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Dissertation Defense Announcement
To:  The George Mason University Community

Candidate: Ezzat Kimiya Dadkhah
Program: PhD in Biosciences
 
Date:   Thursday November 30, 2017
New Time:   1:00 PM
Place:  Bull Run Hall, Room 249
              George Mason University
             Science & Tech campus
             
  
Title: "Microbiome Analysis in Colorectal Cancer"

Committee Chair: Dr. Patrick Gillevet
Committee Members:
  Dr. Ancha Baranova, Dr. Donald Seto, Dr. James Goedert

All are invited to attend the defense.

ABSTRACT:
Colorectal cancer (CRC) results from a complex interplay between gene and environment. Recent data have put at focus the gut microbiome as one of the players in colorectal tumorigenesis. High throughput sequencing techniques have added a new dimension to the mining of gut microbiota for biomarkers and for therapeutic targeting in CRC. Current approaches include quantifying relative abundances and diversities of microbial populations along with presence of disease-specific markers.

In this project, the 16S rRNA sequences of bacteria present in stool samples of patients with CRC, benign adenomas and non-cancer controls were analyzed using three different operational taxonomic units (OTUs) identifying techniques - UPARSE, UPGMA, and UCLUST.

Among three clustering methods, UPARSE was the fastest and had the lowest number of detected OTUs. The UPGMA method required the largest amount of memory, and UCLUST was the slowest method which returned the highest number of detected OTUs. The patterns of alpha and beta diversity obtained using all three algorithms were comparable.

The analysis of samples collected from subjects that have undergone routine colonoscopy to detect the presence of polyp was performed. Various statistics and classification techniques were used to identify the microbiota that can discriminate disease states from each other and from healthy condition. OTUs significantly different in their abundance between subjects with polyp (polyp-Y) and without polyp (polyp-N) were used to build classifying predictor of the polyp status.

The prediction power of classifiers to classify samples into polyp-N and polyp-Y groups was highest when the OTUs that were significantly different in polyp-Y versus polyp-N groups comparison were used into the model. In conclusion, microbiome analysis may improve the power of CRC diagnosis by capturing and integrating into the predicting model the dynamic interface between gut and residing microbiota.


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