Leveraging IT to Link Genomic Research and Patient Care in Pittsburgh

So that makes us unique, because we have 140 patients for whom we know all of their clinical information that sits in the UPMC system, and we know all their genomic or molecular information that sits within the consortium. And the nice thing about the consortium is that that data is all made public; it’s made public to everyone.

All 140 patients have the same mutation?

No, they all have different mutations. Ultimately, once we have the system built, we’ll be able to translate what we’re beginning to learn, into actionable care plans for individual patients. For instance, if you take Tylenol and another patient takes Tylenol, you’re going to respond differently to the Tylenol, you’ll metabolize it differently because of your genetics. So by understanding the differences in tumors, we hope to personalize the therapy; it’s the whole idea of personalized medicine.

How far away are you from the concept of personalized or precision medicine?

As you know, it was in the news recently that the actress Angelina Jolie had a family risk of breast and ovarian cancer, and they sequenced her DNA and found that she had a gene, BRCA1; and because she has that mutant gene, she was at 80-percent risk of getting breast cancer. So she had a prophylactic mastectomy. Some women respond well to what’s called a PARP [poly ADP ribose polymerase] inhibitor. The therapy is the inhibitor, you want to inhibit PARP, because they also don’t have BRAC-1. Once they lose BRCA1, they become susceptible to PARP.

So we are in an unparalleled time at the moment in terms of collecting and analyzing data. We now have great tools that help us; but the bottleneck has been around the ability to store data and share it. And we’ll need things like Internet2 to help us. I think most of the supercomputing centers are on that network. It’s not only about the storage of the data, but also about the transfer of the data, and finally, analytics. So we’re trying to solve the storage issue, and so the storage is this warehouse. And the warehouse does two things with this Oracle server: we’re trying to combine together all the clinical information in a central warehouse, and then alongside it, bring together all of the molecular characterization data. And we would then like to rapidly do searches between them, and that’s what this Oracle system is really good at. The architecture allows you to rapidly search clinical data.

So where are you now in that process?

We’ve tested the system, and it’s all worked; we kind of tweaked the architecture. It went live [in the third week of June]. So it’s installed on the UPMC system; it uses the Exadata server, and we can now log onto the system called TRC and analyze these 140 patients. So the first goal was, can we load them, and can we execute on them? It was a lot of work, with a lot of people. It took nine months, and we had to overcome a bunch of obstacles. But the fact that we could do it and reach that goal was very important, because this is simply the building block for the bigger picture. Now we’re going to try to load more patients and more data, and move into other cancers; next, we’re going to begin to load ovarian and head-and-neck cancers. The timeline hasn’t been set yet, but it will probably be early next year. The more we can load and the more we can ask the data, the more we can learn.

So working with or manipulating data like this requires this level of supercomputing?

Yes. You can see the scale. The New York Times has run several articles on big data challenges of hospitals in New York. This is like what happened with Google and web search and with Amazon and retailing on line. We have a limitless capacity to produce data, but still a limited capacity to store, share, analyze, and use data, and that’s the problem.

What is going to come together in organizations like yours around the country, the next couple of years?

Well, I think you’ll see that the use of the data is going to lead to rapid improvements in care delivery; that’s the end goal for us, to produce personalized care. We will see that on the provider side, just in terms of outcomes. And I think you’ll see that consuming the data and creating knowledge from it, will shift us more towards evidence-based medicine. You see this already; we have this health information network in Pennsylvania where the hospitals are sharing data from the network. For example, the patient comes in unconscious, and they know nothing, but there’s data in the HIE.

Sharing information is of great benefit; and you’ll see that in the research sphere. There have actually been recent crowdsourcing efforts, where you use social networking efforts to advance thinking rapidly. Think now about your daily life; there’s virtually nothing you do that doesn’t use a computer. It’s hard to predict in 10 years where we’ll be. Most likely, most of us will be sequenced by then; and likely that data will be in your EMR; this will change the way you see your primary care physician, basically.

And one important point of the project is that it’s scalable. It will grow over time, and hopefully, others should be able to install it and use it in their systems. So I hope we set a model for how others can do it.