DeciBio's mission is to provide the life sciences market intelligence and strategic insights that drive disruption and innovation in the research tools, clinical diagnostics, health technology, and adjacent markets.
I had the pleasure or speaking with Dr. Ashley Van Zeeland, co-founder and CEO of Cypher Genomics and current CTO of HLI (Human Longevity, Inc.). HLI’s goal is to give everyone access to the power of data-driven health intelligence, using multiple data streams to aid in the interpretation of genomic information. We talked about using genomics in the context of phenotypic and clinical data to drive health intelligence, how advances in technology are enabling this analysis, and some of the challenges facing HLI and personalized medicine as a whole. It was a fascinating conversation; listen below, or scroll down to read the transcript.
DeciBio's mission is to provide the life sciences market intelligence and strategic insights that drive disruption and innovation in the research tools, clinical diagnostics, health technology, and adjacent markets.
Dr. Van Zeeland’s background probably sounds pretty far from her current role. She has a PhD in neuroscience from UCLA, where she worked on the neurogenetics of developmental disorders like autism spectrum disorder, primarily using brain imaging techniques, but combining that with genomics. She then did a post-doc in statistical genetics and bioinformatics at the Scripps Research Institute, where she became even more interested in sequencing technologies and informatics. It was then that the era of sequencing really started taking shape. After earning her MBA from UCSD, she spun out an algorithm from her time at Scripps that helped automated the interpretation genomic data. That company, Cypher Genomics, was acquired by Human Longevity, where she now serves as CTO.
Craig Venter Human Longevity started just over 3 years ago with a mission to radically transform healthcare, primarily by making it much more genomics-based and putting the genome in the context of lots of clinical and phenotypic information so that we can come up with this concept that we call health intelligence. If you’re familiar with business intelligence, it’s looking at multiple data streams and deriving insights from that to guide your business. Health intelligence is very analogous to that. It’s looking at your genome as the core of it, but you have lots of other data streams that are meaningful to help aid in the interpretation of that genomic information. It’s your blueprint of life, but you have to look at it relative to other information.
That’s exactly right, and I think that was part of Craig’s brilliance: from day one, understanding that the genome can be the core of it, that’s your blueprint from birth, but you want to collect as much high-quality other information about an individual as you can on that journey and on that interpretation path. To do that requires a massive cloud compute infrastructure. People ask, “Why did you start HLI now?” It’s because of the convergence of cheaper sequencing, massive cloud computing, major advances in machine learning and AI, and then also a shift in the healthcare landscape towards much more value-based care, trying to keep people healthier longer, not “sick care, pay for service.”
It’s really both. Having come from the bioinformatics field, anyone who’s familiar any kind of data analytics would tell you “garbage in, garbage out.” You have to really pay attention and focus on building a very, very high-quality pipeline all the way through your analytical process: pre-analytical, analytical, post-analytical. But then once you have that high-quality raw data, how do you derive those insights? How do you interpret that genomic information? That’s where we see a huge opportunity for machine learning and AI, and we’ve done some interesting proof of concept studies there to demonstrate the power of that. For example, we can predict the entire structure of your face, your skin color, your eye color from your genome.
I think it helps people understand the power of this in a very tangible way, but it also starts to raise some interesting privacy questions. If we can predict that — a photograph is personal health information (PHI) – how do we treat your genome? It’s very interesting, the power of machine learning on this data type.
Given our namesake, Human Longevity, we’re really interested in focusing on the top causes of premature mortality. Some sobering statistics: for males between ages of 50-74, mortality is 39%, so almost 40% of males will not see their 75th birthday. The number is a little bit lower for women, but still significant: it’s about 25%. The top 3 causes of death in those categories for either gender are things like cancer, cardiovascular disease, metabolic diseases like diabetes, respiratory disease. So we’re focusing our initial efforts on categorizing earlier detection and prevention in those disease areas. Again, while the whole genome is a platform and we can read off any number of disease risks and traits from that, we really are trying to move the needle in early identification and then extension of life by intervening when you find a cancer at Stage I, when it’s a couple of millimeters and you can resect it, versus Stage IV.
That’s exactly right, but again we see the genomic report as the great first-step, but really increasing the power of that report and personalization by then triangulating it with your metabolomic profile, with quantitative whole-body imaging, quantitative-imaging, looking at other biomarkers, your microbiome, and then putting it into an integrated analysis, and saying, “I see you have early pre-clinical signs of this disease, you have genomic risk for that, I see your metabolomic profile is dysregulated in a way that would be consistent with that disorder, etc.” It helps you focus in on an early-stage disease state versus the potential risk which has always been one of the drawbacks of taking a genomics-only approach.
