All right, we're good to go. Good afternoon, everybody. My name is Luke Sergott. I cover life sciences, tools, and diagnostics at Barclays. It's my pleasure to have the man, the myth, Muken, here with me, Ross Muken, CFO of SOPHiA GENETICS, and Kellen Sanger, running strategy and IR for the business. So, you know, the evolution of the business and the platform, why don't you just kinda give us a sense of, you know, the one-on-one of the SOPHiA DDM platform? I mean, it was-- you started off as, like, a genomics data analysis package, and really kind of how that's evolved across the different demand and customer applications that you guys have built out.
Sure. So, you know, a number of years ago, probably over a decade ago, we saw the shift of sequencing to the clinic, right? And with that, we also saw that there were going to be quite a lot of vendors that played across the continuum of that production process of precision medicine data. And what was obvious to us was all of that diversity of instrumentation and all of the ways the data was gonna be produced was gonna create a lot of noise, and it was to be quite hard to operate a lot of these sequencers in a decentralized format.
And so we thought that a platform, software platform, that would allow for the production of that data in a harmonized way, would be something that would be well-needed, versus all of the different institutions developing their own code, and ultimately working on this, you know, N-of-One. And so we built the DDM platform for that purpose, to allow laboratories and hospitals and, the like, to be able to produce data any way they want, any mix of instruments, reagents, automation, et cetera. We'll correct for it on the back end with algorithms, and no matter where you are in the world, you can get pristine, highly accurate data.
Then it became, okay, if you can do that and connect all of these institutions in one common network, you should be able to also advance the science by doing knowledge sharing and collective intelligence, right, across all of these different institutions producing data. So it became very much a feedback loop, that the more data you shared and the more data that you produced, the better the community insights were, and ultimately, the more you can involve advanced care, both in oncology and rare disease. Now, the evolution of that became not only were we able to support single gene or 5-gene or 10 or 25-gene testing in the early days, then we started to see panels, right?
Mm-hmm.
More significant gene content, 50-gene panels, 100-gene panels. Now we're seeing CGP 500-gene panels, or we're seeing exomes in the thousands, right? And eventually now, whole genome. And so we provide the analytical support for any type of application, no matter, you know, sort of the area of interest, for rare disease and oncology, on the same platform, right? We also have different types of universal library preps that we work with our partners on, and there again, across all of the different applications, you can use the same type of prep. So very efficient, very low cost, and ultimately, again, best-in-class accuracy. Now, the next sort of iteration of the model became, as we saw the N of the data grow to such a size level, you get what's called network effects, right?
The data you have, you'd start to see things in that data where you can transform it into a new product, essentially-
Mm-hmm
that you can then use in another arena. And so we saw the ability to take that data and bring it to pharma, right? And so now pharma is another sort of entity in the network, where they're producing data or asking questions. And again, you're getting with that, the typical kind of flywheel effect, that the more questions they ask and the more data they contribute, the more the network produces and the more the network pushes back to them, and vice versa, right? And so that's causing growth. And then the last piece of it, and more recent, was sort of our journey into multimodal.
Mm-hmm.
So then it became, okay, it's really interesting to look at sort of the biomarker-level data, but what if you can do that plus the radiology data, plus the pathology data, plus the phenotypic data that exists in the EMR, and look at that in a, you know, sort of multivariate way and be able to draw new conclusions, right? And that's sort of where we've been pushing with the data ingestion, and so more patients, more applications, more data content. And so, you know, for that, we think, you know, the world is in the very early stages, right, of being able to produce and harness all this data. I mean, a lot of the investment's been made in terms of the CapEx.
You go into a hospital, they have multiple sequencers now, they have lots of MRI or CT machines, they have tons of anatomical pathology microscopes, but none of that data gets kind of aggregated anywhere or shared, right? And certainly not, insights drawn off of it. And so we see in the future a very different view for the patient, for the oncologist, or for the clinician. And again, all of that feeding to each other to create this collective intelligence, to ultimately advance the care of disease and improve patients' lives and allow people to live, right? And, and that's kind of the end of it.
I mean, all these disparate forms of data, you know, from anatomical pathology, where you're looking at slides versus multi-panel sequencing, you know, it's not all standardized and able to be analyzed. So talk about the upfront, you know, the processing, the investment that you guys had to make to actually standardize that data so that it could be just a single-page readout to the clinician.
