By mining a vast trove of genetic data, researchers are enhancing physicians’ ability to treat cancer, predict patient outcomes, and determine which treatment will work best for individual patients.
By mining a vast trove of genetic data, researchers at the University of Virginia School of Medicine are enhancing physicians’ ability to treat cancer, predict patient outcomes, and determine which treatment will work best for individual patients.
The researchers have identified inherited variations in genes that affect how well a patient will respond after diagnosis and during treatment. With that information in hand, physicians will be able to examine a patient’s genetic makeup to provide personalized medicine, according to the study authors.
"Oncologists can estimate how a patient will do based on the grade of the tumor, the stage, the age of the patient, the type of tumor, etc. We found [adding a single genetic predictor] can improve our predictive ability by 5% to 10%. Many of the cancers had multiple inherited genetic change that were predictive of outcome, so if we add those in, instead of a 10% increase we might get a 30% increase in our ability to predict accurately how patients will do with our current therapy. That's amazing,” said Anindya Dutta, MBBS, PhD, chairman of UVA’s Department of Biochemistry and Molecular Genetics, in a press release.
Dutta said that reviewing the inherited genetic make-up of a patient can provide similar benefits for predicting outcomes and choosing therapy for many other conditions, from diabetes to cardiac malignancies. Therefore, the approach represents a major step forward in physicians’ efforts to tailor treatments specifically to the individual's needs and genetic makeup.
The researchers set out to answer why some patients fair better than others, despite having the same cancer at the same grade, stage, and treatment. Physicians assumed up until this point that there may be some tumor-specific mutations that some patients have but others do not, but it occurred to the researchers that with genomic data, there may be another hypothesis they could test.
To determine whether genetic differences in the patients could be the answer, the research team did a deep dive into the Cancer Genome Atlas, a large repository of genetic information assembled by the National Institutes of Health's National Cancer Institute. The researchers sought to correlate inherited genetic variations with patient outcomes.
"With the help of cloud computing services at UVA, we managed to download all this genomic sequencing data and identify what are known as germline variants—not just tumor-specific mutations but the mutations that were inherited from the parents and are present in all cells of the patient,” Dutta said.
Once the researchers learned that they could compute a large amount of information, they went on to download the genomic sequencing data for all 33 cancers and all 10,000 patients. The process took approximately 6 months.
The researchers said they are eager to share the findings in hopes of finding collaborators as well as inspiring researchers and private industry to begin mining the data for other conditions.