Okay, this is David Zierler, Director of the Caltech Heritage Project. It is Tuesday, March 25th, 2025. It is my great pleasure and honor to be here with Mr. Don Liswin. Don, it's wonderful to be with you. Thank you so much for joining me today.
Thank you and good morning. Don, to start, would you please tell me your titles and institutional affiliations past and current?
Oh, Lord.
Well, titles, you know, I've been a CEO six times in the technology business. I've been on, in the cancer business, I've been on the Fred Hutch board. I'm on the Stanford Canary Center board, and I am an adjunct professor of radiology at Stanford. There are a lot of other titles, I guess, but that's probably the best. In the business side of things, besides the CEO roles, probably the most formative one was being able to be the EVP at Cisco Systems during what I call the right 10 years, 1990 to 2000. And at that point, I had a great opportunity to run a big part of that company and learn a lot of things from the CEO, John Chambers. Now, why was that the right 10 years, as you called it?
Well, they had just gone public, and that 10 years later in March, I think, 21st, I've got it around here somewhere, it became the most valuable company in the world. So it passed GE and it passed Microsoft for a brief moment there. Not like the stratosphere of NVIDIA's today at $3 trillion. It was, you know, half a trillion, which we were very proud of. But that 10 years, I held on to all of my options and stock, and I didn't sell until I left to become a public company CEO. And that allowed me to create a financial infrastructure to get Canary going.
Todd, now I wanna ask, sort of at the broadest possible level, what made you think, coming from a finance and technology perspective, that you could create such a massive historic impact in cancer research? What was the confidence? What was the audaciousness? What was in it in you that made you take this leap in your career?
Well, I would say, you know, what I'm best at in terms of business is market development and technology development. So at Cisco during those 10 years, one of the biggest things I ended up doing was creating an IBM internetworking series of technologies where IBM internetworking was an oxymoron. You weren't supposed to interconnect with IBM on anything. And so that was a whole new market for us. And so that's what I became good at, trying to analyze a new market, see how we could insert either through acquisition or organic growth. So I figured that those skills were transferable. And furthermore, I mean, the internet building it was a giant collaborative effort. I always joke and tell my kids, I didn't invent it, but I installed it. And so we were way ahead of the scientific community in terms of being able to collaborate with each other. Scientific community in the 1990s was, you know, one pig, one farm. And so I think we were, I brought that teamwork mentality and not being afraid of anything. I brought a great naivete into the scientific world, which was good in some regards and naive in others in terms of, I thought I'm 25 years into this project. I thought I will have succeeded. And I certainly have on a number of measurements, but in terms of having the goal that I set out 25 years ago, I haven't reached that goal yet. Don, I'm curious, also in the 1990s, this is the beginning of what we now call big science, things like the Human Genome Project that was the transition away from, as you called it, the one pig, one farm. Were those sort of technological and sociological developments in science, was that important as you were thinking this pivot in your interest? Yeah, certainly the whole genome...
Getting, quote, discovered and tools from Illumina and others trying to understand that. The other technology that had a lot of promise, which didn't go as well as people hoped, was proteomics, so the study of proteins. And then really, one of the things that differentiated us was, from Stanford, Dr. Sam Hanbeer joined one of our teams, and the whole idea that this has to be a two-step process. No surgeon is picking up a scalpel based on one test result. Generally, they want two, and the second one is either a biopsy or an image. And that's still to this day something that we try to really hammer into new additions and new scientists that you're trying to solve part of the problem. This isn't like drug discovery where it's, aha, I have the magic pill. This is, you want to narrow down the problem so the next test can narrow it down further and give confidence to act.
Okay, now let's sort of take our discussion and sort of the broad span of early cancer detection in historical perspective. Relative to when you first started thinking about these things deeply and where we are today, where's the major progress that's been achieved? What feels like, you know, the future is still in front of us?
I'd say we've made progress in terms of cancer care on three legs. One in terms of prevention. Obviously, there was a lot of smoking going on 25 years ago. Still is globally, China and other markets. So prevention really, really helped. In early detection...
