Hi, everyone. I'm Jennifer Pottage, Head of Investor Relations at SOPHiA GENETICS. I'm so pleased to welcome all of you today to our first ever Investor Day in New York City. It's so great to have so many of you here with us in person. I'd also like to thank those of you who are joining us on webcast. We really appreciate your time and your attention. I'm gonna spend the next few minutes orienting you on what to expect this afternoon. We're gonna start with some management presentations and then have a quick 15-minute break. After the break, we'll finish up the rest of the presentations and then go into a live Q&A session. Then after the Q&A session, we'd like to invite all of you on the terrace for a cocktail reception.
I'm not gonna read the entire cautionary notices slide, but please note, we will be making forward-looking statements for those of which actual results may differ materially than indicated. Please read this slide and then also see our public filings. Without further ado, I'd like to welcome to the stage SOPHiA's Chairman of the Board, Troy Cox.
Hi, everybody. Welcome. Welcome again to the first SOPHiA GENETICS Investor Day. I'm Troy Cox, the Chairman of the Board, and I thought as a brief intro, I would share with you what brought me to SOPHiA GENETICS. I grew up professionally in biopharma with Schering-Plough, Sanofi, UCB, and then seven years at Genentech, where I led oncology. I also sat on the late-stage portfolio committee, the body that governs all the development decisions for Roche portfolio. I joined as CEO at Foundation Medicine, where we achieved some pretty cool things. My old boss became my new boss again, and Roche acquired Foundation Medicine. After lining up a successor, I started what is now my new chapter that I'm loving, and a big part of that is SOPHiA GENETICS.
I'm very pleased to be associated with SOPHiA. One of the main reasons that brought me to the SOPHiA GENETICS team is this guy, Jurgi. He's a special human. Like, all founder CEOs, and I've met quite a few, and I'm sure you have too, they're wired differently, aren't they? You know, it's just personal. It takes it to the ultimate level of accountability. But what's different about Jurgi is that he, there's something more contagious about his passion and his accountability, and that's attracted a really impressive team, many of which that you'll meet today. The other thing that I've seen Jurgi, while he started as a scientist, and is credentialed, but he's grown faster than the company.
He understands all the key functions and the place that he likes to play the most and spend the most time is at where the rubber hits the road, that interface with the customer. I'm very pleased to work with him. Of course, there are other reasons why I joined SOPHiA GENETICS, and I'm sure influences many others. It's starting at, you know, at a high level, just simply the value that we offer, the business model itself, I believe in. What that boils down to in its most simplistic form is better informed decisions. These are really important decisions. These are healthcare decisions, oftentimes life and death decisions, that we have a huge impact on. We do that via our connected global network of over 750 institutions and centers, leveraging the knowledge base from there.
Those same customers, they expect and they want a decentralized solution. Just like we expect things to be more in our pocket and more at our fingertips, they want things local, they wanna be involved, they want it customized, they want it fast, and they wanna be involved. All of these things together, I think was particularly important for you to understand, if there's one thing that you take away, of how well we're set up as an organization, to be an enabler for all different types of companies. You'll hear a lot about the strategic partnerships that we have, in fact, even a couple of new announcements of strategic partnerships that truly brings value by enabling these companies to serve all of our patients better.
I wanna thank you again for joining us today, but before we turn it over to Jurgi, I would like to share with you a short video that I think gives you yet additional information to understand what's so special about SOPHiA GENETICS.
Our health is our greatest possession. It is our most vital asset. We depend on it for our livelihoods, for our most important relationships, and our own peace of mind. Every day, researchers and clinicians strive to improve the health of patients by collecting a broad assortment of information. Meanwhile, more and more patients are being diagnosed with cancer and other life-threatening diseases while holding out hope for more effective treatments. Cancer is one of the leading causes of deaths and disease burden in the world, and the American Cancer Society estimates that by 2040, over 27.5 million new cancer cases will be diagnosed. With over 16 million patients tragically succumbing to it. Thanks to technological advancements over the past decade, there has been an explosion of data that has accelerated our understanding of human biology and medicine.
Yet many of these valuable data are locked away in silos across institutions, limiting the potential learnings and insights that have the ability to enhance patient care. The name SOPHiA means wisdom in Greek. True to our name, we developed the SOPHiA DDM platform to take the data of patients today to inform researchers and clinicians on how to better diagnose and treat the patients of tomorrow. The SOPHiA DDM platform lives in the cloud and uses artificial intelligence and machine learning to compute and analyze complex data to create meaningful insights and unlock wisdom for patients and caregivers. The SOPHiA DDM platform is highly compatible, allowing us to serve a network of hundreds of hospitals, academic centers, labs, and biopharma institutions around the world.
With the benefits of collective intelligence and SOPHiA GENETICS' highly accurate algorithms, we are able to provide statistical insights and analytics to compute data for more effective treatments. Enabling institutions to transition from hypothesis-driven medicine to data-driven medicine. With the help of our growing network, we are democratizing data-driven medicine together to protect our most precious resource, our health. This is the future of healthcare. This is SOPHiA GENETICS.
Hello, everyone. I'm Jurgi. It's a great pleasure for me to be here today. About a year ago, we went public in New York, in the Nasdaq. Most of our colleagues couldn't attend this event because of COVID, and so it's pretty emotional for me now to see my colleagues here and the one who are as well watching us from Switzerland, Boston and France. I would like we applaud them as well because SOPHiA wouldn't be what we are today without our colleagues. As you hear, I have an accent, right? I'm not American, I'm not English, and I don't know many of you because I've not been living in the U.S.
I was advised to first speak about myself, which for a scientist, I can tell you, it's a bit awkward. This is where I grew up. I grew up in a very special region in Europe called Basque Country, which is one foot in France and one foot in Spain, right? As you can see from the pictures, it's a beautiful region. You know, when people like to joke with me, they ask me why I didn't become a surfer. The answer is I was not good at all. This is where I grew up, and I had the chance while living there to grow up not only with beautiful nature but as well with diversity. I would grow up speaking Basque, French and Spanish since I was born, right?
I think this is diversity that makes us sometimes unique and better. I hope today, as our executive team will be presenting, that you will recognize the diversity that we have in our team in terms of origin, in terms of experience, and that this diversity will inspire you as well and make you understand why maybe SOPHiA as a team is better equipped to solve complex problems. Because with all these great talents, we have different perspectives, and with different perspective, we can take the right decisions. Now, more importantly, professionally, I indeed grew up as a scientist, as Lars said, and actually is my drive and my willingness to be in an environment that would impact more quickly that brought me to the world of companies starting SOPHiA and co-founding that with Lars and Pierre.
Lars, who is one of the co-founders of SOPHiA and co-director of the Stanford Genome Technology Center, is with us today. Great pleasure having you, Lars. In this last 10 years, where basically I've been the executive CEO of SOPHiA. For me, it has been personally as well, a fantastic journey, right? Because I've had a lot of mentors like Troy, and I could learn many different jobs. From a scientist, indeed, I learned how to sell, and then I learned how to market, I learned how to build a product, and I learned as well how to tell stories. Hopefully, I will be able to tell you a good story today, and the team will be able to tell you a good story today. More importantly than myself, let's speak about SOPHiA.
SOPHiA GENETICS was founded in Switzerland in 2011, and since then, I think our journey has been pretty exceptional, and we moved from a dream to a real company with a global impact. We've been impacting now on over 1 million patients by computing their genomics profiles into our platform. Not only we have done that exceptionally well, but we've done that supporting a very decentralized network. Supporting patients across 70 countries and 750 healthcare institutions that had adopted our technology. This is something I'm very proud of because as we speak about impact, definitely changing the life of 1 million of patients or having an impact on the life of 1 million patients is something that drives me a lot. Drive as well all the SOPHiANs that are looking at us today or watching us today.
Beyond that, speaking about the SOPHiANs, the other thing I'm very proud of is that we have created 500 jobs. For an entrepreneur and CEO, I can tell you this is something that counts a lot. Now, how we started back 2011 and what was our vision? We would see a huge opportunity out there in the market. It was pretty obvious for us that new technologies like genomics would change the way we would practice medicine. It was obvious for us as well that to completely leverage on these different technologies, one would sit on top of the data, compute them, and deliver the best possible insight, right? That's why we started addressing this market opportunity, supporting a decentralized world with a tech platform that would sit in the cloud.
Our vision was that this would be important so that we would be able to create a collective intelligence by breaking information silos across institutions, computing the genomics data. That there will be a tech player that would go beyond, and that this tech player will not only break information silos across hospitals, but as well across modalities. Now, 2011, it was the beginning for SOPHiA, right? Actually, we had no technology. As Troy said, I would go and pitch a lot to our potential clients, and by doing so, understand what were their needs. We decided to start with genomics. Why did we start with genomics? Remember, in 2003, for the first time, we are being able to decode a full human genome. This is 19 years ago only.
This is not so long ago, right? This was a breakthrough in technology and in science. This would enable us to understand from 2003 to 2011 that a lot of diseases, at minimum, rare inherited disorders as well as cancer, would be driven by mutations, variants in our DNA. On top of that, in 2011, this is when you would see a big company in our space that would decide to move from research to clinical. Basically miniaturizing their boxes, like next generation DNA sequencers, and making them affordable for the hospitals. While starting with genomics and delivering good with our first generation of SOPHiA DDM in the cloud, we are being already demonstrating that our model worked.
We have been connecting around the world 750 clinical institutions, which are hospitals, cancer centers, and private labs, that would day in, day out, using their next generation DNA sequencers, produce data, add the data in our cloud platform, have us compute this data with our SOPHiA artificial intelligence, and deliver them the right insights so that they could take the right decisions. As you can see here, our penetration has been different according to the countries, right? Remember what I said, I'm not Native American, right? We started the company in Switzerland. Obviously from 2015 when we launched the company to about 2019, we focus in our local market, which was Europe, hence the bigger penetration in Europe.
The other number I want you to focus is the one in orange, because this is where we see the biggest opportunity to grow our market penetration and our network. Now taking a step back and if you think about the patient journey in cancer, this is how a patient is being diagnosed, treated and so on. Please follow me. If unfortunately you would have a cancer, probably the first diagnosis would be done with an imaging modality, right? A CT scan, PET scan, an MRI. Then at some point from the tissue, one would characterize what would be driving the cancer, looking at the mutations. Then after that, while the patient is being treated, we would basically monitor the response to the therapy.
Then eventually when the patient would not respond anymore, a second therapy will have to be prescribed to the same patient, right? While with generation one of SOPHiA DDM, we have been very good by computing genomics data globally in addressing the needs around diagnostics and treatment decision. This was only part of our journey, right? This was just the beginning. With the generation two, and some of you who have been following us in our earnings calls have heard about it, we're now covering more than that. We can see the picture of the patient looking at the molecular information, but as well as the longitudinal perspective of the patient by following the patient with imaging computing capabilities that you will hear from Zhenyu Xu and Philippe Menu. This is, I think, pretty exceptional for SOPHiA.
We're probably today the most advanced in this radiogenomics capabilities, which I think will be essential if we want to build a collective intelligence that will serve good to all the patients suffering from cancer around the world. This is a SOPHiA DDM Gen 2 powered by the CarePath module. Just to keep it simple, because you will have a video afterwards of our Chief Medical Officer, Philippe Menu, what is really SOPHiA DDM version two doing. The three takeaways are enabling to visualize the patient in this patient journey. The second one is comparing this patient versus others that are being already recorded in the platform, and this is what we call cohorting. The third one is eventually, with more and more data that are being ingested in the platform, being able to come with predictive models.
How we are being delivering and, executing upon the promise of this multimodal world? Well, we focus on one specific cancer type to start with, which was non-small cell lung cancer, stage four. Why? Because there is a big unmet need around this cancer. What we did is basically follow patients that would suffer from this cancer and would be treated with immunotherapy, and first demonstrate that beyond computing genomic data, hospitals would trust us for computing other data modalities, right? As you can see, this is now proven. From launching this initiative in at RSNA in November last year to now, we've been able to onboard 23 different sites, including multiple in the U.S., and Philippe will highlight them. Recently Mayo Clinic, which as you know, is one of the most renowned clinical institutions around the world.
I think this demonstrates not only that we are being trusted by the institutions to compute their data, but shows the power eventually of how by computing these data modalities, you may be able to come with predictive algorithms. This is still a retrospective study, so we will have to do a prospective study. If we figure out that this kind of model works, imagine the power we could deliver now to the oncologist. 23 sites today. Remember, zoom out, we have 750. Imagine the power of the collective intelligence we're going to create while moving our 750 existing customers using SOPHiA DDM Gen 1 with our genomics capabilities to this multimodal world for non-small cell lung cancer.
How by doing so, we're going to improve the diagnostics, but eventually as well, the treatment path of the patient that will be supported by the oncologists that are going to adopt this technology. Beyond that, with the same model, what about going to other cancers where we know that today we're still dying from them? You will hear why we're going to focus on the one you see here from our Chief Medical Officer, Philippe Menu. In a nutshell, what I've been telling you is, kind of the SOPHiA story, right? Our mantra has always been about patients, and our mantra was about patient data today benefiting to the patients tomorrow. I think this speaks a lot about the effort we all need to do around sustainability.
Because when we founded SOPHiA, a lot of people were talking about sustainability, but not in healthcare. For us, it was crazy to think that data were trashed and not leveraged for the next generation of the patient. To be able to do that, you need a platform, you need to be in the cloud, you need to be real time, we need to be real world. In that journey, we're not alone, and we don't pretend we're going to solve this problem by ourself, right? For the one who have been reading our F-1, so the equivalent of the S-1, but we are a foreign private issuer.
