Okay, welcome back to the 2023 Stifel Healthcare Conference, day two. We are on the life sciences and diagnostics track here. I am Dan Arias. I'm the life sciences and diagnostics analyst, and we're happy to have with us our next company, Bionano Genomics. CEO, Erik Holmlin, is with us. I think, Erik, maybe you're gonna take us through some slides, and then we'll see what we have left for a little Q&A if we have time. Yeah, that sounds great, Dan. Okay. Thank you. Thank you for the opportunity to participate, and it's good to look out across the room, see some familiar faces and some new faces. So excited to tell you about what we've been up to at Bionano. I think, you know, these next two slides, we have one nice, long, forward-looking statement slide, but now we have another one, and I feel like, you know, these lawyers need something else to do besides give us long lists of disclaimers to put in our presentations. But please make sure that you look at our filings on the Internet. But if you don't know Bionano, if you're new to us, we're a commercial stage company in healthcare innovation, life sciences tools, diagnostics, and we're really focused on oncology, genetic diseases, and a really important area for us is the contributions that we're making in cell and gene therapy, and we've been pioneering something called optical genome mapping, OGM for short. Optical genome mapping is a technique or a workflow that really transforms an area of anatomic pathology called cytogenetics. Cytogenetics, I'll touch on how big it is but our technique really replaces three main workhorses there, karyotyping, FISH, and microarrays, but there are four or five other, or two or three other assays, four or five altogether, that can really be replaced by one workflow, optical genome mapping, and we'll talk about why it's better. What we sell is this end-to-end solution for optical genome mapping, and so it's an instrumentation platform at the center, but we have sample prep automation and really amazing software for interpretation and reporting. Commercial stage and, you know, revenues have been growing nicely, so this is, you know, over the last several quarters, looks like 11 quarters there, and, you know, steadily growing revenues, and that black line represents our installed base. So as of the third quarter of 2023, we reported $9.3 million in top-line revenues, and our installed base has reached the 300 mark, so 301 installed mapping systems. You know, what we've seen is pretty consistent growth in that installed base, about 40% year-over-year growth, and revenues are growing in that roughly 30% range. The idea is to build this installed base of mappers and then have the consumables revenues take over. So if you look on this chart, you see about two-thirds of the way down on the list of bullets on the right, flow cells, those are the unit consumables tied to a genome that gets analyzed by a mapper. Now, those are growing at 55% year-over-year, and so we've seen the consumables growth start to outpace the installed base growth, and that's exactly what we're looking for. So we wanna see more and more people adopting the technology, but then those who do adopt it, use it more and more on a regular basis. And we just completed a $80 million registered offering and concurrent placement which is extending our cash runway. So when we look at the areas that we're focused on, it's really about addressing unmet clinical needs in these four markets. So hematologic malignancies, leukemias, lymphomas, myelomas, constitutional genetic testing, solid tumors, a pocket of it, the area that's focused on fresh, frozen tissues, and then a big area of utility for mapping is in, you know, R&D, preclinical, and clinical development of cell and gene therapies. And, you know, the areas that we're trying to address are covered in these four bullets that you see at the top there. So you may not be aware of this, but about half the time when a patient's sample is tested in a cytogenetics lab, whether that's for cancer, for genetic disease, the results come back, and they're totally uninformative. Like, it's very common in leukemia for a complex disease to have a normal karyotype, and yet the oncologist knows that that patient has leukemia. The impact of getting a normal karyotype back is they don't know how to treat and you know, treat that disease and manage that patient. So that happens 50% of the time, same as same happens in genetic diseases. Now, based on some of the work that's been done with mapping so far and some of the papers that have been published, we now know that about 20% of the characterization, so prognostic scores in leukemias, are wrong. So when we look at how standard of care does that compared to what mapping does, what we see is that there's a massive reclassification. About one in five patients would either be upgraded, so that they would be, let's say, treated by with a transplant or some other intervention, or they'd be downgraded, and they wouldn't get treatment at all. And so the standard of care is just not adequate for properly characterizing these diseases. And, yeah, I think we all know, and we're familiar with the fact that cell and gene therapies are struggling to progress, and one of those reasons are off-target effects and other aspects of a deterioration of genome integrity through the manufacturing process. Well, optical genome mapping can help characterize that and play a role in the QC, both in preclinical development, but in cases of you know, prior to infusion in patients. So this optical genome mapping is really our economic engine. The idea is that it replaces the traditional cytogenetic tools that are out there. And this is a slide that really, I think, it's intended to tell a story