Dylan Glubb

Genetic epidemiology of endometrial cancer

Endometrial cancer is the most commonly diagnosed invasive gynaecological cancer in developed countries. In contrast with many cancers, the incidence and mortality of endometrial cancer is steadily increasing, largely due to increasing rates of obesity, the strongest risk factor for this disease. Through leadership of the Endometrial Cancer Association Consortium (ECAC), our lab runs the largest genetic study of endometrial cancer. To date, we have identified 16 genetic regions associated with endometrial cancer predisposition by genome-wide association study (GWAS), which account for ~25% of the genetic heritability attributable to common genetic variants (O’Mara et al, Nat Commun 2018). Incorporation of existing GWAS data with newly acquired GWAS datasets from international collaborators will identify further genetic regions associated with endometrial cancer risk. Additionally, we have approved access to large, well-phenotyped international datasets (e.g. UK Biobank, N = 500,000). This allows us unparalleled ability to examine the genetics of endometrial cancer, as well as explore its relationship with risk factors, such as obesity.

Aims: To identify new genetic risk regions for endometrial cancer, by performing the largest GWAS meta-analysis for this disease. To use computational approaches to identify and explore risk factors of endometrial cancer. To use genetic data to construct and test risk prediction models for endometrial cancer.

Approaches: This project will use standard GWAS pipelines to identify genetic variants associated with endometrial cancer risk, including imputation, QC and association testing. Post-GWAS analyses to explore novel regions could also be performed (e.g. eQTL analyses, integration with functional genomic datasets). The relationship between endometrial cancer and potential/known risk factors will be performed using approaches such as genetic correlation (LD Score Regression) and Mendelian randomization. Endometrial cancer risk prediction models will be constructed using polygenic risk scores in combination with endometrial cancer environmental risk factors and tested for efficacy in independent datasets.

TO APPLY FOR THIS PROJECT, PLEASE CONTACT THE PROJECT SUPERVISOR/S

• A/Professor Tracy O’Mara

  Tracy.OMara@qimrberghofer.edu.au

• A/Professor Dylan Glubb

  Dylan.Glubb@qimrberghofer.edu.au

Identifying the regulatory targets of common endometrial cancer risk variants

We and our international Endometrial Cancer Association Consortium collaborators have identified common genetic variation at 16 genomic regions that associates with endometrial cancer risk. Although we have identified potentially causal risk variants, at most regions we do not know which genes these variants target. However, we have conducted global (HiChIP) analyses of DNA looping to identify physical interactions between genes and regulatory elements at endometrial cancer risk regions in endometrial cancer cell lines. These experiments constitute an essential step for the translation of genetic findings into advances in our knowledge of endometrial cancer biology and the identification of potential targets for therapy.

Aim: To identify high confidence gene regulatory targets of endometrial cancer risk variants using DNA looping analyses and other functional genomic datasets.

Approach: Depending on the applicant’s expertise, this project could have either a wet-lab and/or a bioinformatics focus. We already have a wealth of endometrial cell DNA looping data that can be coupled with complementary datasets (gene expression, histone modification and transcription factor ChIP-seq) for bioinformatic analyses to prioritise regulatory target genes. To extend our findings from DNA looping analysis of endometrial cell lines, we are also interested in performing analysis of human endometrial organoids from normal, hyperplastic and tumoural endometrium. These organoids should provide experimental systems that better recapitulate the morphological and genomic features of human tissue.

Outcome: Through the identification of high confidence gene targets at endometrial cancer risk regions, we will gain a deeper understanding of endometrial cancer aetiology and identify potential targets for endometrial cancer therapy.

TO APPLY FOR THIS PROJECT, PLEASE CONTACT THE PROJECT SUPERVISOR/S

• A/Professor Tracy O’Mara

  Tracy.OMara@qimrberghofer.edu.au

• A/Professor Dylan Glubb

  Dylan.Glubb@qimrberghofer.edu.au