Microbiome Analysis in Colorectal Cance

Colorectal cancer (CRC) results from a complex interplay between genes and the environment. Recent studies have focused on the gut microbial population (the microbiota) and its aggregate genome (the microbiome) as one of the environmental players in colorectal tumorigenesis. High-throughput sequencing techniques have added a new dimension to the mining of gut microbiome for biomarkers of CRC and therapeutic targets. Current approaches to microbiome analysis include quantifying the relative abundancies and diversities of microbial populations along with the identification of disease-specific biomarkers.;In this project, the 16S rRNA sequences of bacteria present in stool samples of patients with CRC, pre-cancerous adenomatous polyps, and non-cancer controls were analyzed using three different operational taxonomic units (OTUs) identification techniques - UPARSE, UPGMA, and UCLUST. UPARSE was the fastest algorithm and identified the lowest number of OTUs while UPGMA required the largest amount of memory. UCLUST was the slowest and identified the highest number of OTUs. The patterns of alpha diversity (diversity within a sample) and beta diversity (diversity between samples) obtained by each of these algorithms were not substantially different.;In this dissertation, we report the analysis of samples collected from subjects that have undergone routine colonoscopy to detect the presence of polyp(s). Various nonparametric statistics and classification techniques were utilized to identify the microbiota characteristics capable of discriminating between disease states and from healthy colon. OTUs significantly different in their relative abundance between subjects with polyp (polyp-Y group) and without polyp (polyp-N group) were used to build classifying predictors for the presence or absence of polyps.;The predictive power to discriminate between polyp-N and polyp-Y groups was highest when the model was built using OTUs preselected for statistically significant differences in their relative abundance. In conclusion, we showed that 16S rRNA microbiome analysis could be utilized to generate OTU abundance-based feature sets for further development into the predictive models. Eventually, these models will improve the power of CRC diagnostics and aid in defining the dynamic interface between the gut and residing microbiota.