I recently had the opportunity to sit down with Déborah Heintze, co-founder and CMO at Lunaphore. Lunaphore is an exciting series C spatial biology company whose goal is to reduce the barriers to multiplex immunofluorescence (mIF) testing. The team at Lunaphore recently featured their new COMET platform at AACR. COMET marks an instrument-centric approach to address the difficulties associated with mIF. This approach caught my attention and led me to reach out to ask if we could sit down for a chat. Our discussion covers the new tech, as well as the spatial landscape more broadly. This is a space we monitor closely via custom research, data products (like DeciBio BioTrack), and market reports. This discussion is in no way sponsored by Lunaphore – we seek to speak with cutting-edge stakeholders to understand the innovation going on in our industry – if that sounds like you, please feel free to reach out.
Déborah, thanks for joining us, I’m so excited to chat with you. I want to start this discussion with a bit of an introduction to who you are, how you came to Lunaphore, and a brief history about the company.
Thank you, Cameron, for inviting me. It's my pleasure to share about Lunaphore and myself. I have a background in bioengineering from the Swiss Federal Institute of Technology (EPFL). I also spent a year in Boston, at the Harvard-MIT Health Sciences and Technology program where I was doing research in microfluidics applied to cardiac tissues. That was basically the link to Lunaphore. Then I came back to Switzerland to continue work in microfluidics, and that's where I met my other two co-founders. They were also working in microfluidics as part of their PhDs, and since there was this common link on microfluidics, we met and discussed this common interest in launching Lunaphore as a company.
From then on (we founded the company back in 2014) I was COO, but in a startup, that can mean everything and nothing. My role was everything apart from fundraising and R&D, which the other two co-founders handled. That means operations, customer support, sales, and many other aspects, but the common link along that, is everything related to product management, marketing, market research, and strategy, which I've kept also in my role today as chief marketing officer.
I think what's most interesting to me is the link between you and the other co-founders with microfluidics and running with that as an interesting differentiating factor for Lunaphore. We'll touch on that as we come to our product discussion later down the line. But thank you for that introduction! I'll spare everyone my introduction. I'd love to jump into more of an overview of the current landscape of spatial biology, thinking about where we are now and where you see us landing around that five-year horizon. Obviously spatial is super-hot right now – everyone and their cousin seems to have an interest in spatial.
To start the discussion, I’d like to talk about plex. This is something that obviously we see differ on the clinical development spectrum, but I'm just curious to get your input on where current customers are plex-wise.
As you're saying special biology is a huge trend. If we're thinking a few years ago, the word spatial biology did not even exist. We're to some extent past now the education stage about what is spatial biology and what it brings to the field. Now, we’re more at the point of understanding how you can implement the tech more efficiently, or how can we create the right tools that are going to accelerate the adoption of spatial biology in every lab. From a plex perspective, we need to consider where the general interest came from for spatial biology. The fact that you get more information in a spatial context, obviously, but also because realizing that the more markers you can have, the more information you have for analyzing and understanding what your tissue is showing.
I would of course still make a clear differentiation between discovery stage, translational and clinical. There is this trend of going as high plex as possible for discovery. That's when you want to go in without a hypothesis and just get as much information as possible. As you move along towards the clinic, you would want to nail that down to what makes a bit more sense also to be able to run it on larger cohorts. In terms of plex numbers in the discovery space, you always see claims about going 100+ plex, which is about showing capabilities. What we hear from customers is that this is so heavy in terms of analysis that you wouldn't necessarily want to go that high.
Then if you're going towards translational, we're more in the range of 10-30 plex, and towards clinical research, you would go even down to 5-15 plex. I think there's a trend of increasing plex because we're advancing our understanding from the clinical diagnostics which have only one or two markers and are beginning to unearth new information with signatures that involve more markers that will allow you to make better decisions on the therapy side.
I think that aligns well with my understanding as well. We see claims for 100+ plex…and, well we hear that the bioinformatics pipeline does not exist to make sense of all that information. In the abstract analysis that we've done, and in talking to users as well, we see anywhere around 15-20 plex for translational work. Then, toward the clinic, we're seeing a de-plexing toward 2-3 markers.
We'll just have to see. We've been waiting very patiently for our first FDA approval of a triplex CDx assay. So, hopefully that happens soon. The next set of questions I have are around particular markers of interest. We consistently see in abstracts, trials, and publications, a conserved set of maybe five or 10 of the top markers. Do you see a similar trend? Have there been any changes in markers of interest or grouping of different types of markers over time?
