Episode 100! Illumina’s Phil Febbo on the New Era of Low-Cost Genome Sequencing
Welcome to episode 100 of The Harry Glorikian Show! Tune in and read the full transcript of the episode below:
Harry Glorikian: Hello. I’m Harry Glorikian, and this is The Harry Glorikian Show, where we explore how technology is changing everything we know about healthcare.
Believe it or not, this is the one hundredth episode of the show.
We started in the fall of 2018 with the idea of creating a show that would help listeners understand the intersection of biology and data, but make it entertaining as well as educational.
And here we are at episode 100.
A lot has happened between 2018 and today.
For one thing, during the journey, we changed the name of the podcast.
We started out under the name MoneyBall Medicine, which was also the title of my 2017 book about the digital revolution in the healthcare and drug development businesses.
Around episode 71 we decided to change the name of the show to The Harry Glorikian Show.
That was part of a general broadening of how the show is focused, to reflect the publication of my subsequent book, The Future You.
Since then, I’d say the show has held on to its general focus on how technology is transforming healthcare, but with more of a focus on the ultimate impact on patients and other consumers.
And for our one hundredth episode, we wanted to bring on a special guest.
This is Phil’s second time on the show, and to be honest I hope to have him back many times in the future, because there aren’t many companies having a broader impact on healthcare consumers right now than Illumina.
The San Diego-based company is the leading maker of high-speed gene sequencing machines that are at the core of the precision medicine revolution—not to mention the sequencing of viruses..
The company has an 80 percent market share in the market for gene sequencers. Which means that if you or your loved one has had any sequencing done for any reason, chances are your samples were sequenced on an Illumina machine.
For example, if you had a tumor biopsy and your doctors sent it in for DNA profiling to see which drug might be most effective against your form of cancer, then that sample, too, was probably sequenced on an Illumina machine.
The point is, gene sequencing is already a key part of both diagnostics and treatment decisions for many diseases.
And its use is only going to expand as the technology gets faster and cheaper.
This fall, Illumina announced that it’s coming out a new gene sequencing machine called the NovaSeq X that can sequence a genome more than twice as fast as Illumina’s previous top-of-the-line machine, and at a lower cost.
That’s bound to speed up progress all across the field of genetic medicine, drug discovery, and life science research.
And that’s where I started my conversation with Phil.
Harry Glorikian: Phil, welcome to the show. It’s so, so great to have you back. I’m was looking back at our conversation. It feels like not that long ago, but it was actually like, you know, in the early days of COVID. But so much has changed with you guys, I wanted I wanted to have you back.
Phil Febbo: Well, thanks, Harry. It’s kind of like that Twilight Zone episode. Has it been a long time? A short time. Time has lost its relevance in some ways. It’s been some time, Harry. And it’s great to be back with you. Let’s say that.
Harry Glorikian: No, this is great. But I came to your event that you guys had in San Diego, which was a blast, great seeing tons of old friends, from the ABI days and so forth, and also make some new ones. But, you know, I’m going to go back a little here, right?
So in September, you guys announced a new high speed sequencing device you’re calling the NovaSeq X series. And the data that your CEO, Francis DeSouza, shared at the launch event was that the machine can sequence an entire genome in 12 hours for $200, and is capable of two and a half times the throughput of any of the prior sequencers.
And so this is a big advance in my mind. And I feel like we’ve been living through these advances now for quite some time. But I’m wondering, like, if you can put it into context, say, for non-experts and people who may be who may not follow the world of high throughput gene sequencing that are listening, like can you start by just explaining what does a gene sequencer do, what goes on inside the machine, etc.?
Phil Febbo: Yeah, sure. Harry And it was very exciting at the Illumina Genomics Forum to announce the launch of the NovaSeq X, which is our highest throughput, our fastest, most reliable, most accurate sequencer yet. And what sequencers do is they decode the human genome, they decode any organism’s genome, SARS-CoV-2’s genome, and we’re seeing millions of SARS-CoV-2 genomes being submitted to the public domain.
So we can track that virus and track the pandemic and the emergence of variants. But what sequencers do at a basic level is they read the genome one base at a time, an A, a G, a T or a C, that is the code of genetics, those four bases. And the challenge is there’s a lot of those bases in the human genome we have 3 billion bases lined up.
And our sequencers will take a genome. And before putting the genome on to the sequencer, you have to break it up into little pieces. And then we, through a combination of chemistry and optical scanning, we read those bases at about 150 read lengths at a time, so strings of 150.