Exactly, and I think nobody knows that better than Craig Venter, who’s had his genome sequenced for 15 years in the public domain and the insights that were derived from that in that period of time.
We’ve taken two approaches to really demonstrate the utility of this, because we know that it will be a while to make the economic case to payors. Regulatory bodies are struggling with how to handle this space. We have within our research construct the ability to put clients through this Health Nucleus experience, which I started to describe a little bit. We have a flagship site in La Jolla, and it’s a comprehensive 8-hour day of physical screening where you do whole-body MRI, 40 echocardiograms, gait analysis, cognitive tests, the whole genome, the whole microbiome, your whole metabolomic profile, etc., and we partner with you and your physician to return that integrated health summary back to you. We just recently submitted a paper. We’ve seen over 600 clients through this program since it opened about 18 months ago. On the first 209, we did a snapshot observational study and have submitted that for publication. What we found in that is that 1 in 12 individuals urgently actionable, something they need to act on in the next 30 days.
Exactly. Presumably healthy, this is a wellness screening, this is establishing a baseline for them and their physicians, most of them between that age bracket of 50-74, though one was 20 and one was 90, so it’s a broad range. But 1 in 12 had something actionable, in 1 in 4 we detected an early-stage neoplasm, so a cancer that could be resected. Other ones had a couple of other interesting urgent phenotypes that were correlated with a latent genetic risk that they wouldn’t have otherwise known. We’re excited to see that publication come out because that’s the really of the proof point to start showing the economic value, showing the payers, showing the physician community that this type of screening has a role to play in preventing highly expensive life-shortening diseases.
The follow-on care model is one that we’re very actively exploring with our clients who’ve been through so far. Because personalized medicine, precision medicine is tailored to you, this is a great baseline, and as you serve as your own baseline you can monitor your own trends. We do quantitative neuroimaging over time, and if you can monitor trends as your hippocampus, the part that stores memory, shrinking, that’s an early signal of Alzheimer’s. There are certain tests you may want to repeat routinely, other ones less frequently. It’s a journey with our clients.
I really think the opportunity for better, faster, cheaper sequencing, additional sequencing technologies that get long reads, that get more complicated mutations will help us push into the last frontier of what we don’t understand about the genome. You have to have that cost curve driven as low as possible to make this accessible, to democratize this and make this a routine part of healthcare. I’m supremely excited about that. Also, the maturation of cloud. It’s not just the ability to store all this data. We have tens of petabytes in storage currently that we have to manage, and that’s only going to grow. If you get to a million genomes, that’s exabytes of data. That makes you rethink the paradigm. But also, big data computes. Technologies like Spark platforms and other big data solutions are making queries across that much data possible in a reasonable amount of time, as well as privacy around that information. Because if we can predict your face, even it’s not by letter of the law PHI today, we want to treat it very securely and deliberately.
I think it’s critically important. If we limit ourselves to thinking of genomic biomarkers as simply the single nucleotide variations, you miss the complexity and the beauty of the genome. And we know there are classes of diseases that are caused by copy-number variations, that are caused by structural variations. To completely ignore that does a disservice to the opportunity for utilizing genomics in diagnostic cases. We’re really excited about that, obviously it’s a bit more expensive and computationally complex. We’re working on it to get that clinically ready, but I think that should see the light of day very soon.
Epigenetics is hugely interesting. We’re looking for additional markers, what we’re calling the functional genome. You might know there’s a big push to do exome sequencing. Exome is just capturing the protein coding parts of the genome, which is just 2% of the genome. That leaves 98% of the genome untouched, but we know we’re much more complicated than that. We’ve also submitted a publication looking at just over 11,000 whole human genome sequences to define what we the functional genome, and that includes of very relevant non-coding elements of the genome that have almost 100-fold higher mutation rates for pathogenic variance. We have to understand that as well, and how that may relate to epigenomic markers is to be determined, but there’s so much in that last frontier that I’m excited about exploring. You don’t have to go all the whole genome, there’s this middle ground.
I think immune profiling is really important, and the way that the immune system interacts with your genome and the flux and the dynamism in that system with what’s always been perceived as a static, base of your germline DNA is interesting. Initial applications are obviously in tumor profiling, immune profiling looking at your tumor microenvironment and how that intersects with the somatic mutations that are driving the tumor growth. But for a host of other diseases, there’s more and more evidence that immune system has a role to play in everything from neurologic to metabolic disease. So if those are the big killers, those three, that’s where we’re focusing our efforts.
