Dr. Joellen Schildkraut is a recent recipient of two R01 grants from the National Cancer Institute, one in 2017 and one in 2018.
Title: Genomic and Transcriptomic Analysis of Breast and Ovarian Cancers
Ovarian and breast cancers share common genetic and lifestyle/environmental factors. GWAS have identified more than a hundred genomic regions containing common variants associated with risks of these cancers, several of which confer risks to both cancers. The molecular features of the aggressive subtypes of these cancers–high grade serous ovarian cancer (HGSOC) and estrogen receptor (ER) negative breast cancer—are also remarkably similar, suggesting common genetic and biological mechanisms driving disease development.
In the current proposal, we aim to focus on pleiotropic mechanisms underlying susceptibility to ovarian and breast cancer, through tissue-specific transcription-wide analysis of gene expression associated with common variant risk alleles for ovarian and breast cancer identified by GWAS. We will apply a new gene-based association method, PrediXcan, to test the molecular mechanisms through which genetic variation affects ovarian and breast cancer development. The proposed integration of germline genetic data with annotation of whole genome transcription in the relevant tissue types effectively reduces the multiple testing burden faced by GWAS by grouping together multiple risk loci at the gene-level, and further simplifies additional
characterization of implicated pathways. We hypothesize that the biological relevance of predictors provided by PrediXcan will allow us to overcome tumor heterogeneity and identify novel genes/pathways for ovarian and breast cancer. We then propose performing functional analyses in experimental models of breast and ovarian cancer to validate the genes and pathways we identify using PrediXcan.
This proposal incorporates novel methodology integrating multiple sources of genomic and transcriptomic data to identify the role of genetically regulated gene expression traits in the pathogenesis of ovarian and breast cancers. Mortality is highest for the specific subtypes of these cancers that we will focus on, since the mechanisms underlying the pathogenesis of these cancer subtypes are poorly understood. Genotype and phenotype data from the GAME-ON, OCAC and BCAC consortia, along with the publicly available datasets, TCGA, METABRIC and GTEx, represent a unique opportunity to apply the PrediXcan approach in a largescale for a well-defined group of cases and controls with high quality epidemiologic data resources.
Title: Exploring factors related to racial disparities in ovarian cancer incidence and survival: the OCWAA consortium
Invasive epithelial ovarian cancer accounts for 5% of malignancies and, while it is the eighth most common cancer in US women, it is the fifth leading cause of cancer deaths. Many of the established and suspected risk factors for ovarian cancer show racial differences in the prevalence and/or timing of exposures. Racial differences in incidence and survival are likely due to a combination of factors, including differences in the prevalence of risk factors, treatment received, prevalence of comorbidities, and socioeconomic characteristics. The newly-formed consortium, Ovarian Cancer in Women of African Ancestry (OCWAA), brings together investigators experienced in studying the epidemiology of ovarian cancer and dedicated to understanding racial differences in risk and outcomes in ovarian cancer. The OCWAA consortium includes an ongoing population-based case-control study in African American (AA) women, three completed population-based case-control studies that include both AA and white cases and controls and two unique cohort studies with large numbers of AA participants. To address racial differences in incidence and survival, we will comprehensively examine reproductive and lifestyle exposures which differ in prevalence and/or timing between AA and white women, access to care, treatment and socioeconomic factors, and determine whether they explain racial disparities. We propose to harmonize risk factor, clinical and outcome data from the participating studies in the OCWAA consortium in order to accomplish the study goals and carry out race-specific analyses of incidence and survival. We will use traditional methods to calculate population attributable risk percent (PAR%) for associated risk factors. We will use a novel and exploratory approach, a Markov state-transition microsimulation model, to estimate the extent to which racial differences in patterns of exposure to known risk factors explain racial differences in ovarian cancer age-specific incidence and use this same method to estimate the extent to which racial differences in patterns of exposure, treatment, and prognostic factors explain racial differences in ovarian cancer survival. The proposed study will be the largest of its kind and the first to be adequately powered to evaluate epidemiologic and clinical factors affecting incidence and survival of ovarian cancer in AA women. The infrastructure generated by this application will enable OCWAA to create a base on which to support future scientific proposals.