Really, the progress was in getting people to understand the available screening. I mean, my gosh, my cousin died at 51 a couple of years ago of colon cancer because the standards in Canada weren't ready for him to have a colonoscopy. So what's out there?
being utilized has really been the thing that has changed. And then on the imaging front, more advanced imaging. For instance, now it's emerging as one of the standards of care on prostate cancer is to have an MRI, which was never the case, and MRI-guided biopsies and others. So we haven't had, you know, the breakthrough. Grail, as you may know, is an Illumina company who's working on a multicancer biomarker test, and biomarker just meaning some marker of biological fluid, urine, blood, whatever it might be. We haven't had the breakthrough there that we wanted and expected.
Now, it's interesting because of all of the ways you could have come at the cancer problem, you focused on early detection. If you could walk me through, what was your inspiration for that and what were you thinking, even perhaps from a market perspective, in terms of where dollars were being invested versus the biggest bang for the buck?
Ja, well...
So there's sort of two sides to that early detection coin. My dad had a colonoscopy and we've discovered that we are genetically susceptible on my paternal side. And I ended up having bleeding and I went in at 20 something, which I think many 20-year-old men wouldn't. And they found polyps, which back then it was like, well, we'll take them out. But today we believe that polyps are precursors to colon cancer. So that early detection may well have saved my life. On the flip side, my mom at 62.
got misdiagnosed with a bladder infection, and they gave her antibiotics for what was a stage 4 ovarian cancer. And she was my best friend.
Sorry.
It's okay.
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And finally, my sister, who is a nurse, wrangled her into the hospital where she got diagnosed and it was too late and she died. So, you know, I...
They only have lived through this. My first wife ended up getting vaginal cancer at 28 years old, which is unheard of. Her doctor had a good day. He found it on a pap smear, which is very unusual, but ultimately it was just too late. So, sorry, I'll pull myself together here. That's okay. This is what it's about. It's okay. It's what it's about. It's script.
So, so that's what influenced me. And, and then with my mom, my mom died, actually, I'd left Cisco and gone to two public companies that joined. For those of you who are out there thinking about that, don't ever do that. That's, that's murderous on everybody involved. And I remember being with her, and the CFO called me when I was in hospice, and she had just died and said, we missed our quarter. And I said, well, that's, that sucks, and uh but I got to go take, take care of this. So it was, it was really that moment.
I'd done my thing as a public company CEO. I finished up that, got that company back strong. It was weak and...
We had merged the two companies just as the markets in spring of 2001 began to crash. I ended up working through laying off 1500 out of 3000 people, going through 9/11 with 200 people stranded globally. I sort of, after 25 years, I thought I'd done my thing for business. I had been financially successful.
And then where could I take my skills of market and business development and technology and apply them to a bigger problem? And what became clear, specifically through ovarian cancer, is early is pretty easy to treat. Outcomes are fantastic if you find it. The problem for most women with ovarian cancer is the symptoms are very much like menopause. So the age group is 60 to 70 for ovarian cancer. They're taught as women to shut the hell up about your bloating or you're uncomfortable with this or that or the other thing. And so...
The idea was being able to figure out a way to sort out early detection, and this is true for almost all solid tumors. When you find them early, you can treat them. They're quite treatable. But we just have diseases, the worst two, I guess, are in terms of not being able to find them, are pancreas and ovarian cancer.
