GeneDx Holdings Corp. (WGS) Earnings Call Transcript & Summary

Hey, everyone. Good morning. Thanks for joining us today on the third day of our healthcare conference here. I'm Tejas Savant, and I cover the life science tools and diagnostics sector at Morgan Stanley. Delighted to have Sema4 join us this morning. And representing the company, we have Eric Schadt, CEO and Isaac Ro, CFO. So welcome, gents. Before we get into the Q&A, just some important disclosures, please see the Morgan Stanley research disclosure website at morganstanley.com/researchdisclosures. And if you have any questions, do reach out to your sales rep. So Eric, before we get into the weeds on the story, can you just please give us an overview of the genesis of Sema4 and its evolution for those on the webcast not as familiar with the business model?

Sure, Tejas, and thanks for having us today. So Sema4 is a health intelligence company, born and raised in the Mount Sinai Health System in New York. We span out about 4.5 years ago and the charter at Mount Sinai had been how do we enable large-scale health systems, patients, physicians to cogenerate and aggregate more and more high-dimensional data around patients to offer better guidance on -- from diagnosis to treatment paths to risk predictions and so on. So how do we leverage that digital universe of data to make precision medicine a standard of care within a health system or in partnership with patients and providers. We do that through one of the primary platforms we're leveraging on the precision medicine front is genomics. So we have a full suite of genomic testing solution technologies, but then also a standardized information platform that consumes all of the data we generate in the genomics lab, all of the data that exists health related in the digital universe, all of the electronic medical record data and partnership with health systems and patients, and we're integrating all of that information and providing the most state-of-the-art interpretations for patients, again, in partnership with their physicians.

Got it. So quite a bit to unpack there, Eric. So maybe we'll start with Centrellis. How big is the database? How quickly is it growing? And importantly, for what fraction of these patients do you have longitudinal data as well as EMRs or perhaps other data modalities paired up with genomic information?

Perfect. So Centrellis is our information platform, so think large-scale data management system today managing on the order of 36 petabytes of data. That data is growing at a rate of around 1.2 petabytes a month. And of course, the rate of that growth is also growing at a super linear pace. So it's definitely an amazing growth hack engine we've locked into to grow that information platform. But just having all of the data, managing it, structuring it, integrating it, is just Phase 1 of Centrellis. Centrellis also has an analytics platform that sits on top to derive the insights, the predictive models from that data. And then very importantly, an API that allows the wiring in of those insights into standard of care flows. So a lot of different applications can leverage that platform to make a state-of-the-art information hub. Today, we're roughly -- yes, should have this at the top of my head, 15 million to 20 million patients represented in the database. Probably 9 million to 10 million of that, we have very high-quality, high-dimensional longitudinal clinical medical record information on those patients. And of that, maybe 500,000 to 600,000 have comprehensive genomic information around them as well. Because Centrellis is matched by a standardized genomics platform where testing is carried out on an axolotl [indiscernible] genome backbone. And we're running at a run rate of around 250,000 next-generation sequencing-based tests a year. Think of that 500,000 to 600,000 worth of clinical genomic data we have today is growing at around 250,000-plus patients a year and that volume is growing at probably 30% to 40% pace.

Got it. And if I ask you to put on sort of your industry thought leader had, Eric, for a second, when you're evaluating all of these different data sets out there that genetic testing companies are sort of highlighting these days, how does the biopharma partner, for example, decide which database is likely to give them the highest utility?

Yes. The way I think about it, Tejas, is a lot of companies are consuming data on 1 billion patients, say, that's largely claims data, some summary of electronic medical record information and so on. Some of -- so that's kind of like one of the spectrum. So think of it is like kind of a low-resolution view of somebody's health put on a really broad scale. Sema4 is kind of at the other end of the spectrum, which is like how do you -- like just having claims data, having a summary EMR data is absolutely not sufficient to build the kind of predictive models. We want a standard of care for precision medicine and health care systems. And so the data we're accumulating and curating and abstracting from unstructured nodes, structuring those data, integrating them with the structured data, the EMR, genome scale data matched with that clinical record information. So we may only have in the millions today, but think of that as a much higher resolution view of kind of what's going on in the health of the patient and getting sufficient to kind of construct highly accurate, clinically actionable models with disease. So it kind of depends on, like, what does the pharma partner need? Do they need to survey and say, what geographic regions can best support my clinical trials for this drug. In that case, you don't necessarily need high-resolution data. But if they want, hey, give me the patient that has this and this mutation and has been treated with this drug because we need them in this kind of trial. Like, that's the granularity of data and the connectivity to the patient that Sema4 has and will continue to grow.

