Mucosal Microbiome in Patients with Early Bowel Polyps: Inferences from Short-Read and Long-Read 16S rRNA Sequencing.

Numerous studies have correlated dysbiosis in stool microbiota with colorectal cancer (CRC); however, fewer studies have investigated the mucosal microbiome in pre-cancerous bowel polyps. The short-read sequencing of variable regions in the 16S rRNA gene has commonly been used to infer bacterial taxonomy, and this has led, in part, to inconsistent findings between studies. Here, we examined mucosal microbiota from patients who presented with one or more polyps, compared to patients with no polyps, at the time of colonoscopy. We evaluated the results obtained using both short-read and PacBio long-read 16S rRNA sequencing. Neither sequencing technology identified significant differences in microbial diversity measures between patients with or without bowel polyps. Differential abundance measures showed that amplicon sequence variants (ASVs) associated with Ruminococcus gnavus and Escherichia coli were elevated in mucosa from polyp patients, while ASVs associated with Parabacteroides merdae, Veillonella nakazawae, and Sutterella wadsworthensis were relatively decreased. Only R. gnavus was consistently identified using both sequencing technologies as being altered between patients with polyps compared to patients without polyps, suggesting differences in technologies and bioinformatics processing impact study findings. Several of the differentially abundant bacteria identified using either sequencing technology are associated with inflammatory bowel diseases despite these patients being excluded from the current study, which suggests that early bowel neoplasia may be associated with a local inflammatory niche.

Multivariate Analysis with the R Package mixOmics.

The high-dimensional nature of proteomics data presents challenges for statistical analysis and biological interpretation. Multivariate analysis, combined with insightful visualization can help to reveal the underlying patterns in complex biological data. This chapter introduces the R package mixOmics which focuses on data exploration and integration. We first introduce methods for single data sets: both Principal Component Analysis, which can identify the patterns of variance present in data, and sparse Partial Least Squares Discriminant Analysis, which aims to identify variables that can classify samples into known groups. We then present integrative methods with Projection to Latent Structures and further extensions for discriminant analysis. We illustrate each technique on a breast cancer multi-omics study and provide the R code and data as online supplementary material for readers interested in reproducing these analyses.

Genomic, Microbial and Immunological Microenvironment of Colorectal Polyps.

Colorectal cancer (CRC) develops from pre-cancerous cellular lesions in the gut epithelium, known as polyps. Polyps themselves arise through the accumulation of mutations that disrupt the function of key tumour suppressor genes, activate proto-oncogenes and allow proliferation in an environment where immune control has been compromised. Consequently, colonoscopic surveillance and polypectomy are central pillars of cancer control strategies. Recent advances in genomic sequencing technologies have enhanced our knowledge of key driver mutations in polyp lesions that likely contribute to CRC. In accordance with the prognostic significance of Immunoscores for CRC survival, there is also a likely role for early immunological changes in polyps, including an increase in regulatory T cells and a decrease in mature dendritic cell numbers. Gut microbiotas are under increasing research interest for their potential contribution to CRC evolution, and changes in the gut microbiome have been reported from analyses of adenomas. Given that early changes to molecular components of bowel polyps may have a direct impact on cancer development and/or act as indicators of early disease, we review the molecular landscape of colorectal polyps, with an emphasis on immunological and microbial alterations occurring in the gut and propose the potential clinical utility of these data.