Seqing Knowledge Ep.2 – The Future of Genomics in the Clinical Market

The future of genomics in the clinical market

Deidre: Welcome to Seqing Knowledge a genomics podcast from The Sequencing Center. I’m your host Deidre Casey, here at The Sequencing Center lab in Fort Collins, Colorado. Each week we’ll take a look at genomics-related topics from current events to new ideas with the hope of inspiring fellow knowledge seekers like you. Today we’re joined by Ryan Casey, head of product and business development at The Sequencing Center to talk about the future of genetics in the clinical market. Welcome, Ryan!

Ryan: Thanks Deidre, I’m really excited to be here.

Deidre: I’m really curious, as the head of product at The Sequencing Center what is your vision for the future of genetics?

Ryan: It’s a pretty interesting question and I think the reality is a lot of people think about it from one facet and a lot of that is just purely the future of sequencing. I think what’s really interesting about the clinical market specifically is that we look at it from a holistic perspective. That includes everything from the upstream all way down to the downstream. What happens when you look at it from that view is the market starts to expand dramatically around what’s possible and so we’re thinking about everything such as how do we do full end-to-end clinical pipelines where we actually influence the upstream sample collection to biobanking all way down to the informatics and data storage and even the analytics of the clinical market.

I think there’s that view of it, I also think that there’s a lot of specific types of sequencing pipelines and testings that are going to come out. What I mean by that is one of the largest revenue generators we have in our company is HLA typing and we have entire end to end pipeline built out about that and we think that there’s going to be a lot more opportunities within the market to do that in different areas – for example with myeloid leukemia and a handful of other different areas.

The biggest limiting factor though is the FDA needs to approve all these for clinical usage. Where it really starts though is in clinical research and so a lot of the activity we’re seeing right now is kind of building up these pipelines inside on the clinical research side and that translates into actually being FDA approved for clinical diagnostics. So right now we’re kind of sitting on more of the clinical research side and our hope and vision for the future and where we think the biggest market opportunities and expansions will be is actually getting into clinical diagnostics.

Just the other day I was reading an article about how one of the hardest parts of being a medical doctor is just doing things like inputting medical records and then referencing history around medical records. When they talked about introducing genetics into that factor people were saying I don’t have the time to dive deep into the data of genetics and then figure out what the correlations may be and filter throughout the false positives between the phenotypic and the genotypic information. So when we think about that, we really say okay, that means we need to provide very prescriptive, concise information and whatever we produced to the doctors doesn’t impede their day to day progress. Again, I think when you look at that from a diagnostic perspective there’s a lot of clinical opportunities. The market is constantly expanding so for us I think we’re going to be focusing heavily on just the clinical end-to-end pipelines for clinical research and doing a handful those and then expanding out. I think a lot of other companies are going to follow that same suit because it’s very risk mitigating and it helps you get to the market in a little bit easier of fashion rather than trying to hit the whole shebang right out of the gate.

Deidre: So, many of our listeners might be thinking this too, but it certainly came to mind for me – what do you mean by a fully integrated pipeline can you explain a little bit about what pipelines are?

Ryan: Yeah, sure. So when we talk about pipelines there’s a lot of different terminology for different areas of it. Historically there’s been the notion of bioinformatics pipelines and that’s purely taking raw sequencing data and running it through multiple stages of informatics analysis. That might look something like your alignment and then after your alignment comes variant analysis and something else potentially. For us though, we want to look at it in a bit of a longer and holistic view and what we mean by that is the pipelines that we’re looking to build and are currently building not only span from the bioinformatics side but they also go all the way up to sample collection. When we say we want to collect HLA typing samples we actually influence how the samples are collected, how much of what type of samples are collected, and then we actually have an entire pipeline all the way down to the data management and storage of that.

What happens when you’re able to do that is you not only get a higher quality pipeline from end to end you can actually do the QC steps at each of the different stages to ensure the highest quality data is produced from the sequencer. You also get really interesting efficiencies from that as well. Let’s say we collect blood samples and we have a bunch of different tools we run these through. We could not only do genome sequencing on the blood samples but we could also do hematological analysis checking out different types of blood levels. Instead of only looking at one view the reality is, especially within medicine, that there’s an infinite amount of variables and what we’re trying to provide from an integrated clinical pipeline is a suite of tools or analyses that help clinicians make the best judgement that they possibly can with the most amount of data and the most accurate data possible.

Deidre: It sounds like you not only get a deeper view of the patient’s status with this but you also get a higher level of quality from the lab side, which is going to be reducing the risk of each stage of the process. Not only that, but you’re using that data for comparative analysis with other projects. So, for example, you’ve just gone through and sequenced a novel gene and acquired some unique results or maybe you found a better way to process that sample along the way and you can use this information and these new techniques to improve future internal processes of a similar nature.

