Using Genomic Data to Personalize Cancer Treatment

LEADING RESEARCHERS in biomedicine have made tremendous progress in developing new ways to collect and analyze mass volumes of biomedical data, including DNA sequence data. Today, researchers are using genomics research to better understand how genetic variation contributes to human health and disease.

While a number of large-scale genomic projects are underway, collecting genomic data is only one step of the process. Ultimately, in-memory computing technology is needed to analyze and interpret mass amounts of genomic data. In order to derive meaning out of the chemical base pairs that make us who we are, genomic data must be combined with clinical data and include diagnoses and symptoms of individuals. By integrating genomic data with clinical data, scientists can determine associations between regions of the genome and the predisposition to certain diseases like cancer.

Why Cancer?

Cancer is among the leading causes of morbidity and mortality worldwide, with approximately 14 million new cases and 8.2 million cancer-related deaths in 2012, according to the World Health Organization.

According to Bill McDermott, CEO of SAP, a company developing technologies to help defeat cancer: “Fighting cancer is fundamentally a data challenge.” By developing common data standards in medicine and using big data analytics, research centers and physicians will be able to identify better treatment plans and individualized care for cancer patients.

Applying Genomic Data to Individualize Treatment

Through the use of in-memory technology, large-scale genomic variant data has been analyzed in near real-time, revolutionizing the work mode of researchers. Instead of waiting hours or days for their analyses to return, researchers can now interactively ask more and more questions of the data. Once collected, that sequencing data can be shared with physicians to help them make informed decisions and devise more individualized cancer treatment plans.

Personalized Pharmacogenomics

Pharmacogenomics, the study of how genetic variation contributes to an individual’s response to drugs, is another example of how genome testing can influence clinical decisions. Researchers have identified a few hundred genes in an individual that are related to drug metabolism, and they are continuing to identify more. The Clinical Pharmacogenetics Implementation Consortium released guidelines for prescribing drug dosing or alternative drug recommendations for individuals expressing certain genetic variation. With a relatively inexpensive genome-based drug metabolism test (ranging from $200 to $500), a doctor can determine the rate at which an individual can metabolize specific classes of drugs, including cancer drugs.

Education and Overcoming Roadblocks

While genomic sequencing and big data analytics have started to change the way oncologists can treat cancer, many roadblocks remain. Researchers are still discovering new associations between genetics and disease, and while the sequencing itself can be completed within one day, processing and analysis can require a team of genetic researchers to manually map and interpret the data. The processing and analysis of genomic data has not yet been standardized, but the precision U.S. Food and Drug Administration program, currently in beta mode, provides a platform where researchers can validate their processing pipelines for genomics data. In addition, in certain situations, genome sequencing is covered by only some insurers or coverage exists for only a small number of specific genetic tests.

An Improved Sequence in Cancer Care

Despite these challenges, the potential for affordable genome sequencing tests and in-memory computing to revolutionize cancer treatment is enormous. Providing personalized care and individualized drug therapy for patients can significantly improve outcomes and reduce the overall cost of cancer care. As the data pool grows, researchers and doctors will gain more insights from genome testing. They will be able to carve out precision-based treatments by making sense of vast amounts of available DNA data, ultimately improving the lives of millions of people fighting cancer around the world.