about genetic variation and illustrate to people that a lot of times when we think about genetic variation, yeah, we have this idea, well, it's one base pair changing and that's true. One base pair can change and that can cause a deleterious effect in a gene or some other area of the genome. But actually, the whole, there's a whole spectrum of genetic variation that is characterized by the size, and it ranges from one base pair all the way up to full chromosomes. And so the technologies that are used to look for these events have limitations as to the size or type of event they can detect. So you can see sequencing in the orange bar on the right, very, very good at looking at one base pair, and we know about the amazing capabilities of Illumina and now all the other sequencing companies that are coming into the space. They're not gonna be able to detect anything reliably above about 500 base pairs. Now, long-read sequencing, PacBio, Oxford Nanopore Technologies, they've come out, I mean, incredible progress over the last decade but still significant limitation in terms of the range of variation they can detect. Long-read sequencing reliably up to about 5,000-10,000 base pairs. And that's why this whole area of cytogenetics exists. The current standard of care is fluorescence in situ hybridization, so staining chromosomes with fluorescent probes, microarrays, and then karyotyping. So growing cells up in culture, popping them open, spreading the chromosomes out on a slide, and having a trained technician stain and read those slides. Incredibly laborious, requires two weeks of cell culture to get done. And optical genome mapping, you can see in the blue bar, covers the entire area that traditional cytogenetic covers but then importantly, bridges this gap over to sequencing and actually covers an area that's completely otherwise uncovered, that space between about, you know, 50,000-100,000 base pairs down to about 5,000 base pairs. The thinking is that within that window, there's a ton of clinically relevant and biologically relevant variants that are not being picked up. When mapping picks those up, let's say in a leukemia study, those can be used to further manage the patient. That's why we're getting better answers in at least 20% of the cases. The idea is that optical genome mapping will replace traditional cytogenetics and then provide a very nice link to sequencing. If you're looking at this slide, you may ask, "Well, what's the future of long-read sequencing?" I think that that's a valid question. I think optical genome mapping and short-read sequencing is a very powerful combination. And so when we think about what are we trying to do, on the left-hand side, fundamentally accelerate revenue growth, and I think we're in an environment which everybody here is familiar with, we're also familiar with it and that is that we need to operate as efficiently as possible and get to profitability. And so what we decided to do is really focus in these four primary areas. It's about selling this end-to-end workflow for optical genome mapping, primarily in hematologic malignancies, as a tool in pharmaceutical companies for cell and gene therapy. And then we're addressing this rare disease community as cytogenetics labs pick the system up. But selling this end-to-end workflow, sample to answer, is key, and actually, no other genomics company does that, right? So Illumina, for example, you're gonna buy a lot of equipment and a lot of tools and a lot of reagents from other companies, and that's fine. That's worked well for Illumina over the years, but what we know is that customers want a single vendor to deal with, and so we're giving them that. Something that we need to focus on and have been focusing on is clearing the path for reimbursement. So the big economic opportunity for us is to replace traditional cytogenetics. Well, those are used in clinical applications, and so in order for a lab to convert over, they need to have confidence that they're gonna get paid, and that's been. There's been a ton of progress. There have been some codes that have been priced recently, about $1,900 for a leukemia assay, and so that's progressing well, but we need to keep doing more. That's not just here in the United States, but on a global basis. Then we're seeking regulatory clearance for this end-to-end workflow in the U.S. and in Europe. It's not something that we require for commercial adoption today, but we just see that the overall climate is focusing on the importance of FDA registration and so we wanna be ahead of that. So that's something that we're working on, and of course, doing it as efficiently as possible. So these are the four pillars of our Elevate Strategy. This is the end-to-end workflow. So we have sample preparation kits and a system that uses a technology called isotachophoresis, or ITP for short. It's called the Ionic system. We acquired that through an acquisition of a company called Purigen Biosystems. That does the DNA isolation on the front end, which is very unique in optical genome mapping. We now have two instruments. So the Saphyr system, pictured at the bottom, is the instrument that's been around since 2017, and now we have its next generation, the Stratys system, which is pictured up at the top. Stratys has four times the throughput of the Saphyr system. Why is that important? Labs want to adopt, but they have found that the Saphyr system for higher throughput labs just doesn't process enough samples, and so we developed a whole new instrument. That's now in early access, so about 10 of them for sale this quarter, and we're filling those orders, and systems have been placed, and we'll go into a full commercial release at the beginning of next year. Then software. So our software