To start, immuno-oncology is where we see most of the markers come from, and that's going to stay there for quite a while, probably increasing in the number of plex to markers that you would need in that panel. Now, in terms of the grouping itself, it will very much depend on the type of project that every lab is running. We see that if you come with a very fixed panel, especially if you go higher plex, it's not going to fit every lab. That's where you must have the complexity in terms of what are you defining as the core panel and the flexibility as well. Having flexibility for every lab to add their own markers of interest on top of something that they know is already validated is extremely important. That's the way we perceive to address this ‘top markers’ question.
It sounds like customization and flexibility are key in being able to provide something that answers the specific research questions for your customers. What are the main hurdles that you have when you're taking this customized approach, as opposed to just having preset groups of top markers?
Our technology is one of our key differentiators and allows us the possibility to add any type of marker at any point. A lot of customers come to us because of that potential. On other platforms, you don't necessarily have that flexibility, or you would have a significant amount of work to be able to add your own markers. It's also inherent to the fact of using pre-conjugated antibodies versus in our case, off-the-shelf antibodies. People want to use the same antibodies they already use. All that flexibility is extremely important.
That's, in my opinion, the game changer. We see some of our customers, optimizing five panels of 30 plex from scratch in less than three months, on their own, without our help, and they manage to get extremely good data. And that's what's going to be our differentiator. Lowering the barriers of adoption for spatial.
To respond to your earlier comments on the customizability, we've seen that trend across different research tools, for example in the genomics space. Very early on it was clear that investigators wanted to have a validated panel that was extremely robust and well thought-out – i.e., the ‘top offenders’ – but they also wanted to be able to add their flavor of the week, whatever they were interested in discovering or researching, and to have that combination of the proven winners and the genes of interest.
And to address the markers of interest, we routinely analyze AACR, ASCO, ESMO, and SITC activity to determine the top immune markers that are being used. You have your usual suspects, like CD3 CD4, CD8, Ki67, PD-L1, PD-1, etc. It’ll be interesting to see over time, if the average group size and plex increases, and to see, if next gen checkpoint inhibitors like LAG-3 or others shift us to a newer generation of markers.
Next, I'd like to then jump to what we really see as a huge potential barrier to this market, and that is image analysis and bioinformatics.
So, just to start with the overall landscape here, how have you seen that change since you started your work with Lunaphore, where were we when you started and where are we now? Then, what do you see as the next steps or, maybe the inflection point to where bioinformatics may be more established and more robust?
Compared to a few years ago, we were talking IHC and H&E, and not so much multiplex immunofluorescence, and especially not immunofluorescence on the translational or clinical side of things. Today, we see a lot of companies popping up and coming out with new solutions that would help you analyze samples, images. That's a more general trend aligned with spatial biology itself. If we're going to see mIF move into the clinical space, we’re going to need new companies to come up with new capabilities to analyze the mIF.
That's the key trend we see here [the need] and then on another axis, there's everything related to the size of all of those images. What are the data management infrastructures that we can put in place to make sure that you can easily download your images to analyze them? This is a space where there's still a lot of improvement to be done, to facilitate the way users can make those kinds of analyses efficient. On top of that as well, is that today it's even more likely that an expert is going to analyze those images. It's probably a different person in most of the labs that is making the slides, doing staining, and capturing images, and then someone else who would be analyzing that image because it's all different type of expertise.
Where I see this evolving is in the user interface, or the way that someone can just run those types of analyses simply. To the point where one person would be able to run the whole process. I think that's where we're going to have to evolve. Where today we need experts, tomorrow it will be accessible to a larger set of users.
That definitely makes sense. And from our perspective, we’re seeing this tendency to form ecosystem partnerships between digital pathology and multiplex tissue analysis companies. I’m just curious, is there the desire to be able to have this all under one roof? Or is it inherently advantageous to have each company deal well with its own portion of the workflow?
Very interesting question here. We see that right now every company is working with every other company. I do believe it's meant to stay to some extent because it's such a large workflow you're trying to cover – from staining, imaging, etc., I mean, every company has their core competencies. From there, it's always a make or buy decision of, do you want to raise a lot of money in and do it yourself, or are you just much more efficient partnering up with a company that can do it for you? I think the other part of it is that every customer has also their preference. If you think of image analysis, every customer we talk to has a preference and so it’s good to have multiple options.
What we want to do is make sure that we're giving all the tools possible for making or lowering the barriers of adoption into spatial biology for our customers and users in the lab. The way to do this can be very diverse in the sense that if it's about recommending a partner’s solution that we think is a great solution, then that's the way we will be doing that. It doesn’t prevent offering as many tools as possible all from under one roof.
