Use of the Cox Proportional-Hazards Model for Survival Analysis and Prognostic Prediction of Glioblastoma (GBM)

Nari Kim




Assistant Professor of Informatics

Project Summary

This past summer I had the opportunity to work with Dr. Dokyoon Kim and graduate student Jake Leiby in the Integrative Omics and Biomedical Informatics Laboratory, where the primary research focus is to develop and apply various data integration approaches to improve the diagnosis, treatment, and prevention of complex diseases.  

The goal of the project I worked on was to explore genomic predictors of overall survival in glioblastoma (GBM) patients to identify key genes that may be used as prognostic factors and potential therapeutic targets for future GBM treatments. This is because GBM is a common and aggressive brain tumor with a rapid relapse time and poor prognosis with a low survival rate. With only one approved drug for it at this time that has not been seen to have much effect in prolonging survival in GBM, it is imperative that key genes be identified for the development of a more efficient treatment plan for those with GBM that may hopefully help improve the overall survival of GBM patients. Using publicly available gene expression, copy number alteration, and survival data from The Cancer Genome Atlas program (TCGA) GBM cohort, regularized Cox Proportional-Hazards models were built to predict overall survival and identify import genes.

Through my research experience, I learned to work with genomic data and data integration methods to train and fit a model as well as gained increased exposure to employing various packages in R. Furthermore, I was able to gain insight into the field of data analytics and learned to diligently analyze datasets and persevere through continuous running of models with trial and error. 

As one who is on the premed track, I felt that participating in this research project had a valuable contribution towards my educational experience as I was able to expand on my previous experiences with conducting independent research projects to develop therapeutic treatments for cancer and diseases. Not only was I able to grow my knowledge in programming and statistics, but I feel that the skills and experience I gained through my research this past summer will assist me on my future career path in medicine. 

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