Bionano Genomics, Inc. (NASDAQ:BNGO) Cowen 43rd Annual Healthcare Conference March 6, 2023 1:30 PM ET
Erik Holmlin – Chief Executive Officer
Conference Call Participants
Tom Stevens – Life Science Tools & Diagnostic Team here at Cowen
All right, so should we get started?. So I’m Tom Stevens, Life Science Tools & Diagnostic Team here at Cowen. We have Erik Holmlin, take us through with his presentation and have agreed to name it, take it away.
Great. Thank you very much, Tom, and thank you and your colleagues account for the invitation to participate. It’s great to give by now this platform and tell the story. I want to congratulate everybody who made it all the way down the hall to Fairfield that’s a long stretch. So, we’re ahead of the game. I actually spoke at the Cowen meeting in 2019. And I think that there was one person in the room and it was the speaker giving the next talk. So I think, on a percentage basis, we’re way up. But it’s obviously glad to be back in person, publicly traded company. So we’ll be making certain forward-looking statements and I would want to refer everybody to our filings on the SEC website.
So Bionano, if you don’t know us is a commercial stage company. We’ve focused on healthcare innovation. And our purpose is to elevate the health and wellness of all people. Now, we’ve been undergoing a really tremendous wave of success over the last few years. We sell something called an optical genome mapping system, and we’ve gotten 240 of them installed worldwide. Our revenues for 2022 were right around $28 million. And that represented some 55% growth over 2021. And a statistic that’s really important and is illustrating the traction around the adoption and utilization of our platform is the number of clinical genomes that are now being published. So 3000 of them have been published. And it’s a very powerful statistic. I’ll show you the dynamics of that later on in this talk.
What our business is focused on is transforming cytogenetics really taking a common routine clinical workflow that hasn’t changed much for 50 years, and bringing it to a modern industrial scale. We do that through this digital workflow, which gives a massive enhancement in overall resolution, and somewhere about a two fold increase in the success rate for samples that are coming into testing labs. And that results in better outcomes for patients and less costs.
Cytogenetics if you’re not familiar with it is a field of pathology and it’s focused on large chromosomal rearrangements and their analysis, the way they fit into different diseases like cancer and genetic disease, and in fact, cytogenetic analysis is the recommended first line diagnostic analysis for millions of patients worldwide. And these medical societies, you see their logos listed at the bottom of the slide, all recommend cytogenetic analysis as first line.
So it inherited disorders like autism spectrum disorder, intellectual disability developmental delay, all recommend microarray first followed up by Karyotyping and other follow on analyses including sequencing. Throughout cancer, karyotyping is a standard-of-care especially in hematologic malignancies, the first line analysis is a cytogenetic analysis by karyotyping to determine your prognostic risk, and be used for therapy selection.
So cytogenetic analysis is really a cornerstone of clinical analysis is in the market today. And our methodology, optical genome mapping has the potential to really transform that workflow. And so if you look across the top of this slide, you see a scale here, we call the variant continuum. And it turns out that genetic variation or variation across the genome can occur in many different flavors across many different sizes. In fact, the medical community sees it as analog, anything that happens along this continuum, if it impacts the function of the gene has the potential to cause disease, could be a treatable target, or somehow a valuable diagnostic biomarker.
And so you see down below the array of tools that are available to analyze variants of different size, and you see where sequencing fits, that’s on the far right hand side of the slide there, sequencing is really good at looking at what we call sequence variants. So primarily, variants of a single nucleotide change, long read sequencing, which has really improved over the years has gotten better and better and more and more accurate at the single nucleotide stage, but can get only get to about 10,000 base pairs in terms of seeing these larger chromosomal rearrangements.
That’s where cytogenetics comes in. And so you see, the existing standard-of-care in cytogenetics, comprises karyotyping, which was introduced in the 70s, really popping cells open and staining them and looking at them through a microscope slide. Humans do that. And then they cause the resolution of karyotyping is about 5 million base pairs. That’s the smallest event that you can detect by the standard-of-care if you get cancer today. First line test is going to be karyotyping. Microarrays do better, they have higher resolution, but they’re only sensitive to gains and losses. And then there’s fish, fish is very powerful. It can pick out particular events in the genome that are known to be prognostic in nature or guide therapy. But fish is targeted. You can only use fish if you know what you’re looking for.
