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.