The role of precision medicine in cancer care continues to grow, as Big Data projects provide meaningful insights that aid in research and clinical decision support. However, despite significant progress from these early efforts toward the development of new cancer treatments and drugs, many cancers still remain difficult to treat, particularly those in advanced stages.
The world’s leading researchers in biomedicine have developed new ways to collect and analyze mass volumes of biomedical data, including genome sequence data. Researchers today are using genomics research to better understand how genetic variation contributes to human health, and more specifically, to deadly diseases such as 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.
At SAP, our Connected Health team is focused on developing innovative ways to help defeat cancer in the global population. SAP’s CEO Bill McDermott has stated that “fighting cancer is fundamentally a data challenge.” We are using a trifecta of strategies to help the healthcare community combat cancer. By working to develop common data standards in medicine, collecting genome sequencing data, and using Big Data analytics, we can help research centers and physicians in important ways.
This type of precision medicine enables oncologists and researchers to see new insights for discovery through access to a massive body of de-identified data—uncovering trends among millions of patients by analyzing cancer patient medical records. This methodology reveals patterns that can improve patient care and enable more data-driven decision support.
Collecting genomic data
With recent advances in genome sequencing technology, costs for genetic testing have plummeted, thus making individual genome sequencing more economically feasible. Sequencing a full human genome costs about $1,500 and takes about one full day with today’s next-generation technologies. In approximately 27 hours, we can get the entire genetic blueprint that makes a person unique. The processing and analyses of this genomic data still takes considerable manual effort and time, but is continuing to improve as new processes and tools become validated.
Understanding the genetic diversity that predisposes us to certain diseases and makes us who we are will require large databases of genomes. A significant number of large-scale genomic projects are underway. Ultimately, we need to use in-memory computing technology to analyze and interpret these massive amounts of genomic data.
To derive meaning out of the chemical base pairs that make us who we are, researchers and physicians need to combine genomic data with clinical data, such as the diagnoses or symptoms of the individuals fighting cancer. By integrating genomic data with clinical data, we can determine associations between regions of the genome and the predisposition to certain diseases.
Applying genomic data to personalize cancer 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 minutes, hours, and even sometimes days for their analyses to return, researchers can now interactively ask more questions of the data. Once collected, that sequencing data can be shared with physicians, who can use it to make informed decisions and devise more individualized cancer treatment plans.
Genome sequencing data is also a resource that can be useful in drug development. Advances in using data-driven diagnostics in genome sequencing aid in individualizing cancer treatments and matching patients to new drug trials. This includes selecting the most effective medication with the fewest side effects, as well as developing a personalized treatment pathway tailored to the characteristics of a particular patient's cancer type.
Personalized pharmacogenomics
Pharmacogenomics, the study of how genetic variation contributes to an individual’s response to drugs, is another example of how genome testing is influencing clinical decisions. Researchers have identified a few hundred genes in an individual that are related to drug metabolism, and are continuing to identify more.
The Clinical Pharmacogenetics Implementation Consortium (CPIC) has 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 $200to $500), a doctor can determine the rate at which an individual can metabolize specific classes of drugs, including drugs used in HIV and cancer treatments.
Overcoming roadblocks
While genomic sequencing and data analytics are starting 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 analyses of genomic data has not yet been standardized, but the precisionFDA program (precision.fda.gov), currently in beta mode, provides a platform where researchers can validate their processing pipelines for genomics data. In addition, genome sequencing is covered by only some insurers in certain situations or coverage exists for only a small number of specific genetic tests.
Improved cancer care
Despite a handful of 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.