We have multiple partnerships, including one which we believe is very powerful with GE HealthCare, and we have here, two of our friends from GE, to whom you can, you know, go and talk in the breaks or in the cocktail later today. We believe that this type of partnership can be the one that are going to accelerate our journey to create this collective intelligence in the world of data-driven medicine. Now, today, beyond these industrial partnerships that, you know, have proven to be good in our model, I'm extremely proud, I'm super excited to share with you a new partnership. This partnership is with the biggest healthcare institution that you all know, maybe the most renowned in the world. This is MSK. As you know, MSK has unique capabilities, right?
They've been investing a lot in the space of cancer and genomics. Over time, they've built an amazing clinical genomics database, and they've built amazing technologies such as MSK-IMPACT. As we do, MSK wants to impact around the world. What we agreed is that we would be the technology partner, a bit in the spirit of open innovation, that would leverage on the fantastic technologies that MSK has been developing to not only impact on the millions of patients that could benefit from these technologies in New York and New York area. Through us, through our network, through our platform, for the billions of patients that we can .
This is a very, very important partnership for us, and we believe with MSK that this may as well show the way to other cancer centers in the US and around the world, and that may want to join this movement to create this collective intelligence in a real win-win spirit, where the data go back as well to these academic centers so that they can leverage on it as well for the research and for the patients. That's a breakthrough, and this is a grand moment for us at SOPHiA. Now taking a step back, speaking more about numbers. For those again that read our F-1, you know that we envision our TAM being $35 billion, $21 billion in the clinical market, which is where we started. $14 billion in the biopharma market.
You will hear about how we sell in both of these markets. Just to bring the message around MSK home, right? That you understand that at SOPHiA we're obsessed about details and obsessed about efficiency. The reason why this partnership is important for us is because it's going to enable us accelerate the execution of our existing total addressable market, both in the clinical space and the pharma space. You will hear that this will be mainly data from Peter on the pharma surveys to start with, and this will be mainly decentralization of MSK-IMPACT and some of the data in the clinical space. Now, while this for us was an amazing news for today, and I hope that you share the excitement, we have another news to announce.
We signed another partnership, not with a healthcare institution, but we just signed this week a partnership with a biotech company which may have the chance to some extent revolutionize how we treat patients suffering from cancer. This is Boundless Bio. Boundless Bio is still private, very sophisticated, based in California, and they've been developing a technology around what we call extrachromosomal DNA, which may apply to any type of cancer. Peter is going to develop around this opportunity and is going to be on the development phase of Boundless. Peter from Boundless is with us as well today, so great pleasure. I think I like how SOPHiA can be a good partner as well at different levels for the pharma. In that sense, you will hear Peter talking about our 3D strategy.
Now, I being the one speaking, but we are a team, and I hope we will give you the sense that we are a team. I think, I hope what we'll feel is that at SOPHiA we are people of action. We don't have values, we have virtues, because virtues are about doing, and values are about beliefs. As I speak, I hope that you will feel that we're adventurous, we're nimble, we're curious, and we're driven in a mission. With that, let me welcome our Chief Revenue Officer, Ken Freedman. Before I leave you, today, I would like all of you to go away from this conference first understanding that you are part of it and you're contributing to something which is greater than yourself, but as well with having us answer three questions.
How we sell, how we innovate, and how beyond creating a sustainable healthcare world, we as well grow as a sustainable company. Thank you with that, for that, excuse me, and let me welcome Ken.
Thank you, Jurgi. Welcome everyone again. It's a pleasure to be here today. You just heard from our CEO, and you heard from our chairman of the board, talk a little bit about how we got started, where we are today, and very importantly, where we're going tomorrow. Jurgi also left you with three questions. The first, how we sell, which is what I'm gonna help answer right now. My name is Ken Freedman, and I'm the Chief Revenue Officer here at SOPHiA. I joined the company back in January of this year. I come from a background of technology, working for data-driven companies, very similar to how SOPHiA addresses the market, but in different spaces. Spent the last eight years working for a company owned by Vista Equity Partners. Some of you may know Vista. Vista is a large PE firm based in Austin, Texas.
Vista's known for their what they call VSOPs or Vista Standard Operating Procedures. Vista is a very process and data-driven company, which appealed to me, because I'll share a little bit about myself later in the talk, but I've always been a big fan of data and understand the value it brings. When I had the opportunity to come join the leader in data-driven medicine, it was an easy decision. We talked a little bit about it, Jurgi talked a little bit about it, as did Troy, about our core platform. Just something to keep in mind as I go through my presentation today, as Chief Revenue Officer, I'm focused on, obviously, revenue. For us, what we focus on with laser focus is generation one.
We'll also talk a little bit about how our team and process is set up as we bring Gen 2 on and other things that you'll hear about later from my colleagues. We have the team in place that will take those to market as well. Now, one of the things to think about as we think about how we sell is what are our key value propositions that we offer to the market. One of the real nice things about SOPHiA and the DDM platform is that no matter who we're talking to in a healthcare institute, we have a great value proposition. Oncologists, pathologists, lab directors really appreciate our top analytical performance. They appreciate how fast we can get them data insights from that. If we're talking to the C-suite or business development people, they're always interested in two things, revenue and cost savings.
We have that covered as well. We talk about how quickly we can bring assays to market. We talk about our hosted solution, which you'll hear a little bit more later from Abhi, our Chief Technology Officer. We can take cost savings out of the equation. As Jurgi mentioned earlier, we have a very large current network with over 750 connected healthcare institutes around the world. Why this is so important from a sales-centric view is we deploy what we call a land and expand model. Basically, we look for two things, and I'll talk to you a little bit about how our team's set up. We look for new logos to land or greenfield, back in the tech space where I came from, and then we also look for white space or additional applications we can sell.
As you all know, it's really hard to land new customers. One of the advantages we have at SOPHiA is because we have such a large base and, pretty cool, we have a lot of products to sell to those existing customers. You know, part of our strategy has always been we wanna land the customers. Typically, it's with just one application. We could, for example, land a customer with a Myeloid Solution and then offer them an RNA solution or BRCA or any number of different applications later on in the sales process. This helps us have a sustainable growth model. In fact, if you look at these numbers, I think they're pretty telling about how big an opportunity we have before us. Only 13% of our current customers, remember, 750 worldwide institutes, have four or more applications.
As you saw on the previous slide, we have a lot more than four to sell. In fact, only 50% of our customers are using one. Huge market that we can go after on our expand model. This is an example of a large central lab that is a customer of ours, and this is a great example of how once we land the customer, we have an amazing expand engine built in. We started this customer about four years ago, and they started with one application, spending about $10,000 and doing about 200 analyses. As you can see from the stairstep, we've taken them to today, where they now use 10 applications, do over 9,000 analyses with us, and are approaching $2 million in revenue.
Now, we won't be able to have this kind of success with every one of our customers, but this is the model that we deploy. What's nice, again, is all 750 have an opportunity to add at least one more application, sometimes in, you know, this case, you can add up to 10. How do we do it? The sales organization of SOPHiA is approximately 100 people. Of those 100 people, we have 20 dedicated customer success executives. Those customer success executives get up every day, and they're focused. Their main focus every day is customer satisfaction. How can they help our customers be successful, which will enable us to be successful and for you as investors to be successful as well? They look at everything from white space opportunities.
They work on retention, customer satisfaction, as I talked about earlier. We've proven this model out. It works. We're very excited about the opportunity to continue to grow this model. While we've landed 750 customers, which is great, it's an amazing base to start from, we have a massive opportunity to grow our reach. We have over 5,000 potential customers worldwide, including many right here in the U.S., that we can go after, and we have the team to go and get them. As we talked about earlier, our commercial team is about 100 people. We also have a very dedicated team of global sales executives. They go out, they wake up in the morning, and they have two goals every day.
What they're looking at is finding new logos to go after, to talk to, and then the ones we're already talking to, moving them through the process, moving them through the sales process, getting them to become a SOPHiA customer. 'Cause what we know is that once they become a SOPHiA customer, we have the expand model to grow this business. As Jurgen talked about earlier, we don't do this alone. We couldn't do this alone. It's too big an opportunity. In addition to our direct sales force, we work with a number of distributors worldwide and tier one partners like GE and Microsoft right here in the U.S. We have a lot of help, and we help each other grow the business. Growing up, and you may have guessed from my accent, I'm not from Basque Country. I'm not from New York either.
You might have guessed I'm from Boston. I grew up as a tortured Red Sox fan. It was tough in the time that I grew up 'cause it had been since 1918 since we had won. You know, until that fateful day here in this fine city when we broke the curse, and everything has been better since then. Growing up as a baseball fan, one of the things that I loved about the game is I loved the statistics. I'm a statistic and a data guy, always have been. I used to, I actually created statistic-driven baseball games as a kid. I was a stat geek.
One of the things I like to look at in baseball is how what happens in baseball can be brought over to sales and life in general. One of the things I always talk to my sales team about, and they probably get sick of hearing this, but it's great to get the big wins and hit the home runs, but the way we grow our business is through hitting a lot of singles, just like you do in baseball. Because we've proven if we hit a single, we can turn them into a home run later with our expand model. Another thing that I like to look at in statistics is KPIs. You know, Jurgen talked a little bit about KPIs earlier.
I'd like to share with you some of the KPIs that we track on the sales side, and the way I like to look at this is through the whole customer journey. At a high level, we break down the customer journey in four steps. We start in the awareness stage, where through our marketing efforts, we go out and we find new customers, we connect with people. Once we have that prospect, we bring them into our sales process, and we run them through our funnel to hopefully get them to the stage where we close and sign a deal. Then we go into an implementation stage, and finally a loyalty stage. Each step along the way, we have certain KPIs that we're monitoring and tracking. These are all designed to do one thing.
It helps us bring those customers through the journey as quickly as possible, get them into routine using our product so that they can be successful and we can be successful. One of the key things that is or a key indicator where you know that you're doing a good job in a customer-centric organization, which SOPHiA is when your customers become your best salespeople. No disrespect to it. We have an amazing sales team, but when our customers become our best salespeople, that's when we know we're doing something right. With that, I'd like to introduce Ana. Now, Ana Gabriela is the Executive Director of LATAM for a large healthcare institution based in São Paulo, Brazil. This company is called Dasa, which some of you may have heard of. Now, Dasa owns 11 hospitals.
They own 1,000 labs throughout Brazil and the rest of Latin America. Rather than listening to me, I'd like to hand it over to Ana and let her tell you a little bit about what her relationship is in working with SOPHiA.
SOPHiA GENETICS was one of our very first partner here in Brazil. Basically, we grow up with the support and drive of SOPHiA GENETICS technology. For that, we almost didn't exist. We rely on SOPHiA to work towards joint genomics innovation. Not by just providing traditional service, you know? Of course, we have implemented routine service like, the cancer panels that help us to incorporate modern and complete panels for our clients that have robust analysis and also helped us to differentiate from other competition here in Brazil. We proactively proposed to SOPHiA to develop the HRD panel in 6 months. This is a very important information from zero. We did not deliver just the product, but we also met the deadline we proposed and become the first one around the world to offer commercially the HRD, except from the U.S.A at the moment.
The panel was totally personalized to our clients and to our needs as well. This was very our best case here in Brazil. What we have discovered that SOPHiA is not just looking for the basic things that others are doing today. We are always looking for the future and seeing what we can do together to be the first one to have the first movement, not only in Brazil, but also in Latin America. Because when you think about SOPHiA, I always think about innovation and how can we construct things for the future.
Since we are working together, I never heard from you like, "No, we can do this." Here in Brazil, we are almost five years behind the United States or Europe, so that's why SOPHiA got to us also a very improvement for our service in Brazil and Latin America, giving us another way to do genetics for our patients, also for the physicians. Yeah, with SOPHiA, we are able to offer the greatest technology and call it at a very affordable price as well. We are also talking about market access for public and also private access. This goes to the way we are going to get more volume and access for all the patients in Brazil, because we have more than 70% of our people that are not in the private system.
I always have been supported by technical and also commercial team, so they are always available and not just to think what we asked for. You are always trying to take us to the future with you.
Great. Well, thank you, Ana. It's been our pleasure working with Dasa as well, and we look forward to growing our relationship. Before I hand the baton over to Peter is our Chief Biopharma Officer, and he's gonna talk about how we're selling into the biopharma market and the tremendous opportunity we have there. I wanna leave you with three takeaways. The first one is that we've, as we've heard from Troy, Jurg and myself, we have a large and growing market, a huge addressable market that continues to get larger. Our proven land and expand strategy is in place, and it allows us to do things like we talked about earlier, which is land a customer with one application, add more applications, and continue to expand the revenue opportunity. Just as importantly, we have a great team in place today.
Our team today, while they are focused, laser-focused on generation one and selling those applications, we have the team and the process ready for whatever comes next, which you're gonna hear a lot about later from our colleagues. Without further ado, I'd like to welcome Peter to the stage and hand it off.
Great. Hello, everyone. Pleasure to be here. Again, as others have said, thank you for taking the time out of your day to come learn a little bit more about SOPHiA GENETICS. You know, I get the privilege to talk to you a little bit more about what we're doing in biopharma. This is an industry that much like Troy, I've grown up in, although not as many years as Troy. Sorry, Troy. I mean, this is an industry that is very near and dear to my heart. I mean, I truly get pleasure at speaking with some of our biopharma partners and, you know, Peter here today from Boundless Bio, Christina from AstraZeneca here today, and just learning about some of the cutting-edge technologies, innovations, therapies that are gonna be coming to the market.
When I first talked to Jurgen, this was about a year ago, you know, and he's telling me all about SOPHiA GENETICS. Now, granted, I had never heard of SOPHiA, right? I've been in this industry for almost 17 years now. He started talking about his vision and what he wanted to accomplish, and the platform, the decentralized platform, the approach to computing data in real time. I'm sitting there, and I'm nodding my head, you know, and the wheels are spinning. Then I said, "Tell me more. What else are you guys doing?" Then he started going into the new cutting-edge technologies that they were developing.