Now, to be clear, when you did this shift, were you thinking that you were going to leave the technology industry altogether, or was that a soft landing? What were you thinking at the time? Yeah, I certainly was thinking I was going to leave operating roles, you know, in the Cisco days and others, there's no such thing as 9 to 5. Yeah. 5 to 9 was the basic day back then. I continued to do technology investment. And to this day, I have my little venture company, Lisbon Ventures. I probably have 20 different investments that I do in and around areas that I'm familiar with. But I have done more recently med tech, a company called Rapid AI, revolutionized stroke care globally and changed the global standards where that company probably is saving, versus the old standard, hundreds of thousands of more lives. Also, I'm involved in handheld ultrasound, which later on in the story is important because I think handheld ultrasound machines that cost on the order of $5,000 to $7,000 can become the everyday imaging tool for the general practice, which can really help in this continuum of, okay, we do have a blood test. It's high. Let's go do this imaging test, but you don't have to go anywhere. We'll do it in a GP office place. So I continue to invest and be on a lot of boards, public company boards, private company boards, but not operating roles. And I really was operating as the CEO of Canary.
Was there a playbook? Were there other people that you emulated or referred to coming from technology and finance who made this pivot to healthcare?
Well, the vision of this was from a guy named Dr. Lee Hartwell at the Fred Hutch Cancer Center in Seattle. So how that all came about is I sent out a series of emails on a Saturday to a number of institutions, development departments.
And said, hey, I'm interested in working in ovarian cancer.
And the development director, Pat McGowan, emailed me back about an hour later, which always is good for me in terms of, are you on it? And it was Saturday, nonetheless. And she said, yeah, we have this great program, Nicole Urban.
And we'd like to see you. So I went up to Seattle, and when I went to Seattle, I met Dr. Lee Hartwell, who was the institute's director at the time. And this really was his vision. His vision really was this two-step process, biomarker and what we call molecular imaging. So not just imaging like a CT scan, but we put something into your body that can really be specific and home to the cancer so we can be sure there's not false positives. So this was his vision, and he's the one who originally helped me convene a team. And then I learned along the way as I went, imaging from Sam, genetics from Peter, and so on. And so that's how this all started. Now, geographically, you were in the Bay Area and you went up to Seattle, or how was that connection? Yes, yeah. So I was in the Bay Area and went up to Seattle, and that's where my primary major funding went. Nicole was a data mathematician, and she needed to get a wet lab.
And she was about 24th on the list as a data mathematician. So I gave her the first million-dollar gift I'd ever given, and she got a lab to begin to work. She was working on ovarian cancer biomarkers. And I continued to fund her.
And as Lee and I got a better relationship, you know, I said, shouldn't there be a place...
Where we can do all of the work, all the multi-omics work, together, and wouldn't Seattle be a...


Good place to do that. And he said, yeah.
So I ended up negotiating with him and discussing with him and gave the Hutch a $10 million gift to create a center for early detection at the Fred Hutch. And a bunch of that money was used to bring in proteomics people, Dr. Sam Hanash and Dr. Mandy Palavich, because there was no expertise there. And so that's how the program started. Were you in a financial position where you thought you could do most of the giving yourself that allowed you for a certain level of control for the vision of what you wanted to accomplish? Certainly at the beginning, yes. You know, there were, we built a whole variety of models. There were team, big teams, which we can talk about a bit in the future. There were lunches where I'd sit down with scientists and they'd pitch some idea and I'd pull out my checkbook and write them a $50,000 check to go and see if the idea, you know, made some sense and held some water. But the hallmark of Canary over the long term has been leverage. For every dollar that I've put into the foundation, there's probably been five more dollars from other people. And for every of those dollars, we get leverage of around 10 to 1 from National Cancer Institute and other granting institutions. So the whole idea was to build some leverage. But at the beginning, I had enough resources to get things going. Don, being in the Bay Area, Stanford being right next door, when did you establish that connection?
Well, so Lee and I discussed, and he said, what we should do is to build this team. And we don't have everything we need at the Hutch. Notably, what we don't have is advanced imaging technology at the Fred Hutch. And Stanford had, bless his soul, he's passed, but Dr. Sam Gambier as a world leading, one of the top three, I think, and arguably one of the best in molecular imaging. So Lee convened the meeting, which was held at my home here in California in Woodside.