And then on the sequencer, now, we have a processor called the Dragon processor, that takes those 150-base reads and aligns them to the human genome and to each other. And after all that aligning, you end up with a consensus copy of whatever genome you put in. And this used to take weeks, months even, to sequence that many bases and to get to the human genome.
It used to take millions of dollars. With the announcement just a few weeks ago of the NovaSeq X, it takes less than 24 hours and it costs about $200. And so now we can all have access to our genomic code that is foundational to our health for $200 of cost of sequencing, in just over a day.
And that’s super exciting for me as a physician scientist and someone who’s always worked to bring genomic insights into the care of patients. It just opens up a whole new level of possibilities in health care.
Harry Glorikian: So, I mean, I just want to give people relevance of progress here because I think we’ve beaten the pants off Moore’s Law.
But you know what would have been, say, the cost and the time requirement to sequence a full genome say, 20 years ago. I remember almost how expensive it was. It was ridiculous. Yeah. And 10 years ago and five years ago. Can you put that in a context for people?
Phil Febbo: Yeah. So 20 years ago, that was, you know, just after the completion of the first human genome, the big race between Celera and the NIH with Eric Lander, Francis Collins and the teams working very hard. It’s estimated that cost anywhere between $200 million and $300 million for that first human genome. Ten years ago, we had made a lot of promise.
The sequencers were getting better. We were still at considerable sum, probably to the tune of between $10,000 and $20,000. So big, big, big improvement, but still not really practical for use in a health care setting, just too much of an expense burden. And it took a long time and the software to assemble it was, we didn’t even have the speed of processors, with a few rare exceptions, to crank through that much data efficiently.
So as sequencers, so five years ago, I think we hit about $2,000 to $3,000. Three years ago, we got to $600. Two years ago we got to $600. And then this year to $200. And, you know, we do talk about Moore’s Law, and that price did drop faster than Moore’s Law. But I think Moore’s Law is important because we wouldn’t be able to do it within 24 hours if we weren’t benefiting from Moore’s Law and having computing improved so much over the same time period.
Harry Glorikian: Oh, yeah. I remember when we launched the 3700, which was the workhorse of the original, you know, the first human genome, total human genome. Had they not released a new chip from Intel, we couldn’t have had that thing run 72 hours unattended.
Phil Febbo: That’s right.
Harry Glorikian: So we’re dependent on Moore’s Law doing well.
Phil Febbo: We are indeed. Indeed.
Harry Glorikian: So again, you know, and I and I sort of have to ask this because I’m sort of, you know, my friends and I will always argue over the iPhone 13 versus the iPhone 14. How much of an improvement is this machine compared to the top-of-the-line sequencer that came before it. Is it incremental, or is it really like that big leap that we like to see from an innovation perspective?
Phil Febbo: Yeah. So the NovaSeq X, and Francis mapped this out at IGF, it can generate, over a year, 20,000 whole genomes and has two and a half times the throughput and it’s twice as fast. And, you know, I think that if you look at the workflow, a lot of times that may seem to some people saying, well, it’s not tenfold, right? It’s not a log, it’s log base two, but it’s not log base ten.
Twice as fast, and with that kind of throughput, really compresses the workflow of a whole genome at scale to the workflow of a clinical lab, which, you know, has to kind of keep pace with turnaround time expectations. And that 24 hour period to get to the whole genome and whole genomes at scale really fits into the workflow of a clinical laboratory where things can get set up in the morning, run, ready by the next day.
The analysis, the report can go out late the next day. Right. And that’s kind of getting to turnaround time expectations that are now reasonable to work in. So that’s the incredible importance. Now what that doesn’t, and that’s very impressive high level stats, but what it doesn’t get into is what we had to do to realize that benefit.
And that means new optics, new flow cell surface chemistry, new enzymatic chemistry. We redesigned the enzymes involved in sequencing by synthesize, and we put the Dragon server on the, integrated into the sequencer so we could have real time informatics processing the sequencing data as soon as it’s complete with each run and each cycle.
So all of that working together got to the realization of that kind of improve[ment] in output and increase in time.
Harry Glorikian: So historically, you know, there’s a lot of people that were always throwing around the $100 genome. I mean, I can remember we’ve been talking about the $100 genome. I feel like forever like, you know, it’s almost like Nicholas Negroponte, as, you know, $100 laptop sort of thing, which actually never really happened. Right. Do you think we’re not far off from that magic $100 number?