Got it. From a competitive standpoint, I mean, turning to the central lab aspect of the model, Eric, I mean, where do you see Sema4 lying in that continuum vis-a-vis say a Natera, Sequenom, an Invitae or perhaps even a Tempus?

Yes. So I think on the genomic testing solution front, we're definitely comparable to the type of companies you mentioned. I do think Sema4 does stand apart today though and that we've already adopted the standardized genome-wide genomics platform, off of which all of our tests will run. So whether you're getting a test that involves a single gene for some diagnostic application or 500 genes for an expanded carrier screening test that genomes worth of information is being generated. In addition -- so we are already pushing that -- at the end of the day, you need a whole genome scale information if you really want to holistically model a patient. So we're kind of there. And the other differentiation -- take -- Tempus is a good example of like on the somatic tumor profiling, we run a whole exome tumor genome sequencing assay in addition to the germline whole exome in addition to whole transcriptome. But the difference is, we don't just use that germline for better variant calling in the somatic genome. We can clinically report on that germline worth of information on over 7,000 diseases and conditions, including heritable cancer, which, as you know, is now standard of care for patients like with prostate cancer and breast cancer because your heritable cancer risk variance in certain genes will dictate what drug you should be on and that's from your germline. So just a much deeper information play that's more comprehensive. And then, the other differentiator would be, of the patients rolling off our genomic testing solution platform, when they engage us digitally for like genetic counseling, to directly get their results, to assess heritable cancer risk, like whatever it is, roughly 85% are consenting. So we're directly engaging those patients. Remember, hundreds of thousands a year, and we're consenting them in an IRB-approved way. Like this isn't a Google digital click-through consent. This is an IRB-approved informed consenting process that at 85% or so are consenting to where we now have access and partnership with that patient and medical record information ever generated on that patient, the ability to hook it up to that genome's worth of information. So just a super valuable resource, whether it's drug discovery or better population health management for building those models and so on. So there's quite a few distinctions. And finally, I'm sorry to go on, I would say our belief that it has to be this partnership with a health system, if you really want to impact patient care from a precision medicine standpoint, it's not just about flipping a 200-page PDF report to an oncologist that can't understand more than the front page of the report or have the time to act on it for the patient's care that you need to help that physician and the system in which they operate, kind of manage those complex journeys over time. And we believe that by wiring in, in a more intimate way with those health systems, being a partner with them to provide state-of-the-art care, not just a vendor, also a very distinguishing feature.

Got it. A couple of small points of clarification. What was the reason why you decided to first focus on germline relative to oncology and over what time frame do you expect oncology to scale? And secondly, you mentioned sort of 85% sort of consenting to participate in research. How are you thinking about -- I mean, are all of those patients essentially reliably recontactable, Eric?

Yes. Perfect. So on the first part of your question on why the germline versus cancer, so for better or worse, I came from industry into Mount Sinai. Actually, it was my first academic job. I took on this big department, genetics department, founded the Icahn Institute for Genomics and Multiscale Biology. And that department was one of the first medical genetic residency in the country. It was a rare disorder mendelian genetics department. And so it was literally when I thought about as an information guy, like, how can I enable this department, with this clinical arm, giving care at Mount Sinai to benefit from this information revolution, it was easiest to sort of engage this department I ran. And so that was focused on germline. So rare disorder, reproductive health and so on. We did a lot of research in cancer, published a lot of the pioneering papers around useful genome [indiscernible] for treatment guidance for cancer. And so that now all came with Sema4. And I would say we had to get to New York state approval. We're the only lab -- commercial lab in the country today that can run a whole exome-based heritable cancer and somatic profiling, whole transcriptome suite of tests. So it's not like everybody has that. And so that took time to get to all those approvals and kind of build the evidence. And I would say over the next year, Isaac may have this cleaner in his head on the exact percentages we've come out with, but the cancer arm and the engagement with pharma around that will be the fastest-growing components of Sema4. And then you have the second part of your question, which I now forgot.