And we ended up with Pat Brown from Stanford, who invented the DNA microwave. We had Frank McCormick from UCSF. We had Peter Laird, a methylation expert from USC. Sam came in as the imaging guy. Sam Hanosh came in as the proteomics guy. Marty McIntosh came in as a data scientist for us. And we all came together. And so that's where the two Stanford links really came in, the majority of which over the long-term was Sam, as Pat left after five or seven years to start Impossible Meats. Dom, I see here in the story so far, there is the philanthropic world, the foundation world. There's academic scientists. What about government-supported basic research? What's the role of FDA, NIH? How are you thinking about public initiatives in this development stage? Well, what becomes clear as you develop in this world is if you give scientists with a good idea and a good scientist some seed money when they get early results, they're about five times more likely to get a grant from the government. So that was the whole idea. How do you give them a head start? So if let's imagine there's five people trying to do this proteomics study, and four of them have an idea, and my canary person has an idea and preliminary data, they're always going to win that grant. And so that was a model which we used. And we also used models of most of my major funders early on in the days were more senior venture capitalists that I knew. The most noteworthy of which was Don Valentine, who is the founder of Sequoia, who was my father-in-law. Oh, wow. And then the probably the most important to the story was Bill Bose. And he was really the anchor that helped us start building infrastructure at Stanford. But to this day, it's about leverage. I've got a call recently from a new foundation just formed. They sold their company for $5 billion to another high tech company, and they put a billion into...
into work and they want to work on early detection of pancreas cancer. So I think we've got to a point where the brand, we have a trusted, respected brand, and that's also another way that we can create this leverage outside of NIH and CI. Who knows what is going to happen this day and age with the grant.
From that institution. Don, the idea that, you know, you're in a position as an individual investor with a vision to fund research in a much less conservative way. You can write a check if somebody has a good idea with the dream being that if it works, they could go on to, you know, a much larger phase in funding from government sources. Is that a investing philosophy? Is that a perspective to achieve success that comes sort of directly from the Silicon Valley VC investing kind of world? Oh, I think absolutely. I mean, you should think of me as the seed investor, right? And then we go out and look for our Series A people like Bill Bowes or Mr. Valentine, or for that matter, a great example of that, and I know he doesn't mind me sharing, Frank and Denise Quatrone, who is quite a famous banker in the world. Frank's dad died early in his very early.
50s of prostate cancer. And I knew Frank both from a business and a personal point of view, and I approached him, and he and his wife gave us the first money to start our prostate team. Now, it was one of our more challenging programs because we had to commit to milestones. So one of the differentiations and one of the skills was, you know, I always used to say at Cisco, don't confuse efforts with results, right? And so with the prostate team, we're like, you know, Frank's going to give us the next million dollars.
If we do this, or we get close, you know, to this. Because one of the things about cancer philanthropy, which is frustrating to people who give, is it just feels like a giant black hole. You raise money, it goes in, and you never hear a damn thing back, or for that matter, you don't hear, which would be better, well, we failed, and here's how we failed, and so now the aperture of our next attempt is smaller because we know not to do that. And that's, again, for future, one of the advantages of Canary is that we know what doesn't work. And so we say no to that. And so that skill came from Cisco. We built giant ASICs for our routers. I didn't know how to build an ASIC, but I knew when one was gonna work and one wasn't gonna work. And after the second try, one never worked after three turns, ever.
And so after the second turn, if it didn't work, we canceled it. And so we had that kind of attitude.
we're not canceling programs, but giving them enough to get going and having them prove it, versus a scientist who gets a three-year grant and five years later you know whether or not something happened.
Don, it's a question that you can apply always to sort of in the history of great ideas.
What is your sense of why no one thought of this approach before you? In other words, if it's so obvious the importance of early cancer detection, and it was so obvious that this was a part of the, you know, research process that needed funding, why did Don Listwin need to come along in the late 1990s when these problems had been around decades earlier?
Well, I think there were two big problems. One is there wasn't collaborative science models, right? There were no funding mechanisms to do that, so we were one of the very first that pulled that together with Lee's support. The other big problem, which still exists to this day, is the business model in the world for early cancer detection is terrible.