Phil Febbo: Well, yeah, we’re not very far at all. And certainly there’s a path to get there over the next few years, and there’s a path to get there with scale, continued scale. So I think we’re there. Quite frankly Harry, I don’t, as a physician scientist, I don’t think there’s anything that magical about $100. It’s a nice number.
It’s certainly a nice, we did see the impact pricing had on, for example, consumer genomics when 23andMe brought their price of their genotyping test down below $100, their market took off. So, you know, it can have impact on how many people want to order and can order it.
But you know, at $200 for a whole genome, that kind of cost of sequencing fits within reimbursement for many genetic tests. You know, right now labs can be reimbursed several hundred dollars for a single gene, Harry. A single gene. Right. Labs can be reimbursed $3,500 for 500 genes.
And now we’re talking about $200 for the whole genome to fit within that. Right? So the cost now is such that when there is value to information in that genome for patients such as cardiovascular disease, have you inherited a gene that makes you more prone to having early cardiac death.
Are you at increased risk of cancer? For diabetes? And we think about cancer genomes and understanding if someone has a cancer that is more going to respond to a specific targeted therapy. You know, that information can now, the cost of accessing that information has now dropped well below the price that those tests are usually reimbursed at, and that opens the opportunity for clinical labs to provide this and have resources to have a sustainable business.
Harry Glorikian: Yeah, I mean, that was going to be one of my next questions, which was, you know, someday soon, do you just see doctors ordering a genomic screen for almost any condition linked to some sort of genetic variation?
Phil Febbo: Well, I think that happens over time, and it will happen in higher risk populations. So right now, where whole genomes has been shown to be best for individuals and best for health care systems, are children born suspected of genetic disease.
Pioneering work at Children’s Hospital, Stephen Kingsmore and others, have shown you can do a whole genome within 24 hours of birth, and that information provides a definitive diagnosis between 30% and 50% of the time.
And management for those children changes between about 30% and 50% of the time. And it improves outcomes. Not only that, as opposed to what has traditionally been done, which is no testing until much later, that immediate whole genome saves healthcare systems dollars.
So you take better care of the patient, you have better outcomes, and the healthcare system saves resources. And that’s the quadruple aim you hear about in today’s healthcare world, where you want better patient outcomes, you want a better physician experience, you want better outcomes, and you want better—and we want to find healthcare efficiencies because our systems are so strained.
Harry Glorikian: So, you know, going to one of the, technically the rate limiter, right, is how many doctors know what to do with the results of a genome screening. In other words, like is the healthcare world getting better at using this information?
Phil Febbo: Yeah, I think more and more. So right now, Harry, I’d say very few doctors could handle a whole genome, right? I couldn’t handle the whole genome as a practicing oncologist. And I work at Illumina, right?
I’m chief medical officer here. I’d probably do better than most, but, you know, it’s a lot of information. So what we’re finding, though, is in different areas, doctors are becoming more understanding of the importance of genetic determinants of disease. So disease has multiple determinants.
There’s social determinants which are massive now. There are behavioral determinants, environmental determinants with the disruption of the environment. And there’s genetic determinants. And so to really understand what’s happening in a patient, you have to understand the full context, and genetics are increasingly being part of that.
So if someone’s diagnosed with cancer early, very often they are being tested for cancer-causing genes like BRCA1 or BRCA2 in women with early ovarian or early breast cancer. That’s becoming more, people are more aware of that. In early cardiovascular events, cardiologists are more aware of the hypercholesterolemia genes. If they have a cardiomyopathy or arrhythmia, there’s more awareness, there’s more ordering.
Phil Febbo: In some situations. There’s also more awareness of the importance of something called pharmacogenomics, where we all have different abilities to metabolize drugs.
That’s absolutely measurable with the whole genome or a panel, and it has major impact on which drug you should get if you have, for example, mental health, disease, depression, bipolar, or if you need pain medications to control pain.
And more and more physicians are asking the questions as far as what are the pharmacogenomics of this person so that I can prescribe the right medicine to get them a relief as quickly as possible, as effectively as possible. And so you see windows of where testing is being integrated.
The genetic determinants of disease and management are being integrated into care, but it’s more piecemeal right now, Harry. We haven’t seen a kind of holistic embrace of integration of that information into the healthcare system with decision support, so that that kind of holistic picture of the individual can take.
And that’s going to take more time. And it does take, exactly, that full integration into the workflow of a very busy health care practitioner, be they a physician, a nurse, a PA, a genetic counselor. Time is probably the most limited resource for healthcare providers right now. And unless we integrate it into their workflow, it’s just not going to be information that’s utilized.
