All right. Good morning. I'm Doug Schenkel. It's my pleasure to welcome Jurgi, the Founder and Executive Chairman of the Board at SOPHiA GENETICS. We also have Kellen Sanger, the VP and VP Head of Strategy. I can promote you if I mess it up. I don't want to demote, but it's-
Thank you.
Good to have you both here. In terms of roadmap, we're going to discuss SOPHiA's strategy, commercial adoption, and business model. We're going to explore your work with hospitals, labs, and biopharma companies, along with all the other opportunities that lie ahead. There's a lot for us to unpack. Maybe the best place to start is with the big picture. Jurgi, you and I have known each other for a long time. I know a bit about your journey, but I'm really curious, as you've moved from the CEO role into the Executive Chairman role, can you take us back to the original idea behind SOPHiA and how that founding vision has evolved and actually how you're going to be involved in the next chapter?
Yeah, absolutely. Pleasure. Great seeing you again. When we founded SOPHiA, it was quite clear that in precision medicine, data would become critical, actually more and more important. Initially, people were focused on just data production, but for us, it was clear that the value would be on the data themselves. We were very clear back then that genomics would become the key data modality in precision medicine, even including drug side. At the time, maybe it was not so clear, but for us, coming from research and systems biology, this was very clear. Because of these two, it was clear that the data would be very complex. Hence why we thought there would be a need for a platform, AI-native, to compute the data and make it at scale, right?
This is what we've been doing since the beginning. We put our platform in the market, it's called SOPHiA DDM, in 2015. Right now, we're supporting over 1,000 hospitals in over 70 countries to compute data that they produce themselves. The one thing that, at the time, maybe we haven't purely, I would say, captured and matured over time, is that the genomics data wouldn't be sufficient by themselves, and that one would have to combine genomic data with other data modalities to follow patients longitudinally. This is something that I think started to get clear over the last five, six years, rather than 15 years when we started at SOPHiA. As Executive Chairman now, actually have the privilege to be back to real innovation, technology, science. I love leading operations as it was.
As well, Kellen will be able to give you much sharper answers on the traction on the business-wise. But I'm really dedicated on things where we think the future will be. Definitely AI, technology, new technologies to produce data, but as well new technologies to compute the data.
Yeah. I'll stick to our topic list, but as I hear you describe this, I mean, this is really getting you back to I mean, you're passionate about all things SOPHiA, but this is what's always made you most excited, what you're working on now.
Absolutely. It gives me the opportunity as well to spend more time with people who are defining the new category of companies and systems that will be set up.
Now, when I talk to people about SOPHiA, sometimes what I hear is, "Very interesting approach, but all of a sudden, it doesn't seem like there's a shortage of bioinformatics tools. There's all sorts of different sequencing platforms. There's more data repositories." What do you think SOPHiA is solving for different constituents in a way that nobody else is?
I think in the end, it's the scalability of our platform, the accuracy of our algorithms, plus the ability of delivering new and subtle algorithms very quickly into the market, right? It's not by chance. This is because since the beginning, we've accepted to be a tech player, to compute data that would be produced by hospitals, data that would not be harmonious, would have to be computed with sophisticated methods to basically find the signal. This has become our competitive advantage. A bit like Swiss watchmakers pretty much working on the mechanics of the Swiss watches. Now we're in capacity to deliver data compute at scale, accurate, and to make it adjust according to the new needs that are emerging in the market.
One thing you mentioned in response to my earlier question was the scale of the company today. You're in 70 countries. Does the diversity of the data that you have in your data lake, does that put you in a position where you think you're uniquely positioned to partner with biopharma in a way that others aren't?
Well, I can start, and then maybe Kellen can give you some examples of recent work we've been doing with pharma. Definitely, our moat is the network, is our algorithms, and is the data. The fact that we are plug and play in so many systems makes the value proposition we can offer to the hospital, excuse me, to the pharma, distinct and complementary to what central labs can do, right?
Basically, this is being materialized on sometimes CDx efforts, with the intent to be able to touch more patients around the globe, not only in the U.S., but as well sometimes it can be around real-world evidence data production capabilities, where pharma may eventually need to understand how people are being tested and treated today, how they're responding to the existing treatments in the market, to be sharper on how they're going to develop the next generation of treatments, and how they're going to design their clinical trials to maximize the chance of having the drug approved versus the standard of care in the market.