That was just around the recontactability of the patients.

Yes, the recontactability. So yes -- so today -- so those patients that consent, they also consent for recontacting. So yes, all of those patients are consented for recontacting. We've got all that molecular information hooked up with our medical record information, and we have an increasing number of agreements with pharma. Like, again, that's part of the growing expansion on the use of that data with pharma to better characterize patient populations and identifying and facilitating recruitment in the clinical trial. So we see that kind of application starting to move as we build the right scale of resources. And as we get more partnerships with health systems, like, outside the Mount Sinai Health system. So we now have AdventHealth, North Shore Community -- University Community System in the Greater Chicagoland area and Avera Health in the Midwest.

Got it.

And, Tejas, just to put some numbers on it for you, cancer today or in Q2 was about 5% of our total test volume. So it's a small percentage of our mix, but it's growing in a big 3-digit kind of way. And so there's a bunch of things we're doing to expand the menu that Eric has already started with and there's -- we see a ton of white space for us in our business nationwide as well as within these health systems to grow. So the cancer strategy is pretty much the #1 area of pipeline development that we're focused on.

Got it. Eric, on the point that you mentioned about your preference for going after health systems versus a more traditional sort of central lab model. Walk us through, I mean, how open these health systems are to sharing their data? And what are you doing internally to sort of shorten that sales cycle, if you will, and get new systems onboarded?

Yes, it's a great question. And of course, the first systems we signed were -- took a long time. It's a heavy lift. There's lots of concern around the use of their data, the protection of privacy of patients and so on. And so it was really about just establishing with the system, but what value can we help drive to the system through more than a vendor relationship. And then importantly, how do we enable you to benefit more directly from the scaling of data, the curation annotation of that data, the hooking that up to the molecular information and so on. So it's a top-down driven model. It's getting to a large kind of framework agreement where the system is kind of buying in because what they have to provide in terms of resources to make this work are not insignificant. So it's a lot of investment on their part as well as on our part. So it's -- and it's targeting systems. So we've signed 4 systems as of today, and we have 2 more that are looking good. And that's out of hundreds of systems we talk to. And so it's really identifying the systems like what are the systems that have the right leadership, the right progressive thinking, the right want to have precision medicine as standard of care. They want genomic medicine as a core component of that. So North Shore, a kind of a shining example of that wanting to democratize the access to genomic health screening across the patient population. So then, it's this framework agreement, then you're going in with the specific statements that were, like, North Shore in this genomic health screening program, where we're also identifying, hey, who are the patients who are at very high risk for heritable cancer because they would qualify for -- we can leverage the same data we generated for the health screen to inform heritable cancer risk or whose reproductive health age where now it's just a software query to do an expanded carrier screening and so on. So you can reuse the data over and over, it becomes an integral part of the medical record of the patient. And again, just improves their management of that patient population that retains, enhances retention of patients. It makes them competitive with neighboring academic systems like Northwestern, like it's -- so it's really -- again, it's all of those variables and a very significant kind of consulting, delivery and support teams that are working in partnership with the system. So it's a big lift. But again, you're figuring out how do you wire all of the components together that you need to deliver precision medicine as standard of care. And once you figure that out a couple of times, like we're finding these 2 that are coming, we're going from a cycle time of probably 18 months to 2 years to close those first 1 or 2 to these being closed in under a year. So it's having better fleshed out, better models for them to look at in terms of the health economics of what can happen and established examples of how they benefit in pharma relationships and so on with the increased quality of the data.

Got it. A lot of your peers, Eric, are sort of focused on biopharma collaborations. It's early days on that front for you. But how are you thinking about sort of opportunities to partner with these companies and perhaps even opportunities to capture some of the downstream economics, given the size of the database and the value you can potentially add?

