SOPHiA GENETICS SA (SOPH) Earnings Call Transcript & Summary

All right. Well, good morning, everyone. My name is Marta Nazarovets. I'm from the growth life science tools and diagnostics team. Welcome to the second day of JPMorgan Health Conference. It is my pleasure to introduce our next company, SOPHiA GENETICS. [Operator Instructions] And with that, I will turn it over to Jurgi.

Thank you very much, Marta. Good morning, everyone. Pleasure being here. So I'm Jurgi Camblong, I'm the CEO of SOPHiA GENETICS. And today, I'm going to present you the SOPHiA story, and I'm going to elaborate our 2022 performance as well as what will be our 2023 focus areas. So before I start, as usually, I may be presenting some forward-looking statements. So I want you to take notice of this cautionary notice. So SOPHiA was founded in 2011 with the promise of creating a world of data-driven medicine, basically moving from hypothesis-driven medicine to data-driven medicine. And today, I'm pretty proud to say that we've been not too bad in our mission and being already able to impact on the lives of over 1.2 million patients by computing their genomics profiles while supporting with our tech platform, a network of 750 connected health care institutions across 70 countries. And this was very important for us because in our story, the democratization of data-driven medicine was about equal access across countries to these type of technologies to serve equally every patient around the world. We're today almost 500 employees. We're being headquartered in Switzerland as well as in Boston, and we went public in the NASDAQ in July 2021. So when we started SOPHiA, it was obvious for us that in the world of cancer and inherited disorders, there would be a new paradigm shift, and this paradigm would require data so that we would object device decision-making, both for the diagnosis of patients, for the disease progression as well as the therapy selection. And so across the years, these are the two disease areas where we're being active ourselves and we're really being focused to deploy our tech capabilities. And as you will see, support two type of customers, clinical customers that were our first type of customers and more recently, biopharma customers. Now when we started in 2011, cancer inherited disorders, precision medicine, genomics were very early. But at the time, we were really convinced that this type of data modalities would transform our understanding of those diseases. However, there was one challenge. We would foresee that because of the nature of the diseases and the needs of the hospitals on being able to diagnose patients suffering from somatic cancer tumors from heme/onc from hereditary cancer, from inherited disorders, from cardiology. There wouldn't be a single genomics workflow that would be able to be used in clinical routine by every hospital. And we had foreseen a word where basically this production of data would be extremely complex in the hospitals, where a combination of sequencers and library prep solutions would create a huge heterogenicity of data, which is a problem we're thinking about clinical actionability and decision-making. And so one of the first mission of SOPHiA has been to fix that by developing smart algorithms that would sit in our cloud and be used by our customers post data production to be able to clean up the data, identify the signal, annotate the signal and by doing so, point to actionable clinical relevant outcomes. And while we have been very successful at doing that, when we started SOPHiA, we knew that the same challenge of heterogenicity would apply to other data modalities that would be a must to better understand how patients were evolving in their journey as well as how they were responding to treatments. And in that sense, we have seen, but today we call the multi-model world, a world where beyond genomics, other data modalities like proteomics, digital pathology, radiomics, eventually metabolics, would be required to take clinical decisions routinely in the hospitals. And we knew that as it had been the case, and it would be the case for genomics, the workflows that would be used by the hospitals in producing these data modalities would as well create the same heterogenicity. And think about it, beyond the single heterogenicity on one modality, it was clear that by combining these different modalities, its complexity would further increase. Now I'm very proud to say that as we had envisioned this feature of data-driven medicine, and we started addressing first the genomic needs of hospitals, we architecture our platform to be up for this challenge and to be able not only to harmonize genomics data, but as well other data modalities and by doing so, by combining these multi-modalities, bringing now personalized clinical relevant outcomes to practitioners. And today, this is where we are. Today, we're in the generation 2 of the SOPHiA DDM platform. And this year, we've been publicly presenting what we have been doing, in particular in the context of non-small-cell lung cancer with what we call the CarePath module where we've been following 1,000 patients around the world in 30 centers to be able to offer oncologist the ability to visualize how the disease progression of patients would evolve as they would be treated, how the patients would cohort versus each other and eventually as well build predictive algorithms that obviously require further studies to confirm the clinical outcome with prospective studies. But the reason I insist on this slide is that the word we had seen in the past when we founded SOPHiA is what we are realizing today. And as you will hear at the end of my presentation, this will be a strong focus area as well for SOPHiA in 2023. Now taking a step back. SOPHiA today is not only being used in 750 centers in 70 countries. But as you can see, our distribution has been pretty good, right? So despite we started in Europe and being primarily selling in Europe until end of 2018, we've been able to deploy our capabilities globally very quickly, which tells you about the scalability not only about of our platform, but as well the scalability of our operating model that enable us to land and expand customers around the world. Speaking about customers, I would like to highlight that we are addressing two type of customers: clinical customers and biopharma customers. In our journey to create this flywheel effect of data to create the network and the technological capabilities, we started in the clinical market. And so this is where I'm going to spend most of my time in my presentation, but we did that with the understanding