Welcome to the Proteomics in Proximity podcast, where your co-host Cindy Lawley and Sarantis Chlamydas from Olink Proteomics talk about the intersection of proteomics with genomics for drug target discovery, the application of proteomics to reveal disease biomarkers, and current trends in using proteomics to unlock biological mechanisms. Here we have your host, Cindy and Sarantis. Welcome, everybody. I'm back from holidays and it's my first episode for 2025. Happy to see you all again.
Happy to see Cindy. And I'm really excited to discuss with Jenny and discuss about proteins and, yeah, looking forward to hear from you, Jenny. Excellent. So it's Cindy here, also here with Sarantis and our vice president of product management, Jenny Samskog. Jenny, there's a little bit of a question about how to pronounce your first name, so I'd love it if first you told us about that.
Secondly, if you could tell us about, your role, what you've seen evolve in proteomics, you've got a pretty prestigious title. And today we want to talk a little bit about what's coming up. In the future, we have a this recent launch that we'd love you to characterize. And then we'll talk a little bit about some of the meetings where we'll be attending. Please take it away. Thank you so much for having me. I'm really excited to be in this podcast with you guys. So my first name is Jenny.
So it's a soft J, that's Swedish. And I would just like to comment a bit on, you know, where I come from. So you understand my history and I would say my main common denominator is really protein science and product development. So I did start my career in mass spec proteomics as a researcher. And after that I refocused to support, by biopharmaceutical research and manufacturing.
And my main function has so far been within product management, which in essence means developing new products and ensuring that the existing products that we have are meeting our customer expectations. And you're so good at leading a team that listens to customers. So I just want to acknowledge and appreciate you for that. It's such a pleasure to be here and have a have a product management function that really, really listens.
And I would say, thank you, Cindy, but I would really say, you know, I joined Olink, what can it be like three years ago or something. And their focus on innovation and advancing proteomics is very special and very unique. It was for me, it was a match made in heaven because I could combine having great products out in the market that contributes to cutting edge science. But the culture of innovation at Olink has been there since start and no credit to me there.
But it's really nice to be able to continue that culture of innovation. You picked us and we picked you. It's a match made in heaven. I absolutely love that. What's your why? Why proteomics? Why do you see such promise in this space? Well, you know, I think it's been, I just have to go back to where I started. So I did my research a long time ago, within proteomics and, as CMS or mass spec. And at the time, it was a fascinating area, but it was early days.
So at that time, you know, identifying, you could identify a handful of proteins. And then I was happy, if I could say like 30 proteins or something out from the mass spec. And not only that, but, you know, the way we identified the proteins could be based on one peptide. And that peptide. I had sequenced myself in the mass spectrum. So there were very limited amount of digital tools to support that. Not much like intelligence software or anything like that. So that's where I sort of started.
That's where I, and then I left that for it for quite some years, actually, to go to this more biopharmaceutical, industry and then came back to Olink. And it was, you know, it's groundbreaking how much things have happened since then. So, the fact that you can study thousands of proteins, the connection that we automatically almost have to genomics. It's definitely a new era. So, I would say it's a huge thing that kind of happened in proteomics since beginning of 2000 until now.
Yeah. I'd, I'd say that, and as you know, I've got a history in the genomic space that we've been trying to get at proteomics from the genomics side as well. From our first RNA seq experiment. Right. So those first sequencers that Illumina made, the 1G, they were going out the door for folks that were doing digital gene expression at the time, had been using gene arrays, gene expression arrays and were keen to to understand the links to real time biology.
And now, as part of Thermo Fisher Scientific, we have both the mass spec size and some amazing innovations in the astrol and the stellar there, as well as as this, proximity extension assay component. But what do you think it was there actually, that breaking point that makes a difference for proteomics to be more democratic? Is it the NGS itself? Or the NGS plus other protocols that would be integrated. What is your feeling there?
Well, you know, if we're talking as well about mass spec here now, I mean it's not our a core area, but it's definitely our college area. And you know, within mass within proteomics, mass spec is still a gold standard but also here and remember, this is not my area of expertise anymore. But there's a huge amount of things that have happened here.
