A researcher at GW is exploring a method for normalizing cancer genomics data currently held in various platforms and databases

One of the biggest challenges facing cancer genomics researchers is the lack normalized data on cancer genes. Multitudes of genomics projects have been funded over the years that have resulted in data being placed on several different databases, making a wide array of data and annotations difficult to integrate and compare. While projects have taken place to standardize data, they have not successfully enforced those standards between projects.

Raja Mazumder, Ph.D., associate professor of biochemistry and molecular medicine at the George Washington University (GW) School of Medicine and Health Sciences, recently received a $1.2 million grant from the National Cancer Institute of the National Institutes of Health to bring all this data together.

Mazumder and his research team are developing two databases, BioMuta and BioXpress.

"There is a lot of data already generated, and that data is growing exponentially," said Mazumder. "Projects like this one will make utilizing that data much easier for researchers."

The funding will allow Mazumder and his team to pull the data into a framework, standardize cancer terms to ensure that they are mapped correctly, and provide interfaces and applications that allow users to easily access the data. One of the benefits of developing a space to house all cancer genomics data for expression and mutation is that research can then look at cancer genes in an evolutionary context, which helps to understand how cancer genes are expressed across various populations.

With the standardization of cancer genomics data, researchers will be able to search for data within the restrictions they are following for their own studies. For example, if a researcher wanted to look specifically at whether or not a cancer gene is overexpressed in Caucasian women, within a certain age range, with breast cancer, they could more easily find it in BioXpress.

"This project will allow for connecting cancer genomics mutation and expression data within an evolutionary context," said Mazumder. "The primary outcome will be that users of BioMuta and BioXpress will be able to identify high priority experimental targets for biomarker discovery for diagnostics for therapeutics and prognostics."