Yeah, and everything that you just mentioned is built on just this breadth of data.
Right.
You mentioned 75-plus countries. We're currently doing somewhere north of 400,000 patients per year, so one of the largest diagnostic players in the space if you were to compare us with some of the others. And the global nature of what's being produced by the second, we can look by the minute at every patient that's being analyzed across the entire globe. This is an incredible network of data that you're able to do things like you just mentioned, building CDxs and real-world evidence and other types of products off of it for both biopharma and clinical.
Kellen actually, could you, just building off of that, recognizing SOPHiA has, even though there's always been the focus on data, the tools really have been successfully deployed in the relatively early days at hospitals and labs. Do you think what we're talking about now is driving a notable change in pharmaceutical companies, biotech companies working with you? And beyond just the breadth of data, which is really important, are there other capabilities that investors should be contemplating that you have that others don't have when it comes to working with biopharma?
Yeah, there's of course the data, but also the network that exists globally, right? You mentioned that we're now working with biopharma on CDx projects. This started with a lot of sponsored deployments, so if you just think pharma is interested in having their therapies and drugs reach as many patients as possible across the globe, who better than the company with the largest global network of hospitals and labs and patients across the world? Just activating and identifying patients in different geographies, moving that into more regulated products with CDx, so identifying patients in conjunction to a specific drug and quickly turning that on across all geographies.
Instead of a biopharma having to have many different vendors in the U.S., in Europe, in Latin America, in Japan, you're able to come to one network on one platform and look at all data that's being generated globally. Then as you run more CDxs and collect more data, you're also able to build interesting algorithms or predictors and answer specific questions for pharma about specific drugs. Specifically exclusion or non-exclusion criteria, or building what we're doing more recently and what we announced last week around AI predictors for certain indications. We announced bladder cancer. We've done similar in lung cancer and some in prostate.
What you described and the AstraZeneca collaboration, how important were the attributes you described in moving into a partnership with Astra?
Yeah, they've been obviously a great partner for us on many different areas, and starting in a lot of those data deals that we had mentioned where we were looking at different cohorts and therapies and how do we get the best drugs for their trials, but then after that drug is on market, how do we help identify patients across the globe? Throughout all those stages, we've been proving our expertise and our right to play there. As we start doing that with AZ, all of the other pharmas are now very interested as well, because we're helping improve our risk and prove out our product.
Is there an opportunity through these partnerships to actually boost your clinical offerings? Obviously, CDx labels would help, but do you get some access? Is there any flywheel effect where you can learn from the biopharma and actually iterate on your own assets?
Yeah. I mean, this has been the thesis since day one. Even as he was describing his original hypothesis for the company, he was a bit humble. I remember he was showing me in one of the Series A fundraising decks, this is back in 2014, he was using the word artificial intelligence and applying this to genomic data and multimodal data in order to generate insights for patients across the globe. So, quite visionary on some of the AI/ML techniques. No, absolutely. I mean, the clinical biopharma flywheel has been kind of native to the business from day one, that the idea of once you have a CDx, for example, on the market, this helps increase the clinical utility, and you have the premier application that others want across the world, that the data that's generated on the clinical side creates interesting products for pharma.
As you win more as pharma, you are collecting more data about the patients, so getting closer to the patient, getting closer to the oncologist and the point of decision that's being made so that we can build additional tools such as clinical intelligence on working with the oncologist and how you look at patients. So, it all is very connected.
Amazing. I have made it to, I think, 11:00 A.M. this morning or something like that without saying AI, so I am glad you brought it up. You guys have spoken about moving from data interpretation towards clinical intelligence, including decision support tools and digital twin concepts. Which AI applications, where do you create value first, and then over time, how do we think about more value being created, but also being able to monetize this from a franchise perspective?
I can start with the value we're creating and how we're thinking about the evolution of that. We started very much on harmonizing data. This was the way we would be able to enable hospitals produce high-quality data that they would trust locally. At some point, we started to realize that this was good, but that eventually, beyond doing that for genomics data, bringing them capabilities on following longitudinally patients, including how patients were treated, how they responded to treatment, could bring some additional value, right? I do not know if you read that, Doug, but today it's estimated that we as humans spend about $250 billion-$280 billion in cancer drugs yearly. The estimation is that by 2029, we as humans will spend almost half billion, sorry, half trillion, $500 billion in cancer treatments a year.