Yeah. You know, this is, this is probably the piece, I think, in our world, right? You hear a lot about AI now, and it's hard, I think, for many people to understand sort of what's unique, what's differentiated, what's mid-scale advantage, what's not. But the reality is, you know, you, you think about how businesses like Google or Facebook, others kind of grew up, it's, it's always the same thesis: you have to have-- you have to capture an N of users.
That N of users or folks utilizing the network have to have a certain level of diversity, and then from there, your algorithms get more powerful, and then as you keep challenging those algorithms, they become better than anything else, and then you basically get to a point where your lowest, you know, sort of marginal cost per output and your lowest cost to sell it beats everybody else, right? And then you win the whole network. And so if you think about that in our case, right, we've been getting to an N of patients now, where we produce more genomic data than anyone else in the world. And then from there, you can then do other things, right, where then you win the right to be able to say, "Okay, my algorithms are super powerful here.
Will you let us test those algorithms as well in radiology or pathology, et cetera?" And so if you look at what we've done with DEEP-Lung and other initiatives, these were sort of the early stages of us showing that our algorithms are now so powerful, right, we can apply them to different data sets, right, or data types. And again, there's obviously evolution of the algorithms in each of the different settings, but the principles and kind of the backbone of the architecture supports it. And so the $400 million, right, which is a lot of money that we've put into this in the 10 years, really now allows us to benefit from those network effects as we scale the architecture.
And there's an argument that as we grow bigger, we actually become, right, more differentiated and more unique, and, and theoretically, can expand the growth. Because you get to a certain size where there's no single lab or institution in the world, right, that could match us on accuracy or cost or, or utilization.
So on the penetration, the market penetration, as you guys see it, you know, how many labs can you, as you look at the landscape, like, could be users, and where are we on that trajectory?
So you know, obviously, the next-gen sequencing landscape incredibly well. So if you look at Illumina's numbers, right, and you look at the low to mid-throughput market, you're talking about thousands of laboratories, right, around the world. That, I would say, is our sort of target customer. Typically, they're in academic settings. We have also quite a lot of reference and central laboratories, specialty labs, increasingly, obviously, there's pharma and other. But the sheer number of places where sequencers are present continues to grow every year.
Mm-hmm.
Not only that, the power of these sequencers expand, so what you're willing to do on a sequencer or the type of work you can do in-house has changed materially, even in the last two years with the introduction of the X and some of the other competitive boxes. We think all of that dynamic is quite favorable for us because the world we foresee has a lot of diversity of instrumentation and consumables, which is good for a player that deals with diversity well, and then we can also apply that to many of the other modalities.
Let's talk about that, with that comment on the X, if people want to keep doing it. Are you seeing more mid-throughput customers move upstream and essentially, further centralization of that sequencing model? And then, you know, obviously, they're stealing those volumes from the mid-throughput, but also, you know, their old NS6 volumes going to the X. Like, give us a sense of what you're seeing from that shift in the market.
It's obviously not just them, right?
Yeah.
We see Element, we see Complete, we see PacBio, increasingly Oxford. You know, there's quite a number of players in the market that have very differentiated offerings. And again, Illumina remains, you know, the predominant player, but, you know, it's, it's a different mix than it was in the past. Again, they have fantastic technology. They all do. You know, we're agnostic, but, ultimately, we are seeing different use cases for, for different applications. You know, in terms of some of the higher throughput boxes, you know, I think what's ultimately happened is, you know, the breadth of centers that have adopted these, probably broader than you would have guessed, right? So we see an entirely unique spectrum and, and, all over the world, frankly, of different people that have upgraded their sequencing capacity.
Now, with that, it doesn't mean all of them are obviously running full volume-
Yeah
...near 24/7 on these boxes. Some are only able to run one lane at this point. Now, our guess is over time, between the fact that you're seeing people move, let's say, from exome to whole genome, or they're moving from a myeloid panel or a solid tumor panel to a CGP, that alone will drive some level of absorption of the capacity, and then obviously, as well, just the sheer growth and number of patients being tested. I mean, in a lot of areas, there's still quite a lot of folks, right, that are not being tested on next-gen.
Mm-hmm.
And so there's still quite a ways to go there. You know, for us, at the end of the day, you know, we're fortunate. We just want more data output.
Yeah.
