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EAGLE will help cancer research soar
 Researchers from OICR and other institutions have created a new software program called EAGLE that mines data to understand the interactions between a person’s environment and their genetics. The tool has far-reaching uses, including oncology, and can provide researchers and clinicians with important information that can help personalize treatments for patients.

Dr. Hilary Edgington

 Researchers from OICR and other institutions have created a new software program called EAGLE that mines data to understand the interactions between a person’s environment and their genetics. The tool has far-reaching uses, including oncology, and can provide researchers and clinicians with important information that can help personalize treatments for patients.

To learn more we spoke to Dr. Hillary Edgington, a Postdoctoral Fellow in OICR’s Informatics technology platform, which is led by Dr. Lincoln Stein. Edgington and her collaborators recently shared their research in the journal Nature Methods.

What was reported in your recent article?

One of the most important goals in biological research is to understand the ways that our genes can be impacted by the environment around us. The activity of genes can change due to a number of external factors from medication use to air pollution. This study introduces a new software tool called EAGLE to investigate how interactions between a person’s genotype and environmental exposures affect the way his or her genes are expressed.

What is unique about EAGLE?

EAGLE takes advantage of the fact that sometimes the two copies of a gene are unequally expressed, which allows us to compare small differences in those two copies within an individual where they operate in the same environmental conditions. This tool was shown to improve, in both power and accuracy, on detecting associations over standard interaction testing methods. Using EAGLE to test for interactions in two large cohorts (the Depression Genes and Networks study cohort and CARTaGENE) revealed significant associations between gene expression and environmental variables, including depression, exercise, blood pressure medication use and body mass index. This information is critical in advancing personalized healthcare initiatives, as it gives researchers and clinicians information with which to predict an individual’s health risks based on their unique genomic profile and lifestyle factors.

How can these findings be used in the area of cancer?

EAGLE is a tool that can be applied to data from any source. The information gleaned from the application of EAGLE to data provided by cancer patients – including testing for interactions between patients’ specific mutational profiles and exposures such as therapeutic treatments, the microenvironment of the tumour, or properties of the immune system – could help clinicians make more accurate predictions about individual patients’ prognoses and therapeutic options in the future.

What challenges did you and your collaborators face while creating EAGLE?

One of the main challenges with developing any new method is making sure that the results will be consistent across different groups of individuals. It is critical to perform tests in different groups in order to make sure that there is replication of any findings. For this reason, collaboration between different research groups is critical, and this is what brought the groups from OICR and Stanford University together on this project. At OICR we were able to use the resources that we have through the CARTaGENE cohort to perform a replication study. It showed that the associations EAGLE detected in the Depression Genes and Networks cohort are consistent across populations.

What are the next steps planned with this research project?

We will be able to use EAGLE in many future projects as a way to discover previously unknown interactions between any environmental variable of interest and the regulation of genes. As a follow-up to this study we may look more specifically at the gene-environment associations we observed in order to determine what the mechanism is that causes differences in gene expression.