Dr. Van Zeeland: A real need is not only educating the physicians but also the consumers and patients about what personalized medicine is and also what it is not. We have to manage expectations about true outcomes. Some people think personalized medicine is a magic bullet.
Sterling: Although you said earlier that the big problem is coming up with an effective business model, are there areas in personalized medicine that still need a technological breakthrough to move this science more quickly into the clinic?
Dr. Nelson: Many companies are working hard to bring technologies to bear on personalized medicine. I think we’re getting close to being able to sequence more efficiently, even looking at things like RNA and DNA in circulating blood. Again, technically, we’re making good strides.
If I had to pick one technical challenge that we face as an industry it would be interoperability, which is much more important than what everyone in the industry seems to be fixated on, which is standards. Many groups are using completely different methods, and the assumption is that we should all get exactly the same result or answer. But the challenge with that is that people have spent decades trying to solve the standards problems, often to no avail.
The much more thoughtful solution is to say, look, what we’re really trying to do is solve a particular clinical question using a specific technology. Each test should be judged on its fitness for use around a certain clinical question.
Clinical questions are driven by one question and one answer. Does the analyte you’re looking at have clinical utility for that particular application? We may all be measuring different things that may all have inherent value. And so one of the challenges we face is how do we take all these disparate methods and gather all that useful information to make it interoperable inside of the healthcare system.
For example, rather than trying to force everybody to come up with the same sequencing results or the same protein results, the real question should be does your test work to measure what it needs to measure to provide clinical utility? And then how do we provide a solution for interoperability to take that result and feed it into the medical system in a way that’s meaningful?
Dr. Solomon: Yes, some technological challenges are more tactical while others are more strategic. As we consider what it’s going to take to advance personalized medicine, we can acknowledge that the diagnostic side and our ability to measure genomic parameters have come a long way. Going forward, I think technology breakthroughs in front-end sample preparation and back-end analysis can really change the game. Many tumors and organs that aren’t readily accessible for sampling, as well as the cost of taking and preserving those samples, require major technical advances. On the back-end, we need improved data analysis tools to generate better information, higher success rates out of trials, and lower trial costs. To me, these are the tactical opportunities we need to address to boost the workflow today.
On a more strategic level, we need to better understand disease and how to target therapeutic developments. The therapy issue is by no means trivial. It’s one more layer of personalized medicine. And so, while we’ve come a long way in genomics, we need to focus our strategy on a more holistic view of understanding and treating disease. The integration of DNA with RNA, proteins, and metabolites is going to be key to more effective and efficient discovery and development of next-generation therapeutics and biomarkers.
Toxicity is also an important challenge for drug development and personalized medicine. The ‘omics’ technologies are on the verge of enabling new paradigms for in vitro systems toxicology. Because in vitro methods based on cell lines have the potential to be more predictive of human response and more cost-effective compared to traditional animal testing, they will further accelerate translation of research into clinical practice.
Dr. Van Zeeland: For me, three primary advances need to take place. The first speaks more to electronic health records (EHR). The EHRs have to be reconfigured in a way where you can start adding personalized medicine-type of information in a reasonable way and access it. But this is not just for the benefit of a single patient. If you push this information into the cloud, and you can start drawing inferences between patients with interesting phenotypes or profiles, you start moving from just a genomics understanding to a more holistic patient view and outcomes. Then you can start creating personalized medicine in a distributed system.
Sample prep does not require a huge technological breakthrough. It’s just a paradigm shift involving tumors. We need fresh, frozen tumors. No more FFPE. This type of situation would truly start changing things and indicate what types of information we can draw out of the tumor. Circulating tumor cells and being able to monitor them in real time is going to be key.
The third advance relates to the “big data” question. Again, how do you create the algorithms that provide the clinical decision support for a patient in light of the fact that there is simply too much data for physicians to handle on their own. You need these advanced algorithms to help guide somebody through this process because there is often a ton of data.
Dr. Erlander: Harkening back on some of the points other people on the panel have made, I agree that information technology is really where the problem is now. We are generating lots of data, and it’s really about trying to understanding what ARE the key pieces of data and how to integrate all this. This is a massive problem because it’s not only an information problem, but it’s also a biology problem that requires drawing inferences that actually make sense. You may find something important in one cohort of patients but then you have to go validate that information in another cohort. These things all take time and then you need to convince people like oncologists and pathologists that the findings you indicated were important were indeed so.