Back to your question on AI utility and clinical intelligence. Well, one will need to make best use of the data, so this will not be sustainable, right? As a society, we're not going to be able to continuously expand our expenses. I think beyond harmonizing the data, at some point, it's going to be about bringing this clinical intelligence through this multimodal data because of having computed massive amount of patient cases in decentralized setup. For example, as health care systems or insurance, think about who should be tested, who should be treated with a certain type of drug, when and where, and at what cost. I think this is going to open a very, very large market. It's still very early, but on the data monetization, I think it's going to be about this kind of insight.
When you talk to biopharmaceutical companies today, do they view it the same way you view it?
I think they understand, because for them as well, it's very important to make their own market sustainable. If we end up as humans not being capable of basically purchasing or having the resources to purchase our own drugs, it's going to explode, right?
So, and I think in that vein, this part of their market access activities as well. And again, having technologies and companies like SOPHiA that can enable the system, but enable the pharma understand, okay, these are the cases where it's not worth providing a treatment to a patient, while these are the cases which are very rare and very hard and where it's worth providing a new type of treatment or a combination of treatment, despite its expenses, because there is a clear benefit on this group of patients, is something that is very, I think, pertinent, but as very much top of their mind right now.
I brought up the question of how biopharma thinks about this. There are new AI, either AI companies that are moving into drug discovery and drug development, or basically companies that have been set up as AI/biopharma from the start. Are they partners or are they threats? When I think of the differentiated foundational models you've built-
and I think about what they do, I could see where they would love to partner with you, but I could also see where you could start to overlap in terms of what's proprietary over time. What's the right way to think about that?
It's a good question. We don't do drug discovery by ourselves, right?
Right.
Back to the point of Kellen, our main advantage is the network, the scalability of our platform, the ability of computing data with very sophisticated algorithms.
When we are discussing with pharma companies in particular today, and maybe tomorrow with some companies who may be AI-driven drug discovery companies, the main proposal for them to partner with us is the deployment capabilities. It's not rather on the identification of a specific biomarker, but it's from the identification of this biomarker, on the ability of repeatedly, replicably, and with high accuracy, identify patients around the world with the same biomarkers so that then they will receive a specific treatment. I think we're rather complementary. We may be involved in some co-developments when it comes to new biomarkers, but we're happy if we're not the one owning the rights of the biomarker, but rather are being paid on deploying those biomarkers into the market.
Are you seeing interest from these types of companies?
Yes, I think, yeah.
Yeah, we are. I mean, if you just like all of the hypotheses in this space about AI, there's a central kind of agreement that the goal would be to help improve healthcare for patients and humans in general. I think when you compare and contrast the work that we're doing, we really understand the clinical deployment, so real-world use cases of AI in a clinical setting to determine how you might diagnose or treat or can ultimately care for a patient. This is something that's missing with a lot of the theoretical models that exist where you're able to identify something that sure, it sounds interesting, but in our case, adoption and use is the true innovation.
All right, I'm going to take a step back and talk about traction in the U.S. And again, this is kind of ancient history, but for those who followed SOPHiA for a while, it's not where you started. In a way, you were playing catch-up. You've done a really great job of doing that. What have you learned from bringing larger health systems onto the platform? What determines how quickly those deployments actually Once you get in there, what determines success and how quickly you can ramp from a revenue perspective?
I will let Kellen give you the specifics, but as you said, we started probably the hardest way. Not because we chose it, just because we funded the company in Switzerland, we started in the clinical market because we wanted to be this tech player owning or controlling that network. Then we moved to the U.S., and now we're moving to the biopharma, right? Indeed, it has been a journey. When we went public in 2021, there was questioning whether our model would be compatible with the U.S. model. I think over the last two years, indeed, our performance has proven that even in the U.S. market, our model was very pertinent.
Yeah. I mean, more specifically, we've been on fire in the U.S. lately.
Right.
