Much of the discussion on big data has focused on claims information from insurers and EHRs from providers, but a collaborative effort underway at the Stanford School of Medicine with SAP is hoping to tap into a different set – genomic data.
The possible benefits of sharing genetic data on a wide scale have great potential for both global population health and for drug makers alike, said Dr. Carlos Bustamante, who heads the Department of Genetics at Stanford. Benefits range from possibly learning why certain drugs never make it out of clinical trials because of what population they are tested on to a more inclusive global snapshot of differing populations and what drives their health spending, among others.
Take Mexico, for example: It has one of the highest rates of type 2 diabetes in the world, which has been attributed to a number of factors, including diet. But, based on the study and sharing of genetic information, researchers have found that much of the population actually is genetically predisposed to the condition, which in turn can lead to better, root-cause treatments.
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Those types of findings, and the data that comes with them from genomic sequencing, are starting to be shared on a much wider scale, Dr. Bustamante said. And the partnership with Germany-based SAP will help further accelerate both the dissemination and processing of that data, he said. Already, more than 2,000 genomes have been entered into a database that researchers can see in real time.
A key part of the effort, though, is processing the data, which has traditionally been an extremely slow and costly effort compared to the actual sequencing, which is a comparatively quick process, Dr. Bustamante said.
“We ship the hard drive, then a team comes together and puts the data together. It’s weeks of sequencing, months of interpretation,” he said. “We really want to break that cycle. If that information is made available and shared, then you can see the power of that and put it together.”
SAP is providing the platform and the software to help speed that process up, and Stanford says it has seen between 17 and 600 times faster computations in analyzing its genomics data since last November. The collaboration also includes participation in the Global Alliance for Genomics and Health, a global effort to “responsibly share and analyze large amounts of genomic and clinical data,” according to SAP.
Another motivating factor, Dr. Bustamante said, is how disproportionately clinical drug trials are done on people of European descent versus other populations, say Chinese, African, Indian or South American. It will be of particular concern as those countries grow in wealth and develop more complex healthcare needs.
“One of the things that particularly concerns me is that many studies are focused just on the population of Europeans,” he said. “Those that do not tend to have a really small sample size. We think that’s a problem not only from an ethical standpoint, but you leave a lot of interesting biological data on the table.”
Just as the big data proponents on the hospital and insurance side of the equation maintain, big data sharing with genomics has the potential to reduce healthcare costs by better understanding the root causes of a chronic condition, not simply treating that condition, Dr. Bustamante said.
“Just imagine if you do this with diabetes” he said, as just one example. “How do we know that the causes of diabetes are the same throughout the world? Could it be that different populations have different causes for diabetes?”
The same approach can be used for any number of chronic conditions across a wide swath of the population.
“It might give you a biological target in lots of other populations,” he said. “Often times you can make things that benefit every population. The health burdens in Africa and India and South America are quickly turning out to be the same complex diseases that we have.”
Another example, Dr. Bustamante said, is that genetics can predict your LDL cholesterol, and thus your chance of having a heart attack. Part of the treatment to high LDL has been to prescribe medications that in turn increase levels of HDL cholesterol, with the assumption that the good cholesterol will counter the bad cholesterol.
But “your HDL is it not related to cardiovascular risk, but LDL is,” Dr. Bustamante said. “There are four to five drugs that have failed because they raise your HDL but don’t improve your cardiovascular health.Where should the biomedical focus be? It should be on LDL lowering drugs.”
Getting a better sense of the genomic data and what makes one predisposed to certain conditions, like those associated with high cholesterol and cardiovascular health, can then help better inform health experts.
“A major challenge in healthcare is the billions of dollars it takes to bring drugs to market, and most drugs fail because they do well in animal models or in certain settings, but in full clinical trials, they fail, and that’s where it’s expensive.”
That’s partly because the information used by a given drug developer doesn’t include genomic data, because it’s largely not available on a wide scale. Traditionally, such data might have been available to a small set of researchers, or it would have been proprietary information.
“Wouldn’t it be great if we could solve this general problem of data sharing from the biomedical enterprises and have data really in the context of care? That’s where it’s going to be really illuminating,” Dr. Bustamante said.
While much has been gleaned from genetic data recently, now the effort of sharing that data needs to be appropriately scaled.
“We know how to do the analyzing, it’s the question of how do you really scale this up on an industry-wide level and make it fast enough, so you can ask the questions you want to ask,” Dr. Bustamante said. In addition to advancing global public health, such data can likely boost innovation in the private sector, too.
“It also creates a lot of great opportunities for businesses – where there are opportunities to get disruptive technologies to collapse X, Y and Z,” he said.