solution is called VIA. It stands for Variant Intelligence Applications. Again, we acquired a software company, and then we adapted this software to optical genome mapping. But what's so powerful about it is that labs can not only analyze optical genome mapping data, but they can put NGS data in that same software solution. So today, they would need to be generating two completely separate reports, giving the oncologist an NGS report, giving the oncologist an OGM report but now that can be integrated. And it's not limited to just NGS and OGM, but you could put microarray data in there as well. This really provides a very streamlined workflow for getting OGM done, and it integrates well into what's existing. On the left-hand side, what you see here is the traditional lab workflow in cytogenetics, and I can't even read the things that are on this slide, but the idea is to illustrate it's pretty complex. There are multiple workflows depending upon whether you're, you know, looking at and this is just in hematologic malignancies. Different workflows, depending on what initial diagnosis has been named for the sample coming into the lab. Optical genome mapping in contrast, is the same workflow. It doesn't matter what the sample is, it doesn't matter what the initial diagnosis is and so that really simplifies and streamlines the workflow, but then it shortens the time. So traditional cytogenetics would take something like up to 3 weeks, and in fact, some of our customers that have been adopting had a 4-week workflow, and we shrink that down to 3-5 days. And so you can imagine when it involves getting a patient on chemotherapy, a savings of 2 or 3 weeks is likely to have a very, very big impact in outcomes, and oncologists love that. The other aspect is kind of illustrated down here below. Multiple reports, 3-5 different reports coming out at different times, and we consolidate that into a single report. And so the workflow is really attractive for these labs. When we think about cell and gene therapy, how does optical genome mapping fit in? Well, they use cytogenetics today. They use it initially, you know, whether you're going allogenic or autologous, you get cells from a patient and then introduce your therapeutic payload. At this stage here, when you have your edited cells, you need to characterize those, gene modifications. You're looking for off-target effects. You're seeking to confirm that the desired on-target effect has been delivered. So that's a first point of cytogenetic intervention. It's a place where mapping can make a big difference. But then, the next phase involves growing these edited cells up in culture to give you a manufactured batch. Well, it's well known that during culture, genome integrity can degrade, and you can lose all of your intended effect, or you can create toxic effects. And so optical genome mapping is again used to evaluate cells here and then evaluate them prior to infusion in patients. And so I think I'm going to show a slide at the end of the deck, but what we've seen is like a doubling in the adoption by pharma companies on a year-over-year basis, and it's for this application. So how do we think about the size of this market? We think about it in probably two different ways. In one of the earlier slides, we talked about the number of samples that are being tested in traditional cytogenetics. That's about 10 million samples a year. And then we think about what are the number of labs that would adopt this kind of technology. There's about 10,000 labs on a global basis doing some form of karyotyping, FISH, microarray. Karyotyping, by the way, is the global standard. There's way more karyotyping that happens than sequencing that happens today. And then about 1,400 therapeutics companies working in some area of cell and gene therapy. So I think that there is the opportunity to place thousands and thousands of our mapping systems and process millions of patients per year. And economically, it costs about $500 to process a genome or a patient sample. And so that's how we think about the total available markets here. This is the methodology outlined here. You know, I think it kind of goes through four stages: the prep stage, the image stage, the mapping stage, and the interpretation stage. I won't spend a ton of time talking about it, but there's a couple of unique aspects. The first one is the DNA isolation. So one of the reasons that sequencing isn't very good at looking at these large rearrangements is that sequencing processes tiny fragments of the chromosomes. You have 150 base pair reads coming from molecules that are, you know, a few hundred, maybe 1,000 base pairs long. Well, at that point, all of the structural information in the genome has been lost and destroyed. And so what we have developed is proprietary methods to isolate what we call ultra-high molecular weight DNA. So we're talking about hundreds of thousands to millions of base pairs long. So here you have all of the structural relationships intact, and what we do is then label them up, sequence specifically, and then linearize the molecules in a proprietary chip called a nanochannel array. When those molecules have been spread out, we image them doing single molecule imaging. That's where the instrument comes in, and then software takes over through a mapping and then visualization and interpretation stage. But this is the OGM workflow, and it gets the job done in 3-5 days versus 3-4 weeks which is what is required in the traditional workflow. Now, for some people here, I know not Dan, because I've been talking to him forever about structural variations, but this idea of structural variation may be new. It turns out it's not new. It's new a little bit to the sequencing community because sequencers