Yes. So -- for sure. So we -- it doesn't escape our notice either that pharma are early adopters of the more sophisticated modeling, larger-scale data, they're running more progressive trials, that demand better characterizations of patients. So again, we have a growing pipeline of pharma relationships and deals that kind of span the spectrum of drug discovery to post-marketing surveillance. So we have everything from the collaboration with Sanofi around asthma to help them identify next generation of targets for asthma, so a basic research, drug discovery program, all the way to real-world evidence type studies. So again, because of that growing, like that's a very significant pile of data around patients and it's not just constrained to cancer. And there are lots of other areas like autoimmune, autoinflammation that have the same opportunity through abstracting from physician, structuring those data, understanding better what patients are responding to what drugs just like in cancer, where physicians don't know how to get those autoimmune drugs like they just kind of experiment until they find when that works. So there's those kinds of deals. And then again, the facilitating, the recruitment, identification and recruitment of patients in the trial. So you would be looking for kind of those channels of revenue in partnership with pharma to be growing at a good clip over the coming year or 2.

Got it. I want to pull in Isaac here on the -- you decided to sort of not provide '21 revenue guidance and sort of withdrew your target for '22 as well. Can you share some of the goalposts near term in terms of renegotiating your payer contracts and when we can expect to get a little bit more clarity on that front?

Sure. So before I do that, just quickly on the long term, I want to reiterate and that's what we're solving for is our long-term goal of reaching $500 million of revenue in 2023, and we're very much on track to do that. The metrics that we think are going to be most important to reaching out our volume on the testing side, and that will, of course, in turn feed the data that Eric talked a lot about. So all those are very much on track as we move towards the latter part of 2021. And as you pointed out, in the meantime, we are going through the typical transition that has often been seen in diagnostics where when you get big, number one, and number two, when you get public, you kind of get put under a different lens of scrutiny. And so the process of payer contract transitioning did begin in earnest more than a year ago, but it's not done. And the -- again, transition of growth as well as going public did accelerate those conversations. And we really were faced with the decision as we moved into the second half of 2021 as to how we wanted to address expectations for our investors. And so we certainly did not want to provide a really, really wide range, and that would have been necessary to commit to numbers because of the potential range of outcomes that we're dealing with. But as we sit here in September, I think we have a very good sense of strategy, an improving sense of timing and all those will lead us to provide revenue guidance as soon as it is reasonable to do that. But that isn't the case today in September. And I think if we look at sort of the key moving parts, there are not that many. And again, they're very well precedented in the industry. We have, as you know, a great structure of Board members and executives and team members, all of whom have a lot of experience in this industry. And so I think we're going to be able to run through the process here as many other companies have and get to a point where revenue growth and volume growth in our business will be far more synchronous. And that really is sort of the key challenge we're facing. But as I think about goalposts, most of those are probably not appropriate to share publicly because they're sensitive, they're competitive and all these things. But you can assume that we are running a process that involves many people, and we feel pretty good about the range of likely outcomes. So as we can share that, we, of course, will do that.

Got it. Fair enough. And then as we think about that $500 million revenue target for '23, Isaac, can you just walk us through -- first, I mean, what would be the approximate mix? Second, is it an organic number? Are you assuming some M&A in there? And how many sort of health systems or lives does that assume?

