Title: Ovarian Cancer: gene expression profiling and proteomic pattern analysis

Speaker: Eileen Kraemer

Abstract

Epithelial ovarian cancer is the fifth leading cause of death for women in the United States. Although early stage ovarian cancer can be effectively treated, symptoms of early disease are sufficiently vague that accurate diagnosis is often delayed until the cancer has progressed into more advanced stages. Treatment of early stage tumours (I through IIa) is associated with a 5-year survival rate of approximately 95% while survival rates drop to less than 30% when diagnosis is delayed until later stages (stage IIb through IV).

In this talk we present two approaches to promoting effective early diagnosis and treatment strategies: microarray data analysis of tumor samples and proteomic pattern analysis of serum samples. The microarray data analysis studies are complete and our findings indicate that 1) gene expression profiling can reliably distinguish between benign and malignant ovarian tumours, and 2) expression profiles of samples from patients pre-treated with chemotherapy may be useful in predicting disease free survival and the likelihood of recurrence. The proteomic pattern analysis work is preliminary, and involves the application of genetic algorithms and self-organizing maps (SOMs) to SELDI-TOF data derived from serum samples. The ultimate goal of this work is to develop a simple diagnostic screen for ovarian cancer.

Biography

Eileen Kraemer is an Associate Professor in the Computer Science Department of the Franklin College of Arts and Sciences at The University of Georgia. Prior to joining the faculty at UGA, she served on the faculty at Washington University in St. Louis in the Computer Science Department of the School of Engineering and Applied Science and as director of the Computer Visualization Laboratory. She received her Ph.D. in Computer Science in September of 1995 from the College of Computing at the Georgia Institute of Technology in Atlanta. At Georgia Tech, she worked with John Stasko on the visualization of parallel and distributed programs, in Karsten Schwan's group on the Falcon Project, and with Mark Borodovsky in the School of Biology on problems in computational biology. She earned an MS in Computer Science from Polytechnic University in Brooklyn, NY and a BS in Biology from Hofstra University in Hempstead, NY. Her current research interests include interactive steering, perceptual and cognitive issues in program visualization, and tools for visualization and interaction in support of computational biology.