The Australian Cancer Risk Study aims to identify whether and how genomic risk assessments could help improve early detection of cancers, by tailoring screening recommendations to people’s cancer risk. Our work focuses on the four most common cancers in Australia: prostate, breast, melanoma and colorectal cancers.
Specifically, we are looking at the potential of so-called “genomic risk scores” or “polygenic risk scores” for these cancers. These risk scores combine information across many hundreds (or even thousands) of inherited genetic variants: each of the genetic variants only has a small effect on cancer risk, but together they capture a substantial amount of risk information.
The Study includes four interconnected
and complementary research components:
We have generated a lasting resource of Australian genomic data, building on the extensive Australian 45 and Up Study cohort. Specifically, we have generated a map of inherited (“germline”) genomic data for over 7,000 participants, including people with and without cancer. These genomic data are particularly valuable as they can be combined with existing in-depth questionnaire and health information on participants, to provide insights into cancer risk.
We are evaluating new and existing risk predictions for cancer, using data from the latest international genomics studies, and assessing predictions in large-scale international and Australian population-based cohorts (45 and Up Study, QSkin Study, Melbourne Collaborative Cohort Study, UK Biobank). Here, we are considering genomic risk information alongside some non-genomic risk factors (such as age). These analyses help us understand how accurate current risk predictions for cancer care.
We are establishing public preferences for potential genomic risk assessment and subsequent risk-based cancer screening, using a health economics technique called discrete choice experiments. These insights can help design potential future risk-based screening strategies to meet population needs.
We are applying microsimulation modelling to assess the potential benefits, harms, costs, and cost-effectiveness of some example strategies for risk-based cancer screening and early detection based on genomic risk information. Such assessments are vital to identify effective and cost-effective strategies. Our work will help identify promising avenues for further investigation.