He talked about this HRD solution that you've just heard about, and then you will continue to hear about from Zhenyu, about the beautiful way in which they took a panel to look at mutations in HRD, but then layered on a deep learning algorithm. This is. You know, these are pretty innovative approaches, right? Then on top of that, was able to deploy it via the decentralized platform. I'm sitting there, and I'm thinking, so you're like a diagnostic company, but much more than that. You can develop smart tests, but then you could also deploy in record time. Oh, and by the way, you're capturing all this data, real-time, real-world, and you can bring this back to the pharma so that they could have better insights for decision-making. It was at that point I said, "Where do I sign?" Really?
I mean, this is how powerful this was for me, right? Because I could just see it. A little bit about myself. I mean, again, I've grown up in this industry. I started at Merck Research Laboratories. I was a lab geek, you know, with the code, developing and validating new assays that were used in research. They were used in translational, they were used in clinical trials. Soon after finalizing my MBA, I then moved on to some of the larger CROs out there, like Labcorp and NeoGenomics, others that were working with biopharma customers, not so much on data, but focused on the biomarkers, right? This is. We're kind of entering this area of an explosion of biomarkers and, in particular, companion diagnostics.
When I was at some of these companies, this is where, you know, you've heard some pretty breakthrough medicines like KEYTRUDA and OPDIVO, you know, targeting PD-1, PD-L1 therapies. Again, these were biomarkers tests and solutions that were really targeting a single biomarker, right? A single assay. What I started to see as the years went on is that the industry is starting to move beyond that, right? It's not just what's the next biomarker, but the set of biomarkers, gene signatures, algorithms. This is the future. This is how pharma is now looking to understand how to stratify and select their patients that are gonna be most targeted for their therapies.
That's when I moved into some other companies like Tempus and CellCarta that were looking at, again, a little bit more beyond the single biomarker and looking at multimodality, right? Now as I've heard Jurgi talk about the importance of multimodality, the importance of this collective intelligence, generating all this data, real-time, real-world, powered by AI and machine learning. You've heard Ken talk a little bit more about this land and expand strategy, right? We're landing new logos, new institutions in real world, but then we have all this white space to go after to apply new applications, generate new usage, right? This is all the global data that is gonna be the fuel that ignites what we do in the biopharma community. Look, the industry has been fantastic, right?
I mean, many of us are sitting here, whether it's you personally or loved ones that you have, where some of these innovative medicines have saved the lives of our loved ones, right? I mean, you cannot put that in question. It's not without its availability for improvement, right? I mean, there is room to improve here. This is where I believe data truly is gonna play a role. I mean, just look at some of these stats, right? It's still 10+ years to go from IND to approval. 80% of clinical trials still fail to meet enrollment timelines. We're not finding these patients. We're not testing enough, right? It's all the same things you've been hearing about. Look at the cost, $2 billion average cost for drug development.
To me, while data I don't think is the answer to all of this, it is gonna help. It will move the needle. It will move several basis points. That could be the difference of getting a drug to market much quicker and saving a loved one. That's the importance that data has and the importance of a company like SOPHiA GENETICS, right? It's more just about data. There's a lot of data out there. There's a lot of noise. Not all data is created equal, not all is high quality, right? What the core competencies that SOPHiA GENETICS brings, and again, I just talked about them, the global footprint, this global clinical network, this ability to compute in real-time, in real world. That is a major differentiator of SOPHiA GENETICS. We are actually using the source data, right?
We are not just tapping into EMR systems. We are generating source data here, and it's multimodal data now. As we move into generation two, it's beyond genomics. It's radiomics, it's clinical data, it's biologic data, and the future is gonna be proteomic data, digital pathology. There's other omics, other modalities that as they become more and more into the clinical routine space, we now have this platform that can be deployed, and we can compute in real time. All of this is gonna be important to our pharma colleagues as they look for those insights to better target their patients and their therapies. I, you know, keep going back to the original conversation I had with Jurgi around what SOPHiA was doing. When we talked, you know, it was still.
We've got all this great stuff we're doing in the clinical space, but how do we crack pharma? You know, Jurgi was determined. He's like, "I know what we're doing has value. How do we get into the pharma market because I believe they can truly benefit?" Of course it was a no-brainer for me. I know there are pharma companies out there. You know, there's a lot of good real-world data players as well. Again, when you're talking about the source data, where we can then take that data, we can do new development, and we can deploy in real time, real world, we have something special, right? When I got with my team when I first arrived here back in January, this is the three pillars that we believe are.
This is really our own land and expand strategy, if you will, right? Because we're going after stakeholders within biopharma companies where we can do certain data projects. We can look at things like biomarker positivity rates, prevalence, incidence, co-mutational analysis. These are all things that are very important as you're doing trial design and drug discovery. We can do projects that are all founded on data, and now with multimodality. We can develop new solutions, new genomic solutions, new algorithms leveraging on that data. Of course, we have the platform to deploy. This is where, you know, from an addressable market standpoint, if you look at our catalyst for growth, this is the framework for us, right? The data, the development, the deployment, these are not just silos, right? There's an interplay here. One feeds the other, right?
This is the land and expand strategy, similar to what Ken mentioned, that we use, that my team uses as we start to engage with our biopharma customers. Because ultimately, you know, it's very hard to just pitch everything, right? We wanna get to that point where they see us as a holistic solution, but we have to start somewhere. Whether it's data needs that these customers might have, again, or development needs, like with Boundless Bio that I'll talk about, or deployment of solutions, we have these buckets and we can execute on them. We bring it back now to MSK. Again, we have some of our colleagues here from MSK. Thanks for joining, guys. This is. You know, when we first started talking with MSK, you know, I...
It's hard for me to contain my excitement, right? I mean, I'm not from New York. You can probably tell from my accent I'm a Philly boy, but you know, lived in New York for six years. Lots of respect for Memorial Sloan Kettering. When we first started talking to them about their vision, right? We have Todd here who mentioned they wanted more columns, more rows, right? That was the mantra. How do we get access to more data? MSK is doing fantastic things. In order for them to prove out, similar to what SOPHiA GENETICS wants to do to prove out our models, they need access to more data. That was the beauty of the partnership. That was our North Star.
We knew we had something and we were aligned, and now how do we get it there, right? What we're first gonna start is with their clinical genomic database. This is a large database, tens of thousands of patients that have been tested with their MSK-IMPACT assay and other genomic solutions that they have at MSK, but also has the rich, high-quality data, clinical data, right, biologic data, other radiomic images, multimodal data that we are gonna use that is gonna then accelerate and power our CarePath module to look at these patients, follow them longitudinally. In order to do that and accelerate that, we're gonna take some of their key assets, some of their key genomic solutions like MSK-IMPACT, and we're gonna offer that out globally to the world. That then is gonna feed the data back into our platform.
You can see this beautiful virtuous cycle happening, right? This is what we wanna do similar with other large academic centers, right? To pour their data into this collective intelligence, and let's leverage it so we can use with both academia but also with biopharma, right? To treat the patients of tomorrow by looking at the patients' data of today. Again, Peter from Boundless Bio here. You know, this is another very aligned relationship, right? Boundless Bio is a company that's really on the cutting edge looking at extrachromosomal DNA as a target, right? So ecDNA-targeted therapeutics. But the interesting part is that they've developed a prototype algorithm, you know, a very exquisite algorithm to look at this ecDNA. When we started to talk to Boundless Bio about what their vision was, right? They wanted to have the.
They basically wanted this algorithm not on a single test, but to apply it across multiple different NGS technologies. That is where SOPHiA GENETICS comes in. This is the beauty of a platform approach, 'cause as you will hear Zhenyu talk a little bit later today about how, you know, we've computed now over 1 million profiles, but that's across a number of NGS technologies and chemistries, right? We're leveraging and harnessing all that data. We can then take that prototype from Boundless Bio. And we can further optimize it for them, right? We can get it working on the platform so that we can now apply it to multiple NGS technologies, and this can be leveraged around the world, right? That's the ultimate goal, to get more access to more patients. Then the third bucket, the deployment, right?
We talked about data with MSK and how that also spills into some of the other Ds. We talked about the development aspect with Boundless, and then there's deployment, right? Again, these are the things that are really empowering our conversations with key stakeholders within biopharma. We had a press release earlier this year with AstraZeneca about how we're working with them to deploy local HRD testing solutions within Europe, right? This is important, right? Because these are locally delivered HRD testing. You don't have to do a send-out to a centralized lab, right? You save turnaround times there. You get results back to the oncologist to the patient quicker, and it saves costs, right? These are two important factors that a decentralized platform can allow.
On top of it, those institutions get to keep their data in a privacy-preserving manner. Very, very important. This increased range of HRD detection, you know, this full data control, we're now looking with other CROs and other lab partners to extend HRD testing solutions beyond Europe and into LATAM, into the US, right? Again, it's just another model of how we can take a very exquisite genomic solution that was developed, this one in-house by SOPHiA GENETICS, and deploy it on the platform so that folks around the world can leverage it. This is the plan, and this is how we're discussing now other types of solutions similar to Boundless, where we can take that and deploy. You know, the...
Again, the ultimate goal here for us is to just be on that data strategy roadmap with our biopharma customers, right? We are not trying to be the end-all be-all. Right? I mean, there's some really good companies out there, and I think we're very complementary to what they're doing in the real-world data space and in the solutions space, right? Again, we're not trying to be the one-stop shop. It's a cliché I hear way too often, and I've never been a fan of it. The important part for us is that the way the platform sits today, what we're doing in looking at multiple NGS technologies, computing this data globally, global footprint, real world, real time, then disseminating that information, that data back to our customers.
We are well-positioned to be integrated into many of these biopharma customers to help them gain the insights that they need. These are just a few testimonials that really kind of prove this out, right? You know, we've redacted some of the names, of course, but if you just look at some of the testimonials here, right? We're on the data strategy roadmap. Our global footprint, the universal approach, we're looked at as one of the true holistic providers out there. I mean, these are important testimonials that, you know, what we're doing, we're on the right track. Have we figured out everything? Is there a perfect product-market fit? No. Right? We evolve, we're flexible, we're learning from our customers, and this is a big part of what my team is doing today, right?
Out in the field, talking to customers, finding out what their needs are. We have solutions currently, but they might need to be adapted, right? Or we might need to get different datasets for them, different indications that they can't currently get their hands on. Because of our global network and our ability to work with source data and with these key opinion leaders, because we're not just tapping into EMRs, this is very, very important. You know, from a KPI standpoint, right, similar to Ken, how is the team doing? You can see the traction is there. We are building the momentum. 2022 isn't over yet, but you can see the large increase just in pipeline opportunities. Customer meetings, you know, because we're looking at this land and expand strategy, has exploded.
Our booking projections are very, very healthy. Right? We're gonna have a fantastic year this year, so shout out to my team back at home. It's interesting, right? Because we've gone from the awareness, customer meetings and ideation, and now we're into delivery and execution. Really, you know, this is just the tip of the iceberg for us. I mean, for me, a picture speaks a thousand words, and that's exactly where we are today with the biopharma business unit. Again, still refining that product-market fit. We have fantastic offerings today. With our customers and that voice of customer, we're learning, right? We're learning what else they need.
Now we're starting to have conversations around things like real-world comparator arms, pragmatic clinical trials, how we can leverage the clinical data and the genomic data to better match patients in a trial match service. Again, lots of opportunity here, lots of excitement. Some of our key takeaways, right? Informed strategy and targeting, much improved. Our 3D, you know, mantra that Jurgi mentioned, this is at the core of what we're doing in biopharma, the 3Ds, the development, the deployment, and then leveraging on that data. Then really positioning ourselves. To me, multimodality is the future, right? We are going beyond single biomarker, single test. We are looking at other modalities. We are building predictive algorithms, as you will hear from Philippe about CarePath and the multimodal future.
Similar to MSK, right, what they're doing with the Mind Initiative. This is the future, and we are positioned very well for it. With that, I will turn it over to Jennifer.
Thanks so much, Peter. Right now we're gonna have a 15-minute break. We invite everyone to go out in the other room. We have light bites and refreshments. We'll see you soon.
Hi, guys. Welcome back. For the first part of our program, you learned about who we are and how we sell. The second part is gonna be how we innovate and grow sustainably. Without further ado, I'd like to welcome on stage Dr. Philippe Menu.
Good afternoon, everyone. I hope you are all appropriately caffeinated to get us through the second half of the event. It's a pleasure to be here. My name is Philippe Menu. I'm Chief Medical Officer here at SOPHiA GENETICS. My background is I'm a medical doctor by training. I have an MD PhD, molecular biology of mutant cancer, got an MBA, and I spent about 10 years at McKinsey & Company. While I was at McKinsey & Company, I ended up co-leading the Global McKinsey Cancer Center, which gave me a unique opportunity to consult top biopharma CEOs and C-level executives on a wide range of oncology topics. Around 2015, they all started having the same question, which was, How do you think about data and analytics in oncology?
Because it was very clear then, just as it is now, that data analytics had the potential to complete returns from oncology and the healthcare space at large. As a consultant, you like to have these questions that have no easy answers, right? Because essentially, while the promise was clear, the challenges were clear as well, and those are mostly infrastructure-related challenges in a sense that we're facing data silos across instruments, across institutions, lack of data standardization, lack of knowledge sharing. You have a whole raft of issues to actually solve. When I met SOPHiA and I came across the model, it was very clear, very easy to understand, and this had the potential to be a huge part of the solution.