It's been a huge growth driver for us. I think when you think at a macro level what's happening in the U.S., if you were to put yourself in the shoes of any of the large hospitals or health systems, you're just looking at a model where, first of all, clinical utility is being more proven. The reimbursement rates are increasing. The denial rates are decreasing. So, bringing testing in-house is becoming easier than ever and more profitable than ever.
You're constantly contemplating, "Should I let this patient leave my health network or my hospital, or should I do this testing in-house?" Being reimbursed the full services revenue on the patient, sometimes $2,000-$3,000 per patient, pay SOPHiA a piece of that, and then profit ultimately upwards of $1,000 plus per patient, which is a huge amount of economic influx for some of these hospitals and labs. On top of that, some of the other players in the space have proven that the genomic data of the patient is incredibly valuable to pharma, so the monetization of that data on the back end, we believe in not only driving those economics to the institution, but also helping them keep control of their data so that they can use this for research projects or for partnering with biopharma.
It's just a mechanism for them to stay competitive. I know we've mentioned that we've been growing 50% plus year over year in the U.S. It's now become our largest market globally, and we see no signs of that stopping.
Maybe a weird question to ask given the success you're having in the U.S., but at some of these sites, what are the biggest remaining bottlenecks? I guess part of it is, you have customers that are on the platform to ramp.
Yeah.
Well, then I'll come back with a follow-up.
Yeah, yeah. That's true. It takes obviously some time to complete the verification and validation when you're bringing a test up
Yeah
for the first time. But in general, there's some of the larger centers where we're coming in with one application, so we will land them on, for example, solid tumor or enhanced exome on the rare disease side, and then ultimately, we've done a very good job historically of expanding to additional applications. You're seeing some of the initial land thrusts across large accounts in the U.S. So, we mentioned that we've signed two of the largest health systems in the U.S. recently to our enhanced exome application. This is something where eventually you can expand within them, but it just takes some time.
Yeah, that's the right way to think about it. You get in the door with a beachhead. You prove that this can work in one application, and then
And then
eventually the gear starts to go away.
Yeah, with the eventual goal of capturing the entire testing volume of the hospital. Some of this is us switching from homegrown solutions that they are doing themselves internally. Some of it is instead of sending to the central player, bringing that in-house. Some of it is changing from one provider to the other, and we are there to help them across all of those.
As you know, Doug, in the tech world, this is called a classic land and expand approach.
Right.
Right? It is almost like Monopoly, so you are owning this street, and then you need to build your houses. Every year, and Kellen Sanger knows that because he is very involved in the budget efforts, we need to arbitrate whether we want our sales people to be more focused on land versus expand. The two are important.
From a partnership standpoint, how important is it to work more closely with the sequencing companies, essentially the technology platforms, versus other players in the ecosystem in terms of just trying to remove any resistance to decentralizing with SOPHiA?
Yeah. Look, so we are technology agnostic, right? We accepted that basically hospitals and labs would need to have the freedom of basically producing the data with whatever technology they wanted. This was not easy because again, we had to harmonize the data, hence why we built our algorithms. But definitively, for us, a way to go quicker to the market, and we were discussing with one investor just before our first chat, is the way to expedite the adoption. I think all the system would benefit from that. I think the sequencing players would benefit from partnering with us because it means that there may be less enough stress by some negotiations with only the central labs, so they can sell a bit more broadly to more people, that volume scales as well everywhere.
Today, I do not know what are the exact numbers, but probably when you get a solid tumor being diagnosed with a CT scan, it is for sure less than 50% of the people who are being tested on their genomic profile, right? So, I think working for us hand-in-hand with sequencing partners would be a nice way to, in general, increase the impact of the industry on society.
One more on the large health system wins and land and expand. How long does it take? Again, these are awesome wins. It is early, so maybe it is a little bit of an unfair question, but is it years and years? Is it quarters? How do you guys think about how long it is going to take for this to actually show up in a meaningful way on the P&L?
Yeah, it is starting to show up. Even this recent quarter is one of the first quarters in which they started to ramp, and they are using some of our technology and routine. But typically, it takes between six to nine months. We talk a lot about bookings. Our typical sales cycle is we are there at the account, we are talking to them for several months, and then ultimately they sign to adopt our application. They have a pretty good idea of the number of patients they see every year, so they commit to a certain amount of volume. We call this a booking, and then it takes six to nine months for them to eventually ramp up, completing verification and validation. Some are faster than others.
