Functional Genetics

Identifying noncoding regions that, when genetically altered, promote the development of breast and ovarian cancers

Professor Juliet French

Program Director

Research Focus

The Functional Genetics Laboratory combines genetics and functional genomics to pinpoint the key genes and pathways implicated in the development of both breast and ovarian cancers.

We are interested in understanding how inherited and/or acquired genetic variants located in noncoding DNA contribute to cancer development. More particularly, we investigate regions that are either transcribed into functional RNAs such as long noncoding RNAs (lncRNAs) or those that act as DNA regulatory elements such as enhancers.

Identifying the lncRNAs affected by noncoding genetic variants and the protein coding genes they regulate is necessary to identify therapeutic opportunities for primary and secondary cancer prevention. These can be achieved by identifying novel targets or using existing therapies that can be repurposed for the prevention or treatment of either breast and ovarian cancers.

Gallery

CRISPR-Cas13 knockdown screen to identify lncRNAs involved in breast cell proliferation
Research Focus

Research Projects

Current Research Projects

Understanding how common genetic variants associated with cancer risk contribute to cancer development

Understanding lncRNA mechanisms governing breast and ovarian cancer development and progression

Identify novel lncRNAs in the human breast and ovaries using high-throughput sequencing data

Past Research Projects

High-throughput mapping of promoter-enhancer interactions to identify new cancer risk genes


Research Team

Dr Haran Sivakumaran

Dr Xue Lu

Isabela Almeida

Sneha Nair


Funding

  • National Breast Cancer Foundation
  • National Health and Medical Research Council of Australia

Publications

Bitar et al, Redefining normal breast cell populations using long noncoding RNAs. Nucleic Acids Research. 2023. PMID: 37144467

Moradi Marjaneh et al, Non-coding RNAs underlie genetic predisposition to breast cancer. Genome Biology. 2020. PMID: 31910864.

Beesley et al, Chromatin interactome mapping at 139 independent breast cancer risk signals. Genome Biology. 2020. PMID: 31910858.

See Google Scholar


Further Information



External Collaborations
  • Breast Cancer Association Consortium
  • Associate Professor Joseph (Sefi) Rosenbluh, Monash University