And so, at Bionano, we’re focused on transforming this cytogenetic workflow by fundamentally replacing it with something we call optical genome mapping. And so optical genome mapping will connect the whole chromosome analysis that karyotyping thing does, all the way down to sequencing. And so what we believe is that optical genome mapping will become the standard-of-care, in cytogenetic analysis, and that standard-of-care will evolve to be based on sequencing and mapping together because they’re the two most comprehensive, fastest, highest throughput, most affordable tools.
We believe this opportunity in cytogenetics is large. So there are about 10,000 labs worldwide that are doing some form of cytogenetics, mostly karyotyping but fish microarrays they’re looking at about 10 million patients on an annual basis. Another area of cytogenetics that we’ll touch on briefly is the applications in gene therapy and cell therapy where it’s critical to evaluate target effects in genome modification as well as on-going genome integrity and the scale up process. And so we estimate that this market overall is worth about $10 billion, so substantial market.
These labs are located in academic medical centers, reference labs, which you’re all familiar with, and then these therapeutic companies and you can kind of see the breakdown here of those labs. So about 1000 Academic Medical centers in hospitals, 4000 or so regions regional reference labs, and a relatively limited number of ultra large reference labs, but those run a substantial amount of the volume. And so these are our target customers for our platform. We’re active in China. It’s a big cytogenetics market for us. And we have seen a lot of progress with OEM partners in China getting NMPA clearance for our reagent kits. And so we expect to be able to penetrate hospitals, and independent clinical laboratories in China. And then, of course, we have applications in cell therapy that pharma and biotech companies as well as academic medical centers are adopting to measure the effectiveness of different gene editing methods.
Now, the indications that we’re going after, generate about 10 million samples per year. They’re primarily in constitutional genetic testing, hematologic malignancies, and in solid tumors. Within constitutional genetic testing, we’re looking at indications like prenatal testing. So really converting amniocentesis over to the optical genome mapping platform. In postnatal testing, reproductive health, developmental disabilities, other genetic disorders, and the China market is another significant opportunity for us as well in applications in non-invasive prenatal testing follow up, as well as family planning and so that’s about 1.7 million patients overall on an annual basis. Hematologic malignancies is the killer application for optical genome mapping.
So here, we’re talking about diagnosis, therapeutic response analysis, and on-going surveillance in a variety of leukemias and lymphomas. You see them listed here AML AML, AOL, COL, CML, all of the array of traditional indications within hematologic malignancies, but Myelodysplastic Syndrome, which is a precursor to leukemia is now being converted over to routine testing on optical genome mapping and taken together, the heme malignancy market measures up to about 5 million tests or 5 million analyses conducted on an annual basis. And then of course, there’s the solid tumor therapeutic guidance market as well as different research applications.
So taken all together, there’s a substantial amount of volume going through the cytogenetic workflows on an annual basis. And our focus is on converting that volume over to optical genome mapping.
Now, I talked about Cell Bioprocessing QC being a significant opportunity for us. And this slide is a summary of that opportunity. And so I think if you’re following this area, of course, you’ve got allergenic cell types, and autologous cell therapy, isolating cells, delivering a therapeutic payload through gene editing. And these payloads are now more and more complex all the time. And so to be able to measure on target and off target effects is critical in establishing efficacy. And what we now know is that companies that are submitting packages such as IND applications to the FDA are getting feedback that karyotyping which they rely on a regular basis, simply doesn’t have the resolution that’s necessary to monitor these effects. And they’re telling these companies to come talk to Bionano. We know this because they reach out to us.
And so this is a real significant opportunity for us to evaluate target effects, but then to monitor genome integrity through the scale up process, and post infusion to really be able to sample patients throughout the course of therapy. And so this is another big opportunity, which we valued at about $3 billion, and so $7 billion in the traditional cytogenetic side, plus another 3 billion in Cell Bio processing is makes up the $10 billion dollar opportunity overall.
Now, a big question we get is really you can’t do this with sequencing. And the answer is sequencing is not reliable for detecting large structural rearrangements. And the reason is, is fundamental and so to try to explain that we think about a book. And if you were to take a book and shred up all of the pages to a level where you just had the single word that’d be a lot like the sample isolation process and sequencing. You run it through a hydrogen column where it’s heavily fragmented. But then before you complete library prep, there’s a further fragmentation process that’s critical. That’s how sequencing goes as fast as it is it does, because you’re doing parallel sequencing, highly parallel sequencing of short fragments all at the same time.
So if those fragments were just single words from this book, you’d be able to spell check them. But you wouldn’t know the story. You wouldn’t know what chapter, what paragraph and so forth, that those words came from. It’s very powerful to know the spelling of the word I’m not my daughter, saying it doesn’t matter what the spelling is, that’s very powerful. But it is not the whole picture.