As a small story, I remember sitting during my interview process in Jurgi's garden, 2019, and he tells me the vision, made the pitch that he gave, just gave you before. As a good consultant, what do I do? I take out a piece of paper, start writing a slide about the model. Okay, you should take this data, that data, notional view, inform oncologist decision-making, biopharma potential. At some point, I, you know, I raise my eyes, and he's looking at me a bit weirdly, nodding, smiling, takes a laptop, opens PowerPoint, and shows me a slide of 2011, right? Showing me the exact same model. Why am I telling you this story? First, I take credit that my slide was better looking than his, honestly. Second, I do give him credit for having the idea 10 years before me.
The wider point is everything that you hear about SOPHiA today has been 10 years in the making, right? That was the vision all along. The design of SOPHiA has always been data-driven. You've heard earlier today from Jurgi about the vision for the platform for the company. You heard from Ken how we plan expanding our customer base, and you've heard from Peter how we think about the biopharma market potential. My focus today is gonna be around the global multimodal collective intelligence we're building and how we think about the potential that brings us going forward. Let's start with a step back. What happened in oncology over the past 20 years? There's been one fundamental shift, which has been the move from an organ-level disease view to a focus on molecular alterations. What does that mean?
Given our increasing knowledge of genomics, we've been able to move from advanced lung cancer as one disease in one organ 20 years ago, where everybody would get chemotherapy, to a stage today where you have dozens of lung cancer subtypes, all of them with their different therapeutic options. This has been enabled by a great wave of innovations, both on the diagnostic side and on the therapeutic side as well. If you look at the diagnostic side, we come from a world where 20 years ago, we're looking at a single biomarker in a single disease area to today, where we're looking multigene NGS sequencing, looking at complex biomarkers such as HRD, TMB, and MSI. You can see that the complexity has grown dramatically on the diagnostic side. On the therapeutic side, the same story has unfolded.
We started 20 years ago with the first targeted therapy for one specific biomarker in one disease to today, where we have several tumor diagnostic labels from the FDA, where you don't really care anymore what the organ is. You're looking for a specific molecular alteration across a range of different cancers. We can see again that both on the diagnostic side and on the therapeutic side, great innovations, but also increased complexity throughout. Where does that leave us in terms of patient outcomes, which arguably is the only metric that actually matters? The good news is we're making progress. This slide that you show here shows you the five-year survival rate for a range of different cancers in the U.S.
You can see the right-hand side of the arrow gives you the value in around 1970s, and the left side of the arrow gives you the value in the early 2010s. You can see that generally speaking, the trend goes in the right direction towards the right. We have better patient outcomes, which is what we all want. You can even see that if you go to the top right of that slide, prostate cancer, for example. Today, arguably the vast majority of patients will die with prostate cancer rather than of prostate cancer. At the same time, we need to stay very humble because if you look at the bottom left of that slide, look at lung cancer, pancreatic cancer, brain cancer.
All of these still have a very dismal prognosis, unfortunately, sometimes with five-year survival rates that are in the single-digit range. The message is great progress, but huge amount of unmet medical need remaining. Where does that leave us today? Today, we face a very simple reality, which is likely every single cancer is different. This is the picture for lung cancer, but you could say the same story for any type of different cancers. It shows you the mutational landscape of lung cancer. What you can see is that driven by clinical genomics, we're essentially turning lung cancer into a collection of rare diseases. You can see the slivers of patient subpopulations, 1%, below 1%, so rarer and rarer mutations that are increasingly more complex to detect as well because these are gene fusions, they're exon-skipping mutations. Challenging situation there.
That gives you a view of the complexity, but it's just the tip of the iceberg to go back to Peter's illustration, because today we're not looking at that level anymore. We're one level below, which is looking at different specific variants. We're looking at KRAS mutations. We're looking at KRAS G12C. We're looking at co-mutations. We're looking at KRAS, STK11, TP53 mutations. We're looking then at how this mutational landscape evolves over time in the context of the interplay between the tumor heterogeneity on one side and the treatment that you throw at that tumor on the other side.
Clearly, for me, that illustrates the point about why SOPHiA GENETICS, and specifically Gen 1, is so critical here because you need to have the confidence that you can pick the signal from the noise with high accuracy and that you can detect these increasingly rare and challenging mutations. Otherwise, essentially, you forego a chance of better outcomes. Now, you would think that the diagnostic complexity was high enough, but the problem is it's true also on the therapeutic side. Previous slide was all about how lung cancer is, you know, full of oncogene-driving mutations. The reality is that it's only at the end of the day, about 30% of those patients. The vast majority would be candidate for immunotherapy today because there's no oncogene-driving mutations. You would think that that choice of treatment is actually simple. It's actually not the case.
An oncologist today is facing that other level of complexity, which is even if I don't have a mutation, I have about a dozen options to treat that patient. Now, in reality today, what will happen is that oncologists would only ever prescribe a couple of these options. Why? For a very simple reason. We don't have the real world data to make sense of which patient would benefit from which treatment. Comes down fundamentally to a simple equation, which is today what we are doing to make it very simple, is statistical medicine. We're looking at randomized controlled trials where we have treatment A, we have a treatment B, we compare them. Treatment A shows better outcomes, therefore everybody gets treatment A.
Our vision is you should rather start from the patient, and the patient is faced with options on the therapy side, and you need to be able to match this patient to the right option for the patient. You can deal with both the diagnostic side and the therapeutic side. Today, the oncologist is overwhelmed by complexity. I think that's where SOPHiA Gen 2 enters, because this is already solving a lot of these issues and reducing a lot of the complexity by being able to bring different data modalities together in a single environment to facilitate decision making. Building a collective intelligence on a global scale, that's really, really important. Why? Because I showed you that some of these cancers are rare diseases, right?
In a single institution, your average hospital, you might see only literally a handful to one, maybe none of these patients on a yearly basis. Whenever you see one of these patients, you want to be able to learn what other patients have gone through, that are similar. Underpinning all of this is the algorithmic capabilities that we have that give you the confidence that we have high accuracy in picking the signal from the noise. What does that look like in practice? Let's take an example, and we go back to lung cancer and immunotherapy specifically. You all know that immunotherapy has revolutionized the cancer management, right? That's true in lung cancer, it's true in many other tumor types. In lung cancer, specifically today, even in the metastatic stage, immunotherapy has the promise to offer cures for small severe patients.
I mean, if you just stop here for a second, that was science fiction five years ago, right? Here we have a truly unique opportunity to transform outcomes for patients. Now, the issue is the vast majority of these patients will be non-responders, but you do expose them to potentially severe side effects. Some of them may be even lethal. There's a very clear financial toxicity for our care system. Some of these therapies, order of magnitude, cost $100,000 per patient per year.
To top that off, we have no biomarkers to make informed decision at a patient level, saying, "You should get immunotherapy and you shouldn't." If you take a step back, you're faced with a situation where you have a fantastic tool and you absolutely want the right patient that would benefit to get them, because then you can potentially have them cure. But you're faced with the prospect of potentially exposing tons of patients to inadequate therapies, and you would like to have these patients put on other therapies that could give them better outcomes chances. Our belief is really that you need to move from a statistical medicine to an individualized medicine.
The only way to do that today is through multimodal data analytics, which means putting the genomic information of the tumor in the context of other phenotypic information, in the context of a global collective intelligence, to understand what works and what doesn't for patients that look like the patient in front of you. Now, luckily for us, the data that we need is everywhere. This is an illustrative patient journey for lung cancer today, from diagnosis to advanced lines of treatment. You can see that on a daily basis, we're producing an avalanche of clinical data in digital format today. Clinical data imaging. We have our colleagues from GE HealthCare here. I mean, imagine the number of millions of CT scans, MRIs, PET scans are introduced every year in the world for the cancer management. We have pathology data, we have clinical data, genomics and lab data.
All of this data exists here. What are we doing in practice? Jurgi mentioned this in his introduction. The DEEP-Lung-IV initiative that we are pioneering, it's exactly the same idea. We basically take the data I showed you on the previous slide, genomics, put that in the context of other phenotypic information such as imaging, clinical and lab data. Train machine learning algorithms to learn from that data in their real life and be able to stratify patients for likelihood outcomes. Now, you're sitting here listening to this and you're saying to yourself, "I've heard that story before." Of course you have. You've seen that at ASCO probably, right? You've seen that at the level of single center institutions, right? One academic center with maybe 200 patients, very limited data diversity exposure.
The point is, anybody can do this, but if you want to prove the clinical utility of these models, the only way is to go big, right? You have to go big, which means lots of patients. We're following about 4,000 patients across the world notionally. It means going big on data diversity. We're targeting about 30 centers across 10 countries to be exposed to as much data diversity as possible so that we don't end up having a model that's trained in San Francisco, of which we're not really sure what the performance would be in Paris or somewhere else. Now, even if you think about all of these single center data that are being produced, which are very valuable, how do you think about deployment? Wouldn't you need something like, say, decentralized tech platform with a global footprint to make that happen?
I think that's where we can also help. This study, which is currently ongoing, we've been very humbled, honestly, by the reception of different sites. Jurgi mentioned only in the U.S. Mayo Clinic joined the fray over the summer, joining about half a dozen competitive cancer centers. We have other top-tier institutions from across the world, France, Germany, Spain, Italy, Israel, Canada, two more Brazil. We have discussions in India and Japan as well to broaden the footprint of the study. Also for us, very important to see is that we are very promising early data. Which is early data of course, but we see about 80% predictive power, which makes us think that the vision makes sense and is achievable. Obviously, we have to confirm this data in larger data sets in the validation stage. Now we've been speaking a lot about lung cancer, right?
I want to leave you with the message that this is the tip of the iceberg once more. We have about a dozen different RNA programs in flight. I've here highlighted the five key ones that are the front. Beyond lung cancer, we're looking at breast, kidney, brain, and colorectal cancer. You might wonder, well, why these five and not five others? The answer is because we really prioritize our efforts, and we typically prioritize them on three type of metrics. Number one, patient outcomes, so medical and met need. Number two, health economics discussions regarding the sustainability aspect of healthcare systems. Number three, biopharma market potential.
If you look at these five indications together, roughly speaking, as an order of magnitude, they represent about 50% of new cancer cases in the U.S. on an annual basis, 50% of the deaths as well, and about $60 billion in combined pharma market spend. The idea is, as we develop these different models, the patient stratification and predictive modules, this has the potential to fuel new applications into our SOPHiA CarePath module of the platform. You've heard about this wonderful deal and strategic partnership with MSK. This is gonna be exactly going in the right direction to accelerate our efforts in this field. That offers me a very nice segue into SOPHiA CarePath itself. Jurgi showed you the key building blocks, data visualization, cohorting, and prediction.
Data visualization is all about breaking silos across data modalities and bringing to an oncologist the full patient picture. The cohorting function is all about placing that specific patient in the context of similar patients that we've seen across the network, so we can learn from real experience from other centers. The predictive analytics is about, as we accrue more data in the real world, training machine learning algorithm that can help decipher multiple signatures that are predictive of response to the level of individual patient prediction modules. Obviously, that last part is tied to specific regulatory considerations. I'd like to wrap up my talk by giving you a quick snapshot of SOPHiA CarePath today. If you look at the visualization part, you can see here data from our DEEP-Lung-IV initiative.
We can see a specific patient, so you can navigate the view. You can see this is an advanced cancer patient with one liver metastasis, PD-1 at 50%. You are able to navigate the events. Here we've loaded onto the database a strong dozen events across the modalities, which you can navigate, of course, by date. You can also look at different imaging modalities, for example. Here I'll show you what the radiomics platform looks like. This is this same patient. You see the tumor that's been segmented on the right-hand side, so the lung on the left. You can recreate that segmentation in 3D, and really what you've done is you focused with the algorithms, the image specifically on the tumor. What we'll do next is we'll extract radiomics features, which are about 200 radiomics features that we routinely extract.
To make it very simple to understand, we turn the image into data. Data that basically look at the morphology, the heterogeneity of the tumor, and really informs of what you're seeing in a data-driven way. We mentioned before this patient has a liver metastasis, so of course, you can also segment the tumor here in the liver. What you create then is a longitudinal view of the tumor burden for that specific patient, both the thoracic tumor and the liver tumor. These are four different data points along the patient journey, so four images for the same patient. Again, you can extract the features, you can start plotting then the radiomics indicators on a timeline, and you can really start tracking for each lesion at the individual lesion level in a data-driven way what happens over time.
If you go back now to the visualization stage, you can obviously navigate all of the other events for this patient, including the genomics, where you would be able to access all of the different variant information. You can get access to treatment plan, number of doses, toxicity. You see that this patient was treated with pembrolizumab and chemotherapy combination, having a PD-1 at 50%. You can look at the medical history, you can navigate the blood analysis, essentially all of the relevant data points that the physician wants to have access to. On the cohorting side now, I'll start with a very basic view, either from your institution or for the full cohort across the network.
You can place your patient in the context of other patients, whether it's on response metrics, subtype metrics, and just understand how the patient fits into the picture. That's a basic view. Now, the most interesting part for us is the smart cohorting feature, where you start asking CarePath to generate your own cohorts, and you can literally tailor the cohorts to the patient that's in front of you. Now I will show you a very simple and obvious example. We'll just ask the platform to do a cohort that looks at clinical response at first evaluation, so three months after treatment start with immunotherapy. Then we'll ask what's different about patients that have had a complete response or a partial response, so an objective response to the therapy, and how do they differ from the rest of the network cohort.
What you see basically on the left-hand side, you have the PFS Kaplan-Meier curve. You can ask the platform to produce OS, you know, metastatic load or burden, evolution over time. Then you have the statistical breakdown on the right-hand side that allows you to understand what's different about your cohort versus the rest of the patients. Remember that you'll be able to slice and dice all of the different categories of data. In the predictive modules, this is obviously in flight and being validated in studies such as DEEP-Lung-IV. The vision is to be able to give a percentage likelihood range, if you want, for a specific outcome of interest, and most importantly, to give the explainability.