We are very good at helping them along the way because we have done this so many times, and so in many cases, we offer services to help them expedite. But in general, the pros and cons of this is we have a very good insight into what our revenue will be. We said on our earnings call that today we have 95% of visibility into 2027 revenue, right? Because we are signing business today that will ramp and grow throughout 2027. In addition, we have very little churn. Our annual revenue churn is less than 1%, which is probably world-class in terms of metrics. The platform is very sticky, and once we are in, it is very easy for us to not get displaced, but also expand with the customer.
The six to nine months is probably on the initial application, right?
Correct.
That's almost completing the land in a way, right? Then the expand comes after that?
Which is typically much quicker. Once they've already used our platform and they are using it in routine, it's adding another application to the platform, which goes much quicker.
How do you think about menu in a Sitting here today, and there's going to be more and more applications over time. But again, probably using land and expand too much, but as you expand, how broad can the menu get on these sites?
Our menu today is a nice proxy for the entire testing market.
Right.
We offer everything from hereditary cancer to heme-onc to solid tumor to liquid biopsy to others on the rare disorder side. We are seeing growth across all of those applications. We typically will land with one of them and then expand across the others. We have amazing new applications in liquid biopsy, which is with our MSK-ACCESS test, which we now have in 100 institutions globally, which is a pretty incredible number in such a short period of time, with liquid biopsy growing quite quickly. The others are as well seeing growth.
There might be new ones in the future, like mRNA for tumors.
Yeah, it is a pretty broad menu.
Transcriptomics, right?
Yep.
We ended up, as you know, we were probably best in class more than first in class. We let others demonstrate the clinical utility of a new application or a new data modality like transcriptomics, and then we help hospitals to adopt and scale that, rather than demonstrate the clinical utility first.
Looking ahead, how do you expect the mix of core clinical versus even as I'm thinking about this, it's probably hard to parse it out, but clinical versus software versus biopharma. You're an important clinical partner for a lot of your customers, but there's a lot of tech attributes to the story. How does the mix of revenue evolve over the next several years?
Yeah. All of it is software for us because we're all working on the platform, right? Today, the biopharma business and working with pharma companies is less than 10% of our revenue.
Yep.
We think long term, this could be half of our revenue. This is just the growth we're seeing. We're signing really large accounts, really large deals, and in the medium term, it could be 50% of our overall revenue, as with the U.S., right? The U.S. today is maybe 20%, and this could become as well 50% of our revenue in the long term.
From a profitability standpoint, does this mix There's no negatives in this mix. It's more leverage on the existing infrastructure, correct?
Yes. It's a lot of leverage. I think as a company right now, we're approaching some very exciting milestones. We're reaching $100 million of revenue. We're growing 25% plus. We're going to be profitable from an adjusted EBITDA perspective in the fourth quarter of next year.
Yep.
We've mentioned this quarter, we lost $6 million of adjusted EBITDA, so we're dropping down year-over-year, or each quarter, around 60% of our incremental revenue growth. So you're seeing the leverage of a true software-native company.
I wanted to close with, and you kind of touched on some of these already, Kellen, but if we think about two or three key milestones over the next 12-18 months, what would you want us to be looking for as we think about success over the business in the medium term, call it 12-18 months?
Yeah. Financially, I just mentioned a few, and obviously, we're committed to continue growing at our current levels or higher. I think from a strategy perspective, we have four pillars going into the next three years. We're going to continue to win in genomics. Our clinical genomics business is growing north of 20% on a volume and a revenue basis, which is great. On the CDx perspective, which is pillar number two, which is winning more CDx deals with pharma, we just won our first two in our company's history, which were announced in Q2. To continue making progress there, moving into more regulated products, collecting more data for the patient, which is pillar three, and building a real-world data product. We just this quarter announced an AI predictor with AstraZeneca for predicting lung cancer, so doing more of leveraging our data.
The last is building into clinical intelligence, so getting closer to the patient, closer to the point of decision-making, working more with the oncologist to build tools for clinical decision support.
That's great. All right. We'll leave it there. Jurgi, it's always great to see you. Kellen Sanger, I'm really glad you could join us. This is fantastic. Thank you guys for taking the time.
Thank you.
Thank you.