Seminars

A Transcriptome Analysis by Lasso Penalized Cox Regression for Pancreatic Cancer Survival
Tongtong Wu - Assistant Professor, Department of Epidemiology and Biostatistics, School of Public Health, University of Maryland-College Park

09/16/2011, 2:00PM, GHC-6501

Abstract

Pancreatic cancer is the fourth leading cause of cancer deaths in the United States with five-year survival rates less than 3% due to rare detection in early stages.  Research on identification of genes that are highly correlated to pancreatic cancer survival is crucial for pancreatic cancer diagnostics and treatment. No existing GWAS (genomewide association studies) or transcriptome studies are available addressing the problem of pancreatic cancer survival. We apply lasso penalized Cox regression to conduct a transcriptome analysis to select genes that are directly related to pancreatic cancer survival.  This method is capable of handling the right censoring effect of survival times and the ultra high-dimensionality of genetic data.  A cyclic coordinate descent algorithm is employed to select the most relevant genes and eliminate the irrelevant ones in a fast speed. Twelve genes have been identified and verified to be directly correlated to pancreatic cancer survival time.  This 12-gene signature can be used for the prediction of pancreatic cancer patient's survival.

Biography

Tongtong Wu is an Assistant Professor in the Department of Epidemiology and Biostatistics, School of Public Health, University of Maryland College Park. Dr. Wu obtained her Ph.D. from the Department of Biostatistics, UCLA School of Public Health in 2006, and then she worked as a postdoctoral researcher in the Department of Human Genetics, UCLA.

Dr. Wu is a biostatistician with interests in survival analysis, computational statistics, and statistical genetics. While studying methods of survival analysis, she focuses on semi/nonparametric modeling and two-stage design. In the field of computational statistics, Dr. Wu works on multi-category classification and variable selection. Her research can be applied to cancer classification and the genetic determination of diseases and has other useful applications. Dr. Wu also studies longitudinal data analysis as it applies to HIV research.