that if we would own such a network, we would be in a unique position afterwards to serve the biopharma customers. And I would like to highlight towards the end of my presentation what we have been doing in 2022 and how successful we've been. In the clinical market, we are supporting a variety of factors, which are primarily university hospitals, comprehensive cancer centers, reference labs, specialty labs and central labs. And we can support them for a variety of disease indications. And I wanted to highlight here the most important ones. Somatic oncology, heme/onc, hereditary cancer, rare inherited disorders have been our focus over the last years. And as you may have seen a recent press release this morning, we will now get access to a new technology for liquid biopsy, and we're very excited about this promise. This is how we operate, supporting our clinical customers. As I said, these are Tier 1 hospitals and centers around the world. We are today serving 750 that are equipped with next-generation DNA sequencing capabilities. And we estimate the current potential market of 5,000 hospitals that are today being equipped with next-generation DNA sequencers to be able to routinely practice genomics testing. While this is a status today, obviously, with the new sequencers that are coming to the market and new capabilities, we expect not only the volume of data to increase in this field. But on top of that, we expect more hospitals to adopt next-generation DNA sequencing and all of them will need a platform like SOPHiA. So the way we operate is having these hospitals producing the data locally. And as they do so, they control the data as well. The data are being loaded into our platform. In the platform, we have power app, the data computing with our smart algorithms that I refer to, which enable us to harmonize the data, to identify the mutations, to classify the mutations, to basically ease the interpretation, which is always done by the pathologists or by the geneticists in the lab. The more data are being computed in the platform, the more interpretations are being done. These are network effects. The smarter becomes our algorithm, the better we can prepackage the data for the next wave of patients. So while this is important, I would like to highlight as well that over the years, supporting 750 hospitals around the world, we've been able to build a very comprehensive set of applications in the platform, actually dozens of them. And when it comes even to somatic oncology, today, we can help some hospitals with comprehensive genomic profiling capabilities, so enabling them to test 500 genes, while we can help other hospitals, which maybe have less sequencing power on addressing the same somatic oncology needs on the panels for 50 genes. While we have been developing these applications, we took advantage of that to land the 750 customers have been talking about, right? But what I want to highlight to you is that while until now, we've been very much focused on landing customers and creating the network. From now on, we're going to focus a lot as well on expanding our customers. And this data, I think, tell everything. Today, part of our network of 750 customers, only 50% of them are using 1 or more application. 37% are using 2 or 3 applications. 13% are using 4 or more applications of SOPHiA DDM. So as you can understand now, we have a very captive customer base to whom we can sell additional applications and by doing so, bring them new capabilities and being a consumption-based model, the more applications are being used, the more patients are being computed in the platform, the more revenue we generate. So along those lines, I would like to drive you through the journey of one of our customers, which we landed in 2019 with already at the time an enterprise sales which means that we didn't enter with one single application. We won this customer who was starting in genomics and actually is one of the biggest central labs in the world with already 5 applications. But by the time on these 5 applications, they would move to routine and then they would add additional applications. You can see how the number of patients that they would compute in our cloud platform will evolve, right? And we moved from only 200 patients in 2019 to almost 10,000 patients in 2022. So this tells you how this land and expand model can work. It's quite unique and new in the health care sector, but it's a formal in the tech sector. And if you look to what has been made in the success of Datadog or Salesforce or Snowflake, it was really about this land and expand method. And of course, this requires being very close to the customers, understanding their needs and having a decent set of applications that are ready to be deployed into those centers. While, as you can see that these type of customers are growing quickly, what we realized over time is that the largest become our customers, the quicker they grow. So taking a step back in 2019, the average revenue we were generating for our top 20 customers were $385,000. In 2022, this moved up to $675,000. But as you can see, the customers who today are generating over $100,000 revenue at SOPHiA, have been growing from 2019 to 2022 with a 31% compound annual growth versus 19% for the customers who are generating less than $100,000, which probably signify that the largest become our customers, the more they benefit from our platform. The more competitive they become, the more samples they attract. When it comes to the commercial team, I think it's important to highlight that we are ripe. We know how to operate. And we have about 50 quota-carrying salespeople, which are sales executive landing new customers and customer success growing the customers, adding new applications around the globe. And we have as well a very sophisticated team of 40 presales and post-sales experts who are in charge as well of the implementations and the professional services we offer to our customers. So while we primarily sell directly, we sell as well through distributors and through partners. And so I think it would be important to highlight as well along the years, the ecosystem of partnership we've been building around SOPHiA. And these are partners with whom either we build content, either we sell or we co-market solutions. Today, I am really excited to share that we have expanded over the last months and days 3 partnerships. One of them is with Microsoft. You all know Microsoft. We are running our data in Azure. And actually, we're being among the, I would say, fastest-growing partners of Microsoft over the years. And now we've, in December, signed a strategic