatmawati Hospital. Microbiota examination was performed using 16S rRNA sequencing. Bioinformatics analysis was performed using the wf-metagenomics pipeline from EPI2Me-Labs (Oxford Nanopore Technologies platform).

RESULTS

Patients with colorectal cancer had a higher median index value on the Shannon index (3.28 vs 2.82, P > 0.05) and a lower value on the Simpson index (0.050 vs 0.060, P > 0.05). Significant differences in beta diversity were observed at the genus (P = 0.002) and species levels (P = 0.001). Firmicutes, Proteobacteria, Bacteroidetes, and Fusobacteria were the dominant phyla. The genera Bacteroides, Campylobacter, Peptostreptococcus, and Parvimonas were found more frequently in colorectal cancer, while Faecalibacterium, Haemophilus, and Phocaeicola were more frequently found in non-colorectal cancer. The relative abundance of Fusobacterium nucleatum, Bacteroides fragilis, Enterococcus faecalis, Campylobacter hominis, and Enterococcus faecalis species was significantly elevated in patients with colorectal cancer. Meanwhile, Faecalibacterium prausnitzii, Faecalibacterium duncaniae, and Prevotella copri were more commonly found in non-colorectal cancer.

CONCLUSION

Patients with colorectal cancer exhibit distinct differences in the composition and diversity of their colonic mucosal microbiota compared to those with non-colorectal cancer. This study was reviewed and approved by the Ethics Committee of Faculty of Medicine, Universitas Indonesia (No. KET-1517/UN2.F1/ETIK/PPM.00.02/2023).

Detection of colorectal-cancer-associated bacterial taxa in fecal samples using next-generation sequencing and 19 newly established qPCR assays

We have previously identified increased levels of distinct bacterial taxa within mucosal biopsies from colorectal cancer (CRC) patients. Following prior research, the aim of this study was to investigate the detection of the same CRC-associated bacteria in fecal samples and to evaluate the suitability of fecal samples as a non-invasive material for the detection of CRC-associated bacteria. Next-generation sequencing (NGS) of the 16S ribosomal RNA (rRNA) V4 region was performed to evaluate the detection of the CRC-associated bacteria in the fecal microbiota of cancer patients, patients with adenomatous polyp and healthy controls. Furthermore, 19 novel species-specific quantitative PCR (qPCR) assays were established to detect the CRC-associated bacteria. Approximately, 75% of the bacterial taxa identified in biopsies were reflected in fecal samples. NGS failed to detect low-abundance CRC-associated taxa in fecal samples, whereas qPCR exhibited high sensitivity and specificity in identifying all targeted taxa. Comparison of fecal microbial composition between the different patient groups showed enrichment of Fusobacterium nucleatum, Parvimonas micra, and Gemella morbillorum in cancer patients. Our findings suggest that low-abundance mucosa-associated bacteria can be detected in fecal samples using sensitive qPCR assays.

Quantitative and dynamic profiling of human gut core microbiota by real-time PCR

The human gut microbiota refers to a diverse community of microorganisms that symbiotically exist in the human intestinal system. Altered microbial communities have been linked to many human pathologies. However, there is a lack of rapid and efficient methods to assess gut microbiota signatures in practice. To address this, we established an appraisal system containing 45 quantitative real-time polymerase chain reaction (qPCR) assays targeting gut core microbes with high prevalence and/or abundance in the population. Through comparative genomic analysis, we selected novel species-specific genetic markers and primers for 31 of the 45 core microbes with no previously reported specific primers or whose primers needed improvement in specificity. We comprehensively evaluated the performance of the qPCR assays and demonstrated that they showed good sensitivity, selectivity, and quantitative linearity for each target. The limit of detection ranged from 0.1 to 1.0 pg/µL for the genomic DNA of these targets. We also demonstrated the high consistency (Pearson's r = 0.8688, P < 0.0001) between the qPCR method and metagenomics next-generation sequencing (mNGS) method in analyzing the abundance of selected bacteria in 22 human fecal samples. Moreover, we quantified the dynamic changes (over 8 weeks) of these core microbes in 14 individuals using qPCR, and considerable stability was demonstrated in most participants, albeit with significant individual differences. Overall, this study enables the simple and rapid quantification of 45 core microbes in the human gut, providing a promising tool to understand the role of gut core microbiota in human health and disease. KEY POINTS: • A panel of original qPCR assays was developed to quantify human gut core microbes. • The qPCR assays were evaluated and compared with mNGS using real fecal samples. • This method was used to dynamically profile the gut core microbiota in individuals.

GDmicro: classifying host disease status with GCN and deep adaptation network based on the human gut microbiome data

MOTIVATION

With advances in metagenomic sequencing technologies, there are accumulating studies revealing the associations between the human gut microbiome and some human diseases. These associations shed light on using gut microbiome data to distinguish case and control samples of a specific disease, which is also called host disease status classification. Importantly, using learning-based models to distinguish the disease and control samples is expected to identify important biomarkers more accurately than abundance-based statistical analysis. However, available tools have not fully addressed two challenges associated with this task: limited labeled microbiome data and decreased accuracy in cross-studies. The confounding factors, such as the diet, technical biases in sample collection/sequencing across different studies/cohorts often jeopardize the generalization of the learning model.

RESULTS

To address these challenges, we develop a new tool GDmicro, which combines semi-supervised learning and domain adaptation to achieve a more generalized model using limited labeled samples. We evaluated GDmicro on human gut microbiome data from 11 cohorts covering 5 different diseases. The results show that GDmicro has better performance and robustness than state-of-the-art tools. In particular, it improves the AUC from 0.783 to 0.949 in identifying inflammatory bowel disease. Furthermore, GDmicro can identify potential biomarkers with greater accuracy than abundance-based statistical analysis methods. It also reveals the contribution of these biomarkers to the host's disease status.

AVAILABILITY AND IMPLEMENTATION

https://github.com/liaoherui/GDmicro.

Potential role of gut microbes in the efficacy and toxicity of immune checkpoints inhibitors

In recent years, Immune checkpoint inhibitors have been extensively used in the treatment of a variety of cancers. However, the response rates ranging from 13% to 69% depending on the tumor type and the emergence of immune-related adverse events have posed significant challenges for clinical treatment. As a key environmental factor, gut microbes have a variety of important physiological functions such as regulating intestinal nutrient metabolism, promoting intestinal mucosal renewal, and maintaining intestinal mucosal immune activity. A growing number of studies have revealed that gut microbes further influence the anticancer effects of tumor patients through modulation of the efficacy and toxicity of immune checkpoint inhibitors. Currently, faecal microbiota transplantation (FMT) have been developed relatively mature and suggested as an important regulator in order to enhance the efficacy of treatment. This review is dedicated to exploring the impact of differences in flora composition on the efficacy and toxicity of immune checkpoint inhibitors as well as to summarizing the current progress of FMT.