Harry Glorikian: Yeah, I mean, if I think about how long I’ve been talking about this or working on this and how long it’s taking, like I’m like, it takes a little longer than you may anticipate to get this off the ground. But I want to sort of, just for everybody that’s listening, right, so just to state the obvious, right, these machines can sequence any genome, so not just the human genome.
And so it keeps getting faster and cheaper to sequence the genome of, as you said, viruses, bacteria, crops, you know, non-human animals. So.
What are the research implications of getting the cost of whole genome sequencing that low? What possibilities does it open for different fields like epidemiology or the study of DNA from extinct species, going back to George Church? I know there are many implications, but are there any that you’re like, you hear about and you’re like, “Oh my God, that is so cool and really exciting.”
Phil Febbo: Well, let’s break it down. Let’s talk about the human impact and we can go into other species and then go into agriculture. Right? So for humans, we have sequenced a considerable number of human genomes, probably well over, you know, 2 to 3 million.
But the vast majority are northern European.
And we just haven’t, we do not have a complete understanding of the full diversity of what the human genome truly represents. You think about the northern European ancestry is there from the Africa diaspora, you know, tens of thousands of years ago. And it’s estimated about 3% of human diversity at the time left Africa. Right.
And so we have, you know, very few and inadequate number of genomes being performed in Africa on, you know, communities in Africa. We have too little across the entire globe. Latin America, Southeast Asia, South Asia.
And as the price is coming down and really importantly with the NovaSeq X, we now have ambient chip, which, you know, some people might say, you know, yeah, that’s great. You don’t need dry ice.
Well, if you’re going to open up sequencing and start informing, you know, pulling, understanding the information in the human genome that represent all humans and the diversity of human beings, you’re going to have to get those sequencers working in areas that have trouble with cold chain. And that’s where NovaSeq X now can do that.
So rather than 100 pounds of packaging for each run and, you know, several tons of dry ice over each year, it’s ambient chip and a 10-pound box that we ship. And so, you know, these kind of the decreased cost, the ambient chip will open that up and we will start to see more human genomes, more representative of the human race and representing the beautiful diversity we have. And we’ll get to more insights that way.
Phil Febbo: That kind of, those advantages will also be applied to animal husbandry to find what traits in different livestock help with things like efficiency, decreased methane gas production, and ability to be more resistant to disease without having to do genetic modifications. Right?
There’s a lot of communities that are just not open to genetic modifications. But we’ve been doing animal husbandry where we select for traits that are observable for, you know, tens of thousands of years.
This just speeds up the process where you can use genetic information to select for individual animals that have the characteristics that are optimized for the environment in which they will be living and growing. And just one, on the agriculture side, you can do the same things for crops.
In fact, we’re supporting a yam project in Southeast Asia where yams are a major staple. And with the evolving climate, we have to understand how do you select for yams that are maybe a little more drought tolerant, more flood tolerant, higher salinity, lower salinity, higher CO2, you know, all those different characteristics, because that’s a major staple for both calories and to sustain large portions of the population.
And now sequencing could be applied for that. So throughout all of that, from humans to yams, sequencing is important.
Harry Glorikian: Let’s pause the conversation for a minute to talk about one small but important thing you can do, to help keep the podcast going. And that’s leave a rating and a review for the show on Apple Podcasts.
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It’ll only take a minute, but you’ll be doing a lot to help other listeners discover the show.
And one more thing. If you like the interviews we do here on the show I know you’ll like my new book, The Future You: How Artificial Intelligence Can Help You Get Healthier, Stress Less, and Live Longer.
It’s a friendly and accessible tour of all the ways today’s information technologies are helping us diagnose diseases faster, treat them more precisely, and create personalized diet and exercise programs to prevent them in the first place.
The book is now available in print and ebook formats. Just go to Amazon or Barnes & Noble and search for The Future You by Harry Glorikian.
And now, back to the show.
Harry Glorikian: So, you know, just to twist it a little bit and, you know, we always want to talk about AI and machine learning and how all of this contributes to all these areas that we talk about on the show. But last week, you guys announced a new research collaboration with AstraZeneca, where you’re testing whether it would be helpful to combine the two companies’ A.I. tools., if I was reading into what I read correctly. Not everything is always announced—but can you help listeners get their head around why AI would be important at all for the company?