agreement with Microsoft, where we're going to go behind where we're going to leverage on their commercial people as well as on their marketing muscles and where we're going to leverage as well on Microsoft technology, in particular in our multimodal journey to be able eventually to extract information from documents. While this is super exciting, I would like to highlight another expansion of our partnership today. I'm really proud to say that we've been expanding our partnership with Agilent, with whom we're going to work now together on a new comprehensive genomic profiling solution where basically Agilent is going to leverage on their backbone technology together with SOPHiA DDM capabilities, and we expect a very good adoption of this technology from 2023. Last but not least, I'm really thrilled to announce today the expansion of a new category of partnership for SOPHiA in the principles of what people would call open innovation. And this is the one with Memorial Sloan Kettering. And so we had announced that we signed a partnership with MSK where we would get access to 70,000 clinical genomics information. And today, I'm proud to say that we signed an agreement where we're going to be able to leverage on their MSK access technology, which have been already clinically proven to be extremely good for liquid biopsy testing. And this is a technology that we're going to leverage on in the clinical market, but maybe eventually sooner in the biopharma market in the context of clinical trials, which gives me the opportunity to move to our second type of customers. So while for the first years at SOPHiA, we've been really focused on supporting the clinical customers and creating this network of 750 connected health care institutions. More recently, we started working with biopharma companies, leveraging on our technology, on the data we're gathering in the clinical market as well as leveraging on our network. And this strategy has been further accelerated in 2022, where we had really good traction along the line of the 3D strategy, which are data, development and deployment. So the data aspect enable us to work with pharma companies pre and post approval of drugs. The development enabled us to work with pharma companies in CDX-like projects. And the post approval enabled us to impact while collaborating with the pharma in the real world, in the clinical world by deploying new testing capabilities into our network of 750 customers. So this year, we've been announcing some very important partnership with AstraZeneca in develop and deployment, a recent one in November with Boundless Bio, which is a very innovative biotech company, still private working on developing drugs that are targeting extrachromosomal DNA, which basically are circular DNAs that are present in 20% of cancers. We more recently signed an agreement with [ Peraxil ] on the data side on top of the one with Lilly. And today, I'm really proud to say that along the line of our 3D strategy, with AstraZeneca, we've been expanding our partnership from deployment and development to data and that beyond what we have been doing with HRD scoring for PARP inhibitors, we're now leveraging on the data that we've been gathering in the context of CarePath of the generation 2 of SOPHiA DDM for non-small cell lung cancer to offer Astra the ability to get access to this technology and to this data in the context of their own efforts. So now that I have addressed our strategy and our performance in the clinical and the biopharma market, I'm going to move to the financials. So these KPIs are the traditional ones that we shared in our earnings calls. So the size of the network is something that I encourage you to look at. It's very important. As I said, in the clinical world, now we are addressing over 750 customers, which are using routinely every month, our platform, which are the 380, you can see on the site. So to be clear, these are the ones that are -- the 380 are using the platform routinely every month. So 750 are the ones that we have put in the network in total. We've been, over the last 12 months, computing 260,000 clinical genomics profiles over 20,000 a month, almost 25,000 a month, actually. And we've been doing that, respecting, I would say, and being very disciplined on having strong margins that we've been sharing in our Q3 earnings, which were between 63% and 65%. We still maintain our guidance that we shared in Q3 despite, I would say, some challenges with FX, around $44 million in reported revenue for the 2022 exercise. And last but not least, we've been growing, and we maintain that guidance as well in 2022 organically between 30% and 35%. And I encourage you to stay tuned as we are going to disclose our Q4 numbers in March when we will have our earnings calls. Last, I would like to insist on a very important point and very important considering the current environment. So SOPHiA, end of Q3 at $190 million in cash. And while I think this is important, we can recognize that the environment has changed. And I want to tell you that we have taken as well ourselves the necessary cost management actions to be able to put us on a path to profitability in the spirit of sustainable growth. So with that, I'm going to end with what will be our focus for 2023. So first will be somatic oncology. I haven't been talking about HRD today, but for the one who have been following us in the earnings calls, this has been capability where we've been growing triple digit last year and where we expect to grow significantly as well in 2023. Comprehensive genomic profiling will become a very important one. I mentioned the efforts we've been doing with Agilent for a solution that will come to the market in 2023. The biopharma side is going to be further enhancing our strategy around data, which has proven to be very effective, as you saw. And putting in the market the MSK access technology powered by SOPHiA, which we expect to be a significant value proposition for the biopharma customers in the clinical trials. And last but not least, the multimodality, where this year, it was the first year we're talking about that, where we've been having a tremendous success around the deep plan for studies on non-small lung cancer stage 4 where in less than a year's time, we've been able to deploy these multimodal capabilities in 30 cancers around the world, including Mayo Clinic. And where by doing so, we've been following thousands of patients with 200 clinical data modalities, including imaging data modalities and genomics data modalities. So with that, I'm going to thank you and ask Ross to join me so that we can address your questions.