Do you see any missing pieces to this current ecosystem? Or, are all the pieces here now, and it's just a matter of accumulating clinical data and showing the utility?
I think we’re covering a pretty good piece of what is needed here. But still, as I was saying before, like with the image management infrastructure…some things might be not as efficient as they could be. But for me, it's not that we're missing a major piece, but more about again, lowering barriers and optimizing the process. Now you can think of it as a puzzle, where you put together different pieces to try to cover all of the workflow, and there's a lot of efforts that can be made into just making the whole thing much more streamlined for users to do their own analysis.
I think I have a few more questions on the landscape before we dive into the Lunaphore section. This one may be contentious but hopefully it’s interesting to our viewers. Many people in precision medicine are excited about liquid biopsy and a lot of people are excited about spatial, and a lot of people are excited about both. I want to get your take on the future of these two modalities and how they might complement, how they might compete, and just how you see the interplay between liquid and tissue playing out near and far-term.
From my perspective, they're very complementary because of their use cases. Liquid biopsies can be very extremely powerful, for example, for early detection of cancer or in real time for monitoring of treatments. In some cases it's just a better fit for what you're trying to look into. On the other hand, tissue biopsy is still the gold standard in terms of analysis of some of the cancer types. Again, depending on what you're trying to achieve, I really see that you can use both. As we are looking into the future, I don't see one replacing the other one completely. My vision is that in the end a pathologist would probably have a dashboard of solutions to make the right decision on the treatment for the patient.
I'll be really interested to see how that evolves, because there obviously is advantage of having the intact tissue – it's not like our immune cells are just at random moving around. I want to use this as an opportunity to transition, to talk more about Lunaphore just given that we're discussing the necessity for the spatial context and the value that it may have.
To start, could you give us your 60 second elevator pitch for COMET?
Indeed, focusing on COMET for today, COMET is really a fully automated all-in-one platform where you do staining and imaging of your samples up to 40 plex without human intervention. I will probably expand on a few of the value propositions later, but in a nutshell, you can use off the shelf antibodies. That's a very clear differentiator for many different labs who just want to use what they already know how to use. We're lowering a lot of the barriers of being able to optimize your assays rapidly. It's also the fastest and highest throughput instrument for hyper-plex on the market, while keeping high reproducibility. That's extremely interesting for people who want to run tests in the translational and clinical space.
I think a lot of what we're seeing is, this specialization within different plex levels and being able to offer the most robust product within a certain level of plex. It's interesting to hear about the combination of high-plex capabilities and high throughput, I think that sounds very attractive as a cross-stage platform. I'm just curious about who you expect to be the primary user of this platform? Is this ultimately aimed toward clinical diagnostics?
It's mostly translational research towards clinical research, with the aim to move towards the clinical diagnostic space. Having said that, since you can go hyper-plex, many people are using it in discovery with fewer samples or smaller cohorts. In a few months, you can get these initial hyper plex on a few samples analyzed, and then just transfer to a lower plex assay when you want to move to larger cohorts, using the same instrument, reagents and antibodies. We are also positioning ourselves in the translational and clinical space thanks to the reproducibility and robustness of the system. We continuously prove the reproducibility from slide to slide, and day to day, and continue to be attractive to pharma companies that need this level of robustness to move into further stages.
I'd like to get into a bit more of the differentiators and the value proposition for COMET. In researching for this discussion, I was watching some of your previous material and there's a lot of cool technology at the core of your company. I'd love to discuss that and understand how you feel this tech will set you apart from what is becoming a bit of a crowded space.
Many companies innovated on the reagent side. We came from a completely different angle, which is making sure that the reactions happen more efficiently in the chamber or in the microfluidic chip that we're using for our tests. From that perspective, we are suddenly having complete freedom on what are the types of reagents and samples you want to run on the platform. Also, because we control very precisely the flow of reagents inside the chip itself, we control uniformity over the whole sample, which is important for reducing gradients.
That's where you can suddenly start even thinking about quantification. We're not necessarily using amplification – I mean, there's a linear amplification – but suddenly you have a whole dynamic range that you can assess that is giving you extremely interesting quantification information. If you want to add other amplification, you can too. You have all these capabilities because of the open platform. We're focusing on immuno-oncology specifically, but you could do anything. There could be many other markets where people can use whatever antibodies work for them.
So there’s no tyramide amplification step?