It's, you know, the whole story I've heard is Avid Labs has as many trucks as UPS. You just don't see them. They're running around at night picking up blood samples. And they're in, to use the tech analogy, it's a mainframe processing model. There's seven huge sites. And the way the government will pay for them is cost plus. So...
Versus I have a blockbuster lung cancer drug, I become a very rich scientist, I get accolades, and I get pharma money pulling. There was no money from industry pulling here. So part of the technology development we did also, and Stanford has led the way, is in point-of-care devices where there are disposables and that you can build businesses that are very successful businesses in this field, as opposed to the old model of I'm going to sell this biology. So the business model is still...
I'm still broken. I'll tell a quick story about my dentist is always interested in what I'm doing in this area. And we chatted one day many years ago, and she said, Well, my office is the right place to do this. She said, you come and see me twice a year. I can take a drop of blood out of your mouth without you even knowing it. And if I had something the size of a printer next to me, which is what some of these new early detection biomarker platforms are like, I can drop it on there, run 128 tests. The chip costs a dollar, sell it for 50 if you want. Right? And you can make a really, really successful business out of that. So that's one of the things I've also encouraged is I've done a lot of mentoring to a lot of scientists on them starting their own businesses and being successful. Because if all the great minds went to Google, we wouldn't have great minds doing this research. Don, is that to say that even if the goals of your initiative are, you know, so idealistic, it's all about helping people, it's all about improving health outcomes, it's all about making sure that other families haven't gone through what your family has gone through. Does there also need to be a level of, I don't know if the right word is cynicism, but world weariness, that money really talks and it needs to be present in all of these considerations for the whole model to work?
Certainly for aid to scale, it certainly has to be. But, you know, if you can start saving people's lives in high-risk clinics, and that is just the wealthy people, but wealthy people got cell phones first. And that's how technology development goes, right? And you prove it out, and the wealthy can pay for it, and then you get the next generation, and it's two times cheaper, which means four times more people, rule of thumb on consumer electronics, can get access to it.
We've got to the point where, you know, one of the big partnerships we had was with Cancer Research UK.
who were told genetics, genetics, genetics, genetics, and they put money into genetics for 10 years and didn't have any change in outcomes. And they came and they started talking to me, and I said, well, here's where we think the leverage is, particularly for you in the UK, because you have a public health system. So if you get something working, you've got the tools, the money, and the infrastructure to roll it out, which is far different than here in the United States. What was most important for you to convey in those early years that...
There was money to be made, this was not a financially losing proposition. What was the case you made and who needed to hear it?
Well...
Originally, the case was just made to funders that, you know, everyone goes on these great binges of excitement on, you know, proteomics and then genomics, and the next thing, and liquid biopsy, if you recall, that was, you know, that's the latest and greatest. We needed to show people that there was a pragmatic way that could begin to save lives. I think sometimes people want all the lives saved. You know, if we could save 10% of the women's lives in ovarian cancer the next five years, that'd be a huge home run. And then 20% the next five years and 30% and so on. So, you know, we're just in those conversations globally with trying to roll out early detection technology.
But we have a very long way to go to convince any government infrastructure because up until now, right, all the questions are, well, here's all these untreated masses, how do you get to them? And I say, we get to them when the technology gets to the price that we can get to them. And the only way that happens is with generation after generation of working on them. Don, I wonder if you can explain sort of the pipeline where you're talking to scientists, they're telling you what they need, and now this eventually goes to biotechnology to create the devices, the diagnostics that give the scientists the tools they need.
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Give me that question again. I'm not quite sure what you... The pipeline that goes from you talking to scientists, the scientist telling you what they need technologically, diagnostically, how you take that information and then bring it to biotech so that they create these devices.
How does that pipeline work? Most of them to date have been...
