Project summary
Precision medicine utilises large data sets that combine omics with clinical information and health outcomes to optimize disease diagnosis, treatment and prevention specific to each patient.
The Computational Biology Program will integrate and interpret ‘omic (i.e. genomic sequence, protein, metabolite, and microbiome information), biological, and clinical data to accelerate the translation of research findings into a clinical environment and improve patients’ access to new clinical trials, as well as, inform new programs for prevention and earlier diagnosis of disease in the community.
This will ultimately lead to more effective treatment strategies for sick children, and their families so that they live longer and better, and reduce the burden on the healthcare system.
The predictive models insights will also undoubtedly have utility beyond paediatrics such as adult cancer and other rare disease disciplines.
What is the issue for NSW?
Existing models to integrate, interpret, and report clinical recommendations have limitations. As precision medicine evolves, there is an increasing gap between what is possible in research and the clinic. Challenges include complexity of data, multiple sites, labour-intensive curation, and limited complete data sets from patients. Implementing precision medicine depends on the ability to achieve automation and efficiency gains in processing and interpreting complex data sets, consolidating storage, access, sharing, and computing power in a secure environment, and upskilling the workforce to achieve high standards of care in NSW.
The outcome from this investment will be that children and families in New South Wales will experience world class healthcare improving access to the right treatment at the right time.
What does the research aim to do and how?
Establish a program and capabilities to underpin and accelerate precision medicine activities by developing analytical and statistical approaches including:
- improved methods for genome & transcriptome characterisation to increase diagnostic & actionability rates.
- improved methods to integrate, and manipulate a child’s clinical, biological, and multi-‘omics data
- new methods for disease risk, prognosis prediction and optimal drug matching
- underpinning screening projects (newborn screening and cancer predisposition), building fit for purpose analytical pipelines
- identify candidates for pre-clinical testing
- build data infrastructure to support a secure environment for data sharing & analysis, and
- establish bioinformatics training platform to build workforce capacity.
Read more about the work of Luminesce Alliance.
Partnering institutions
- Children’s Cancer Institute
- Sydney Children’s Hospitals Network
- University of Sydney
- University of NSW