Ryan: Yeah that’s totally accurate. I mean, what we tend to see while we’re working through these protocols are different nuances and tweaks especially if you have a specific, repeatable pipeline that you’re always going through over and over again. You not only optimize the protocol itself end-to-end, everything from the informatics, to sequencing, data collection all that we also get to reduce the potential margin of errors.

The other thing that I think a lot of people don’t recognize within these integrated pipelines is automation and costs. If it’s super repeatable you obviously can put automation in place and get a different suite of robots to handle the entire thing, but you also have much more consistent purchasing power against your vendors. What happens then is if you know there’s always going to be a specific pipeline around a specific date or many in a certain time frame you can actually leverage that and get bulk discounts or anything of that nature. We use that specifically at our lab in order to reduce our prices, which we then pass on to our customers.

So there’s a lot of different variables and a lot of interesting things that actually happened when you move more towards these specific pipelines rather than everything is just kind of a custom project.

Deidre: Where are you starting to see genome sequencing being adopted in the clinical market? I mean, are people even using it yet do they know how to use information properly with their patients?

Ryan: Yeah there’s definitely a lot of movement happening and I think people are starting to realize the value of it. We work with organizations of all types government, private, universities, etc. We work with some pharmaceutical companies and some really big ones and they’re starting to integrate that into their drug discovery efforts to not only to help prime the research but we’re even seeing genome sequencing within phase one clinical trials. So we’re actually starting to see potential usage in inclusion/exclusion criteria. We’re also starting to see that as another additional data point. What at least the pharmas are starting to realize is if you can prove through the combination of genetic and phenotypic data that a drug has effectiveness, the genetic data helps add some heavy weight when you’re going for FDA approvals.

Not only that but when you look at even things like just clinical research, in general, a lot of it’s happening in immunology. We’re seeing it more within some of the other pipelines like cancer. We’re actually seeing a lot of genetic data-driven decision making from the genetics side taking place so my impression, especially when you look at the general market as a whole, is that there’s a lot of additional movement happening and the market growths are actually pretty surprising. For example, the HLA typing market is currently growing in a 9.9% or 9.3% CAGR, somewhere in that range, and that’s specific for the next generation sequencing side. So you’re seeing an acceleration – compounding acceleration – of movement in a lot of these markets.

I think what kind of happened was you had your height curve up in the early 2000’s when we got the human genome sequenced and there was a torrent of data produced from that. There were a lot of promises made leading up to that and unfortunately when it happened a lot of people didn’t really know what to do with the data. So we spent the next decade and a half basically trying to understand what these things really mean, what do the genes tell us, what are they capable of doing. Now that we’re starting to understand that with deeper sequencing technologies, even on the computation side, we have more computing power to help identify these things like population genomics or anything like that. When you look at deep learning helping us do expression profiling and things of that nature the computing side has really caught up and helped us leverage the torrent of genetic data.

Again, I think with all those variables in mind you’re now starting to see us come out of kind of this trough of dissolution and move into actual value extraction from the data. I think you’re going to continue to see more and more of that, not just in a linear growth but an exponential growth.

Deidre: So with more researchers and clinicians adopting sequencing what hurdles do you see them facing?

Ryan: I see them facing a lot. Unfortunately, the market’s kind of constructed in polar opposites when you start to introduce genetics. And really the term that you need to know is “precision medicine”. The reason why you do sequencing is that it becomes empirical data you can reference. Within your genotypic and phenotypic data, genetics becomes your empirical data and the phenotypic becomes your qualitative data even though there are a few empirical aspects to it as well.

Why that’s really difficult in the drug approval process is because if we just take drugs in general or even your gold standard clinical pipelines, the way the FDA approval process is set up is they have to hit statistical confidence in mass. And so what you’re saying is great we have the technology and the capabilities to start doing things on an individual basis but in order to do them in a diagnostic way, it needs to be done in the masses. So you’re at these kinds of separating ends and it’s really difficult to get over that hurdle. I think a lot of that’s going to come down a law making and getting lawmakers more educated around the potential benefits.

Now that said, there’s a caveat which is, as we all know especially within genetics, there’s a lot of unknowns still, there’s a lot of false positives, correlations don’t equal causation in this field by a long stretch. There’s an additional kind of caveat that you have to understand which is data confidence in quality; ensuring that what you’re doing not only from the production of data and the lab side but also the informatics. You have to be really, really confident that when you’re going to make a diagnostic that it is the utmost quality, so I think that’s another huge one.