Right? And so that's happening. Now, the challenge for some, right, is, you know, the mix change, right, from a cost perspective in terms of the cost of producing precision medicine data. I would say, you know, of $100, it used to be maybe analytics would get 5% of it. You know, now that the cost of sequencing has come down and even the cost of the boxes have come down, the software piece, because of the data proliferation, is actually becoming more and more prominent, and so we would expect that, of that hundred, to take a much bigger slice of the pie over the next, you know, five or 10 years.
And when you think about the shift here, I mean, you're talking about a lot of your presence is on the academic or translational. Talk about how you think that this is ultimately gonna shift into the actual clinic, and, like, how you guys are ultimately enabling that.
Yeah. So I guess we have to be careful. I mean, we do... You know, clinical means different things in different-
Yeah
... parts of the world, right? Like, RUO here is quite different than RUO in Europe, for example. And so in many parts of the world, we are used in the clinical setting, right? Even though the products may not be labeled. You know, we do have products in Europe moving toward IVDR. We'll obviously have to think about sort of the new legislation here in the U.S. around LDTs and what we do from an FDA standpoint, similarly as we enter Japan and other countries. But I would say, for the most part, majority of diagnostics done in the world in the clinical setting are RUO at this point, or LDT. I think it'll probably be some mix like that in the future. What that looks like, we'll have to see.
But for us, we can support, you know, any sort of setting that exists. Again, we designed the platform under a QMS system and with what's called design controls, to be able to have it ready for any regulatory environment that evolves. And I would say Europe's probably been here at the forefront.
Mm
... frankly, of regulating products like ourselves. But, you know, there's still a lot of, I would say, evolution that has to happen in many of these different markets.
Yeah. So, you know, the MSK went 360, went off, now it's the... Was it the Access launch? So dig into that. I mean, you guys, on the fourth quarter, you guys had a lot of announcements, so just kind of walking through... and how it's, like, driving this transition into the deeper and deeper clinical market. So what is the DDM plus Access? Like, what-- ultimately, what is that? And then, you know, what is that answering?
You want me to talk about liquid biopsy?
Yeah, sure.
So the MSK partnership has been really exciting for us. Sometime last year, we started talking to MSK about decentralizing and deploying a few of their tests globally. And so they actually selected us to be one of their partners to take the test that they're running in-house for liquid biopsy, which is MSK-ACCESS, as well as their solid tumor test, CGP, which is called MSK-IMPACT. So they selected us because of our cloud based platform, our ability to replicate the analytical performance that they have with in-house across the world. And so this has been a huge growth driver for us in an area where we see a lot of excitement, specifically, liquid biopsy and the MSK-ACCESS test.
So we launched a privilege access program or early access program with a few customers late last year in December, and we'll have that full commercial launch in April. And we're already seeing a lot of demand. We've announced customers such as BioReference, Tennessee Oncology-
Mm-hmm
... Dasa, who are already adopting this solution. They... First of all, when you bring the MSK name, especially in the U.S., but also globally, this is really exciting. You, you, the customer usually perks up. They, they like the solution. So it's been relatively easy, or exciting for them as they're looking to add new applications. But there's also a really interesting biopharma angle. So as we're working with MSK, they, they've introduced us to many different biopharma companies or bring some more to the table there. And we specifically announced a partnership with AstraZeneca, where AstraZeneca will actually be subsidizing or sponsoring the deployment of MSK-ACCESS globally, across new customers.
So this is exciting for us, obviously, as we deploy MSK-ACCESS to new areas, but also for biopharma, as they have more patients who are being diagnosed and tested for liquid biopsy and eventually eligible for their drug, but also the data coming off of those tests is exciting for them as well.
All right. So, like, take a step back and just think about the overall workflow. I mean, you guys, as a software, you know, as a service, essentially, you're software that sits on the sequencer in this democratized environment of sequencing. So how does the sample throughput... Like, walk us through the logistics of the distribution model that you guys ultimately offer, 'cause you're, you're offering a diagnostic test, so you still have to do the isolation, but, like, the lab that in Dasa is gonna do the MSK-ACCESS, instead of having the sample sent to MSK, they just do it locally. So, like, how do we think about the changes from the logistics that you guys have-
Sure.
Currently enough?
So I think, you know, particularly here in the U.S., folks are much more, you know, I would say, used to the central lab model, where the sample is sent out-
Mm-hmm.