Because we’re in the very early days of adoption of genetic inquiry as part of your healthcare journey, I think those services provide a huge educational opportunity for consumers to understand the concept of risk and of relative risk. Very few things in genetics are deterministic; there are a few, and those are diagnostic. But by and large we’re going to be uncovering risk profiles that then give you that opportunity to modify your behavior, make it actionable. The fun things, your percent Neanderthal, your traits, do you sneeze when the Sun comes out, those types of things help people learn the language of genetics and genomics and some of the basic concepts. Then should they be in a situation where they’re confronted with needing a genetic test, whether it’s NIPT (non-invasive prenatal testing), cancer tumor profiling, etc., they have that basic foundation. I think there’s a role to play for both the fun, entertainment value of this to help people understand and get comfortable with accessing their genetic information, but also a very real role for clinical genomics.
That’s right, and if you think about even the first clinically relevant gene that was ever sequenced, CFTR for cystic fibrosis, they thought, “We found it. We found the gene that causes this disease. We’ll crack it.” That was 30 years ago. We’re understanding more about it, there’s finally targeted therapies for cystic fibrosis for certain mutational profiles, but you still have issues with incomplete penetrance and hundreds of mutations within that gene, each with slightly different effects. BRCA is another example, where I think just educating the population of it’s not “if you have it then this will happen,” it’s all around relative risk and which kind of mutation you have.
With respect to specific assays, I think being able to do cheaper, better long reads is important. People get excited CRISPR, you have to mention that. I think the horizon is quite a way out, so it’s not necessarily something we’re playing around with today. We think there’s a lot of actionability on the screening and diagnostic front, but ultimately how do you roll in gene therapies and what kind of diseases can you target with things like CRISPR? That’s really exciting to think about.
I think it has to be a combination of both. Clearly, just because you’re trying to interpret billions of data points potentially from the genome, you need very large sample sizes to crack that from a statistical perspective. However, in combination with these quantitative biomarkers, you increase your power for those studies. The better we can get at capturing those high quality quantitative biomarkers through things like advancements in imaging techniques in combination with the genome, I think we’d be surprised with how far you can get even before you hit millions of genomes. For example, one of the very cool techniques that we’re using at the Health Nucleus is looking at diffusion in whole-body MRI. Cancer cells have different water diffusion profiles relative to healthy cells. There’s been a lot of concern around incidentalomas. So you see some kind of mass on a whole-body MRI, and by overlaying that diffusion scan on it, you can say whether that mass is likely to be healthy tissue or cancer tissue because they have different signals. And that’s quantitative. By pushing all frontiers, we’re get very far on a smaller number of samples than we ever thought possible.
Sure. Importantly, we refer to all of our visitors as clients because they are presumed healthy. So, our clients come in, and because it’s a lengthy day we try to make it a very welcoming environment. At our flagship site, you have a private suite and you’re no more than 50 steps from all our different modalities. It’s really a lovely day. We do the whole genome as I mentioned, your microbiome, your complete metabolomics, so those are simple blood draws or sample collections. And then we walk you through 8 hours, effectively, of different tests in these private suites. A couple weeks later, you get your integrated report. We have both a digital application to provide this information to you, because the alternative is a 700-page report binder because it’s so complex, and it’s so much information. We spend a significant amount of time educating our clients and their physician partners on that information as it comes back to them and work with them on whatever recommendations they may glean from that information.
I think that’s the key thing, those are two very different contexts. We have multiple customers if you think about who needs this information to make decisions. You have the individuals, the clients, who want to take this information and potentially make family planning decisions or changes in their diet. How can it be interpretable for them? On the other hand, if you have an oncologist who has a patient, we have oncology products out there. You have to help them get to the highest priority information quickly so they can make an informed decision, they can start treatment as quickly as possible and be confident in the direction that they’re going. So we have a multitude of different customers as we’re supplying this genomic health intelligence back to the market.
We’ve seen the gamut, we really have seen the full spectrum of people who are interested but not well-versed in genomics, per se, to the very early adopters who live and breathe this. I think that’s important for us as a field to recognize, and going back to the question of consumer genomic companies in this space or the role of any other participant in this market is that joint education of the people who are not comfortable or genomically literate yet, because in the next 10 years it will change, and we have to help people on that path.
Sure, that’s a complicated issue and one that’s hotly debated in the field. Is it for public research efforts? Is it publicly owned? Is it owned by the client? Is it corporately owned? It’s a complicated issue that people are very actively thinking about. I’m not sure there’s one right answer, but I think that irrespective of where ownership ultimately lies, there is an obligation for the stewards of that data to recognize the value of that data for whomever contributed it and recognize that we don’t know all the answers yet, so they have to treat it very deliberately.
