A/Prof Mark Cowley

Group Leader, Computational Biology

Computational Biology

Research goals

- To improve outcomes for children in the Zero Childhood Cancer national clinical trial
- To develop computational methods to improve the quality of precision medicine
- To develop computational methods to investigate the impact of non-coding mutations on cancer

Associate Professor Mark Cowley is a bioinformatician whose research is focused on developing innovative computational approaches to improve health outcomes. Mark joined Children’s Cancer Institute in 2018 to establish a new computational biology group, bringing his bioinformatics expertise to the Zero Childhood Cancer personalised medicine program as well as a number of other research programs at the Institute.

Best-known for his translational bioinformatics work, Mark previously worked with the Australian Pancreatic Cancer Initiative, and the Kinghorn Centre for Clinical Genomics at the Garvan Institute. Here, he led the development of clinical-grade whole genome analysis, which has now been commercialised, and developed into one of the first whole-genome-sequencing-based pathology tests in the world. He was also instrumental in leading the development of the computational biology underpinning the now-national MoST program, Australia’s largest precision medicine trial for adults with rare cancer.

Mark and his group are conducting genome analysis as part of the Zero Childhood Cancer clinical trial, analysing the tumours of participating children to look for mutations that may provide important clues about causes and appropriate treatments. Longer term, Mark aims to use the data generated from these analyses to answer larger questions about cancers and a range of other diseases. He is also exploring the impact of ‘non-coding mutations’ of children with cancer – mutations that occur in parts of the genome that don’t encode genes yet are thought to be important in other ways.

‘Precision medicine is the context of my work,’ Mark explains. ‘The aim is to improve outcomes for patients by understanding the molecular drivers of their tumours. We can use this data to improve outcomes in patients today, through making better treatment recommendations, as well as long-term to better understand cancer.’