Research Areas

Research Working Group Lead: Simon Gayther, PhD
Team Members: Justyna Kanska, Kruttika Dabke, Norma Rodriguez-Malave

Research in the Gayther Laboratory is largely focused on understanding the underlying causes of ovarian cancer initiation and development. Simon Gayther, PhD, has a long-established track record in defining the heritable component of ovarian cancer and the functional role of both common and rare risk variants and their target susceptibility genes in the early-stage disease pathogenesis. The overall approach of research in the Gayther Lab is to integrate genomics and epigenomics analyses to identify molecular markers associated with disease, using cell biology modeling studies to validate the role of novel molecular markers in disease biology. The goal is to translate the findings from these studies into the clinical arena to improve risk prediction and prevention strategies, early-stage screening, and disease diagnosis and targeted therapeutics.

Research Working Group Lead: Michelle Jones, PhD
Team Members: Alberto Reyes

The Gayther Laboratory uses next-generation sequencing methods to generate data to map genetic variation and apply population-based approaches to identify disease risk variants. Large population-based studies using genome-wide association study (GWAS) approaches have identified more than 30 germline risk loci for ovarian cancer. The Gayther Lab is now focused on understanding how these loci increase disease risk and how they direct disease initiation and progression.

Using genetic epidemiology tools to integrate genetic information with clinical, demographic, and environmental data will improve risk estimation by allowing clinicians to enhanced monitoring and disease prevention. The Gayther Lab applies statistical approaches to model how germline risk variants combine with environmental risk factors to contribute to heritable disease risk. Lab members also using novel statistical methods to investigate how noncoding variants in gene regulatory regions contribute to heritability.

Now that a number of ovarian cancer risk loci have been identified, it is critical that we understand how these variants lead to ovarian cancer. Our group works on functional annotation of risk loci with genomic datasets. In collaboration with Dennis Hazelett, PhD, and Simon Coetzee, and the Functional Genomics and GWAS working group, we are working to identify the functional role of credible causal variants that are identified by fine mapping.

Research Working Group Lead: Jasmine Plummer, PhD
Team Members: Stephanie Chen, Brian Davis

The Functional Genomics Team is focused on the use of next-generation sequencing methods to identify candidate genes from genome-wide association studies (GWAS). Currently over 30 GWAS regions have been identified for ovarian cancer, and we are now focused on understanding how to link these GWAS loci with their most likely causal gene. Current projects include identifying various regions of the genome with functional effects on the transcriptome, with regard to ovarian cancer risk. Using epigenetic profiling methods, our plan is to provide a comprehensive landscape of the genetic determinants of gene expression in ovarian cancer in order to best prioritize genes and loci for further functional follow up.

GWAS studies have identified thousands of common variants associated with numerous diseases, but for the vast majority of genetic associations, the underlying functional mechanisms are unknown. Unlike Mendelian disorders, approximately 90 percent of trait- and risk-associated alleles lie outside of protein coding regions, suggesting that common variants lie in regulatory regions and cause disease by regulating target gene expression. In post-GWAS studies, the overall problem becomes identifying (i) the correct susceptibility gene(s) for each locus and their functional role in disease pathogenesis and (ii) the causal genetic variant(s) that drive disease development. Common variants that influence the activity of specific regulatory elements, such as enhancers, may affect target gene expression through direct, physical interactions. We use 3C/4C technologies to identify physical interactions between susceptibility cell lines. Using epigenetic profiling spanning all ovarian cancer histotypes, we are establishing histotype-specific enhancer profiles. Enhancer marks that overlap with positive 3C/4C SNP interaction are later validated in in vitro neoplastic models of ovarian cancer.