And I would say mainly or a lot of things, of course, on the technology side to make sure that we actually can, have a much greater proteome coverage, obviously. But also, the digital tools, I should say, how do you understand the results, how did you quality assess the results, etc.? And the supporting tools to do that. I think that's been for me. When you've been away for a few years and you come back to see that both in within Olink, obviously who is really spearheading this market.
But also, what I have seen from the outside has happened in the mass spec area as well. Yeah. So just specifically around that Olink, component within the Thermo Fisher environment, you know, the proximity extension assay, first launched on the qPCR readout, in 2020, launched on the NGS readout. So that transition to be able to look at more proteins across the proteome, particularly in plasma CSF, some of these liquid media were mass spec may not be able to see those low abundant proteins.
I think that that was game changing for me, and that attracted me to this team and this technology. Jenny, you just had an announcement from your team about this new reveal products. Can you tell us a little bit about where that fits in to the democratization? Yes. And so yeah, and thank you for for highlighting the democratization because that's one thing. So proteomics has been you know, it's not for everyone or hasn't been for everyone yet.
One of our overarching goals, within product development is to make sure or to enable our customers to utilize proteomics, and make it accessible to a broader research community. And some of these accessibility challenges that we're trying to address, have been noted elsewhere many, many times. One of them being cost. So, to be able to run large proteomics studies or to be able to run proteomics clinically, the cost needs to go down.
There is also, apart from that, a perceived complexity within proteomics, right or wrong. And there's also, a need to better understand the data and to get more support in understanding and trusting the data. So those are the three things that, you know, at least we can talk about today. And and before I go into our new product, I just want to mention and we haven't really talked about that, you know, we talked about, what has happened within proteomics, in the last years.
But I really want to mention something that really made a big difference. Is of course, the, you can be first project, where the data has been public now for over a year. And we have so many, new publications coming out from that project already. So, and already now it's, you know, and that is sort of a game changer within proteomics I really just want to highlight that. Well, and those publications are highlighting which diseases folks can dig into.
So just for context, the UK Biobank Pharma Proteomics project a few years ago, 13 pharma partners agreed to run Olink as the technology of choice against almost 60,000 samples in the UK Biobank. Now, there has been publications around, the 54,000 samples that have been part of the flagship paper that came out in nature in October of 2023, and then there have been 200 publications that I know about over 200, but that's been cited. That flagship paper has been cited over 300 times.
And so that certainly builds, more of a comfort with the actual data themselves. It doesn't allay people's fear. And I'll say geneticists fear, but just because I talked to a geneticist around what we call pre analytical variation, and I think you allude to this in your complexity comment. Jenny, you mentioned right or wrong, they're perceived as complex and I certainly think that's true. From the genetics point of view. And I know Sarantis has a history in this as well.
Actually that was also my question, I'm guessing that the daily life is not only happiness in product management. You have a lot of challenges to go through. And you mentioned the cost. You mentioned, the time that you spent on developing. But any other challenge, especially from the technical variation, that you may be facing? That'll be great to hear.
Yeah. So I think that that is really one important aspect, especially as a supplier, to really make sure that we can guide, our customers in understanding their data. So maybe we can go through that a little bit because. So when I talk about trusting the data, that's very critical but very often overlooked. And proteins are different from genes. They are a little bit more sensitive.
You have to really take care when you do the sample collection and how you handle the samples, and really ensure that, you know, you have you can assess the data quality at each stage to build that confidence. So one of the things that we're doing is to develop tools to help our customers, help our researchers understand if they have pre analytical variation and then guide them through, what that could mean. Are they going to discard the data. Can they use them anyway.
So it's sort of a like an understanding, like an intelligent support of understanding your own samples, your own results. And so it's really critical, within proteomics to really take that into consideration. It's a great point. And I think at the end, our main goal is the precision medicine right at the end is like, having high quality data where we can enable precision medicine.
I'm sure Cindy, you have a lot to share, about this field where you are really looking closely recently on that even more, you know, and happy to hear your thoughts about how do you see this precision medicine being enabled by proteomics? And where do you see these going, proteomics in this respect. Yeah, absolutely. So I think we're better characterizing disease risk in individuals because we're capturing real time information.
