Statistical Methods for Predicting Survival Outcomes from Genomic Data

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DESCRIPTION (provided by applicant): Dr. Annette Molinaro is an Assistant Professor in the Division of Biostatistics in the Department of Epidemiology and Public Health at Yale University School of Medicine. Prior to arriving at Yale, Dr. Molinaro was a Cancer Prevention Fellow at the National Cancer Institute. Her long term career goal is to develop statistical and computational methods which elucidate mechanisms of cancer pathogenesis to be used for the purposes of cancer prevention, diagnosis, and treatment. To reach this goal she has outlined two areas which are in need of enhancement: 1) her knowledge of functional genomics specifically related to carcinogenesis; and, 2) her proficiency in computer programming for the purposes of searching for and extracting pertinent information from vast data structures. A comprehensive understanding of the biological mechanisms behind carcinogenesis as well as the advanced computational skills necessary to implement novel statistical methods will propel Dr. Molinaro's independent research program. To meet these needs, Dr. Molinaro will: 1) attend classes at Yale University in genomics, bioinformatics, computer science, and molecular biology; 2) participate in world renowned courses at the Jackson and Cold Spring Harbor Laboratories in mammalian genetics, computational and comparative genomics, and complex trait analysis; and, 3) attend scientific meetings and workshops to present her K22 research, build collaborations, and engage in scientific discussion on current issues concerning statistical genomics. Her proposed research project entails a comprehensive, aggressive search of genomic, epidemiologic, and histologic data for the purposes of predicting a clinical outcome of interest, such as time to recurrence or death. Dr. Molinaro has established a univariate approach to this problem; however, she now needs to expand this to a realistic biological setting. The primary aims of this research project are: 1) to account for missing values in the genomic variables; 2) evaluate measures of variable importance; and, 3) extend this approach to encompass other statistical models such as wavelets and splines. This K22 grant will enable Dr. Molinaro the protected time and resources to accomplish her training in the molecular biology of cancer, establish collaborations at Yale University and beyond, and provide the scientific community with a much needed tool for associating genomic data with clinical outcomes. Relevance: Dr. Molinaro's research incorporates genomic, histological, and epidemiological information in order to predict a clinical outcome, such as time to disease progression. It is methods such as this that will provide greater clarity within the complexity of carcinogenesis and allow for more targeted methods of cancer prevention and control.