You see here the local explainability chart that really allow the oncologist to understand what is driving the model, because the key for us is really to have interpretable modules that people can understand and track. I hope this gave you a good overview what SOPHiA CarePath is. Remember, this is one example with lung cancer, but in the end, we will fuel this platform with many more use cases and applications. I'd like to leave you with three key takeaways. Number one, our vision of moving from statistical to truly individualized medicine. Number two, how this requires us to break data silos across instruments, across institutions, and how our platform is uniquely positioned to help do this. Three, really the value and importance of building a truly global and multimodal intelligence. With that, I thank you for your attention.
I would like to introduce now my colleagues, Abhimanyu Verma and Zhenyu Xu, who will tell you about how we develop and deploy our platform in practice every day. Thank you.
Thank you, Philippe.
Good afternoon, everyone. Zhenyu and I will talk about how we build and operate the platform of the future. Just to briefly introduce ourselves, and recognize many faces from my country of origin. Abhimanyu is the first name. Abhi, it's easier to pronounce. I call myself a healthcare technologist, and I've been with the industry for 18 years, previously with Novartis, where I joined Novartis after finishing my MBA at the Indian School of Business. Along the way, I did a Master's in Science and Pharmaceutical Medicine.
At Novartis, I played various roles, obviously, with such a long tenure, but a lot of that was focused on the technology charge, on real-world data, real-world evidence, really defining what the strategies are and the technologies needed to do that, the standardization of data and applying AI and new technologies in those contexts. From that frontline experiences, it was clear to me that the future of healthcare and medicine is going to be leveraging the power and the information and data. Whether it's in the context of biopharma or in the context of healthcare systems. That goes beyond just the IP boundary that you would find, right, in a typical biopharma model, which is more chemistry and biology-driven, right?
Which is what started my personal journey in search to be in the driving seat of this journey towards personalized medicine that Philippe so nicely described, and this notion of right therapy at the right time and the right dose. That's what brought me to SOPHiA. I think it was two years of conversations and a lot of discussion and finding the right spot. I'm super excited that I joined SOPHiA s the chief technology officer, and I'm very proud to lead the team that engineers and operates the SOPHiA DDM platform. Today, along with Zhenyu, we'll be telling you all about what goes on under the hood and how we do it. Zhenyu.
Yeah. Hi, everyone. My name is Zhenyu Xu. I'm the Chief Scientific Officer. I'm leading the data science department in SOPHiA GENETICS. I graduated from University of Cambridge, so I did my master's there, and I did my PhD at EMBL, so Heidelberg University, with Dr. Lars Steinmetz, who is also sitting here. Hi, Lars. I'm the second employee of SOPHiA GENETICS. I joined SOPHiA GENETICS almost like 10 years and seen basically the growth of the whole company. I'll tell you later about what we do at the algorithm level.
I'm going to keep the conversation at a fairly high level, right? While giving you enough detail, I'm gonna use a little bit of a hook here, right? That is, imagine a high-performant electric race car, right? It's not even built. It's getting built. It's there, right? There's some characteristics around it, right? Obviously, it brings together innovation from multiple disciplines. It is precise, highly performant, it has to be safe, adaptable to the different race conditions and leads in the race pack. Those are the attributes that characterize the SOPHiA DDM platform. Delving a little deeper as to what that means.
The SOPHiA DDM platform, in its essence, has a very modular architecture, which allows extensibility and scalability across different data modalities, across different features and functionalities and applications, as we go across different customers, different geographies, different disease areas. From a technology stack perspective, we use a number of technologies listed there, which, for those of you who are familiar with the tech platforms, these are fairly, I would almost say, leading and standard. A combination of open source as well as commercial technologies. That blend works very nicely for us because we are able to leverage the power of innovation coming from the global tech community via the open source movement, as well as leverage the commercial side. Platform, as you've heard, is obviously cloud-based.
While we get the advantages of cloud in terms of extensibility and scalability, that global deployment allows us to process the data in region where the customers are in a very privacy-preserving way, like Peter also mentioned. Which allows us to be compliant, right? Then we are able to bring this together in a collective way by anonymizing the data or using semantic technologies and federated data querying to create that knowledge network, that collective intelligence, which then creates that real-world, real-time insights for our customers, whether they're on the clinical side or they're on the biopharma side. Coming from biopharma, that is rich, 'cause any real world data that pharma has access to today is six months to a year old, right? At a minimum, if not older.
All of this comes together, of course, to power the algorithms that Zhenyu and team develop and we help productionize. Of course, that leverages some leading frameworks around AI, ML like TensorFlow, Keras, and UNITER. We take the responsibility that our customers place on us as our primary duty of care because this is, at the end of the day, healthcare data. Towards which, and to help us on that, we have an independent and a very strong regulatory and compliance function, which keeps us on our toes, and we have regular audits. We are GDPR and HIPAA compliant. We follow ISO 27001 guidelines. Recently we just achieved IVD certification for the analytical modules on our platform, which means we can start to do diagnostic applications.
That also sets the platform for more powerful engagements and collaborations with biopharma. Now, the oil which powers this car and the engine is the data. We all understand that, right? You heard Philippe talk about how the future of healthcare is around the aspect of our multimodal data and how that is leveraged. Now, all of that is possible, and you saw a good example of that in CarePath, but the underlying mechanisms, right? The platform supports those, right? The architecture and the processes and the structures we have support those. To be able to capture and ingest this massive amounts of multimodal data capture from multimodal data, so like we're processing terabytes of data every day, organizing that, cataloging it, organizing it, making it what.
You know, for those of you who come from the data management space understand this FAIR principles, right? Findable, accessible, interpretable, and reusable. How we are able to use that organized data which also means filling the gaps and imputing where the holes in the data are and bringing that together in this decentralized way in this peer network which powers the multimodal insights. You saw a good example with CarePath, but then also this collective intelligence. You've heard this phrase, collective intelligence, a few times now. What does that really mean, right? Let me give you a very concrete example, even in the context of what we've been referring to as Gen 1, right?
In SOPHiA DDM today, you have what we call the peer network, which essentially is a feature which allows the user of the platform to be able to share their insights with the rest of the community, which means they're able to benefit and contribute to the larger insights network, but then also they benefit from it, right? You can imagine a physician sitting in, I don't know, Mumbai, right? Or Hyderabad or Delhi, I had to pick those cities, is able to leverage the insights that maybe a patient in Brazil who came into a Dasa lab, right? Which is so powerful, right? It's just from a patient outcomes perspective.
Till date, and some stats and information over there, till date, we have 400,000+ and growing community insights that have been generated on the platform across the 1 million analyses, and this is just the genomic space. Right? The more interesting piece of this is 19% of these flagged community insights that are in the platform today, that information is not available in public databases. Right? Just some proof points and key points that you wanna take away with. Moving on, and a little bit on the how, right? That was what we do, and now this is how the team operates, right? The team, right, our teams and everyone in the company, right, we have this mindset of rapid innovation and continuous improvement.
You heard a lot about product market fit, talking to customers, finding where that fits, taking continuous feedback. That's what we do day in and day out, right? We deploy, we learn, we adapt, and we follow this two-week cycle over and over again, day in and day out, right? We do this not by employing an army of hundreds of thousands of people, right? To do this. This is highly automated, and boss loves us for this, for the automation and the cost efficiency that this brings. Of course, following leading DevOps practices to bring together those enhanced features and analytical functionalities that come together, right? Now, for someone who's been in healthcare and technology and healthcare for roughly two decades, I mean, ever since I had my hair, this is really state-of-the-art. It is not easy.
In that context, it is not easy. The regulatory expectations are high, the compliance expectations are high, your own expectations are high, right? To be able to crack that code.
Yeah.
Getting that degree of automation is remarkable, yeah? I'm extremely proud of the way we operate and the effort the teams puts in to have this cadence. Now all of this is good, but for what, right? At the heart of it is our race car driver, right? Our customer. This customer-centric approach is also how we have created the platform, how we operate the platform. Which also means that it is easy to deploy and easy to use. What does easy to deploy mean, right? Healthcare workflows are extremely complex. We all understand that, right? Introduce any new technology inside healthcare, come interoperability challenges, landing challenges, network challenges, security challenges, and deployments can go on from six months to a year. I'm not even talking about the
This is just on the tech infrastructure side, right? This is not even on the data integration side. It's not even about the analytical performance and trying that out, right? The platform from the get-go allows for this seamless integration. It's designed to operate in the context of our customers' environments. From the time we sign and the customer is ready to go, it's a matter of hours and days before they're ready to, you know, click and go. The user interface intuitive, very intuitive and I've been fortunate enough to be in the last two weeks going around the U.S. speaking to customers directly and gaining feedback and every one of them said that they find the user interface is a pleasure to use, right?
The reason we are able to do that is a lot of the team comes from that very own customer experiences, that those were their previous lives before they joined SOPHiA. They're able to bring that experience forward, and we apply our modern engineering and dev practices to blend that together to create that interface. You heard briefly about this independence of instruments and chemistries, which honestly is the secret sauce. Zhenyu will get into much more detail on that. Purely from a business perspective, what it means is our customers are able to capitalize all their CapEx investments that they made on all these instruments and all the workflows and all the validation that they've done, and they can just go, right? Without getting locked in and get the high analytical performance that they look for.
All of this comes together to create this high-quality targeted insights, right? Of course, the reports are compliant to various regulatory standards that are there around the world. To round off, we are in a pole position, and the reason we are in a pole position is because of the disciplined execution of these fundamentals that I just walked you through. That is how we are innovating across the different aspects of our platform, how we bring the different technologies together, the scalability, how we operate, our continuous improvement mindset, and of course, our customer centricity and having that always at the heart of everything that we design and build. You . some testimonials and you'll hear more about that, right?
I will now hand over to Zhenyu, who will talk to you in depth about the engine of the car, the core of the platform. Over to you, Zhenyu.
Thanks, Abhi. Okay. Before I start, I'd like to introduce a bit, again, myself. This is the world I came from. I build algorithms to enable scientific discoveries. In the PhD, my major focus is more on understanding the mechanism of transcription, and that has lead to, back-to-back publication in Nature. The tools that I have built also enabled a lot of researchers around the world to study the mechanism of transcription. You can see, that's also resulting in quite fruitful publications. At SOPHiA, what I did is something similar. I bring the recipes and industrialize them, and hopefully, this can be beneficial to all the people around the world in a much more scalable way. In every platform, what is driving the adoption is its core.
Like at Google, the core is the search engine. At SOPHiA GENETICS, the core is the algorithms that we industrialized that powers our platform. We have enabled, or we have built the engine that powers or drives the adoption of our system. We also impacted the computed profiles also have an impact of more than 1 million patients. Beyond this impact, the technology that we have established in different fields, like in genomics, radiomics or multimodal, have also enabled our customers or the collaborators to build driving their own research, as you can see. The publication is also increasing every year.
Today, I'm gonna give you a concrete example of taking genomics as one of the modalities, and to tell you how our technology could benefit or influence the treatment of cancer. Cancer itself, it is actually not a long time ago, people start to understand that this is a genomic disease. External factors like environmental or even virus infections or internal factors like your hereditary history, these can all have an impact on your genome. When the damage accumulates, at some point, it leads to cancer. Mutation identification or understanding the damage of those factors towards your genome become more and more important. Because this has a huge value around prognostic or therapeutic or diagnostic manner.
Mutation identification is actually not easy because you identify a stretch of letters that are different in the sea of billions of DNA letters, right? There's already a lot of literature showing that mutation identification is not that straightforward. Taking one example in this JCO Precision Oncology publication. The author shows that if you send one sample to different labs around the world and you get the results back, the results are actually not consistent. Another paper that published very recently in Nature Biotechnology also highlighting the same or similar problems. They all point to something that is very important, that the technology or the combination of different technical factors are the major source of assay discordance. I will just take you to some concrete examples to expose you to what actually it means.
A typical genomics workflow starts with blood or FFPE or biopsy DNA extraction. Once you extract the DNA, you need to convert this into a format that the sequencer can recognize, right? That's what we call library prep. There are many different ways of library preparation, like highlighted here. Major chemistry manufacturers like QIAGEN, Agilent, IDT, Twist, they all have their own way of making the recipes of. Here I'm showing one example of this library prep difference. On the left side, in your case, this is on the right side, the assay one. The black line shows a deletion. This is actually the raw sequencing data. This, the whole thing is just provided by one sample by different assay.
On the right side, what you can see there is a deletion, so those black lines, has been detected properly with this technology, so which is a capture technology. On the other side, so the assay 2, what you can see that the same sample profile with a different chemistry, you cannot detect this mutation anymore. This highlights that for this particular application, the assay 1 probably are more suitable for the detection. While the other one, so on assay 2 side, this is an amplicon-based technology. This does not mean, okay, this technology is useless. In many of the other situations that this technology is actually more superior than assay 1. This is another example, in blood cancer, so in myeloid disease.
What you can see is a variation coming from the same vendor, so which in this case is Illumina, but different type. What I'm highlighting here is two regions of clinical relevance. What you can see is that those bars are no longer real variants, but just simply noise. You can see the noise from HiSeq and from NextSeq for these two clinical relevant regions are very different. What we're trying to give you an idea is that the genomics workflow itself is not that simple, right? The life will be simpler if there's only one, but unfortunately, this is not the case. Probably, this will never be. A good analogy is actually a handwriting recognition problem.