And so the comparisons of polygenic risk score to protein risk scores have been really helpful in that regard. There's some papers out of Claudia Langenberg's lab, as well as Ben Sun, who's one of the one of the joint steering committee members in the UK Biobank Pharma Proteomics project. He's published with the team at BioXcellerate and Optima Partners around protein risk scores.
I think that's going to help us in understanding how to better recruit for clinical trials so that we can have clinical trials that are smaller but powerful sufficiently powerful to see success in candidates. And of course the ability to have more successful clinical trials. What do we say the candidates, you know, 90% of candidates fail when they hit clinical trials. If we can improve clinical trials by just 10%, we'll be the best drug makers in history.
And then I would say that changes everything downstream, because now we're really dialing in the right treatment for the right patient at the right time, which Chris Whelan talks quite a bit about. And he's in our just recent episode of the podcast talking about just that and how proteomics is enabling this and so ultimately those are the pillars I see being moved, the pillars of ultimately precision medicine, which interact with clinical trials and risk stratification.
And then each of those interact with each other. And I see all of those moving upon the foundation of an understanding of how genetics, proteomics and outcome data are associated and linked. Thanks. Yeah, thanks for asking that. And, you know, there was there's been a lot of discussion from our customers and the research community regarding how can we, understand different technologies in this area. How can we understand how they complement each other and can you help us?
Sort of guide us how we trust the data or how we analyze the data. So I think that's going to be and that's normal because proteomics is maturing in itself. So I think that would lead us back to a little bit on the mass spec side where the complementarity between, for example, our technology in combination with mass spectrometry, could help us to better proteome coverage. It could help us assess platforms through mass spec, while we would maybe take more of the plasma side.
So I think those kind of things, and then again, as suppliers and enablers to the research community here, I think we have a role to play to make sure that we really showcase that these are, you know, what we show you, what you see with our technology is you can trust that and you can and we will also guide you in terms of understanding how that performs versus other technologies and how they complement each other.
And I think that's going to be something that, as we are maturing, we're going to see more of and that's going to be a lot of them. And well, investments in digital tools. For that integration and for, for AI, machine learning, we hear a lot. Mike. And also, I wanted to ask you Cindy, for sure you have the overall this trend of suddenly a lot of people, due to the fact that we have a lot of technologies and other technologies. Now we're talking about precision medicine, right?
And there are a lot of events happening around this, especially in ways that they didn't used to have before. What is your feeling and what is your feedback on that? Because you are more in the field and, you know, in discussion with a lot of people. How do you see this moving forward? So I think these two topics are very intimately linked.
You know, your reference to our activities in the field and working with customers and showing up at conferences and our messaging Sarantis, and Jenny, your comments about having a responsibility in funneling data that are as accurate as possible into, the algorithms for machine learning and artificial intelligence that will change our understanding of these large data sets, right? We're not data rich.
Certainly not as data rich is, say, the self-driving car industry, as we hope to be in the future. But we're getting there. And as we get there, we have this responsibility to only put the most specific, well-characterized data into, those algorithms. And I think that's where we on the side of caution. I think that's why we have ostensibly fewer proteins in our assay, because we're very careful about getting those assays into our, products.
And I think in many ways, that's your team, maybe not your team before you joined, but you certainly have supported and resonated for that. And I think customers appreciate that. And just knowing that if we're detecting something, especially if it's a intracellular protein or a membrane bound protein, if we're detecting it in plasma, where it shouldn't be, that has the potential to be an enormous opportunity for discovery, by customers that are seeing it there.
And so our detection or our lack of detection should reflect, I think, true biology. And I think that's our messaging Sarantis at meetings. Yeah. So JP Morgan, we just had JP Morgan I think the messaging at JP Morgan or the take homes that I heard there were, essentially that these companies are in many of the pharma companies are presenting, they're moving into a growth phase.
I think we've had two years of challenges and funding and, and pullback and contraction and, and caution and I think there's this this bullish opportunity with Suisse, some uncertainty around the political climate and the change in leadership here in the US. But some optimism. And it just felt very buoyant there. And then we have right around the corner the Precision Medicine World Conference, which is founded by Tal Bahar.
And they're really building on what that momentum, felt like at JPMorgan or around opportunity and Vision in order to take action. And so to really foster, an environment of partnership, in this precision medicine space. So I think that's, that's very exciting. And Jenny will be talking a lot about reveal. Can you just give us like a high level overview is where does reveal fit into our product portfolio and where can people learn more about it. Thank you, Cindy.