Yes. Good question. So first piece, remember that our core business, which is in the diagnostic testing, mostly for women's health. Those markets are growth markets, and we have a commanding market share in those product categories. So they're going to continue to grow, and we're going to continue to support the growth over the next few years. So as we look at the picture in 2023, I would expect a substantial portion of our business will still be sort of in this nationwide testing business, building off of what we currently have. The big incremental new piece of revenue will be in the health systems, all the work that we're doing today with the 4 that we have should allow us to grow a substantial new business, it's essentially all white space for us. And that will be the sort of second largest piece of revenue with the smallest part being the pharmacies. As Eric mentioned, there's a universe of really compelling opportunities. They take time to snowball. And so I think that will be sort of the smallest of the 3 buckets if you sort of divide it up that way. Now to your second question, we obviously are privileged to have the capital we have with the post-public process. We have $500 million of cash. And as we look at sort of how to deploy those dollars, it's sort of like there are a couple of different ways for us to get to $500 million of revenue. We can take those dollars and invest them organically. We're doing that today to expand pipeline, expand our sales force, all these things. And that's one part of the path to $500 million. What's harder to predict and model is how much of the capital might be deployed for M&A. And we have a pretty strong pipeline of deals that we're looking at. And if those pan out faster, that will be a bigger portion of the path to $500 million of revenue. And if we announce a deal, we will talk a little bit about what that means at that time. But today, in a vacuum, I would say, if we don't do a deal, we would be over the next 2 years, deploying the capital that much more aggressively into organic growth opportunities. So a couple of different levers we can pull to get us there. That's why we feel confident in delivering that number. And then the final piece is if you go back and look at where we are today for health systems in our current portfolio, we had assumed at the start of this year that we would need 5 to achieve $500 million of revenue that, that total of partners would get us to our goalpost. I would say that with 4 today, we're a little bit ahead of schedule, which is why we feel great about the long-term outlook and the trajectory to get there. If we close more than 5, then that will also give us additional paths to upside. But I would say today, we're not going to disclose where we are, but we feel really good about the pipeline. We've got lots of opportunity. And I would expect that if we continue on the pace that we're on, we will probably get to a point where we have to think very selectively about who we want to partner with. As opposed to just getting to 5, we may have to say kind of slow it down and do more only if it makes sense. So again, because whatever we do, we need to do it well. And so somewhere between 5 and 10 feels like a very doable number. More than 10 in the next few years, feels like it would be a very careful decision because we want to make sure, again, that we can execute. So those are sort of like the ways to think about how we get to $500 million. Again, a bunch of different ways to get there, and we feel really good about where we are today.

And just to maybe quickly add, the reason to focus on 5 to 10 is these are very intimate learning-based partnerships. Like, what we're doing -- again, we just don't want to be flipping reports and taking all the data and selling to pharma like that's not the model. The model is partnership. And so it's like learning with this system, like, what are the best -- what are the aspects of our platform that are applicable to all systems and so easy -- kind of on the easy side to drive that. What are the places that require customization, like, understanding for the different kinds of systems, how to best manage the physician workflows and so on. So about learning how to do that and how to scale once we kind of achieve that learning. Once we have that learning and we understand how to scale, then we're going to go to all health systems, then we'll be out. But that $500 million number doesn't depend on that.

Got it. And can you share some sort of color on the types of targets that would be interesting, Eric? I mean is it sort of fair to assume oncology? Is it just lab capacity in a favorable payer jurisdiction? How are you thinking about all the options on the table here?

With respect to the health systems?

No. Just in respect to your sort of M&A strategy, really.

Yes. So the -- on the M&A side, it really is about -- so again, think holistic partnerships with health systems, what are the kinds of gaps we have to facilitate that. So take, for example, on the cancer side. Like, does it make sense -- we have a state-of-the-art whole exome somatic germline, whole transcriptome profiling product. We have a whole exome-based heritable cancer testing product. But we -- and we have all the clinical curation, data annotation services, the tools that go on top of that. But what about a liquid biopsy? We have a therapy selection test that will be out by the end of the year, but consider MRD surveillance, like -- so some of those gaps that would allow us to achieve a more holistic precision oncology solution for those systems is one example. How to better -- so we talked about the big patient population we have. How to facilitate better and better engagement of that population longer term. So think of app -- even consumer-focused app-based development efforts and so on are of interest and so on. So it's all about how do we make that offering more holistic. Again, the aim -- precision medicine is standard of care in these systems.

Got it.