It's processed in the lab, and then a PDF comes back, right? So for us, it's quite different. So in our model, you know, let's use, again, one of our customers here in the U.S. as example. So if I'm BioReference, right, I already have a lot of the logistics that exist in terms of being able to do sample collection. And so I would like to now offer liquid biopsy as a capability with a well-validated test. And so MSK-ACCESS, being probably the second most used liquid biopsy test in market, and certainly one that, you know, clinically has proven, given its capabilities, is really robust, is one that would be quite attractive. So if I'm them, what I can do is I go and I contract with SOPHiA, right? We can then go to their laboratory, see their setup-
Mm-hmm.
Right? Understand, sort of, all right, what automation are you doing? What sequencers do you have, right? What chemistry have you used in the past? And then basically take our kitted version, right, of the MSK-ACCESS that exists, which is our bundle-
Mm-hmm.
and then be able to work with them on their wet lab preparation steps after they've done the sort of sample prep early part, like the isolation, other pieces, to be able to use our sort of probe content on their sequencer, right, on the flow cell, to go run the sample, right, and get the result. And so where our piece comes in is once the sort of sequencer, in their case, it's Illumina, sort of generates a FASTQ file, that goes immediately into the cloud, right?
Mm-hmm.
It is then what's done, secondary analysis happen, which is kind of the variant calling. Then there's tertiary analysis, which is the reporting piece. That then ultimately leads to the end result, which will be sent to a clinician, right, after it's signed off by a board-certified lab director. And so we're doing the piece from the back of the sequencer all the way through to the answer, right? And we do that better than anyone at a lower cost than anyone. And that's where, again, the complexity is happening over time, given all of the different drugs available in market, given all of what we're learning about different genetic content, given the complexity, right, of what we have in terms of many of these diseases.
And so that piece in greater scale is what we do, and then we're paid essentially per patient that gets uploaded off of the back end of the sequencer.
And then, so compare that to other peer models like Tempus and others that are running this. Like, what is the scale advantage that you have that they might not have, or just a differentiation there?
Well, one, you can do all of the analysis close to the patient, right? So let me give you an example. Take Tata in India. I, before, used to send my samples to Foundation-
Mm-hmm.
Right? And then there would be quite a long wait. I mean, just think about the logistics of getting samples from India to Foundation Medicine's lab and back. Again, it's a wonderful test, but here now, I can be Tata. I have my own capabilities. I can use the DDM platform to basically replicate some of those tests, right, that I want to run, hereditary or solid tumor. I can do so in a highly accurate manner in a few days, right? So now I'm getting an answer for that patient quite quickly, pristine results... and I can do it with whatever equipment and other elements I have around my laboratory, right? So now I'm able to lower the cost, I'm able to quicken the turnaround time, I'm able to basically do multiple different types of tests on one platform with one workflow of prep and chemistry, right?
All within the confines of my institution. And so it's, and then what's fascinating is, so now I'm pharma, right? And so, you know, in the last week, I was talking to prospects in Australia, one of the largest cancer centers there, in Brazil, in Nigeria, in France. You can imagine all of these centers now producing their own data close to the patient, where they get to keep that data for themselves. They're not just getting that PDF back, they have the raw genetic data.
Mm.
Right now, they can take that, mix that with their other data, and actually get conclusions on their patient if they so choose over time, right? They own that data. And so from that standpoint, it really enables them to be their own sort of precision medicine lighthouse, and a lot of the intellectual capital stays within the institution, which they can eventually do other research or clinical trial work, et cetera. And so, you know, and the data is now comparable because it's on a common platform across all of those different labs, no matter where you are in the world.
Oh.
The insights and learnings we get from all of those laboratories are also shared across the community. So again, it's a very different thought process versus, again, the send-out, where the lab is doing the sequencing, sending back the PDF, right? And stapling it to the EMR, and then doing the revenue cycle to collect the reimbursement. So even in the U.S., in many cases, given how reimbursement is firmed up in a number of different NGS categories, now this could actually be a profit generator for the hospital.
If I think about it from the LDT perspective and a lot of the liquid biopsy players, like, why wouldn't... What's prevent them from coming on board and, you know, essentially partnering with you on the rest of them?