So again we talked about accessibility being one of our main goals. And as part of that, we are adding, a new product to our, discovery portfolio. So everything that is, detected through and sequencing, and that is Olink Reveal. Olink Reveal is the little sister of Explore HT. It's an inflammation oriented panel, so curated, the assays are curated based on cis-pQTL associations, with a strong connection to UKB. A very strong, inflammation focus. As I said, it's a thousand plex panel.
So it's a good, very good protein coverage, of course, less depth than Explore HT. But, you know, on the, accessibility side, it's much more, what can I say. It's more of a mass market product. And the reason for that being, so we focused a lot on reducing the cost per sample. So the cost is actually less than $100 per sample. Wow.
Which means that it would be much easier to add this, for other cohort studies, where we have less funding, for example, but still, and I think, you know, we should always, aim to add proteomics as one tool in all the big population health studies. So that really enabled that, but not only cost, I would also say what is related to cost, but I will also say something about the, perceived complexity of proteomics. So we have focused a lot with Olink Reveal to make it super simple.
So you should be able to just go in the lab if you have a NGS sequencer and set it up and run it. To get results really quickly. So you can even run it manually or with a simple automation solution. So no big investments to start up, but something that any genomics lab already has. So it's an easy, simple, way of adding proteomics to your project. Actually, as you say, the democratizing protein actually at the end, right, is like a nice example of how we democratize protein. So that's great.
Yes it is, it is. We've been waiting for this. This is very exciting. Congratulations. I know it was a long trip for your team, and a lot of other teams. Congratulations. It's a really great tool. Yes. No, it's been, it's a project that has been ongoing for quite some time at R&D and we're super proud of this.
And really required a lot of data analysis of the data that are out there that are publicly available where we're allowed to go in and play with and see what are the ones that have the highest disease associations, what seem most promising for having future disease associations, where these cohorts just haven't been able to afford to get into proteomics. So I think this will offer quick publications.
And I think tracking the publications in review will be an exciting, time to see labs doing proteomics that have never even ventured in and then, of course, the opportunity to validate orthogonally with mass spec I think will be, also amazing. That's a great point. And I think the choice of inflammation is really crucial because inflammation, as all of us know, is connected to our disease almost.
And that offers a possibility from different types of researchers for different disease areas to explore proteomics finally. That's a great tool. Yeah. I mean even in Alzheimer's disease, right. Where there are clearly endotypes and some of them are associated with information and some of them are not. Being able to stratify those patients in advance of clinical trials, for example, might be some application.
I know that several pharma have reported that, and Chris Whelan talked about this, but they have been able to do post clinical trial proteomics on Explore HT, which does require automation. So that's 5400 proteins over 5400 proteins using a next generation sequencer. And folks are seeing stratification of these, of these disease areas after the clinical trial. And they're seeing that these different endo types of this disease are, are responding differently, to the treatment.
And I think that is laying some amazing groundwork. I think it will help a lot for biomarkers. It surrogates biomarkers. That would be really a great tool for following protein biomarkers really closely. And I have a question actually for both of you. I think we’re discussing about now. We discuss about tools that we’re developing now with a perspective in the future But how do you see the future? What do you see the challenges and the wins we may have from the proteomics lab in the future?
I think Jenny first. Know, yeah. This would be a great wrap up question. I love this this is wonderful. Yeah. No, I, I would say I mean for the future, I think it's going to be or it would have to be, a much more focus on combining different data sets. So again, coming back to what we talked about with, the focus on machine learning and so on.
So I think we we're going to see much more, support to combine proteomics, genomics, transcriptomics data with, disease genotyping, for example, we're going to see much more regarding, predictive power, on proteomics. And obviously, how is that translated into, clinical proteomics. So it's going to be I think we're going to see, you know, also just on the first UKB study, we've already seen that happening.
That you're identifying these really nice protein signatures with a very strong predictive power early on to say, you know, if this patient will actually get a certain disease several years in advance. So I think it's going to be like, you know, from this discovery to this more, clinical applications that's going to happen quickly now. We already had talked about how much has happened in a few years time. Right.