There is a good reason that there's so many different fonts or different writing styles exists. In order to recognize them all, you need to expose your algorithm or your system with different diversities of a different type of fonts or different type of writing styles, so that they can have a gen. They can abstract or gen. Make it more general, how the detection would happen, right? This is what SOPHiA offers. Thanks to our strategic approach that insisting deploying the solution in a decentralized way, this actually enable us or exposed us to more than 1 million different genomics profiles. This almost cover all the different combinations of the technical factors that we have highlighted before.
This gives us a unique position to build a machine learning, a pattern recognition techniques that harmonize the complexity of genomics workflow problem. Right. The harmonization is the key actually driving our land and expand strategy. 'Cause imagine that if we harmonized the different genomic workflow into a same data analysis problem, this means that whoever adopt our solution, and if they would like to expand it to another solution, this will be actually very straightforward because they already experienced the platform, and then that they will share the same data analysis logics. Another benefits is that harmonization is the only way that actually promote knowledge sharing and also insight extraction.
Here, I will exemplify our technology with one of the solution that we deployed, which is an HRD solution, which you heard many times the previous people have talked about. HRD stands for homologous recombination deficiency. This is a phenotype that commonly happens in ovarian cancer patients, but also happen in other cancer patients. If a patient is diagnosed as HRD positive, at least in ovarian cancer cases, they are eligible to a treatment called PARP inhibitors. Okay? If you are HRD negative, you cannot benefit from this, treatment. HRD detection itself consists of either you can identify the root cause, which is typically, the damage of the genes such as BRCA1 and BRCA2, which is very important to responsible for repairing the damage of the genome.
Another way is that you can detect by the effect. The effect is on the consequence, which is shown here, that you see a profile of HRD positive or HRD negative. You are looking for signatures that cross the whole genome. Okay? Our solution actually combines both the detection of the root cause or the effects together. The tricky part is the one that we're highlighting in red. This is a machine learning technique that we adopted to identify the signature. We converted the whole genome coverage profiles into an image, and then we leverage on convolutional network techniques to build a classifier to tell whether this is HRD positive or HRD negative patients.
Thanks to the technology breakthroughs that we have built or established before, this actually enable us to develop and deploy this solution in just one year time. Here are some metrics just showing to highlighting the solution itself. Right. So first, this is a comparison to a reference method, but in a decentralized way. So this is highlighting the performance that we profiled with eight technology centers. This shows you the problem that I highlighted before. There is problems of genomics heterogeneity, right? So that you can use in different library prep chemistry or sequencing combinations. Here, what we just show you is in different sample, that you can see the different color represents this different combination of either a negative sample or HRD negative samples or HRD positive samples.
As you can see, with our technology, the results are quite consistent. The third point is that you like to address a problem that commonly faced when you decentralize the solution. That's lab-to-lab variation, right? Here we're showing, again, different samples, one negative, one positive, but profiled in different laboratories. The result, as you can see, is actually quite consistent. The expertise or the recipes that we have established in genomics can also be applied to other modalities. In radiomics, the heterogeneity comes from the instrument type. This can be a CT scan, MRI scan, or PET scan. Different instruments might be more suitable for certain context of tumor type. As you can see, the problem is very similar to what is exposed through genomics.
We have been able to establish a deep learning algorithm that can harmonize this complexity and extracts more than 200 radiomics features. Most importantly is that we actually harmonize this process, right, and standardize the extraction process. I have showed you some example of how we deal with the workflow complexity in genomics, and also give you just a bit of a sense of how we did it for radiomics. But in general, what you can see that there is some commonalities between these two modality, is that the workflow complexity is given. Meaning that you actually do not have a choice, because different application, there are different workflows that are more suitable for that. You can only address this complexity harmonization problem at the data analysis level.
In order to do that, you have to be exposed to the different diversity of the data, and the decentralization enable us to do that. As you can see, the same recipe or principles could also be applied to other modality, like digital pathology or proteomics, when those technology become more mature and the clinical utility is proven. Here comes to our summary. Abhi.
Thanks, Zhenyu. Three key takeaways, right, from our section of the presentations. One is our relentless focus on execution and putting together these fundamentals, right, in an unmatched way. I'm not gonna. I don't want to gloat a little bit, but this is, so again, in my previous life, I had the vantage point of being where many of you are today and reviewing a lot of companies that went, right, and pitching to, like SOPHiA, right, to biopharma for collaborations. SOPHiA is unique, right? That's the reason I joined. It is unique because of those unmatched platform fundamentals that have been put together, which allows for this scalability across modalities or across the way data is organized. These unique algorithmic capabilities is the second key takeaway, right? Zhenyu gave you some great examples, right?
Again, linking it back to my prior experience, right? Where there were hundreds and millions of dollars of investments flowing, in that, and then seeing how much has been produced over the last few years with this brilliant team and continue to expand and making it more reusable and, applicable across modalities is also very unique. Something to keep in mind. As all of these comes together, the potential to take it into CarePath, into different disease areas and then expansion, the base is there. We just need to build on it more. Those are the three key takeaways. Speaking of building and growing, I'm gonna hand over to Ross, who will tell you how we're gonna do this in a sustainable way.
Thank you. Thank you, gentlemen. As they both said. I'm Ross Muken. I'm our Chief Financial Officer. I joined SOPHiA roughly 18 months ago from another software entity in the digital health space. But like many of you, spent most of my career in financial services, primarily around life sciences, genomics, and the health tech space. For me, when I met Jurgi five years ago in the Geneva Airport, I was incredibly excited. It was one of those meetings that materially outperformed my expectation, and I obviously watched the business evolve. Having been around the genomics space since the very beginning, for me, when the opportunity arose, I was incredibly excited, and there was nowhere else I wanted to be.
I'm incredibly excited today to tell you about how the strategic vision that Jurgi laid out and the way we're attacking the clinical market that Ken laid out, and the way we're entering the biopharma space that Peter laid out, and the way we're helping the patient that Philippe laid out, and now the way we're building a unique tech platform that Abhi and Zhenyu laid out, all translates to us from a financial standpoint and how we're going to create value for our shareholders and be able to grow in a very sustainable way. You know, the first thing I'd like to say is we're starting from a very strong base. Our strategy is already translated into significant commercial success. You've heard a lot today around the number of institutions all over the world. That landing is very hard, right?
We've now built a network all around the world that we can now work together with our partners and grow. That's also translated into very attractive financial aspects of the business. You can see the growth rates we had previously communicated for the year and the strong capital position and solid gross margins that we've already displayed. I'm very proud of saying that this is not just a strategy and a vision, but we've built a real base of a business by which to now grow off of. Speaking on that, we've also made unbelievable progress as a software business. Often you hear me talk a lot about different KPIs. Let me explain to you why these are so important to us from a land and expand perspective. One, new logos.
This is really about that land, and it's the hardest thing we do in terms of getting new customers onto the platform. As you can see, we're already having year to date, in terms of the first half of the year, very strong results. Next, you'll see ARPU and NDR. This is really around the expand, right? I want you to think about that NDR and keep that in the back of your head, 'cause I'm gonna hammer it home why this is such an important metric for us. But on both ends, you can see our ARPU continues to expand, and that's really, again, evidence of us being able to leverage more value to our clients and be able to benefit from it. The net dollar retention is really a function of our same store organic growth with our customers, right?
We've got this great network of 750. How do we enable us within just that network to grow sustainably and in a very attractive way? Now next, you can see our LTV to CAC is around three. That's ideal, optimized, and it's not by chance. As you saw with Ken, we are incredibly disciplined and KPI-centric, and as you saw with others, we're very much about efficiency and performance here at SOPHiA. Not only are we growing, but we're growing efficiently and creating value in doing so. Then the last metric, this is a new one. I haven't shared with you RPO before, and I'm very excited to share it with you today. The $85 million RPO we have, think about that as essentially backlog coverage, right?
We've contracted with all of our clients, and we have fantastic visibility because of it. If I think about what that means is that with that $85 million+ RPO, I have nearly 90% visibility on forward twelve-month revenues, right? That is very substantial, very unique in terms of a business model. Now, that doesn't mean currency or other elements can't come into play, but it's a really, really nice basis by which we can confidently, again, plan and grow our business. Turning to some of the strategic initiatives that is getting us to where we need to get to. You've heard a lot about this today. Super exciting new products. You've heard about CarePath, which is our Gen 2, that will leverage to, again, that large network that we have. You've heard about HRD.
That is obviously a very key application for us, and it's probably the most successful new product launch in the company's history. You've also seen today, again, new examples of really tier one partnerships. You know, MSK, fantastic relationship, shared vision, and with what we can do, not only could we create value, but also impact patients. That, to me, is also the important thing, that we're doing, you know, great by creating value, but at the same time, social good. You know, obviously, GE as well is a fantastic partner. You know, you heard a lot today about imaging and radiology and just the absolute impact it can have in the cancer journey, and we're super excited to be along with them, as a great technology partner to us. Today, you also heard a lot about biopharma. Right.
Peter talked to you quite a bit about the Boundless Bio transaction that we just had, which is fantastic. Incredible technology. We couldn't be happier to have them as a partner. This is going to be, again, another real proof point for us in this growing biopharma business, which I'll talk to you about in a minute. Obviously, you've heard us again. Peter highlighted our work with AstraZeneca. Lastly, on the fueling energy in terms of our growth, it's really around getting into NORAM. We're seeing not only success on the ground in academic medical centers, but also central labs. That's a huge proof point for us in terms of our story since we came public. We've also had some really nice wins in APAC and LATAM, which are also exciting new markets for us.
We're not doing it solely to grow, right? We're doing it sustainably. That means continued focus on FTE productivity, right? There, we're really, you know, we've made the investments. We have a tremendous regulatory and quality organization now. We've got tremendous investments in cybersecurity. You know, all of these different pieces. Now we're gonna leverage a lot of this expertise as we expand. You'll see, again, revenue per FTE for us continue to move upwards. I'll talk a bit more about OpEx in a minute. Gross margins. I'll get to that in a minute, but exciting news there as well in terms of the momentum. Leverage on the cloud compute and storage side. Leverage of our existing customer service operations.
You know, Microsoft has been a fantastic partner to us in the space, and there's a lot more we can do there, again, to be able to deliver to you continued leverage. Lastly, on the OpEx side, we're getting very focused, right? You know, you've heard a number of different elements here in terms of R&D that are very exciting, but some of them, like CarePath and HRD, are very high impact for us. As we do that, we're going to see returns on those investments we've already made. Additionally, Ken talked to you about the sales force investments we've made. We have now a very strong commercial force. We're gonna leverage that. We don't need to grow our sales headcount materially in order to achieve our objectives, and that is a very key element.
Then lastly, you'll hear me talk in a bit around our public company costs and getting some fixed cost leverage out of that part of the model. This is a new point I'm really excited to share. Based on where we are today, we're very happy to say by 2025, we're gonna be well in excess of $100 million in revenue. That's continuing on the strong annual growth you can see here on the page, 30%-35%. This, again, as I'll talk to you in a minute, is very visible, tangible growth. You can see some of the key drivers here that will get us there. Again, as we're growing, the number of patients per year grows, which again fuels the whole enterprise.
Again, very exciting, and I think for where we are in our journey, strong underlying top-line momentum. Now you can understand how our strategy is translating financially. Now, one of the other things we talked a lot about today was our biopharma opportunity. I also wanna show that off of a very small base entering into this year, we're quite confident now of biopharma by 2025 comprising of nearly 10%-20% of our revenue base, right? Very substantial growth. These are very material new relationships that we're embarking on. Again, what you'll find is those two parties will interplay each other in a flywheel effect to actually accelerate growth.
This is very important for us as we think about the evolution and that biopharma will be a really nice complement to our existing base on the clinical side, which is already quite strong. The land and expand that you've heard a lot about today, I cannot tell you how important this is to us from a strategic perspective. Really the expand side, right? That virtuous flywheel that happens as you get into an account, you understand their needs, and you continue to evolve with them and be able to deliver technology and capabilities so that they can continue to grow with you. Just to give you an anecdote here, as I was thinking about this before, right? You know, we gave you the RPO, which gives you forward revenue visibility, but I also gave you the net dollar retention.
There, if you look at our net dollar retention over time, it's been in the 120s, right? If you think about that versus our growth expectation, that means that over 80% of our forward revenue is already coming from our existing customer base. I don't need to land one new customer to be able to achieve the low end of that forecast, roughly. From that perspective, that is incredibly unique, right, in terms of a business model. It really makes, again, us different than most of what you see out there in terms of other entities in our evolving space. Again, when you see that net dollar retention, now you understand why I'm so excited about it. From a CFO's perspective, it frankly makes my job much easier because landing is so much harder than expanding.
Now, another way to give you some confidence that we can do this and to give you some numbers behind it, you can see here our customer cohort breakdown for 2019 versus today in last year in 2021. Sorry. Now, obviously, I'm excited that the number of customers, recurring customers have grown. What's really interesting is that as our customers get bigger, they actually grow faster. Think about that for a minute. Bigger customers grow faster. What that means is all of these new customers we've brought on in the last few years, they're going to age into that growth and become even more material for us over time. Now look at that top statistic, right? If you look at our top customers just 2+ years ago, they were roughly $400,000. This year, $700,000.
Which means not only are our customers moving up that pyramid, right, and contributing more and more to growth and accelerating, but they're actually the ceiling of where they can go keeps moving up materially. Right? You heard from Ken an example earlier where we now have a customer approaching $2 million, and we may have ones that are actually quite larger than that over time, based on what's implied here. What I want you to walk away from is a consumption model takes time to build, right? We've done the hard work. We've put the basis out there of the network, and now as we put more and more applications, we move to Gen 2, you know, we continue to be able to solve everyone's issues in a more sophisticated way as a platform.