And, realistically, two decades of next generation sequencing have not closed the gap. And so I think that the community is now recognizing that sequencers are not going to be a universal solution for molecular genome analysis, because they need to incorporate some structure. Even long read sequencing, which has improved the picture dramatically, has not closed the gap completely. And in fact, there are some really good papers out recently from different consortia that are evaluating long read sequencing. This is the preprints on the left hand side here describing the work of the NIH funded program all of us and what they found was that there was a massive discrepancy, so Oxford Nanopore technology, nanopore long read sequencing, and Hi Fi reads from PacBio, agreed 53% of the time, so, we should have just flipped the coin to guess which ones were going to be in alignment or not.
So there’s this tremendous amount of work still to be done. And our view is that long read sequencing will not close that gap. The GREGoR Consortium has incorporated optical genome mapping and its comparative. And what they have shown is that it’s very common for Optical Genome Mapping to pick up variants that are simply undetectable by sequencing. And so the reason that Optical Genome Mapping does such a good job of picking up these large rearrangements is that we bring the context that comes from these ultra high ultra long reads. And so to use the book analogy, again, instead of shredding the book up into tiny pieces that are only the size of the word, we still have to shred it up, because when you pop the cells open, and the DNA comes out, it naturally fragments.
But we work very hard in a proprietary method to preserve the length of DNA. And so now what we’re going to be analyzing are like full paragraphs, full sentences, entire pages. And so when we look at those segments that are so long, they span all of the repetitive sections of the genome. And so we can now place the chromosomal location, we can determine the orientation. We can determine if there’s just flat out been new information added to that page or to that chapter. And that’s the critical structural information that plays a very important role biologically and clinically, in the proper functioning of the genome. So if we look at our read length, compared across the industry, they’re by far the longest contiguous reads.
So this is important, as I mentioned, because of the repetitiveness of the human genome. Two thirds of the human genome is highly repetitive. I was speaking, I don’t know if it was one of you, but to somebody earlier today about the D4Z4 repeat that’s critical in some forms of muscular dystrophy and other forms of intellectual disability. It’s a repeat array that exists on chromosome 4 and on chromosome 10.
And on chromosome 4, when the D4Z4 array is shrunken, and it goes below a certain threshold, that’s pathogenic, but on chromosome 10, the size of that array has no biological or clinical significance. So if you’re just looking at sequence in NGS of the D4Z4 repeat, you don’t know if it’s from 4 or 10. That’s why today, most of that analysis is done by Southern blot, right? You can’t use sequencing for it. And so that’s a good example of where our ultra long reads 25 times longer than those of average size from let’s say long read sequencing from PacBio. That’s why they’re so powerful at revealing structural variations.
Now, optical genome mapping is a workflow goes from sample to answer, we’ve developed it in house, and our customers like that we are the supplier of the entire workflow. Because if something goes wrong, and you know, it does, things do go wrong every once in a while, and so they have to call us and ask us about it, we’re able to troubleshoot the entire workflow with them. Now, when they work with other partners in sequencing, in particular, they have to rely on the array of different suppliers to troubleshoot, we are an end to end supplier in optical genome mapping, the workflow starts with isolation of this ultra high molecular weight using our proprietary protocols. We then label certain sites across the genome. And if you’re recovering molecular biologists, you may remember restriction fragment length polymorphism, that’s all we’re doing. We’re putting a label at restriction sites, but we’re leaving the DNA molecules intact, then we linearize those molecules inside nano channels on the Sapphire system, and take a picture of those single molecules. And we measure the distances between those labels. And that’s your restriction fragment length.
But now, there’s continuity over hundreds and hundreds of 1000s of base pairs. And so those images are then subsequently digitized. And those digital molecules are analyzed through a set of proprietary algorithms that we’ve developed and read out on our analytical platforms, which includes a very powerful new system that we’ve developed through an acquisition that we made called variant intelligent applications or via, and it reveals all of the structural variations. And so there have been a number of benchmark studies that have looked at the relative performance of optical genome mapping, compared to sequencing and across the board. This slide shows you the fraction of OGM detected structural variants that are revealed by sequencing. And you can see, this is all next generation sequencing. And many of these are consortium driven projects 1000 Genomes Project genome in the bottle, and you see that short read sequencing detects somewhere between about 11% and 30% of the structural variants that optical genome mapping picks up. Everybody says, well, we are what about long read sequencing?
