My primary objective is the development of a knowledge base in cancer genomics and oncology that will complement my knowledge in statistics and enables me to identify and apply my statistical skills and abilities in promising areas of breast and ovarian cancer genomics through my position as Senior Scientist in the Computational and Applied Genomics Program (CAGP) in the Institute for Genome Studies and Policy (IGSP) at Duke University. My proposed program involves formal coursework in cancer biology, genetics, and epidemiology tailored to the specific loci of this grant and the context of oncogenesis. I will apply my knowledge in molecular oncology through participation in the lab of my mentor, Joe Nevins, Ph.D., Director of the Center for Genome Technology in IGSP. Andrew Berchuck, M.D., gynecological oncologist in the Duke Comprehensive Cancer Center, will serve as my co-mentor and will direct my education in the clinical application of my knowledge within the context of ovarian cancer. My education in cancer epidemiology will progress through coursework and research in genetic epidemiology under the co-mentorship of Joellen Schildkraut, Ph.D., Director of the Epidemiology Division of the Program in Cancer Prevention, Detection, and Control and Associate Professor in the Department of Community and Family Medicine in the Duke Comprehensive Cancer Center. The goals of my research plan take advantage of recent developments in the use of genome-scale measures of gene expression and advanced computational tools to identify patterns of gene expression that are associated with or predict various characteristics and outcomes in ovarian cancers. My research is designed to aid in the identification of strategies for developing new therapeutic targets for the treatment of patients with advanced-stage epithelial ovarian cancer who do not respond to platinum/taxane-based adjuvant therapy and may or may not respond to salvage chemotherapy. I believe the importance of this work lies in the fact that chemosensitivity is the major determinant of outcome for advanced stage epithelial ovarian cancer, and the ability to predict outcomes such as chemo-responsiveness will be a powerful tool in disease treatment. A cooperative goal is to determine whether distinct subsets of ovarian cancer can be better defined by simultaneous consideration of etiologic and inherited risk factors and molecular features as expressed in gene expression data. This strategy represents a new approach in seeking to delineate the molecular epidemiology of ovarian cancer and may identify gene polymorphisms which influence relative risk of disease and clinical outcome. The potential results represent a useful tool in determining the most appropriate treatment protocol for each patient. The proposed development program has been designed to provide me with a background in cancer genomics and epidemiology which will enable me to focus the interdisciplinary nature of the CAGP environment on cancer genomic research collaborations between geneticists, breast and ovarian oncologists, statisticians, and epidemiologists.
| Clarke, Jennifer; Seo, Pearl; Clarke, Bertrand (2010) Statistical expression deconvolution from mixed tissue samples. Bioinformatics 26:1043-9 |
| Clarke, Jennifer; Clarke, Bertrand (2009) Prequential Analysis of Complex Data with Adaptive Model Reselection. Stat Anal Data Min 2:274-290 |
| Clarke, Jennifer; West, Mike (2008) Bayesian Weibull tree models for survival analysis of clinico-genomic data. Stat Methodol 5:238-262 |
| Lin, Xiaodong; Pittman, Jennifer; Clarke, Bertrand (2007) Information Conversion, Effective Samples, and Parameter Size. IEEE Trans Inf Theory 53:4438-4456 |