All right. Thank you for the great presentation. So let's start with the Q&A. [Operator Instructions] So thinking about the clinical business, how do you expect revenue and ASP to trend in 2023, given the macro headwinds, especially FX and customer funding constraints?

So I will start with the historicals, and then I will let Ross answer directly to your question. So what has been nice with our model is that over time, the data complexity that we would compute would get bigger, right? And so we've seen a trend in increased ASP. So it's like the way we are growing our business in the clinical market is by having the adoption of more customers, more application per customers and over time, ASP going up. And so that's a trend that we think is going to continue. And Ross, I don't know if you want to add any additional color.

Right. So obviously, we're not providing a formal update on 2023 here, we'll do so with our Q4 results. But I would say if you looked at our third quarter, we did show core growth acceleration into what is a challenging environment. I think if you take a step back and you look at the macro, typically cancer, which is a majority of our business is pretty resilient, right? And if you think about us touching mainly Tier 1 academic medical centers around the world as well as large pharma companies. Again, those groups tend to be quite resilient in a number of different environments. So I think we've been quite fortunate this year to avoid many of the challenges I think we've seen across the complex and the ecosystem. Certainly, we're mindful and thankfully, in our business, we have a huge degree of visibility. We stated at the Analyst Day, we have over 90% sort of look through into the '23 numbers already, right? So I would say on our end, we feel quite good about the trajectory. And again, relative to what we said in Q3. I think our momentum actually accelerated in the second half of the year just in terms of new business environment as well. And I think there, you're not just seeing obviously stability in the kind of regular way business, but you're seeing some of the large central labs and specialty labs and others really look to us as a partner to help them from the cost side of things and labor efficiency and also to be able to be competitive in terms of launching new applications. And so I would expect that trend to continue as well into '23.

All right. And then also related to clinical business, how big is the market opportunity for CarePath? What is the customer feedback so far? And then beyond NSCLC, what other indications are you looking at?