That visible growth becomes even more, I would say, something that we're confident in. Again, this should be evidence to you that what I said before around already having that visibility, we've already been doing it. Now another way to just hammer this home one more time. You saw the left chart from Ken around the number of applications per user. Look at the right. What you see is that over time, as customers stay with us, and again, we have very high retention, they keep adding more applications, right? You can see by the later years they've moved up several fold. That's how we're doing it, right? It's really. It's not that simple, but mathematically it is that simple, right? That's how you see that example earlier, where we grew from 10,000 in year one to several years later, nearly 2 million.
We're looking to do this again and again and again with all 750 of those clients. Today, we have good visibility on the 380 I cited that are recurring, and we're looking to convert the rest of that to regular customers as well. Very exciting high visibility revenue growth. Now, we're not stopping there, right? We're also seeking to deliver very strong gross margin. What you can see is based on our current plan and that revenue trajectory, we're also gonna expand gross margins north of 500 basis points. That's coming from economies of scale. That's coming from improved cloud compute and storage costs, and also labor efficiency on the customer service side.
Again, you know, we are trying to translate that very strong top line into leverage to buy down our operating loss and move again to a very sustainable business. Above and beyond operating leverage, right? You got to get it also from the OpEx line in terms of not just gross margins. Here you can see a bit of a sense of our OpEx base, right? You've got your wages and benefits, so that's our head count. You've got your fixed costs, which is a lot of our public company costs and some of our fixed assets, which you'll see in a minute, are quite modest. Then you've got our discretionary and other variable costs that essentially move per unit of analysis or per patient, et cetera. We're looking to optimize all of these, right? I'll show you in a minute what that means.
Essentially, we are trying to now drive greater than 55% incremental margin on every new dollar of revenue. Again, that's how we're going to be a sustainable business. It's not just growth at all costs, it's growth with a very disciplined operational approach. Let me show you what that means in terms of numbers, since many of you come from the finance world like me, right? You can see here both IFRS and our adjusted figures. Now, as I've said many times on conference calls, wages is a big portion of our base, right? There you can see it's still a majority of our operating costs. Now the next biggest piece is variable and then we also have fixed costs, right, in terms of contribution to that OpEx line. Now what I'm gonna suggest to you is what we're doing, right?
We spoke about the gross margin leverage. You can see that there's over 500 basis points. Importantly, we're able to deliver again because of how much growth is coming from the expansion. That operating plan and the strategy you heard today, without changing the capital base in terms of our head count, not much, right? Look at that level of leverage you're getting in the future as that current 500 basis points base stays pretty consistent again, to deliver this level of growth in the plan. Now, on top of that, we're also seeing really strong leverage on the fixed costs. Again, I mentioned all of what we're doing on the regulatory side to become a public company. There's quite a lot of costs in terms of audit and D&O, et cetera.
We're also getting better there, and you're gonna see quite a lot of leverage, and we don't need to make a ton of investment. Then lastly, we're also fighting obviously every day to keep our variable cost per unit in check. When you translate all of this operational efficiency together, what you end up with is a path to break even. Again, I wanted to just stress we are obsessed with this, okay? We are very, very, very focused on being a sustainable business. For us that is incredibly important for many reasons. Now, what that also translates is into what I shared in our last conference call, which is that we have quite a bit of cash through the period you can see here.
In that, we feel quite confident today based on the plan I've shared with you, that the cash we have can fund that plan and support it. We are really very focused on long-term profitability, ultimately cash flow generation and self-sustainability. All of that coming together today is again factored into what we have already thought about in terms of our current capital position. You know, for me, the key conclusion on my section is, even though we've already been quite successful and established this base of business, we're just getting started, okay? There is still a lot more to go. This is only the beginning and we're very excited again to have all of you hopefully come on this journey with us as we grow sustainably and create value here at SOPHiA GENETICS.
At this time, I'd love to bring back our Founder and CEO, Jurgi Camblong, to the stage to take us home.
Thank you so much, Ross. Thank you, everyone. For me, it has been pretty amazing, seeing my team presenting in front of you. It was, I think, a very good exercise to educate you on what SOPHiA is about, but it's a very good exercise for ourselves as well, to continuously improve our strategy. Along those lines, I hope that today we're being able to answer to you on how we sell, how we innovate, and as just, Ross, I think, brilliantly presented it, how beyond impacting sustainably around the world, we're doing that through a sustainable economical model. The message for me is, we are the future, and we are real. Along those lines, it's not about only us, about the patients, about the executive team, about our employees. It's about all of us.
You know, a lot of you are in the financial sector, but I think when you go back home, you have questions on what are you doing? How are you impacting? I hope that being part of our journey, you will have these answers as well, so that you can tell them, "I'm impacting by investing in a company who is making a difference, who is going to make the world a better world." With that, we're going to move to the Q&A, and I'm going to invite our colleagues for the executive team to join with me. I'm going to present you four new characters that you didn't have a chance to listen because we couldn't have enough time. Please join me on stage.
They've got a chair.
Great. We have still 30 minutes to answer to your questions. If you didn't get the answers on how we sell, how we innovate, how we grow sustainably, please ask any question you want. Before we start, I would like Lara to present herself.
Hello. Good afternoon. Is this on? Hello, good afternoon. I'm Lara Hashimoto, I'm the Chief Business Officer. I am based in the Boston headquarters, and I started working at SOPHiA GENETICS back in April of 2020.
The next character will be Melissa.
Hi, I'm Melissa Fenocchio. I'm the Chief Regulatory Officer. I'm based out of Switzerland, and I joined SOPHiA GENETICS in May 2021.
The next one will be Dan.
Daan van Well. I am the Chief Legal Officer, based in Switzerland, and I joined SOPHiA GENETICS and the team in 2019.
Last but not least, Manuela.
Hello, I'm Manuela Valente. I'm Chief People Officer. I'm based in Switzerland as well, and I joined the company in January 19.
Thank you. Don't hesitate to ask questions as well. I will be the chair, okay, to have others answer, but don't hesitate to ask questions that others may answer, including Dan and Manuela, Lara and Melissa. We have a couple of microphones, I think, in the audience. Raise your hand if you want to ask a question. Yeah, Vijay? Sorry, I don't know all of you personally yet. Okay.
Thanks for hosting the day and taking my questions. This is Vijay Kumar from Evercore. Jurgi , maybe a big picture question for you, right? When I look at the landscape you guys play at, it's FDA-regulated market space. You have approved drugs, approved diagnostic tests. Your decentralized cloud-based algorithm, right? How does that fit in that universe, right? When you look at the long term, what role does SOPHiA play? Because will there be more regulated, you know, tests on the market? Can you still play in that market or, you know, you're expanding with the MSK-IMPACT test, and how does it change the landscape for you?
The first answer before giving it to Melissa will be that we welcome regulation, right? Because we believe that, regulation is as well, in line with, accessibility. That if things are simpler and designed and communicated, this will accelerate the movement of precision medicine and data-driven medicine. Of course, it's part of our thinking, part of our strategy. Maybe, Melissa, I let you elaborate on how the field is evolving and what are our thinkings without giving any concrete milestone here.
Of course. It's a great question. I think the fact that I'm up here and that you heard several of my colleagues refer to the regulated space that we're in, to the tests that we're already doing to make sure that we're ready for the future, is a statement that we do intend to be in that, in that market. For sure, we see that the regulation continues, and our job is also to help make sure that we educate the regulators and bring them along on this journey. That it's the medicine of tomorrow doesn't look like the medicine of yesterday, and that we have a role to play in that.
You heard Peter also talk about the role that we can help with biopharma in their clinical trials in the early stages of drug development, and we know that there's a lot of space for us there. Everything we're doing today with the platform is to build that path also to make sure that it can be in the regulated space with all of the necessary approvals wherever we happen to be in the world.
Just maybe back to you, Jurgi. You know, you mentioned the deep lung study, the MSKCC partnership. What does a partnership mean, and this lung study, you know, when are those results expected? Is that gonna be a new application, a new product that you expect to launch? Maybe talk about these initiatives.
I think there are two questions, right? Or two efforts in your question. One is the DeepLung, and the other one is beyond DeepLung in the same logic, maybe, working more closely with MSK. On the DeepLung, we started that journey in November last year, right? I think Philippe today has been sharing some data with you. We intend to follow 4,000 patients. We captured already 900 patient data points, which each time is about 200 something data. Philippe?
On the clinical side, yes.
Yeah. What we said is that we would deploy the CarePath module in these 23 sites, end of this year. I don't know, Philippe, if you want to further elaborate on what might be the next steps beyond this retrospective study without giving, again, any deadlines. That's one thing, as you know, Vijay. I learned we cannot give you something which, you know, is set in stone.
I think to go back to your question on DeepLung for specifically when the study ends, it obviously depends on patient accrual and recruitment, right? We always said we're on a trajectory to basically get the 4,000 patients by original timeline, which is 2023, 2024, and we'll see what happens in practice. Now, obviously, if you take a step back, that's a retrospective study, so there might be a prospective component coming after or in parallel, depending on what the results show at some point. What I tried to make clear in my presentation as well is that this is just like one example, right? It's one application that can fuel CarePath, but the multiple capabilities we're developing can impact on many different cancer types. The data can come from clinical studies from this one.
It can come from data sets such as MSK or other partners. It can also come from biopharma partners, where there's a lot of interest in these multi-modal capabilities to go back on trials and understand better what I call next-generation patient stratification strategies to understand from a multi-modal perspective, where are your super responders to inform R&D, right? That's kinda the whole portfolio of approaches that we have in front of us.
To continue on MSK now. MSK has built capabilities which are beyond lung cancer, and we are very much looking forward to collaborate with them as well on these capabilities. We share exactly the same spirit, which is a win-win-win. The companies are to win, because it's a matter of sustainability as well for our technology to serve patients ultimately. The patient needs to win, the hospitals need to win, the oncologists need to win, right? This flywheel effect that Peter was talking about is really at the heart of SOPHiA since day one.
Beyond that, maybe on the clinical side, right, not on the data side, Lara, you could elaborate on, you know, how you see in particular the relationship between SOPHiA and MSK for MSK-IMPACT and, how we could leverage on that to help-
Absolutely.
Patients around the world.
Yeah. We really see the combination of MSK's unique clinical genomic database with our decentralized model as being a win-win in that we can now use, as we had mentioned earlier, we can leverage those sites we're already in, which is the 750+ accounts, to generate that flywheel effect of generating more data to actually feed back into informing the algorithms to benefit patients. We also see CarePath as being a way in which we can extend our reach within these institutions. Previously, we've been focused largely around the lab and, say, the C-suite. Now we can extend beyond that, so we really get more call points within a single institution.
On MSK-IMPACT, in particular, some color, comprehensive genomic profiling.
Yeah. This is a great way for us to also enter into the CGP field. This is obviously an early space, so you're seeing that it's not fully penetrating the market. Obviously, as new biomarkers are discovered, CGP is going to be coming fairly soon, I think, in all the markets. Having the access to the MSK-IMPACT will allow us to get better partnerships with biopharma as well as in the clinical space.
If I may, one last one. Ross, you know, thanks for sharing all those numbers. Just to clarify, that RPO number that you shared, $85 million, is that 90% of visibility to street revenues for 2023? Or just maybe clarify what the 90% visibility is. I think those incremental margins of 55%, that's certainly at the high end of life science and diagnostics. The path to operating profit neutrality, is that 25 when you guys hit the $100 million?
Now I get to experience your tough questions in real life, that's good, Doc. I would say on two fronts, right? When we think about RPO, this is a metric we will start updating everyone on. Obviously, I understand on a go-forward basis where I expect my business to be. What I was trying to characterize was not to comment on 2023 revenue specifically in terms of street models, but to suggest that when we communicate around our forward forecast, there's a very high level of confidence, namely because we have a lot of backlog coverage, right? I'm not trying to figure out how many new institutions do I need to land, how many new contracts, how many new boxes, et cetera. I have a very, you know, visible set that I'm drawing off of as we're putting together the forecast, right?
I think, again, as a CFO, this type of a business model is quite advantageous. Now, if you think about the path to operating leverage, again, you notice there was no time frame, right? Although I did give you quite a lot to calculate on your own. I gave you our expectations of revenue growth, and I gave you our incremental margin being above a certain level, right? You, I'm sure, can go back and do some computational math and figure out within some time frame way that may happen. I guess what we wanted to get across was, one, it's something we're very focused on, and two, it's something that is very feasible for us to execute on, right?
We didn't just wake up now and say, "Wow, we've got to eventually, you know, generate profit." You know, we've been very mindful, very tactical around building a sustainable business over time. We've also said since IPO, we've made a lot of investments that you haven't seen payoff yet, right? You saw some of those investments come to bear today in some of the exciting things we're sharing. The key is, I don't need to add significant variable cost as that revenue is coming in. Again, this is very unique, I would say, to a model like ours, which allows for that high incremental margin, which allows for us to eventually get to that break-even point that you highlighted. Again, the point today is really around our confidence in the journey and the path, and that we're very focused on being sustainable.
Thanks, Doc.
Tejas.
Thank you. Jurgi, couple of questions here. Maybe I'll start with the Dasa partnership that you highlighted. You know, one of the questions which investors have had is particularly around sort of the cost in the context of the emerging market sort of cost structures for these genetic tests. And second, I mean, occasionally there is hesitancy around any black box solution. Can you talk to us around, you know, specific to Dasa, but also in general in the context of your OUS customers, how the dialogue has evolved over the last couple of years on those two fronts?
Yes. First, I guess when you're talking about the cost, it's primarily the reimbursement, right? In those countries, which indeed tends to often be less generous than what you would see in the U.S. This applies to Europe as well. If, like, as we would have to help hospitals in Europe and abroad, including a central lab in Latin America, definitely, we would be in market conditions where cost constraints would be more important. Often, this is part of one of the reasons why they go for SOPHiA. The benefits we bring are around top analytical performance, turnaround time, the acceleration of the adoption of a precision medicine application in their lab, but as well, cost control.
