Personalised Medicine is changing the way that cancer patients are treated. New technology is generating huge amounts of data from individual patient’s tumours, data which holds the key to understanding each person’s cancer.

Our group aims to improve outcomes for patients by getting the most out of that data. We develop computational approaches (algorithms) that can identify and prioritise clinically relevant mutations in a patient’s cancer cells. If we can figure out what’s driving those cells to grow, and/or how they overcome conventional therapies, we can use that information to help design the treatment strategies most likely to work.

Personalised medicine is increasingly becoming a data science, and so we also develop methods to integrate the data we generate with data in public access databases. This maximises its impact — the data doesn’t just benefit individual cancer patients today, it also contributes to our overall understanding of cancer. This sort of collaborative multidisciplinary research answers bigger, farther-reaching questions.

Our goals are:

  • to improve outcomes for children with high-risk cancer, through developing methods which improve the quality of precision medicine
  • to develop innovative methods to characterise the molecular profile of each tumour, and use these to analyse large-scale public data sets
  • to develop methods to investigate the impact of non-coding mutations on cancer
  • to understand the increasing role of genetic factors underlying cancer predisposition
    • Translational bioinformatics: Zero Childhood Cancer


      Our group provides the bioinformatic and genomics expertise that underly the molecular profiling used in the Zero Childhood Cancer personalised medicine national clinical trial. We analyse the vast amounts of molecular data (WGS, RNA-Seq and methylation) obtained from each patient’s tumour sample. We develop methods to identify and prioritise mutations, so that the ones that are medically interesting and clinically actionable get focused on. We mine the data for this information in real-time and feed our results back to the ZERO variant curation team and Molecular Tumour Board. This ultimately helps treating oncologists decide on the treatment strategy with the best possible chance of success for each child on the clinical trial.

      We also develop methods to integrate the data we generate with data in public access databases, so we are adding our knowledge to a growing pool.

    • Probing the dark matter of the high-risk paediatric cancer genome


      The human genome is vast, and only 2% of it encodes genes. One of the great challenges in genomics is to understand what genetic variation in the other 98%, the ‘dark matter’, does, and how it contributes to cancer. There are however a number of obvious places to begin looking. We will initially investigate the role of non-coding genetic variation on the sequences which are responsible for controlling gene splicing, gene regulation, and patterns of retrotransposon activation. By combining large amounts of matched WGS and RNA-Seq data from patients, we will start to systematically tackle the non-coding genome and begin to understand how this contributes to paediatric cancer. By investigating these noncoding mutations, we hope to gain a better understanding of high-risk childhood cancer, and potentially identify new drug targets. This project is supported by a Cancer Australia Grant (2019–21).

    • Redefining germline paediatric cancer risk


      Why do children get cancer? We believe that many children who develop cancer were born at a high risk of developing the disease. However, we can currently find a genetic cause in only around 10% of childhood cancers. Our research aims to improve this diagnosis rate by improving our understanding of how a child’s DNA affects his or her chances of developing cancer. We are doing this by investigating new ways in which the genome affects cancer risk, by looking at the “dark matter” of the genome, by examining how genes and the environment work together to control a child’s chance of developing cancer, and by searching for pre-cancerous changes in healthy children. We hope our findings will result in tools to predict childhood cancer risk, tools which can improve outcomes through earlier diagnosis in high-risk children, and better inform the families of children with cancer.

Staff List


Associate Professor Mark Cowley


Dr Maely Gauthier

Dr Mark Pinese


Dr Marie Wong-Erasmus


Patricia Sullivan


Chelsea Mayoh


Rachel Bowen-James