A new system that rapidly determines which cancer drugs are likely to work best for a patient according to his genetic markers was developed by research teams from the University of Delaware and Georgetown University. The first publicly available system of its kind, their database, eGARD (extracting Genomic Anomalies association with Response to Drugs), is described in PLOS One.
When your genes work correctly, they function like miniature factory plant managers, directing the production of life-sustaining proteins. But sometimes, a gene goes rogue and manufactures a cancerous tumor instead. When cancer experts identify these faulty genes, they can devise treatment plans based on past evidence.
However, until now, the data linking genetic factors and treatment results has been spread among hundreds of academic journals. It would take days for doctors, doing nothing else, to find and read all these reports. Now, they may be able to spend that time delivering optimized treatments instead.
eGARD is a text mining system that analyzes words and phrases in medical literature to find relationships between genomic anomalies and drug responses.
"Clinicians have no time to read all of the reports and literature for each tumor," said Peter McGarvey, a study author and associate professor of biochemistry at Georgetown University. "eGARD is a way to help surface the important ones for clinicians, medical geneticists or maybe companies that already are doing this in other ways."
The research team applied eGARD on roughly 36,000 article abstracts, retrieving 50 genes and 42 cancer drugs, including cell cycle inhibitors, kinase inhibitors and antibody treatments.
The research team first trained the system to identify indications of genetic anomalies, with very scientific names such as "over-expression of ERCC1" or "C677T and A1298C polymorphisms of MTHFR gene."
Then, they trained it to look for text suggesting treatment outcomes, such as "significantly poorer response" or "survival rate." Next, they sought words and phrases connecting a genetic anomaly and outcome, such as "correlate," "associate" or "sensitize."
By extracting and processing key pieces of text, eGARD can match genetic signatures with outcomes with 95 percent precision.
"We hope this could make a difference for oncologists and cancer patients alike," said study author Vijay Shanker, a professor of computer and information sciences at UD.
UD researchers developed the code and data processing for eGARD, and clinically focused researchers at Georgetown provided use cases, terminology, curated datasets and insight on what information was most important to clinicians working in precision medicine. Both groups tested and refined the system.
The team will make a public interface for eGARD. It may also be incorporated into other software eventually.