Phil Febbo: Well, absolutely, because any time you’re starting to generate massive amounts of data to find the true information in that data takes advanced analytics, and artificial intelligence is a major and a growing part of those advanced analytics.
You know, in 2021, we estimate about, what is it, 260 petabytes were generated on our sequencing. Just to give your listeners a relative, that’s 2.5 times the entire Netflix archive. So that was generated in one year on our sequencers. And in that information you have to find, in that data you have to find the true information.
And so artificial intelligence is one way where you can train to look for specific patterns. And in a lot of times, what you’re looking for is where an individual’s genome, with a, an individual with a specific phenotype, like a response to a drug or a risk for a cancer, has a variant that is associated with that risk.
And so what we’ve done is embrace the fact that the genome is vast and large, to use different artificial intelligence training to look at variants in areas that are non-coding, around splice variants, splice junctions in the genome, which is a regulatory space in the human genome.
Phil Febbo: We’ve also used evolution in primates to train a model, an artificial intelligence model, because if a variant that is similar in humans and primates has existed in primates for a million years without any selection pressure, chances are it’s not very important, right.
To go through a million years of evolutionary selection and not be deselected or be selected positively or negatively, it’s probably not doing anything. And so we’ve used that kind of training to start winnowing through all the variants that are found in whole genomes, to find, to move as big a proportion of those that are found into the insignificant pile as possible.
And those are just two examples, Splice AI and Primate AI, that our team at Illumina have developed. A nd AstraZeneca have also developed different trained models. And what we’re doing now is as we work to have more genomes on patients that have been on trials, that have had a response or did not respond to specific therapies, you can leverage that information in a way that protects the patient ID and confidentiality.
But you’re looking for associations between variants and response to specific therapies, or variants and the potential to respond to novel therapeutic targets. And that’s where a lot of people are really excited because we’re now getting enough whole genomes and enough really structured phenotypic data on subjects and patients so that we can look for those new medicines.
That will be life changing for patients with cancer, cardiovascular disease, neurologic diseases or diabetic metabolic diseases.
Harry Glorikian: So you’ve got both of these companies sort of working together. I mean, as you said, you’ve got Primate AI and Splice AI, and they are, I think, contributing something called Jarvis, which is interesting because you want to think Iron Man when you hear that, and then something called MTR, which is for missense tolerance ratio, right?
But what is the in your mind, what is the the bigger picture here? Because I feel like I feel like we’re headed towards maybe a cusp of big advances in computer aided drug design as well. And, you know, is there a lot of value to be unlocked by connecting these algorithms and tools that are right now maybe siloed in these different companies?
Phil Febbo: You know, we all we all have to make our decisions on what we prioritize as far as our R&D. And I think my experience in academic medicine and my experience in industry, both, is that when you find a path to true collaboration and bring your R&D teams together, the value to the world and academia, to discovery and insight. and to shareholders and industry, is multiplied.
And that’s what you know, and you can’t always do that. You have to have an aligned, you know, you have to have complementarity and aligned sense of purpose. But when it can happen, it’s much stronger. And I think specifically in therapeutics, we’re seeing some really exciting, we’ve seen, for example, in cancer.
So we’ve seen American Cancer Society has put out the fact that the mortality due to cancer continues to decrease because of advances in prevention, earlier diagnosis and treatment. And a lot of the treatment advances has been to understand the genetics of someone’s cancer and use targeted therapy or immunotherapy.
The next step that we’re seeing that’s quite exciting is more bespoke approaches where you not only, you’re not using something off the shelf, but for example, Moderna, who developed the mRNA vaccine, one of the mRNA vaccines for COVID, has an mRNA vaccine for cancer, where you take 30 of these variants that an individual’s tumor has, and you put that in the same type of vaccine and you train the immune system to target their specific tumor.
Phil Febbo: So it’s a programmable vaccine based on that individual. That’s the kind of opportunity you now have. When you can do a whole tumor, whole genome, find 30 variants that are expressed, that are expressed in a way the immune system has a chance to respond to, you program that into the into the mRNA vaccine, just like they programmed the spike protein sequence into the mRNA vaccine for COVID. And you give that to a patient.
And they already have trials going on. It looked it looked really promising in head and neck cancer. They’re moving forward with other cancers and should have some readouts over the next year. So that’s just one example where you see coming together, therapies and sequencing coming together to have individual successes. That’s not science fiction. It’s happening right now.
Harry Glorikian: And it’s funny because people always ask me like, Why are you so excited about this space? I’m like, Are you kidding me? You have no idea. But, you know, now you mentioned therapeutics. This is a great segue into one of the areas that that I’ve always grown up with, which is the diagnostics.