So this, this makes a ton of sense, right? So this is something we've seen post the MSK announcement, mainly because... And we seek to do it with other of our, our peers. I mean, we have another product in MRD AML that we're currently developing with one of our very large super brand name lab customers in the US, that's in the Midwest, right? And here's, again, very exciting, in terms of what we're able to do with them. But again, it's open innovation, right? We're doing it in, in combination with thought leaders in the space. We can also do this with other, other companies, right? There's a lot of technology being built, right, for the decentralized world, whether they're reagent packages or, or different, other configurations.
It doesn't make sense for everyone to build their own sales force and build their own analytics capabilities when we have it already deployed in many labs in the world. So you can see a model where someone takes their solution, puts that on our platform, we algorithmically support it, can push that out to our customers, and now you've got distribution already in all of those laboratories that will adopt tests like this. And so for us, this is another angle. And what we're really trying to do longer term is just enable, you know, that production of data at scale, right, in a common manner. Because if I'm the lab director, if I'm the hospital, I don't want to run five different software applications for five different solutions, right? All with mixed kind of variability and have to change my prep every time-
Yeah
... I'm doing a new run. It's very hard. There's not a lot of labor in these labs, right? And even with automation, you can't keep up with the volumes as they're happening and the complexity.
So when you're thinking about, you know, almost being plugged into, like, an Epic or, you know, the back end analysis piece of that, of a massive healthcare system software piece.
Yeah, because for us, again, we are a B2B company.
Mm.
And so, you know, with our positioning as kind of a network player, you want to plug into everything, right? We'll go into an institution, we'll plug into your LIMS, we'll plug into your EMR, we'll plug into whatever configuration of equipment you have, whatever reagents, whatever other software you might be using in the laboratory. It's very flexible, right? And the point of that is, because what we want to be able to do on our platform is enable everyone to sort of be more efficient, right?
Mm.
Be able to produce data at scale. And so to do that, you really have to sort of weave stuff together to create a solution and a workflow that's better than what exists. And I would say, you know, it's, you know, time savings, cost savings, and the magnitude, depending on the laboratory, could be very material.
Let's talk about the cost savings, the last question-
And then, honestly, that's a part of it, but then at the end, the patient is also getting, you know, the insight-
Yeah
... that they need. I mean, if, you know, if it was someone in my family, I would want them to be seen by someone using the DDM platform based on the learnings that clinician can have relative to the prevalence of that variant and some of the other conclusions comparative to the community, right? And even in a multimodal sense, where you may see associations, right, relative to drugs, that on a pure biomarker basis, are correlative.
Yeah.
But if you take nine different data points from different modalities, and together you find a correlation, you might actually much better solve for why we have responders and non-responders for certain therapies.
And so as the sell from you, I know it's B2B, but, like, it really requires the physician, because the, the physician right now in sequencing is still the biggest hurdle in the education standpoint. So how much of it is, "All right, we sold to the hospital, now the hospital has to educate the physician, like, you need to be using your patients here, or your—for your patients, they're getting better care." Like, how, how much... Like, talk about that push.
Yeah, this is probably one of the more challenging aspects, right? Because you would think, like, all these folks in the hospital talk to each other, they don't.
Mm.
Like, the pathologist doesn't speak to the oncologist often, and let alone the radiologist or others, right? And so, you know, this is one of the things. So certain customers of ours, one recent one, solved it by mandating it, and they just said: "Look, we've made this decision organizationally. It came from a very high individual. We think this is better for us and our patients. Everyone needs to stop sending out, and they need to now send the volume to our internal laboratory." Then we have other places where it's really tough for them, right? You know, they're still sending a few thousand samples out a year, and, you know, their lab is running one of our solutions, and the clinicians prefer the send-out because they think it's lower risk, or it's easier, or there's white glove coverage, or there's going to be better reimbursement, right?
I would say, again, there's still a lot of education to happen. As we move into our CarePath module, and maybe you want to talk about that, as we start to touch the oncologist, I think that'll become a bit easier.
Yeah, sure. So CarePath is our multimodal analytics module that we launched late last year, and it's basically giving the clinician the ability to track patients longitudinally and do things like compare them to other patients, visualize multimodal data, and then eventually predict outcomes and compare them to others in order to choose the best type of treatment.
Awesome. That's all the time we have. We could keep going-
Thank you, Luke.
Sure. Thanks.
Great questions.
Still got it.
Yeah. Great.