Internship Presentations

Identification of colorectal cancer subtypes

Alhussin Binsaleh (Mentor: Dr. Nathan Edwards, Department of Biochemistry and Molecular & Cellular Biology, Georgetown University)

May 5th, 2015, 2:00pm-2:30pm, Room 1202, Harris Building.

My project is multidirectional extension of the work published by Zhang et al (2014); they have undertaken an intricate proteogenomic analysis of two related cancer types: colon and rectal cancers. They aim to understand the correlation of genomic alteration with that of tumor pathology. By simultaneously analyzing the somatic and germline variants of the proteomes of 95 colon and rectal tumors that were previously characterized by TCGA they show a clear reduction in protein abundance in somatic variants; however only modest correlation was evident with mRNA transcript analysis. Their proteomic analysis identified five colorectal cancer subtypes that reflect known biological characteristics. However, common data analysis pipeline (CDAP) has published protein report that has the same mass spectrometry to the published protein abundance. The aim of my project is to reanalyze this pipeline databased on the correlation strategy of mRNA transcript analysis, Proteomics and genomic alteration published by Zhang et al. Such approach would identify the additional regulatory mechanism that is involved in the emergence of pathological features of specific tumors.

My first goal is to analyze the CDAP spectral count data, and precursor area to obtain cancer subtype cluster using gene pattern. The next step would be comparative analysis of my results with published cancer subtypes. Based on the results obtained, I would proceed to extract mRNA protein signatures and define similarities and/or alterations. The final step would be comparative analysis of newly obtained signature patterns with that of previously published one; the data will identify previously masked regulatory signatures thereby providing additional clues to develop novel therapeutic approaches.

We hope to demonstrate that the CDAP data can provide quantify smeller results to the Vanderbilt University analysis in terms of actionable cancer information.

Spring 2015