But talking about cancer screening. So in August of 2021, Illumina completed the acquisition of Grail. Now, I know you guys are waiting for the whole European, for them to make up their minds, but Grail is a developer of a blood test for cancers. Right now you operate it as a separate company while the acquisition is still working through its thing. But at this point I imagine Grail’s technologies, you got to, you can’t be doing this in a vacuum.
I think I was trying to read some of the comments that Francis had made on this. And, you know, it’s sort of they may not be successful without the resources that you guys are bringing and vice versa. But can you describe to people that are listening, what exactly does Grail do?
I mean, I know they have a test called Galleri that can report and detect 50 types of cancers from a single blood draw. But I’d love to hear you explain how it works.
Phil Febbo: Sure. Yeah. Thanks, Harry. And yes. We have acquired Grail. And yes, we’re holding them separate. So I don’t know anything that’s outside of public domain. But I do know that, and I know that keenly. And Grail has developed a test using sequencing, using Illumina sequencing technology.
And what it’s based on is the observation that was made about 15 years ago that when an individual has cancer, we’ve known for decades that cancer recruits a vasculature system. And we know cancers have cell division, they multiply, cancer cells, but they also die.
They just multiply more than they die, which is the problem, and why you get a tumor. When they die, they break apart. And when they break apart their DNA fragments and it gets into the circulation. And each of us in our blood, we have you know, we have blood cells, we have immune cells, but we also have fragments of DNA.
Now, most of that DNA is from normal cells that turn over and die and your kidneys get rid of it. And it’s not a problem. But what we know is that for patients with cancer, you can sequence the cell-free DNA, which is what it’s called, and you can find changes that are consistent with the presence of cancer.
And that’s being used right now by companies like GuardantHealth, Foundation Health, and others to help with treatment decisions. So if you can’t get a piece of someone’s tumor, you can do a blood test and sometimes you can find changes that you can target with drugs from the tumor circulating in their blood.
Phil Febbo: Well, we were doing testing at Illumina for a different indication, for pregnant women, who are pregnant and wanted to know if their children were at risk, if their fetus was at risk for any of the common genetic abnormalities. Common trisomies like Down syndrome.
They get a blood test about 10 to 12 weeks of pregnancy. About 10 out of 10,000 women had really crazy changes in that cell-free DNA, incompatible with life for the fetus. And what they had is they had cancer.
And what Grail did over the past seven years is they took that initial finding and they did studies with major cancer centers to show that, in fact, in the blood, if you look at circulating cell-free DNA, you can detect the presence of, as you said, up to 50 cancers earlier than could be diagnosed otherwise.
And so they’ve done a number of trials involving over 140,000 patients already to find that signal and develop into a test called the Galleri. I’ve had the Galleri. We offer it at Illumina as a self-insured employer. Luckily, no cancer signal detected. And more and more we’ll see adoption of that.
Then it breaks the mold. So for the past multiple years, decades, we’ve been developing screening tests, one disease at a time. And so we have colon cancer, cervical cancer, breast cancer and lung cancer and prostate cancer.
Phil Febbo: You know, the Nordic study just read out last week on colonoscopy and colon cancer, it took 63,000 participants. It took over 15 years. And it did show a decrease in the diagnosis of colorectal cancer. There was some debate over whether it improved colon cancer specific survival.
I actually believe the evidence does support that, if you look at it. And, but we can’t do it one at a time. So these tests that have been developed now are multi-cancer early detection. You’re not taking on. You’re looking. It breaks the mold.
There’s a lot of frustration and some downright anger from those steeped in screening because it’s such a transformative approach. And we need to do the studies. But so far, the studies have shown very strong performance. And over time, we’ll understand whether these tests can improve mortality.
That’s the gold standard, cancer-specific mortality. It’s going to take another decade to see that. But the studies are underway, and in the meantime, we’ll get more and more evidence whether these studies decreased the stage at which cancers are diagnosed, meaning do you diagnose them at stage 1 or 2 rather than 3 or 4.
They’re treatable, but seldom curable. Stage 1 and 2, you can be cured with oftentimes cured with surgery and or radiation. Are we curing more people? We’ll see that earlier on. And eventually I have confidence there will be translation to improve disease specific mortality.
But it is transformative. And we’re not going to get to, I don’t think we’ll get to a successful moonshot of halving the mortality of cancer in the next 25 years, as outlined by President Biden unless we we do these studies and start to see meaningful adoption of tests like this.