Just one other thing I'd add, Tejas, to the M&A question is, again, we have the capital resources to sort of do it organically or inorganically. So a little bit of the decision tree has to do with speed to market and knocking out a couple of different birds with one stone. So as you may recall from our Q2 call, our volume growth in cancer is extremely compelling. Our reimbursement is not yet sorted. And we, of course, for the long term, want to drive really hard in oncology. So ironically, the faster a cancer grows and the harder we push, in the near term, it hurts our ASPs because our reimbursement is not yet sorted. That's a very tractable solution. It's got a lot of press in the industry. We're extremely confident we're going to get that done. We can do that organically, and we have a couple of paths to do that right now in process or we could speed that up with a deal. And so these are the types of things that if we get in addition to technology, faster reimbursement and/or maybe a team and the commercial ability that we don't have today, those are the types of factors that would roll together into whether or not we deploy capital for M&A versus the organic piece. And again, just to sort of make sure that we give ourselves lots of room for success, we're pursuing multiple paths at once, and I think that's what's good stewardship. But right now, I think we also look at all that within our 4 walls and look at the marketplace and say, the opportunity in cancer is so vast. Our addressable market for cancer within the health systems we're dealing with is virtually untouched. It makes a lot of sense to think about M&A through that lens. And I think if we were to look at where it does make sense to do M&A, there's a long list and you've got to have a priority list, right, and we do. But we're not looking at just doing deals to get bigger. This is a very targeted process, and we're super excited by some of the targets that are out there.

Got it. I'd be remiss if I didn't ask you about gross margins, Isaac. I mean, obviously, part of that ties into some of the near-term COVID, sort of, stocking dynamic, but you also have scale benefits to come ahead of you as your facility expansion sort of comes through here. How are you thinking about the moving pieces on the gross margin line? And at that sort of $500 million revenue number, how confident are you that you can get [indiscernible] I don't know, 50% plus sort of gross margins?

Yes. Great question. So maybe 2 framing statements that I'll make on gross margins. Number one, if you look at sort of the portfolio mix that we have today, in the abstract, there's no reason why we shouldn't have gross margin that's very consistent with the peer group. If you just sort of look for like-for-like testing categories, carrier screening, NIPT, somatic profiling and heritable cancer, there are companies in the marketplace that generate a nice gross margin doing those things. Put it in a blender, we should be able to look very similar as we scale the business, get the reimbursement sorted and that kind of thing. Having said that, the second framing statement is we are building something different than what everyone else is doing, right? We've got the whole exome mindset, the clinical grade data that we're generating there. And then we're also, over time, building a platform of algorithms on top of that chassis with the database. And so if you look 3 to, I don't know, 10 years out, that longer time frame, I think you should assume that our P&L at maturity will look a little different than what you've seen in this industry because we're not just a testing company, right. There's going to be a software element to what we do. You can look at SaaS companies and that kind of thing where maybe gross margins are higher, but there's a different OpEx component to doing that at scale. We feel that over a long period of time, we absolutely have a very compelling path to profitability, and that's, of course, important for shareholders. But how we get there will look different than what you've seen in the industry in the past. And so I think the models that you have today will get you to a certain point and then you have to think a little bit about how we're different on the data side. And I think that second part is probably beyond the 3-year time frame. But in the next couple of years, we're scaling the accounting infrastructure, improving our systems, getting a little bit more efficient in the lab, getting better reimbursement, improving our mix. All those things, I think, are directionality to the upside for gross margin. We're not there today.

Got it. One last one for Eric, and then we'll wrap it up. Eric, I mean, through the stack and the pipe process, et cetera, you've had a whole bunch of investor conversations. What in your mind is most underappreciated by investors today about the Sema4 story?

Yes, that's a great question. It's that -- the company is complicated because we have a scaled revenue business on this lab side. And what they underappreciate is that our ability to get to that scale and come online with reproductive health solutions, where we were behind Counsyl/Myriad, we were behind Natera. Like, they were all years ahead of us. So how did we storm on and secure what's likely more than 50% of the IPF market and so on, it's because it was an information-driven solution. So it's sort of this failure to link up the enhanced competitiveness on the genomic testing solution side through a superior information-based platform, better mastery of the data. And that at the end of the day, it doesn't matter if you're the one generating the data, you're the one generating the data for the test. It's how well do you interpret that information to deliver superior outcomes. And so there's still that -- it's very difficult for investors to see all the revenue coming from this where they just want to match it to Natera and Invitae and so on, but kind of underappreciating that we got to that competitiveness through the Centrellis platform.

Got it. Perfect. We'll leave it there. Thanks so much for the time this morning, guys. This was a great overview of the story, and I hope you have a productive rest of the conference.

All right. Thanks, Tejas.

Thank you.