The goal of the proposed research training program is to provide tailored additional training to facilitate successful career development throughout the completion of postdoctoral fellowship and the transition to independent tenure track professor. The key elements of this plan are: Candidate: I have considerable research experience in developing and applying computational models to understand complex biological systems. The training component of this proposal will focus on acquisition of knowledge in cancer genetics and genomics, integrative computational methodologies, and next-generation sequencing technologies. Additionally, I will receive training in laboratory management, networking and collaborations, and grant submissions. This well-rounded training plan will accelerate my goals of being an independent researcher and developing computational models to better understand cancer biology. Environment: The training environment at Cedars-Sinai Medical Center fosters productivity and collaboration with world class researchers in clinical and basic biomedical science. I have assembled an advisory committee with esteemed experts in the areas of epigenomics, genetics, data science and cancer biology to ensure my success in this training program and to guide me through the successful acquisition of a tenure track faculty position. These include my mentor Dr. Simon Gayther and four advisors, Dr. Benjamin Berman and Dr. Shelly Lu from Cedars-Sinai, and Dr. Bogdan Pasaniuc, and Dr. Paul Boutros from University of California, Los Angeles. Research: A fundamental goal of human genetics is to decipher the relationship between genotype and phenotype. Cancer is a disease comprising a heritable component that confers cancer predisposition and an acquired (somatic) component where accumulation of genetic alterations occurs during disease development. Population based genome-wide association studies (GWAS) and whole genome sequencing (WGS) analyses have identified thousands of germline risk variants and somatic non-coding mutations involved in ovarian cancer development. Often, protein-coding cancer driver genes harbor both deleterious germline risk variants and somatic mutations. This proposal hypothesizes that the same is true for non-coding cancer drivers. With the wealth of epigenomics and regulatory datasets, the goal is to identify genomic regions where there are interactions between germline and somatic variants.
The specific aims are: (1) identify functional regulatory elements where non-coding germline and somatic ovarian cancer variants co-localize; (2) identify non-coding ovarian cancer drivers through multi-omics regulatory evidence by machine learning models. The proposed studies will establish systematic and quantitative models to identify ovarian cancer non-coding drivers and improve our understanding of disease etiology.
Ovarian cancer is responsible for more than 150,000 deaths per year and so it remains a high priority to develop effective prevention approaches and to identify new disease specific therapies to reduce disease mortality. The research proposed here has two components: (1) To characterize the genetic component in ovarian cancer development through epigenomic landscape; (2) to establish the functional relevance of genetic findings for clinical translation. This is a highly integrated multi-disciplinary research proposal, and these approaches to novel discovery and mechanistic understanding are desperately needed to advance our basic knowledge of this disease to develop clinical interventions that reduce mortality.
