Computational Therapeutic Discovery
Many aggressive cancers still have poor survival rates because their complex biology hides actionable therapeutic vulnerabilities. Our group integrates AI-driven multi omics data and large-scale functional screening to systematically uncover genetic dependencies, prioritise therapeutic targets, and enable precision oncology.
Lab head Dr Claire Sun and the Childhood Cancer Model Atlas (CCMA)
Overview
Cancer remains one of the leading causes of death worldwide, responsible for nearly 10 million deaths annually. In Australia alone, more than 160,000 people are newly diagnosed with cancer each year. Despite major advances in treatment, many aggressive cancers still have poor survival rates. One of the reasons for this is that only a small number of known tumour-driving genetic / cellular changes can currently be targeted by the drugs we have available – highlighting a critical gap between molecular discovery in the lab and development of effective therapies for patients.
Cancer is biologically complex, driven by interacting genomic, epigenomic and proteomic changes that are difficult to interpret using conventional approaches. While modern technologies generate vast multi omics datasets, translating this information into new therapeutic strategies remains a major challenge in the field.
Our group develops artificial intelligence-driven frameworks to integrate genome sequencing, transcriptomics, epigenomics, proteomics, CRISPR dependency screens and drug response data. We then use this combination of information to systematically identify hidden genetic vulnerabilities, to prioritise therapeutic targets and develop predictive biomarkers for childhood and other cancers.
Our goal is to expand the number of known clinically actionable cancer drug targets, improve patient stratification, and accelerate precision oncology by transforming complex molecular data into tangible treatment strategies.
The Computational Therapeutic Discovery Group is led by Dr Claire Sun, who also leads the Advanced Informatics Program at Hudson Institute.
“By harnessing artificial intelligence to decode cancer complexity, we are turning data into discovery and discovery into real therapeutic opportunity for patients.”
Diseases we research
Areas of focus
- Using artificial intelligence to analyse large amounts of cancer data to find new treatment opportunities
- Identifying hidden weaknesses in cancer cells that can be targeted with new or existing drugs
- Screening to test which genes or pathways cancer cells depend on to survive
- Discovering biomarkers that help predict which patients are most likely to benefit from specific therapies
- Investigating how changes in gene regulation influence cancer progression and treatment response
- Building accessible data platforms that allow researchers and clinicians to explore complex cancer datasets more easily
Research Group Head | Dr Claire Xin Sun
Cancer remains one of the leading causes of death worldwide, responsible for nearly 10 million deaths annually. By harnessing artificial intelligence to decode cancer complexity, we are turning data into discovery and discovery into real therapeutic opportunity for patients.
News from the lab
Collaborators
Publication highlights
