Genomics and Precision Health

The stream is building and evaluating cutting-edge cancer risk predictions for potential future risk-based 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 is developing evidence-based recommendations 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 has also generated a new lasting cancer-focused resource of Australian genomic data within the 45 and Up Study. 

Multi-cancer early detection (MCED) tests have the potential to identify people with a wide range of cancers who do not have symptoms yet, by using a single test that snapshots many different cancer biomarkers. The technology is rapidly evolving, and the potential benefits could be game-changing. The stream leads a new $3M MRFF-funded collaborative research program on the potential of innovative MCED tests in Australia. This work brings together researchers across many Australian and international institutions, health professionals, and consumers. It aims to establish evidence-based recommendations to help maximise the potential of MCED tests for patient-friendly and more effective early detection of cancers.  

Leveraging large-scale health data, the stream investigates patterns in cancer survival, care, and pathways to diagnosis. We are particularly interested in potential inequities and in opportunities for tailored interventions to improve health outcomes. 

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. 

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.