The other thing that I wanted to mention that helped me understand the value proposition of the Lunaphore chip was this analogy: if traditional staining is like making a drip coffee, Lunaphore is like making an espresso.
It’s somewhat a joke, but that’s just what came to mind when I saw the diagram of the chip. It creates a reaction environment under pressure. I said to myself, “Oh, we do that with coffee, and it's a fantastic variation on how to get your daily caffeine.”
So, a very different approach to the reagent optimization that some others have gone for. How has that worked? What have been the successes of the product so far? Obviously, the launch just occurred a few months ago, so there so may not be too much more to report, but is there any additional commercial activity that you're excited about? Overall, how have things been going so far? How has the reception been to the product?
As you say, it's just been a few months since COMET launched, and we've never had so much success. It's amazing to see top cancer institutes and pharma companies already buying the platform. Some have already bought a second instrument because they are so happy! For us, it's an amazing time because we see the excitement. But on the other hand, we only have a certain number of units we can place for this year, because we're still in the priority access program. Probably a good problem to have, which we're working on. In terms of the projects so far, it’s been very focused on immuno-oncology, with some other projects also for example on COVID.
We have not much data or posters out yet, because we just started placing our instruments, but it’s a matter of time now until we get extremely exciting data out, seeing the great outcomes in labs using COMET. I'm really looking forward to that as well because that's what's going to support us in the scientific community in the end. How can you make sure that people understand that you have a fantastic platform without having data? You just need to produce that data to make sure you can show everyone.
Well, I’ll be excited to see the outflow of data in future conferences and publications. I will just say, as a personal dig, the COMET search term does produce a lot of noise when we're looking through abstracts. We take extra care to be sure that we're finding each and every mention of COMET, but I have to bring that up as someone who's looking through all the data.
Well, that means it might be inflating our numbers.
Maybe it was intentional? Just kidding. So, wrapping up a bit, we discussed ecosystem partnerships in the landscape broadly, and I'm just curious, for Lunaphore, for what does that ecosystem look like? How has the experience been, creating that ecosystem, and what do you expect to see in addition in the future to build the ideal platform with other commercial providers?
We're trying to get the most complete solution we can offer. We've been partnering up with other companies on the image analysis side [Visiopharm, Indica Labs]. Also, we are an open platform from a reagent perspective, but there's always an advantage in pre-validating certain reagents. We’re working with testing antibodies from different reagent companies, to make sure that we can just feed that list of markers that we can recommend. That's also part of some of the work we're doing. I would say those are the main focuses.
I'm just curious, is there any platform or collaborative option where, investigators can share their successes with their particular antibodies in addition to the in-house validated list?
We do have on our website the list of all the markers we optimized, and we would classify them differently on the level of optimization that has been done. For example, if you've tested on different tissue samples, then that will be classified in a different way than if it was just tested on a single tissue type. We also get this information from partners or collaborators when they consent for us to share. As an aside, we saw that customers started making their own user meetings because they got extremely excited, so we always try to get this feedback and hear what’s working.
I want to wrap up soon here, but just a couple more questions. What is the end goal for this platform? When should we expect to see prospects for clinical assays?
To continue on what I mentioned before in terms of reproducibility and throughput, we are in an attractive position in the translational clinical space. We do have already discussions on where we want to be in the end, and that’s always the clinical space. It's just now a matter of time. Of course, in a larger sense, the mission is for us to make sure that spatial biology's available for the larger crowds.
That makes sense. And lastly, what would be your wish list from biopharma, in terms of what could they do to help the space accelerate more quickly?
Sponsor more projects! There's some preliminary work on our side to come up with initial panels to propose to pharma companies, but then we need for them to come onboard and get to the next steps and take it further. They have the know-how on the drugs that they are developing. You need that collaboration to just be able to get into the clinical space. In some cases, we can work through translational research labs in cancer institutes or academia and make that link to pharma. In other cases, we need to work directly with the pharma companies and use the platform directly for the drug development.
Well said. I think that the extent to which we're seeing those collaborations is going to grow and grow. We’re certainly starting to see some of these prospective clinical trials and we're excited about that. Fantastic. Well, thank you so much. This has been a fantastic discussion. It was great to hear your take on the mIF landscape and then to learn about Lunaphore and COMET. I think other people will be interested to hear this too. Again, thanks so much from us here at DeciBio and, we'll look forward to seeing data come out and seeing what's next for Lunaphore. Thanks so much for being a part of this.
Thank you so much. It was really a great discussion and I'm as excited as you are to see all of that come out!