Harry Glorikian: Oh, yeah. I mean, I think it was nine or 10 years ago, I remember writing a piece and published it basically saying, you know, not far away, not too long from now, you know, we’re going to have a blood test and people are going to joke about this cancer thing because we’re going to make it sort of go away.
Now, of course, it always takes longer than we all think. But you know, why do you why do you think it made sense for Illumina to buy Grail? And what can Grail do with Illumina’s help that maybe wouldn’t have been possible before?
Phil Febbo: Well, um…
Harry Glorikian: And this is all stuff that you guys have talked about publicly, but just sort of, you know, for people who are listening.
Phil Febbo: You know, I think Illumina, we’re a global company, right? We have 21,000 sequencers across the globe, about 14,000 unique customers across the range of high and low throughput. To support those customers, we have commercial operations, we have regulatory groups, and we have over 1,000 product registrations across the globe. In over 44 different countries we’ve gone through regulatory approvals.
And we also have incredible experience in reimbursement because for these tests to be meaningfully available, they have to be covered. Not everybody can afford them without insurance. So we have incredible commercial kind of commercial capability, support capability, regulatory capability and reimbursement experience to help bring this test globally.
So as the as the data matures, as more and more people and health care systems see the benefit, we’re really well poised to support Grail and have this test become available faster than it would otherwise. And that was the premise of why we brought them back. Right.
We spun out Grail because they needed five years plus of intense focus and investment to see if they could go from an initial observation in 5 to 10 women to a true product. They’ve done that with those studies. We brought them back in so they could benefit from the scale and the size of Illumina to be successful.
You know, the biggest concerns is would we stop other companies from developing MCEDs? And we’ve put a supply agreement out there. We’ve in the past we we did this with NIPT [non-invasive pregnancy test] and we were able to support a very competitive and pro-competitive environment where there are now multiple different providers of NIPT tests and we can do it again for multi-cancer early detection tests. And we’ve put out made some commitments to make sure of that.
So the long and short of it is, Harry, we felt like we could speed the impact of this amazing type of test on patients globally and on communities globally, while at the same time we know we can foster a very competitive market in screening, cancer screening.
Harry Glorikian: So. How do you how do we take your progress on lowering the cost and increasing the speed of genome sequencing back to Grail’s business? And then, what could they do right, what could Grail do if all their machines were NovaSeq X’s?
Phil Febbo: Yeah. So first of all, what we’ve promised is that Grail will have access to our emerging technologies just like any other company that wants to participate in screening. So there’s not going to be any special privilege afforded to Grail for being part of Illumina.
They’ll get access to new NovaSeq X, just like all our other customers, including those who are or want to move into this screening business. What I didn’t mention is in order to detect that signal, you can imagine, you have a lot of cells, you have trillions of cells in your body, and you want to you want to detect a cancer that may be a million cells, and only a small proportion of which are dying and shedding DNA.
So the DNA signal in that cell-free DNA is pretty low volume, let’s say that. And to detect it, you have to do a lot of sequencing. And without dropping the cost of sequencing like we’ve had, there wouldn’t be a viable path to create a test at a price that could actually be considered in healthcare systems.
But now we’ve gotten to that threshold. So what it means is that commercial laboratories who want to develop a screening test now can sequence at a cost that will fit within a price, that will be successful on the market and have the impact that that I anticipate it will have in communities. So it really opens up the possibility for these tests, the fact that we’ve brought the sequencing price down.
Harry Glorikian: Yeah, I mean, I have this conversation with my friends all the time. I mean, we’re getting to a point where the sequencing cost is, in the grand scheme of things, a rounding error.
We can always bring it down further through more advancements, but we’re at the point where that should not be the barrier to moving forward right now. I know that interpretation can be expensive. But the cost of actually doing it should not be a barrier anymore.
Phil Febbo: Yeah, and I think that’s, I think that’s right. And if you talk to laboratories performing, clinical laboratories performing whole genomes, they’re always going to be happier with lower cost sequencing here. You know that.
Harry Glorikian: Yeah, yeah, yeah, yeah, yeah.
Phil Febbo: But if you ask them, well, if you look at where your spend is in your cost of goods, COGS, increasingly it is on the back end, interpretation.