Through, the Stanford ones have been through startups through venture, so, you know, we'll work on some program, and we're very careful about not getting financially involved in any of these things. We don't want to have any view of conflict of interest. Early on, I did a couple of investments, and a couple of my donors came back and said, well, are you using my money to sort out what's the best thing for you to invest in? And I said, no, but I can see how that might be viewed as a conflict. So,
The startups that are coming out of Stanford right now are coming through the typical venture channels where we helped. And the money that we put into their lab, we don't get any percentage of. It goes to Stanford, it goes to the School of Medicine, and it goes to the researcher themselves.
Was your idea for Canary, would there be any scientists that were directly employed?


It would always be external partnerships.
They were always external. We employed young PhDs that became program managers. So as we grew, we grew different team structures. We started with ovarian, which I can talk about, not because it was my mom's, but because the team thought it was one of the most challenging ones to work on. We then ended up, were found by a family in New York where a well-known banker had died, you know, was a marathon runner, died in his 50s of lung cancer. They wanted to start a lung cancer team. So that's how that team started. The prostate one we talked about was with Frank. So we would convene those teams. They would talk about what the best problem was. And so to our earlier conversation, the problem the prostate team said they wanted to solve was helping doctors and patients decide whether they should get treated. So in prostate cancer, you know, a quarter of the men, you're fine. You should be careful, you should be diligent, but it's not going to progress. The quarter on the top end, you got to go to surgery, radiation, whatever, right away. The big public health problem was, what do you do if you're a man in the middle? And so that was the problem that the team tried to work on.
Because if you could convince people who didn't want to go to therapy to go to therapy, you were going to save lives. And if you could tell people they didn't have to go to therapy and couldn't they were going to, you could save a lot of morbidity. And so those probably, it really depended what problem the team they thought they were going to solve. In the case of both ovarian and pancreas cancer, there are two pretty good blood biomarkers. One's called CA125 for ovarian cancer, and one's called CA199 for pancreas cancer. The problem is that the incidence of the disease is so low that if you do your statistics, the false positives are crazy. So this is where Dr. Gambier said, this is where imaging has to solve the problem. And we've been working on a molecular imaging program for 15 years with the idea that you inject a body with this particular substance, and it goes and finds cancer vasculature. And Avastin from Genentech here locally, knew all those targets because they were targeting drugs with them. So we didn't target a drug, we targeted a little bubble that when you put an ultrasound over it, it vibrated, and then it burst and you could see the cancer and you knew it was specific. So to this day, we've just opening a new center at UC San Diego for...
early cancer imaging detection using ultrasound with a young, used to be a staff scientist at Stanford, Hamid. And so each one of these, if you looked at the care continuum, you said, what's the big missing piece? So in ovarian and pancreas, it's imaging. And in lung cancer, it's also imaging. I mean, the way you handle lung cancer is you get a CT scan. The doctor doesn't know whether or not it's cancer or scar tissue from asbestos or whatever else. So the protocol is you wait 90 days. And your chance of living if you have cancer goes down from 50% to about 10% while the doctor waits 90 days to figure out if it's cancer. So how do you do a test structure that says, no, damn it, it's cancer, go pick up the scalpel. So each of these, we examined the care continuum, what was out there and where you could insert and provide some leverage versus saying, oh, it's the utopian dentist office thing. And we'll genetically change babies to get rid of their bad gene structure. I think we were pretty practical on where was the leverage in the care continuum. Dan, you said something very interesting earlier about getting started focusing on ovarian cancer, not because of your mom, but because it was so challenging.
Fascinating there. Why not go after the low-hanging fruit first? What's the science? What's the investing philosophy behind that?
That's what the scientists wanted to do. Easy as that. It's as easy as that. And Nicole, who I talked about, she was also part of that team. And we convened the first team and discussed a variety of these different things. And I think there's some pretty big minds and some pretty big egos around the table. And they said, let's see if we can crack this one.
I think it's as simple as that, David. And is that to say, Don, that you always let the scientists take the lead, that what they want to do is what you want to do, or are you ever sort of at the forefront of saying, I think this is what we should work on?