don't find it, so they're not talking about it. But all of these medical societies have written guidelines for characterizing patients in cancer and genetic diseases. Every one of the guidelines for leukemia, lymphoma, myeloma, solid tumors, calls for karyotyping, FISH, and microarray. That is the standard of care. Karyotyping and FISH and microarray, as we illustrated before, look at these large structural variations. This is really an established, important area of medicine. What has been going on is there isn't a technology that's really been able to crack the code, and we have that now. This is the area where we're focusing in. Often folks are interested in, well, you're saying sequencing doesn't do as well as mapping. Can you quantify that? Well, there's been tons and tons of papers that have come out, and this is a slide that attempts to illustrate that. So the black bars represent the percentage of structural variations that was detected by sequencing compared to what optical genome mapping detected. So that first line says that sequencing picked up 11% of the variants that OGM picked up. As I said, long-read sequencing is better than short-read sequencing. So here you see 50%-70% of structural variations are picked up by long-read sequencing compared to what mapping picks up. But I don't know about you guys. I have a 14-year-old daughter. She's in 9th grade. I tell her that 50s and 60s and 70s on the test is not good, not nearly good enough, and it's when it's your life and it's cancer that we're talking about, it's absolutely, totally unsatisfactory, right? Some people have said to me: Well, gee whiz, Erik, PacBio has a new system out. It's called the Revio. It's way better. It's not, it's not better, actually. It's faster, but the chemistry hasn't changed. So these data really, really hold. I mean, this is the standard. Optical genome mapping is the best for detecting these large structural variants. That's, that's well established and well proven. Something else about being in the OGM space versus the sequencing space is that we don't have a lot of annoying other companies coming around. Now, sometimes it's nice to have strength in numbers, I guess, you know, somebody else telling your story, but we much rather be where we're at in the OGM landscape versus where the NGS folks are at. I mean, that's definitely crowded and a lot of price competition going on there, where we're selling a very compelling clinical value. And so we love where we're at and position in the space. Now, something for us has been a really intense focus on market development. So the strategic pillar that's connected to clearing the path for reimbursement is really a market development challenge, where we're focused on, you know, addressing key drivers to get the medical community on board and have them include optical genome mapping and guidelines. This is a decade-long process, but, you know, until you start it, it hasn't begun. And then getting payers on board. We've made a lot of progress there, but the key to it is building like a critical mass of data, showing clinical validity, clinical utility, health economics, as many publications as we can get out there, and then we need adoption and utilization. And we had a good discussion with the folks at Palmetto and MolDX, and they said, "Well, you know, who's using it?" I said, "Well, we need you guys to reimburse it so more people can use it." And they said, "Well, we need more people to use it so we can reimburse it." So that's a challenging dynamic, but we're driving that discussion by giving payers specific data that they're asking for, pushing for the guidelines, and ultimately, coding and reimbursement, which is driven by the payers and supports adoption. So this is an ongoing effort, and it's an area where we spend, you know, spend, spend money but I think it's money well spent to drive adoption and get these groups supporting what we're doing. And these are just some of the principal investigators who are involved in our trials. As part of that market development work, we have, you know, a clinical trials program in prenatal genetic testing, in constitutional or postnatal genetic testing, in leukemias and lymphomas, and then one in solid tumors. These are all the different institutions that are involved and the scientists who are leading those, those studies. You know, it's a highly collaborative effort. We see a lot of impact in this work. This is a cumulative count of just human genomes that have been analyzed and published, and I mean, you can see that the hockey stick is happening there. The same sort of thing is happening just to the cumulative number of publications. In 2017, 104 through the end of the third quarter this year, 886. And so why do I show these here? It's because this is really the best leading indicator that we have. It's the literature that really provides new potential adopters with the comfort to adopt. They're happy to listen to me as a salesperson but they'd rather listen to their colleagues who have tested the system and written about it. And this is just a summary of where those publications are hitting according to indication. And the idea is that, you know, we have pretty compelling studies, you know, across all of the different indications where we're seeking to drive adoption. So it's been very nice progress in market development there. And this is a slide that I think shows our progress. So since launching the Saphyr system. Yeah, we've been able to improve its performance quite a bit. There's actually been an overall 14-fold increase in throughput since 2017, and about a 10-fold decrease in cost per genome. And that is what has changed the kind of complexion of sites that are adopting. So in these first couple of