Well, long reads sequencing is better. So on germline analysis, long read sequencing picks up somewhere between 50% and 72% of the variants that optical genome mapping picks up. Now when those genomes get more complex like in cancer, the sensitivity of long read sequencing falls way off and goes down to only about 25%. And so the sensitivity of optical genome mapping is by far, substantially greater than any sequencing method. It’s also got very low false positives.
And so here’s a study that compared are actually three different studies that compare nanopore sequencing and mapping. And it may not be totally visible from where you’re seated. But on this slide, you see nanopore in what are called circles spots above. And the nanopore plots are filled with all these different lines connecting different chromosomes, those are called translocations across the genome.
Across the bottom, you see the events that are called by optical genome mapping. And so all of those tons and tons of variants across the top, those are all false positives. And so the false positive rate of nanopore in these studies was like 90%, which is obviously not going to work in a clinical setting, it’s also not going to be very powerful for discovery research. Because if you have a sense that, substantially, most of your variants that you’re picking up in an assay are false positives, you’re simply not going to pursue that data type.
Optical genome mapping is now known throughout the industry, of being the most specific methodology for detecting structural variation, so very powerful. Now, these are the products that we sell. We sell the Sapphire system and kits that are needed to do the end to end workflow. And through acquisition we brought in very powerful solutions that simplify the workflow first, by automating the DNA isolation. We acquired a company called Purigen Biosystems integrated their isotachophoresis technology which runs on the Ionic system. And we’re developing a module for ultra high molecular weight DNA which should be in the field later on this year. And then we acquired bio discovery, which has an industry leading tool for analysis of structural variants from sequencing and microarray data. And we now have a version of that working in the field and we’re going to go to a commercial launch of it later this year in hematologic malignancies, but that will make reporting of optical genome mapping incredibly efficient. And it will integrate optical genome mapping reporting alongside sequencing and microarray data.
And so now all of a sudden, a laboratory performing OGM sees it alongside of all the other molecular data types that you’re that they’re analyzing on a routine basis. And so our workflow is going to be end to end completely streamlined, very powerful to fit into molecular pathology. But this read length alone is not sufficient to really penetrate this clinical market. You’ve got to score high on all six of these criteria, sensitivity specificity, you must have that sensitivity in very complex cancer genomes and mosaics, the sensitivity must include low Allelic frequency.
So in cancer, the variant that makes you sick and may kill you, is usually not present in abundance, it’s there in a small fraction of the cells. And so it’s critical to be able to detect it. That’s one of the powerful aspects of karyotyping is it’s kind of the original single cell technology, it’s just that it’s got limited resolution. And so optical genome mapping meets all of these criteria.
Now, when you’re evaluating Bionano, let’s say as an investment opportunity, I want to point out something to you that should be pretty compelling. We’re talking about making optical genome mapping standard of care and cytogenetic analysis. And we’re alone in this in this effort. Compare that to what’s going on in the sequencing landscape. Now, in the NGS landscape, in particular, these companies are bringing very powerful solutions to an area where they’re now fundamentally across the board competing on costs. And so we really like the idea of being fully differentiated not only in our technology, but in the market that we’re going after, with a clearly sustainable competitive advantage that will allow us to generate value over the long-term.
A lot of the sequencing companies come to us and want to work with us because it’s clear that mapping and sequencing work together very nicely and are very complimentary. And we say, we want to work with you, we want to work with all sequencing companies, right. So the combination of mapping and sequencing is a very powerful, powerful one.
Now, if we look historically, at where we’ve come from, we’ve been focusing on optimizing the product to really penetrate these markets. And we’ve, we’ve relatively recently hit critical thresholds like on throughput and cost and being able to analyze these capabilities. I’ll talk a little bit about where we are focused going forward. But it’s been those innovations and that progress that’s really driven this consistent growth in revenues on a quarter-over-quarter basis, since the first quarter of 2020. So we haven’t reported yet for the fourth quarter of 2020. But we pre released and so revenues were in the $8 million range full year revenues about $28 million.
And importantly, we’ve been able to increase the size of the Sapphire system installed base to about 20 — 240 systems worldwide. And so we expect this trajectory to continue going forward. One of the most noteworthy transitions over this period of time has been the shift in our customer mix from almost entirely academic basic research and non-human sites, to over 80% academic medical centers, reference labs and pharma companies. And the reason that matters is that those users pull through quite a bit more consumables revenue on a per system basis. And so our products are penetrating the valuable markets that are now our targets.