This is because of how we are computing the data enable us to get more out of the data and simplify, if you like, the workflow, right? We eliminate what we call orthogonal solutions, beyond genomics, and so the cost goes down. I think this was definitely part of our success and thanks to the smart algorithms of Zhenyu Xu ultimately. This was definitely part of our success to be able to have this adoption in Europe, in Latin America. We see now this as a competitive advantage, right? When maybe the economic constraints are tougher now, even in the U.S. The need of precision medicine applications becomes more and more obvious, and more and more institutions have been buying sequencers recently as well with COVID, right?
They need now to use to capitalize on these instruments. We see that as an opportunity to bring them a solution that will enable them to be cost-effectively and locally, which means faster and controlling the data and controlling the cost, right? I think we're pretty well placed now for the decentralization of precision medicine, including in the US. Now, that said, it doesn't mean we don't work with central labs in the US, right? I think the Dasa example, it's a very good one. When we work with large centers like Dasa and others in the US, now more and more our conversation is about the enterprise solution. If you like, as a platform, how you can help me beyond one application. How you can help me basically in the cost of opportunities and in managing my overall cost, right?
Along those lines, we have heard a lot of challenges for labs in finding data scientists, right? If you're working with SOPHiA, you need less data scientists. Even in some hospitals, we've heard about this challenge, that data scientists today are overwhelmed because of the volume of data they have, and they cannot do any more of the research. Okay. SOPHiA let us do more the routine and use these data scientists for innovation and discoveries, right? Along those lines, I think the black box element you were talking about, and I don't know where you heard from this term, but definitively is something where we had to educate the market, right?
The way we do it when we help labs to jump into our platform is we open up the hood, and that's why it takes a bit of time to land customers because we need to show them how we are computing the data as well.
Got it. That's helpful. In terms of the, you know, being a provider to some of these large labs or even MSK, for example, versus, you know, enabling potentially smaller competitors to those same customers, walk us through how MSK approached that decision. 'Cause I think that would be, you know, the whole central labs, you know, frenemy sort of equation with you guys does come up every so often. Just walk us through what MSK was thinking as they approached this.
MSK today, in the setup, is not a client. I hope they will be a client. I hope they will use our platform day in, day out, right? The conversation didn't start like this at all, right? The conversation really started on how together we can impact more broadly. How on the basis of the MSK-IMPACT asset and technology, we can, by adding that in our platform, help other centers around the world basically, and including in the US, do proper comprehensive genomic profiling. You know, I don't want to speak over MSK, but I think their success means as well that today they have a lot of volume, and for them was very difficult to impact beyond, right? They see from a very good angle having SOPHiA helping this decentralization. That's the first element.
Then the second element is that MSK has two duties, right? One is care, the other one is research. As we are as well giving access to this technology to other players in the country and around the world, in the spirit of, you know, win-win, the data that we compute will benefit as well to MSK for their own research, right? This kind of federated learning that you may have heard about, Tejas, is something that we do already in the cloud since day one. I think MSK shares the same view. It's about controlling, about getting access, and about working together so that we can do better research, so that we can have better impact on patients.
We haven't seen, and I would be surprised if we end up having these conversations, but we haven't seen anything that would, you know, give a sense that we can work with them, but not the others. I will tell you, it's even the opposite. I cannot give you names, but they are already planning to introduce us to other comprehensive cancer centers that they know, and they know share the same vision and the same, you know, and share that approach of building this collective intelligence for tomorrow.
Got it. On the OUS piece of the database, I mean, that's clearly a huge differentiator for you guys today. You know, I see the appeal in that on the pharma side of things, but how much has that started to resonate with your clinical customers who often deal with, you know, very captive, relatively homogeneous patient populations?
Look, I should say thank you to all or any question, but to this one, I will tell you thank you. Okay? I showed you where I grew up. I grew up in Basque Country, in a region where we would not necessarily get access to the same technology as the one we would get to in Paris, right? Maybe then in Paris, you would be tested, but you don't have the same population. It becomes more and more important, and I think especially in the U.S., we hear a lot about underserved communities, right? Sometimes, unfortunately, it's the way the system works, that economically it leads to have some communities which are disadvantaged versus others. For us, serving all communities equally is very important.
In that journey, while we are helping customers in India, in Latin America, we even had some customers, but unfortunately, not high volumes in Africa, right? This exposure to this data enable us to build a collective intelligence that then can be leveraged, even in the U.S., to eventually better serve these communities. I think the point, you know, Philippe made on CarePath is so relevant. If you do that in only one center, one region, one town, you have less chance to be exposed to this diversity and help all the communities.
Got it. One final one for me, for Ken here. Can you just walk us through some of the commercial incentives you've started to put in place to encourage the cross-selling across solutions?
I guess here the question is regarding our own teams, right?
Yes.
Okay.
Yes, exactly.
How much detail can he give here?
I think you could talk about some of the work we've done to drive the, you know, behavior we need in terms of hitting our plan.
Sure. Okay. Well, thank you. I think to rephrase the question, you're asking how we motivate our existing sales team to expand our business? Okay. As I mentioned, previously in the slide, we have a dedicated customer success team, and we track a variety of metrics for that team. I talked a little bit about in our KPIs. We have Net Promoter Score and customer health score. That are two metrics that we track. We also incentivize them to find and sell white space.
That's part of the incentive plan as they need to go out and help our customers grow, which is, as Jurgi was just talking about, a win-win. You know, the more applications our customers use, the more successful they're gonna be and the more successful we're gonna be. It's really a pretty simple equation to go out and do it. The other thing to keep in mind is that when we talk about the implementation or we talk about the time from when we close the deal till we bring it to revenue, it's much quicker if that customer already is a DDM customer. We put a large emphasis on building that white space and continuing to fuel that expand engine.
Thank you. Please.
Hi, Julia Qin from JP Morgan. Thanks for taking the question. Just to hone in on the land and expand strategy, I appreciate the chart you guys showed showing, you know, different customer cohorts adding on different applications over time and that 120% NDR rate. How generalizable is that NDR or application expansion pace as you continue to grow your customer base? A second related question, can you speak to your confidence in maintaining or even, you know, maybe a potential upside or downside to that NDR rate and menu expansion pace, especially as you start to drive more Salesforce and SG&A leverage over time? Thanks.
Thank you. First, as you understand, Julia, the NDR, it's as well an indirect indication of how well we support our customers, right? Because growing with your existing customers over 20% a year, it's not a given. Basically, this places SOPHiA among the best SaaS cloud-native public platform technologies when it comes to the NDR. When coming back to your question regarding how NDR should evolve or has been evolving, maybe, Ross, you want to give some color.
Thank you. I think, you know, if you look back over our history, we've been consistently north of 125%. Even after we came public, we're in the 140% range. You know, now we're obviously have been impacted in the first half a bit by currency, which does touch that number as well as a bit on a decline in COVID sales, which was targeted by us. Overall, if we look at it, you know, you have an underlying growth rate of the industry just in terms of the adoption of NGS and other technologies, right?
We have the large number of customers that have come into the pipeline in the last 12 or 24 months that's really starting, as you saw in that example Ken gave, to hit sort of exit velocity on utilization of the platform. We're obviously also constantly, you know, looking at the application set and what makes sense in the context of that customer. For us, again, as we look at it, that is a very visible rate that we should be able to sustain for quite a long time. Obviously we strive to drive it higher. Today, it's primarily our clinical business. Now, if we start thinking about the biopharma business impact on that, and you saw the growth rate, right? It was over 100%.
You know, while it's a small portion of the business, that also is going to buy us, right, some of that as we're, again, landing and expanding in many of these clients. We start with a pilot or some smaller relationship, and then they can scale quite materially in terms of dollar revenue. Obviously, as I mentioned before, our pharma business tends to be much larger, right, in terms of that initial customer than our clinical business does. I think putting it all together, you know, we've now made the SG&A and sort of OpEx investments to enable us to execute on, you know, the base plan. I think, you know, the challenging thing in healthcare is always the time for adoption.
We have certain accounts, right, where we see huge uptick in utilization and broadening of their application suite with us in quite a, you know, expedited period. We have others that are very slow and methodical and move at, you know, I would say, a much more measured pace. Sometimes there's many different reasons, right, why they don't continue to expand at the same level as others. Maybe they can't get enough folks in the lab to operate the sequencers, or they don't have enough money to buy another sequencer, or the automation, right, required to produce more volume is not there. But ultimately, I would say, as you look at, you know, the different base of it, you know, we have a growing portion of increasingly larger customers that, as they get bigger with us, are growing faster.
We still have this very rich base to harvest, right, which is what Ken's team is going to be doing between now and 2025 to get us, right, to that level, while also still landing, you know, those logos so that we go from 750 to a number closer to that 5,000 target, right, which is where we ultimately wanna be. I think what I was trying to get across and what the NDR should get across again is the sustainability of the base growth rates we've given you, and that to the degree you see, you know, shifts in the industry, right, or in adoption or in that biopharma curve, obviously you can see influence, you know, in terms of better performance on the NDR.
Now, on the negative side, as I mentioned, you know, obviously utilization is one piece, so the macro is relevant to that just in terms of how it may influence, you know, the number of cancer patients going into a laboratory. Obviously, FX was the other piece, and then, you know, recently we did call out Turkey, right? So occasionally you will see some economic disruption lead to some disruption actually at the utilization level typically, or reimbursement level. But for the most part, if you look at the portfolio, you know, in every region, in every country, you're seeing improving, I would say, stats in one portion and then others that are moving more slowly along the curve. But the good news is, from what you saw in that chart, they're all moving in the right direction, which is up the pyramid.
Yeah.
Hi. It's Mark Massaro, BTIG. Nice presentation today. I guess my question is, you know, last year there was certainly an explosion of interest in proteomics with IPOs of SomaLogic, Olink and Seer. There's been a lot of financings in digital pathology, you know, Paige, Lunit, PathAI. Maybe just talk to me about your strategy trying to incorporate proteomics and digital pathology into DDM. You know, when can this become meaningful? Are there some partnerships you can do? And how do you think about the timing of these? Do you see meaningful revenue streams in the next year or two?
Thank you for the question, Mark. First, we're thrilled with these new technologies that's coming to the market, right? 'Cause they could be used to better impact on patients. I think as we have seen in some industries, it takes time from life science to diagnostic. Without knowing when this will happen, today, we cannot deliver what we want to the patient if we are counting on things which are not yet being produced day in, day out, right? Our approach, Mark, has been to already deliver technologies, as Philippe demonstrated on genomics, radiomics and other, to be able to support hospitals and so patients on the basis of the technologies that are already being deployed, right?
I think it's probably very much a tech approach, but I think it's the quickest approach to grow and to have an impact. When it comes to other data modalities, we have few partners who are working with us, and we will speak about those partners when we think that the time will be the right time.
Okay. This is just my second and final question. It's just a housekeeping question. I don't think I heard any financial impact or upfronts related to either the MSK or the Boundless Bio. Is there any comment you have about the duration of these partnerships or any financial impact?
I guess what I would say, Mark, is we obviously didn't disclose financial terms in any of the transactions. What we hope is that, you know, this is indicative of long-term partnerships on both sides. You know, I would say stay tuned for us as you think about the evolution. Again, I'll go back to, without speaking specifically to those two examples, that RPO, right? In that RPO, there's a lot of different things, but for me, it's what gives me confidence in what I'm gonna communicate to the street around our forward expectations, right? What I really want you to get out of it is, relative to these transactions or these partnerships, we're super excited. You know, in terms of the financial outlook, there is a lot that goes into that, right?
We have a very diverse customer set. The mix of it keeps us, I would say, quite optimistic on the forward look, as we've shared in most recently, the second quarter, relative to our growth expectations for the year. I would say stay tuned in terms of how some of these different relationships will financially impact us over in the future.
Thanks, Mark. We still have five minutes. Yes, Lars. Uh-oh.
I'm Lars Steinmetz, co-founder of SOPHiA GENETICS, professor, as well of genetics at Stanford University. Obviously, I'm proud to see the progress SOPHiA GENETICS's made. One of the initiatives and incentives initially was to build on numbers, and that's why how to be decentralized. You know, asking a genetics question here, thinking about scale and numbers, often therapies are tailored currently in towards mutations that are detected in particular genes. Of course, as you get more and more patients into your database, you will be able to do sub gene stratification and probably with CarePath, which I'm excited about, we'll be able to learn from these data and be able to predict not only, you know, what path a patient will follow when they have a mutation in a gene, but also at which position this mutation is.
I was wondering whether in your database currently, you can already start to ask questions like that and make discoveries?
Thank you, Lars. I guess you're reading in our minds, right? You know, it's not really a surprise, but we funded the company more than 10 years ago. Definitively, with CarePath's capabilities, we will be following more and more patients longitudinally with imaging modalities. With molecular modalities as well, we will be able to do this kind of make this kind of findings, right? Eventually, maybe in the context of HRD, eventually it might be that, you know, you're an expert of DNA breaks, and DNA breaks may not happen always in the same place for the same patients. From there you may be able to infer information regarding the mechanisms that are not working and maybe have different rules of PARP inhibitors. Definitively, these are discussions that we want to entertain with the pharma industry.
Definitively, this is how we are thinking about the platform in our next, what we call, the generation two of the platform. With that, thank you again, everyone. I hope you get excited about what we shared with you, that you will go home understanding how we sell, how we innovate, how we grow sustainably, that you realize that we are the future and we are real, and that by helping us in our journey, you're contributing to making the world a better world as well. Thank you so much, and please join us for the cocktail now on.