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Genomics and Precision Health

Many approaches to cancer prevention, early detection, and treatment have been designed based on an “average” person. However, people are different, and what works well for one person may not be the best option for another.

Our research aims to integrate individual and population-level genomic, lifestyle, and medical information for successful precision health: tailoring the right intervention and support to the right population at the right time, from risk-stratified cancer screening and early detection, to targeted cancer treatment.

To help effectively and sustainably optimise cancer control through precision health approaches, the stream brings together expertise in genomics, epidemiology, and innovative analyses of large-scale data, with strong collaborative links to Australian and international researchers.

Key areas of focus in this stream include:

Predicting cancer risks and the Australian Cancer Risk Study

The stream is building cutting-edge, evidence-based risk prediction tools for potential future risk-tailored cancer control interventions. In particular, the stream leads the Australian Cancer Risk Study, an innovative program to harness the potential of genomics for improved risk prediction and early detection for the four most common cancers in Australia: breast, prostate, melanoma, and colorectal cancers. In collaboration with a multidisciplinary team of world-leading researchers, consumers, health professionals, and policy stakeholders, the study will produce evidence-based recommendations for effective and sustainable genomics-informed cancer screening and early detection programs to help reduce the burden of cancer. This research program is funded by a $3m grant from the Australian government’s Medical Research Future Fund, and will also generate a new lasting cancer-focused resource of Australian genomic data within the 45 and Up Study.

Uncovering cancer journey patterns

Leveraging large-scale health data, the stream is revealing complex patterns in pathways to cancer diagnosis and patterns of cancer care. These patterns highlight areas of good practice, but also current bottlenecks and inefficiencies in health service delivery. The stream also investigates differences between subgroups of the population to identify potential inequities and opportunities for tailored interventions to improve health outcomes.

Calculating the costs of cancer

The costs of cancer care vary significantly by cancer type and stage at diagnosis. The stream analyses direct costs to the health system and out-of-pocket costs to patients and their families, integrating information from different in-depth linked health datasets. The results provide vital information for the evaluation of potential cancer control interventions.

Bringing genomic medicine to the clinic

To inform effective genomic medicine strategies for cancer, the stream is involved in a multidisciplinary clinical implementation trial to improve testing for Lynch syndrome, an inherited predisposition to cancer. In collaboration with mathematical modellers, the stream also carries out health-economic evaluations of different testing strategies using the Policy1-Lynch platform.

Forecasting future cancer burden for biomarker-based treatment

Forecasts of the future cancer burden are crucial to inform planning, policy, and practice for precision health, including estimates of burden in subgroups targeted by specific interventions. In particular, the rise of genomic medicine has led to new cancer treatments based on the molecular features of the tumour (so-called “pan-tumour biomarkers”) rather than its tissue of origin. The stream is estimating the numbers of future cancer patients with selected key pan-tumour biomarkers.

Research Team

Associate Professor Julia Steinberg

Stream Lead, Genomics and Precision Health

David Goldsbury

Statistician and PhD candidate

Dr Yoon-Jung Kang

Research fellow

Philip Ly

PhD Candidate and Research Assistant

Associate Professor Julia Steinberg

Stream Lead, Genomics and Precision Health

Hamzeh M Tanha

Postdoctoral Research Fellow

Sarsha Yap


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