Our long term growth plan is is sort of summarized in these key pillars which I won’t go through but we’re focused on product innovation, driving up throughput, simplifying the front end, shortening the overall time to resolve and creating powerful integration solutions. We have a large clinical trials program that’s underway to address the need for coding, coverage and medical guidelines. It’s been very productive across these different indications generating a number of publications. And we work with all of the PIs who are part of these key medical societies and will influence guidelines that are set up.
And so all of these efforts have been what has driven this incredible explosion in the amount of clinical data that are published. And so you can see historically, we, plugged along here and there, but we brought in an absolutely amazing chief medical officer. And she initiated these programs, brought in all of these collaborations, and that’s what’s really driven this explosion. And this word of mouth, the network effect that is driven by this is what we believe will drive future revenues. We have a number of product innovations that are on the roadmap going forward, just designed to continue the momentum that we build been building so far. And what we believe is that if we’re able to deliver on all of those catalysts, revenues will continue to grow at this rate. And we believe that we will build a very valuable company with this, P&L at scale that you see listed here on this slide.
So with that, I want to thank Tom and the Cowen Team for the opportunity to present and all of you for your kind attention.
Q – Tom Stevens
I think we have time for a brief Q&A, the last 15 minutes. We could just kick off on the whole long we debate I think you made a really interesting point, when we last chatted on the kind of difference in informatics workflow and how the short read start thing with the long read versus OGM. Could you speak a bit on that?
Well, I think that that you — you’ll our platform is sample to answer in the sense that when you get through it, you press print, and it gives the laboratory report that they can pass on to a clinician, and that’s done with the same technician that isolates the DNA that goes all the way through if you compare that to a sequencing workflow in molecular pathology, there’s a whole bioinformatics team that’s required to really pull those data together and create that report. And if we knew that we could get away with a whole bioinformatics team doing it, we might not have developed these solutions. But now, we’re glad that we did, because it’s really easy for people to adopt and use.
I guess just the last one, we often [Indiscernible] as you go to customers with this kind of value proposition, you go to new genetics, the world gets the quest of the wild, what are the things that most surprised about and one of the bottlenecks that still kind of make them hesitant to adopt?
Yes, I mean, I think I think that the real driver, and the key sort of aha moment, if you will, is just the ability to identify this incremental information that’s not being detected by existing tools, but that it’s immediately clinically and biologically relevant. It’s actionable, right out of the box. There’s also a lot of stuff that is never been seen before. But a lot of the information is actionable right away, which they’re not getting the incremental diagnostic yield through a series of publications about plus 30%. So that’s pretty incredible. The roadblocks are pretty straightforward. If you go to quest, which is processing about 40,000 samples in hematologic malignancies annually, in their Texas, laboratory. If they’re going to convert that to optical genome mapping, they need certainty of being reimbursed for it. And so we need to work out the coding and coverage process for them to get that economic support. But that seems like just a formality at this stage.
The other thing that they need is the throughput. So the current system probably doesn’t address that sample volume, but the next generation system, which is going to be available this year, will.
Right. And with that I’ll open up to the audience for any more questions.
Can you give us a sense of — with the number of samples that are being processed today versus the number of samples that were processed [Indiscernible]
Yes, I mean, I think I think what we’re talking about here is, the growth is tremendous. So it’s — on a on a year over year basis 30% year-over-year growth and work and we’re consistently seeing that quarter per quarter like we report the number of flow cells that we sell. And that’s going up. And so we see increase in utilization.
Now, if you’re out there and you’re analyzing the number of flow cells we sold, and you’re dividing by the number of systems that quarter, you may not see that growth, because that denominator has a whole bunch of brand new systems that haven’t hit steady state yet. So it’s, it’s a little bit disconnected in time, but the growth is tremendous. And we forecast that to continue on a regular basis.
I guess I’ll say that it’s a complicated data type that’s highly algorithmic in terms of just making single base calls. And so, in structural variations are embedded within repetitive sequences. And so, repeats cause all sorts of problems. And I can imagine that for nanopore, which is your algorithmic just to determine a single base pair that repetitive segments would make that more challenging claim for more.
We got time for one more.
We, so sequencing is used and that should continue, right. Because that’s going to reveal the variants that are within the detectable range of sequencing. Right now, karyotyping is being used and other methods that people are sort of cobbling together in an effort to provide good tools, we should be able to replace all of those, the best combination of methods would be sequencing and mapping.
This this Well, I mean, the way that that would work is that somebody would adopt the Research Use Only platform and then validate it, and then as an LDT, and then they can do that. All of our reagents are manufactured in registered facilities. And so it can be done that way now, today.
So I think that we got to call it that, unfortunately. But thanks Ed for coming down. Great.