I hope that a lot of our efforts at Human Longevity will push that conversation forward. We’re just doing it, I think you have to get out there and just show the use cases, put it into practice. We’re publishing everything that we’re learning and finding from that to educate others in the field, and I think education of the physicians is also important. We talked about educating consumers; consumers can knock on their physicians’ doors to ask for these tests. Physicians need to come along for the ride as well, and now the question is how to have that dialogue to make sure that they’re comfortable in handling this information and returning it and making it part of their healthcare practice, as well as for the consumers who are receiving it and trying to interact with it within their daily lives?
It can be either, it depends on that individual client’s situation.
I think, like everything else, there’s a spectrum of humans and so it has less to do with the specialty, per se, than just the individual and whether they’re an early adopter or a late adopter and have to be dragged kicking and screaming to adopt this or they’ll never adopt it. That’s where the market force is, where the consumer education may have a role to play and may change which doctors get the business.
I think that’s a really important question. One of the challenges we have in the field is, whether it’s research or the diagnostics, it does tend to be biased towards those who can afford it, those who can access it, those who are aware that it’s a service that’s available to them. As the price of sequencing drops, there should be an obligation this accessible as broadly as possible. There’s 7 billion people on the planet, we all have at least one genome (depending on if we have cancer, maybe multiple genomes) and microbiome. It’s our information to unlock, it’s our information to utilize to extend our own healthy life. By keeping it separated I don’t think we make the healthcare impact that we all want to. With the current pricing it’s really hard, it’s very expensive and it’s very time-consuming and it is kind of esoteric in understanding. Right now, I do think it’s a limited market, but we have to think creatively and push hard to broaden that over time.
From the HLI perspective, Craig is a scientist and an experimentalist, so the best thing we can bring to the table is data that it works, data that the economic value is there, and try to push that conversation.
So… I’m pausing because I think there are just some institutional challenges that face companies of every age and size and stature, primarily related to the regulatory and reimbursement landscape. These are uncharted waters, so how do we navigate those? Whether you’re big or small, you should be in conversation with the FDA to help inform yourself and get guidance, to blaze that forward to make this a reality. That’s a challenge whether you’re big or small, young or old, but it should be a responsibility that the companies take on to engage in those dialogues. And again, bring that data to bear from the reimbursement perspective to show the economic value.
The first bit of advice is “do it”. This is a really exciting space, it will change the world in the next decade. If you have a way you feel you can contribute to making that happen, this will take will village and we need all the creative minds that we can get to push this forward. How to do it is much more complicated, and I think reaching out to experts in the field, getting good advisors, and just trying to show those proof points as early as possible is probably the best way to go.
Well you know, it takes all kinds. I think for those scientists or scientifically minded who have a leaning more towards the applied space or the business space or the translation space, which is really exciting, you can think very creatively. There’s lots of ways to contribute at that intersection, whether you’re a technical writer, marketing writer, helping get grants for a non-profit, serving in roles like the one I’m in or like the two of you are in, there’s many ways where you can leverage that scientific understanding and language and an ability to communicate and understand the hard sciences with translating that into another application. I think those are key roles as we start leveraging more and more of this hard science into our healthcare. There has to be that translational component, and that’s not easy. If you have that skill, I would say capitalize on it.
Not in a million years, but it’s been an incredible ride the whole way.
I pursued an MBA primarily as a way to understand the language of business to get private financing to support my academic lab. I saw the trajectory of NIH funding as I was writing grants furiously during my post-doc. And looking at some of the really creative public-private partnerships back at the Scripps Translational institute (I was working with Eric Topol and Nick Schork there), that model seemed very interesting to me as a way to supplement drying-up NIH funding. That’s initially what drove me to go to business school, and it just so happened a little bit of the entrepreneurial bug while I was there and then had the opportunity to spin-out company, and it ultimately was successful. I feel really fortunate, but it started from trying to fund an academic lab.
Absolutely. I’ll speak from my experience with the local research institutions here, specifically UCSD. They’re taking a very open-minded approach to exactly this situation, which is that not everyone is going be a doctor, lawyer, scientist, whatever; those are pretty defined categories, but there’s so much more opportunity if you an interest and skills in STEM to contribute to the workforce. They have a number of innovation programs starting from early high school internship program all the way through undergrad and graduate programs to foster that, whether it’s true entrepreneurship or at least the innovative thinking that goes into problem solving, which is a benefit even if you join a very large company versus starting on your own. How do you take those skills and find an unmet need? That’s really the core of it. If universities and educational systems can adopt that kind of thinking, you’ll see great returns in those with an interest in STEM in finding alternative careers that still add major value to our workforce.
I think that’s exactly right, and also the comfort with the unknown and the confidence to go explore hard problems and the language to do that, in addition to those critical thinking skills. That’s the really great thing about STEM education.