And that’s where you know, by putting Dragon on the sequencer and getting from primary sequence to variants, doing that what we call secondary analysis—primary analysis is the actual sequencings, secondary analysis is going from the code you’re reading to the variants, the changes that you’re finding for that sample—while on the sequencer, helps speed that and decrease the cost there, the cost of storage, cost of the bandwidth you need to get the data off the sequencer.
And then with the artificial intelligence and other applied analytics we’re using, we’re hoping to speed and decrease the cost of the interpretation too. So we’re focusing at the full cost of sequencing, from library prep, which is getting better and better; sequencing, which we’ve talked about, the $200 now for a whole genome; and the interpretation.
You have to look at all of it. And you’re right, in the clinical world, that interpretation right now is where probably there’s definitely more cost than on the sequencing side for most tests.
Harry Glorikian: Yeah, I mean, what most people don’t understand is we’re always playing whack a mole, right? Because yes, I solved this, but you know, now I got to work on this one. And you can never make as much progress across all of them as you’d like because you just need different tools sometimes in in each one of these different areas that you need to move forward.
And then at some point you really need to explain it to the physicians and everybody else, because you’ve got to, they’ve got to sort of keep up to a certain extent to what you’re doing. And that’s not trivial either.
Phil Febbo: Yeah, no, I look at it as impedance matching, right. And as soon as you decrease the friction in one area, you’re going to discover it in another area. Right? So last two weeks ago at Illumina Genomic Forum, we announced the NovaSeq X, a $200 genome.
That reduced the friction there. And now it’s immediately interpretation. So the friction point changes based on the, I’d say, asynchronous breakthroughs that occur across the sample-to-answer solutions. And that’s what really physicians want. They want to order it, send a sample, get an answer that changes the management and improves outcomes for their patients.
And that’s what we’re working on, is not only the different pieces, the library prep, the sequencing, the computation, but that how do you pull all those together sample-to-answer. So that physician at the end of the day, they don’t really care if it’s a NovaSeq X, Harry. You know, as much as that hurts us at Illumina, they want…
Harry Glorikian: Just give me the answer so I can do my job, right. That’s all they care about!
Phil Febbo: …they want the answer as quickly as possible, as accurately as possible, and with as the information that they need to take great care of that patient.
Harry Glorikian: Yeah, and you know, it’s funny, I guess in some ways, I mean, just being in this area forever, right from Applied Biosystems and so forth, maybe I’m biased, but when I think about how sequencing affects the human condition, how it affects every other plant, crop, animal, etc., and the work that we can do there, how it affects us in surveillance, for pandemics, et cetera—this is such a foundational technology that on a global basis, if it’s not in place, I don’t know how a country or an industry goes forward anymore.
And maybe, like I said, maybe I’m biased, but I just don’t see how it’s possible.
Phil Febbo: Well, ABI was a storied place and certainly we owe a lot to yourself and individuals at ABI who supported the first Human Genome Project and also has now populated pretty much the entire genomics world. It’s hard not to have a leader from ABI in some part of genomics, if you’re at a company.
But what I will say is yeah, I think there’s growing recognition that, and we’re meeting with health ministries, finance ministries, who really see this as becoming part of the fabric of their countries, for their communities. That raises, people are concerned to make sure they have access to new technologies for health, as we talked about for agriculture.
They also see this as an opportunity for intellectual development and really kind of the development of an industry in-country, through sequencing and participating in the knowledge-based industries. And people are also getting more concerned about security, right, because there’s so much information there.
How do you protect your communities? And so that’s becoming more and more.
Phil Febbo: And I do want to walk back a little bit, I said pretty tongue in cheek that people don’t really care about their sequencer, what the sequencer is. I think that’s true.
I don’t think it should be true. I actually think it is actually very important what technologies are used to sequence for reliability, for trust in that the data is handled with the right security in mind. And I do anticipate in the future, as this becomes more common, people will start to look for what is this report powered by?
Is it powered by an Illumina sequencer? And they should feel very confident that that test is based on the best and most secure technology sequencing technology available.
Harry Glorikian: Well, Phil, as always, it was fantastic seeing you in person when we were together in San Diego. Be It’s great to have you back on the show.
You know, I have a feeling we’re going to, every year or two, we’re going to have to make this a regular thing because of the advances that are happening. It’s like, you know, they have fundamental like huge impact on so many areas and half of them honestly, we didn’t even discuss because there’s just not enough time.
Phil Febbo: Yeah, no, Harry, always great to talk to you and happy return. There’s always more to talk about, for sure. Thank you.
Harry Glorikian: Excellent. Thank you.
Harry Glorikian: That’s it for this week’s episode.
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