So the customer feedback has been excellent, right? And I think it's pretty unique to probably unprecedented, having someone being able to deploy this type of capabilities in 30 centers in a year time, starting from scratch and being able to enable the centers follow over 1,000 patients for 200 clinical data points. So beyond non-small cell lung cancer, we have already presented data at ASCO on triple negative breast cancer, on kidney cancer and on brain cancer. And so you should expect that we will have similar efforts for those disease indications and eventually other ones depending on the pharma needs beyond the clinical needs. When it comes to the clinical market, I would say, it's still a nascent market. But if you think about it today in the clinical work, you have about 5,000 hospitals, which have the capabilities of producing genomics data, while you have hundreds of thousands of hospitals, which have the capabilities of producing imaging data. And in this multimodal capabilities, it's extremely important to follow longitudinally patients. And so as we do so, it might be that as we compute imaging data, we're as well expanding the size of our network.

Now moving to the biopharma business. How should we expect the adoption ramp of DDM CarePath and biopharma competitive clinical?

So SOPHiA DDM CarePath for us as being a way to further address the needs of the clinical market. And by doing so, if like, enable them to do research, but as well capture data that we can use in the pharma market, right? And so as you have seen from our logos, the data angle is the one that is growing the fastest. So you can expect that CarePath will be a very important piece in our pharma story.

So you think it will be similar to clinical?

So the model will be different, and it's not going to be pay-per-use, and maybe I'll let Ross give you some color on financially how one should think about it.

Yes. For us, the pharma business is very exciting. It's quite different, right, contractually than our clinical business and that the contract sizes tend to be much larger, right? They're multiple year duration, but they're not pay-per-use, right? You have sort of almost SaaS component to them. And then you typically will also have other service-related items in terms of the revenue. And so there, we tend to have pretty good visibility, but certainly it's -- because of the size and chunkiness of those contracts, we've been, I would say, generally conservative with how we've modeled and included them in our forecast.

And you mentioned the oncology portfolio saw the biggest growth this year?

There are some questions, Marta.

Sure. Go ahead.

It's great to hear the commitment to getting to cash flow positive. Any guidance you guys can give us in terms of time frame or revenue levels at which we should expect that?

Yes. So what we shared at the Analyst Day was obviously in terms of incremental revenue. We're expecting greater than 55% drop down, right? So you can do some math relative to the growth rates we've shared. I would say, ultimately, on our side, we've really wanted to balance sort of what we're seeing on the revenue and growth side where we're seeing some inflections in a number of our businesses with sort of operating discipline, right? And so we've really taken a deep look at our fixed cost infrastructure, systems, some G&A headcount, professional service spend, some of our other IPO and public company-related costs that were inflated when we came out. And so we're really, I would say, spending quite a bit of time making sure those elements are more measured. But ultimately, on our side, right, again, we're very different than many of the other businesses people look in the life science space. Our main cost is people. We don't have almost any CapEx, right, as a software player. And so it's really trade-offs of where you're deploying your human capital and headcount. And so in that, I would say, we're really trying to titrate accelerating that growth rate with making sure we are being disciplined and sustainable on that side. But certainly, we feel quite confident, as Jurgi shared with our current capital position, and we're certainly mindful of the environment, and we're managing to be able to be self-sustainable.

That's very helpful. And so should we think about the cash and the balance sheet being enough resources to get you to that cash flow positive?

Yes. So again, what we've said is we certainly can manage the business to live within our means, right? And so on that, certainly, at different periods, again, our business is starting to see some acceleration inflection in other areas. I think it's dangerous to box yourself in a bit on giving an exact quarter, an exact date because then you all of a sudden start managing your business to that when you have, again, lots of different levers to pull and create shareholder value. But ultimately, I would tell you the entire business, all 500 of us at SOPHiA are incredibly committed to be responsible with the capital that we've been given and make sure that the returns we're driving particularly on our R&D projects, which is the predominance of where we spend our capital, will deliver solid results.

Any other questions?

Just a technical question. I'm in the imaging space, and I'm curious, it's relatively easy to capture sort of flatter data and digital data. 3D data is not in the imaging space. Are you bringing in sort of just report-based data? Or are you actually internalizing and considering the core imaging information?

So the data are being produced locally and then they are being transferred to us. And from the CT scan, from the PET scans, from the MRI, we apply some deep learning techniques, if you like, to get some signal out of the data, right? And then this signal is being used in our multimodal models. And so the one we highlighted, for example, in non-small cell lung cancer, where we have today AUC of 80% on the first 1,000 patients that have been run there with -- towards response to immunotherapy, both in the prediction of who will respond and not will respond, wouldn't be feasible if we wouldn't have this deep learning algorithm capabilities that are extracting the signal from the CT scanners.