Key Takeaways

• Background in research and diagnostics
• Interested in the applications of genomics in the diagnostics phase
• There is a distinction between precision medicine and personalized healthcare
• Direct to consumer models are a clear path to growth and profitability
• Data privacy is still a concern, given that companies change their privacy policies
• The future of digital health is coming fast and furious

What’s your background in health?
My background is a long time as a research scientist, and currently in molecular diagnostics. So, I have a cancer research background, biology background, and currently in diagnostics.

What brought you to the conference today?
I really wanted to see the panel in diagnostics, and see Carlos talk. Kind of interested in applications of genomics in the diagnostics phase. I am interested in that. Skeptical. Pleasantly skeptical.

Skeptical because?
What I think is that it’s very early days. I’m very interested in it, but I am having such a strong background in research. I’m really trying to understand the biology behind what comes into diagnostics. Just, I think there is a lot of information we get from genomics, but I think it can be disjointed when you think of biological pathways, which often times in disease, there’s hot that many single gene to a disease.

There’s often multiple genes that play into a pathway, so if you have a mutation or genetic variant in one gene and that’s all you see, what happens to the rest of the pathway? I think that having that single variant be something significant. I’m just interested in thinking about that, that isn’t over- over interpreted.

So if this means this, and what was gratifying to me was, I heard on a couple different panels people echoing that thought. So, clearly people in the space are also thinking in the same way, that you’re getting all this data, it’s sometimes can be very variable, like what is significant and what isn’t significant. So I heard talks about specificity, increasing specificity, which helps draw out more significant associations between genetic variants and diseases. If you’re in diagnostics or in medical therapeutics.

And on the other side I also heard people on the diagnostics panel echoing my thoughts about understanding the biology and not over interpreting so I thought that was very gratifying.

How would you describe the distinctions between precision medicine and personalized healthcare?
… one of the panelists separated the definitions, which I really liked. If you think about personalized healthcare. It’s treating each individual as a person and if you think about something like cancer diagnostics, taking one therapy for cancer and applying it to everybody who has it. It’s not going to work on everybody, because everybody has different genetic background in their tumors. And so for me, personalized medicine means that somehow you look at an individual’s particular disease, you have the background of population studies, but you tend to take a more personalized approach in that, and precision medicine can help with that, right?

So you can now start to define down, now more precisely what diseases correlate with what genetic variance and then apply those on a personal basis to the patient. That’s kind of how I look at both. Often they’re also used somewhat interchangeable, but I like that distinction and I agree with that.

What works and what doesn’t work?
I think the direct to consumer products, that’s a clear path. However, that can be fluctuating depending on the trends right? Trendy. I think and I’ll introduce our discussion from earlier, but I think the really good thing about direct to consumer market is that it really brings the space into the public eye. The Ancestry, the 23 and me, the Helix, Color genomics is a bit more on the medical side but they still do direct to consumer and I think all this brings the technology that’s coming into the focus and almost everybody has seen ancestry, even before it had genomic screening

That I think will be helpful to have consumers drive. Again, consumers drive healthcare to have changes. And clearly it’s challenging for the medical field to adopt new, untried technologies, but if consumers push that as well, I think will be adopted faster.

At the beginning we talked about data privacy. Just talk to us about that as a consumer like myself, or generally, how should we think about privacy when we use these products or some of the other products to aggregate data. What’s the prism with which you view those products and how do you view data privacy in the healthcare space today?
I’m cautious about what getting all the data together means and I’ll talk about that first and then I’ll talk about terms that I think are good. From my own personal basis I wouldn’t give away my data privacy.

Because?
I think that from my perspective. All of the data collectors start out with good intentions and most, if not all of them have to have opt out, so that they can’t use your data for anything more, which I think is good, but policies change and that’s what worries me is that it’s such a new field and you don’t know how policy is going to change or how government regulations is going to change, how the approach to insurance policies with incorporating genomic data. Again, how they’re going to look at the fact that if you have one variant that somebody deems as a disease variant, but it hasn’t been thoroughly vetted, how that’s going to be used.

I don’t think that it’s too new of a field. So there’s not enough, there’s a lot of thought about that, but I think more thought on the data collectors and the data managers than on the policy makers. And so I think that’s when you hear insurance companies really getting in on it, or federal government agencies really making a stand on it then maybe they’ll be more … but on the more hopeful, optimistic side, which I always like to leave on. I like the idea of the web exchange? And I think the block chains is the same way, where the technology industry is so good at the coding and encrypting and identifying, if you will, by putting tags on the data, where you can store an entire population’s data in one cloud and just have them tagged or coded, so that nobody can go in and get somebody’s genetic data from A to B.

They can only get out little snippets. I think that’s a good move and I think the technology industry is really going to help, so a lot of the protection and the … I’m trying to think of the technology words … that are coming in now are, security protection that’s going on now, I think that will be able to be applied to a lot of digital health.

I have hope that this will reconcile and there is a way forward. I just think that today is an early day for me.

What do you see as the future of digital health and medicine?
I think it’s coming. It’s definitely coming fast and furious basically. Because of the amount of time that it takes, the short time that it takes to analyze it. In terms of getting the data. Again, coming from a biology point of view, it’s going to take time to wade through all of that and really have an understanding so it turns into really solid treatments. We’re starting to see that in cancer which is really one of the easiest applications of a lot of it, because you can get a tumor, you take it out of a person, then you can get the data from it. And you can say okay, this is a tumor of this type, it has these mutations.

So this is why this is a good starting point, in its early days. I think we’ll get there, if you look in the clinical literature, the clinical studies, more and more they’re introducing genetic variance into the studies that they associate with biological pathways. Sometimes they would vary significance and again, taking those through enough trials. Most often those studies are population stratified, which means that they’re working on a very specific population.

So again, to be a good healthcare marker it has to be, either you have to have a marker for every different ethnic population or else you need to work on a cross population strategies, which there are some companies that are interestingly looking at that. It’s very common when you setup clinical trials or genetic studies to look at a very defined population, but there are studies that say, “We want dirty. We want messy studies, because we want to now see what markers come up out of a diverse population study that then you can apply to maybe a diagnostic across the whole population”, otherwise … okay again I’m personalizing your ethnicity and your genetics passed down from your family as part of your personality. So, that should be taken into account too.

So whether it’s diagnostics or therapeutics from a diverse population or if they have specifics, either would be good. From my perspective I think it’s easier and it’s easier to adopt in the medical field if it’s a single entity across populations.