years, especially in 2017, you see 63% of adoption was in, you know, university research centers, basic research. And I can tell you that people were doing things like hummingbirds and fruit flies and Komodo dragon, and the cover of Science was salamander. And, you know, it was sort of paying the bills, so we celebrated it but our real objective was to get into hospitals, academic medical centers, and so we kept pushing the performance of the product, increase the throughput and lower the cost. And so you see that, you know, a lot of that happened in 2019, and so that has really paid off. And so if you start adding up some of these distributions, it's kind of interesting. Only 30% of systems were installed in academic medical centers and reference labs through 2019, but since then, it's 71%. And then we just checked year to date 2023, 86% are either in an academic medical center or a reference lab or a pharma company. And then pharma is this sort of like light green bar on a year-over-year basis that really doubled. And so that reflects the interest in this cell and gene therapy application. So while we were, you know, back here doing the boogers, bears, and bees, is what I call the basic research area, you know, we've clearly shifted over into human applications but human clinical medical applications, that's really driving meaningful, important value to physicians and patients. So it's an incredible evolution for us and this is obviously the big market and it's the sticky market, right? This is where adoption happens and utilization sticks. And so we have a number of catalysts that we've been clicking through this year, good progress around product launches, clinical studies, work in reimbursement. Where you see a check mark, we've gotten it done. Where you don't see a check mark, it means it's coming still this year. You know, we're just trying to drive that end-to-end workflow into the market as effectively as we can and create the opportunity for folks to adopt it. So I want to thank everybody for attending and listening, and happy to answer any questions. Yeah, that was great, Erik. Maybe I'll just start it. Looks like we have a couple of minutes for a few here. And I just want to hit on a point that you made on the clinical data generation, which is to say, you know, to use the baseball metaphor, if I look at that slide on publications, how far down the path do you think that's taking you in terms of reaching that critical mass and having what you need? And then the second question on top of that, and sort of a related area, is, is it really just the number, or is there something that you think these payers need to see? Is it a comparison of some sort that they're really kind of trying to have in hand in order to change the way that they think? Yeah. I mean, so we made, you know, a huge dent, and I think what we've done is lit a fire in the community, and so the community will continue it. We're also gonna continue pushing, but, you know, there's this incredible momentum, so we've really, you know, t he sort of like step we've taken has been massive. We're gonna continue to support it but, you know, I don't think that the lift going forward is nearly as heavy as it has been to this point. When you talk about payers, this is really interesting. So this is not a new test. It's a new technology, but it's not being used for a new test, and so that really helps us. So when you go through the list of things that a MolDX requires to issue a local coverage determination, clinical utility, clinical validity, those things are already established. Clinical utility is, everybody understands, where it's in guidelines that you should do karyotyping and FISH. Mm-hmm. We're just a replacement. So then what they ask us to show is, you know, how good are we compared to those traditional methods? And the answer is shown in all those publications now, 100% concordant. And then they want to know, well, okay, are you better and by how much? And so then they want us to measure things like diagnostic yield. I mentioned at the outset that about 50% of patients' samples that are tested come back with no result. So what kind of a dent do you make in that 50% of uninformed test results? And the answer is that in heme, it's gonna be like 25%-30%. So almost half of those we're gonna make an impact on. That's huge. And then in genetic diseases, it's somewhere between 10% and 30%. It's a more complicated space. But nevertheless, we make a huge impact in the otherwise undiagnosed population, and that's something that payers value intensely. Because what happens with those patients is they get testing over and over, so super expensive, years of testing, and we can nip that in the bud for a substantial portion. And so, CMS just priced a specific, it's called a proprietary laboratory analysis code or PLA code and they just priced that for heme for two labs that applied for it, and they priced it at $1,900. And, I mean, if you compare that to some of the traditional genetic tests, those are much lower pricing, and so I think that there's some recognition of this incremental value there. What we need now are for, you know, the Medicare administrative contractors to issue coverage decisions, and that's something that we can drive because we have a CLIA lab, but also our customers are doing it. So MD Anderson in Texas, they're doing it. We, you know, we have a CLIA lab in California, so that's Palmetto, and so we're doing it there. Dartmouth, Harvard, they're doing it for NGS up in this North, Northeast area. So, you know, those coverage determinations are next. Okay. Okay, I think we've hit our limit here on time, so I'm just gonna say, Erik, thanks for spending some time with us today. Good to see you. Yeah. Thanks, Dan.