So and just looking forward and then in five years, I can't even imagine, you know, what we're going to do. but I think it's going to go like, you know, it's going to be more multiomics. It's going to be much more support for clinical proteomics. And of course, we as a supplier, have a responsibility to help that to happen. Great, great. Cindy, what do you think? What is the future? Well, I'll piggyback on something Jenny said.
So the the idea of being able to predict disease many years in advance of getting disease. I mean, that was really hot news when Keren Papier and Ruth Travis and Karl Smith-Byrne and Josh Atkins, their paper came out. There's, you know, seven plus years, it was a median of 12 years, 12 years in many cancers of being able to predict disease.
And of course, those are those are many of those predictive of genetic, dispositions of folks that are more likely to get disease, not necessarily that they actually have the disease on board, although they also time stratified to be able to get at a little bit of the detail there. And I just saw they had a preprint on prostate cancer that characterize some of the pathways in the immune system that are predictive of a likelihood of getting prostate cancer.
So I can't wait for that to come out in publication. So that brings up, the recent announcement around us running the entire UK Biobank. That's 600,000 samples. That's 500,000 individual with 100,000 repeat samples at a 15 year mark. Is my understanding, being able to see that across all of the diseases that are represented within the UK Biobank. And some of them longitudinal also, Cindy, some of them also longitudinal also followed, right? If I’m not mistaken.
That's the longitudinal component, is the one that over the 100,000 that are followed up at 15 years. Yeah. So and many of those have imaging data. Right. That's great. And outcome data. Right. So being able to characterize that, that set of samples which have around 8,000 African diaspora samples, have around 8,000 South Asian samples. These are diverse, sets of samples that, as Rory Collins says, aren't enough diversity for us to really characterize everything.
But across the entire UK Biobank, we do, you know, effectively have longitudinal representation, because if you get enough samples, you get folks that are in different stages of disease. So though it isn't longitudinal in an individual, it can be, you know, by being cross-sectional and large enough in size can represent some of the longitudinal aspects of disease progression. So that's what I'm really looking forward to. And I expect those data to be published around 2027.
Which means that that the world will have access to that international resource, which is some of the and we talk about it as the UK Biobank, but it is the internationally access to UK Biobank. So it's an exciting time. And just to add to that the statistical power of 600,000 samples, would, you know, imagine what that would mean for understanding rare diseases, for example, which hasn't been possible, really.
I mean, I know, we had that you could see that also in the, in the sort of smaller UKB, set from before, but with very few samples. So I think that is also something that is extremely important. Already with these, the first papers that we have or the small sample size that you mentioned, Jenny. Yeah, it's a small one. I mean it we were able to see there are great papers from Claudia Langenberg, that we had improvement on diagnosis of disease. Even better than clinical outcomes sometimes.
And that was really impressive. Well, it was really amazing for the first time to see such a thing. Right. Imagine that was tenfold more sample size. What's going to happen. That more diseases, right. More representation of those diseases. Exactly. And ability to you know, propose these protein scores that that that will improve any over anything a doctor has available to them today. Yeah. This is yeah it's a beautiful time. And of course we are biased by our protein excitement.
But yeah, we're happy to be a part of it. So with that, I will wrap up this episode of, Proteomics in Proximity. We will, as we all mentioned, we will be at Precision Medicine World Conference in Santa Clara. That's February 5th through the 7th. We will have a booth. Our booth will be near the stage for track three. It'll be between track three and track four in Hall C, and very close to a little networking station. So reach out on LinkedIn, reach out to me.
Reach out to Sarantis If you want to set up meetings with our team, I will be there in person and would be excited to talk to folks that are there. Would be great. That would be a great event. Really? Yeah. I wish you were here. I wish I was there, but I’m looking forward to hear your feedbacks. I’m sure you have great feedback from that. Yeah, we could do an episode live. Yeah, that would be great idea. Great idea. All right. Thank you Jenny. Thank you for tuning in. Yes.
Thank you Jenny, thank you. Thank you so much. Great to have you. Well, that wraps up this episode of Proteomics in Proximity. Huge thanks to our guests and authors of such impactful publications. I also want to thank you for tuning in. Really appreciate you being here. If you enjoyed the content of this episode, please think about sharing it with friends or colleagues you think might be interested in the content.
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