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Department of Biostatistics Seminar/Workshop Series

Statistical Bioinformatics in Translational Research: Personalized Chemotherapeutics using High-throughput Expression Biomarkers

Jae K. Lee, Ph.D.

Associate Professor of Biostatistics and Epidemiology, Department of Public Health Sciences
University of Virginia School of Medicine

Wednesday, August 22, 2008, 1:30-3:00pm, R Light Hall, Room 512

Intended Audience: Persons interested in applied statistics, statistical theory, epidemiology, health services research, clinical trials methodology, statistical computing, statistical graphics, R users or potential users

We will discuss about two ongoing statistical bioinformatics studies based on high-throuput expression data that are directly associated with human disease prognosis and outcome. First, we will breifly introduce an integrative statistical pathway modeling on disease progression of diabetic atherosclerosis. Next, our recent study of gene expression-based prediction of personalized chemotherapeutics will be discussed as follows. The U.S. National Cancer Institute has used a panel of 60 diverse human cancer cell lines (the NCI-60) to screen >100,000 chemical compounds for anticancer activity. However, not all important cancer types are included on the panel nor are drug responses on the panel predictive of clinical efficacy in patients. We thus asked whether it would be possible to identify common chemosensitivity biomarkers from that rich database to predict drug activity in cell types not included in the NCI-60 panel or, even further, clinical responses in patients with tumors. We address that challenge by developing a novel statistical pharmacogenomic approach "Co-eXpression ExtrapolatioN" (COXEN), which can effectively identify concordant genomic chemosensitivity biomarkers between two independent expression profiling data sets, here extrapolating the expression patterns of NCI-60 biomarkers with those of clinical tumors. Applying our COXEN approach in a prospective fashion, we predicted anticancer drug activities on completely independent bladder cancer, which is not included in the NCI-60 panel, and on breast cancer patients treated with commonly used chemotherapeutics with 80% accuracy. We also used COXEN for in silico screening of 45,545 compounds and identify a novel agent with superior growth inhibition activity against human bladder cancer.
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Topic revision: r1 - 12 Aug 2008, DianeKolb

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