Probably another current, big hurdle but one that is rapidly becoming less of a deal is pricing. There’s a lot of general costs that are challenging to kind of overcome but we’re seeing a dramatic reduction in the overall cost of sequencing as well as other areas of the pipeline. I would say give that a handful more years and you’ll start to get into really widespread adoption and I think that will help quite a bit.

The last hurdle has got to be the supply chain speed. This is, unfortunately, a pretty antiquated field for many aspects of it and when you have a critical diagnostic that you need to make right now if you’re doing it ad hoc the pipeline from a supply chain perspective isn’t fast enough to get there. There’s a lot of archaic nuances with purchasing from vendors, coordinating vendors, getting everything lined up in the right place, everything from documentation to support and things of that nature are really challenging. I classify the supply chain as also the supply of software. A lot of the informatics software is very dated, they’re orphaned, open source technologies and so there are very few kinds of commercial offerings that really hold water.

When you take all those, it sounds doom and gloom and terrible and all this stuff, but when you look at the growth rate from today compared to five years ago, it is absolutely night and day difference. Again you’re seeing a continual investment into genetics being a priority within the clinical aspects, so while we see all these hurdles there’s a lot of goodness happening to actually get past them and start to increase the adoption.

In our lab, we proceed with a little bit of caution and that’s why we’re currently working on more of these clinical research integrated pipelines. When we start to butt up into things like regulations and data confidence etcetera that’s when we’ll start to really take a heavy investment into ensuring that those are all in place.

Deidre: Are you guys at The Sequencing Center doing anything to overcome these hurdles right now?

Ryan: Yeah we have a lot of different initiatives behind the scenes going on. So at The Sequence Center, we don’t play ball in clinical diagnostics. We’re currently heavily focused on clinical research and that gives us a little bit more leeway. There’s a bit more of experimentation process, but we’re leveraging all the learnings from that process and all the fine tuning and nuances that help us produce the utmost high-quality data and increase the conference in the data. We’re taking those learnings and going to eventually apply them into actual clinical diagnostics. So right now I would say we’re in preparation mode for getting into a place where we feel like we could go to the FDA and come up with approvals for actually providing these pipelines but apart from that you know there’s obviously things like the pricing.

We’re constantly dropping our pricing, just recently we dropped our HLA pricing by I think it was like 30% to 35%. Apart from that, we’re doing things like commoditizing different aspects of the pipelines. So DNA extractions are now free with us, basic bioinformatics is free with our facility and these are all really great things to help reduce the barriers of entry in order to increase the widespread adoption. So I think those are really great things. Supply chain’s a little bit more of a tricky situation we just really focus on working with vendors who think like, not necessarily a startup, but they think in agile terms, they think in speed and they really want to build great relationships and so we’re really interested in working with those types of vendors and people who really get our general vision around the markets.

Deidre: Some of our listeners out there maybe doctors and clinicians that have never used clinical genome sequencing before and might not know where to start. What are some things they should know going into this?

Ryan: I think there’s a lot of different things we could recommend in this podcast. I think the most critical ones though are probably sample collection. Obviously, it all starts with the sample, the patient, being able to collect that. If you don’t collect information we obviously can’t really do much from a genetics perspective or anything else for that matter. So I think that’s step one. Not only just sample collection but understanding the quality controls around sample collections, actually collecting samples in a really good manner, making sure using the proper vials, making sure that you’re collecting things in a clean, high-quality way. There’re other things too such as resource guidelines. You can go to NCBI, there’s a lot of information about sample collections at The Sequencing Center as well.

We have our own sample submission guidelines we also have experts on staff who really help understand projects and help guide people through them. I think that’s a really critical point when you’re looking at going with a sequencing vendor – you really want to find someone who cares less about the money and more about making sure that the initial set up of the project makes sense and helps guide you into more on educational perspective. We’ve worked with a lot of clients who initially did sequencing and have come back to us for sequencing or they went to another vendor for sequencing then came back to us for sequencing and I think it’s really important that you find a sequencing vendor that helps you along the process because it’s a very complicated, weird, funky process there’s a lot of nuances and so patience is a big one. Find a vendor who’s patient.

So I guess if I had to recap it really comes down to making sure you’re collecting the samples properly, finding a really high-quality vendor who helps you along with the journey, and kind of a shameless plug that’s some things that we really pride ourselves on The Sequence Center. We have great samples submission guidelines, we really take care of our clients, we try and do right by them so that’s what I would recommend.

Deidre: This is really exciting news to hear how much genome sequencing is shaping a new era of clinical research and diagnostics. Ryan thanks for sharing The Sequencing Center’s vision and participation in this podcast with us today.

Ryan: Awesome thanks so much for having me.

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