Well, at this point, I...
I have some rules of engagement, and, you know, back to the, we've already learned this, I'm not doing that again. So, you know, some scientists want a blood test that's perfect. Well, they don't exist yet. So why don't we find one that's specific, and what that means is, we know it's cancer, even though we're going to miss some, we know the ones we got are cancer for sure.
And so there were guardrails, and of course, after 25 years, I know more than I did at the beginning.
So I trusted Lee in particular and Sam to really help guide scientific principles, but I was the one.
I'm trying to think about how do we get other people engaged, and then how do we bring this to market, not as a company, but through other vehicles so that it ultimately can be successful. And...
So that was the role I played. Don, as Canary was getting started, how did you refine what role you saw it playing in this larger ecosystem? In other words, at the beginning, you have all of these ideas. What did you learn about what Canary's lane should be, where it should leave other aspects to other organizations, and where you saw a real opening that nobody was doing this? I wonder if you could walk me through that.
Well...
Добрый день!
Certainly, most all of the money in cancer is in therapy and drug development, and that's still the case. And when you go to the National Cancer Institute, and I was on the board of the scientific advisors for five years, the early detection group is rolled in with the prevention group. So it's the poor brother to prevention. So there's not, there's not much money at all. So it was pretty easy to be the only one that said, we're going to do this, because the big pots of money in diagnostic company, they go, well, we're just not ready to do that. Now, to this day now, and I'll digress a bit, the prostate team has been doing this clinical trial for 15 years on that sorting out what men should go to therapy or not. There's a calculator now that men and doctors can put in, and with 99% certainty, know where they are in this process. Now, we have hundreds of thousands of samples. That program probably cost $30 to $40 million so far. Now we have biotech companies. So now there's biotech. There really wasn't biotech to think of 25 years ago. We sell them our samples.
And the outcome we're looking for is, they actually get a test that works. They just didn't have the samples because it takes forever to get these done. So, you know, why didn't anyone else do it? I think because...
People worked on it and failed, and then there was none of this big economic pulling. Now, in the future, fast forward, the two huge successes on this, CRUK, which we talked about earlier, decided to shift and make early detection one of their anchor programs and bring their money and clout to it. And the other big thing that happened in the U.S. here in the last decade is Phil Knight of Nike fame.
One day, he came to a podium and surprised everyone and said, my wife and I decided to give another billion dollars to Oregon State Hospital System, but we want it focused on cancer. And a very famous doctor, Brian Drucker, got named the director, and he surveyed the world. And Brian came back and after talking with me and a host of other people, went to Mr. Knight and said, early detection's the right low-hanging fruit for us right now. And so there's a giant institute up there that's working on early cancer detection. Is Canary a part of that initiative? What role do you play in this?
Sam and I played a role in convincing Brian that early detection was the right thing. Sam and I played a role in convincing the leadership team at CRUK, and then, you know, it's at Cambridge that the major cancer work is being done. There's a couple of actually Stanford people that are leading that effort now, and I do, I do, I'm gonna call it sales calls. They had somebody who wanted to give 25 million to the program.
And I said, let me tell you, here's my unbiased view of this. I'm not gonna get a nickel, but let me tell you how this helps the overall ecosystem. And we managed to land that donor, they did, and get that program going. Similarly to this big new foundation that's got this interest in early cancer.
They said, well, should we give the money to you or give it to Stanford? And I said, look, for the beginning of small money,
I have IP agreements done with everybody. I have a great gift agreement done, which I don't think in today's day and age anyone's gonna get the low percentage of overhead that we have. So I said, start with me. If you wanna give $25 or $50 million and you think Stanford's the home for it, I'll be the first person to guide you through the process and where the pitfalls are. I'm just interested, I mean, we started with, you know, six guys and a goat, and, you know, we are now the legions of thousands.
Dan, you mentioned calculations. Inevitably, this prompts questions about your views on machine learning and artificial intelligence, which is obviously a very recent development and potentially is revolutionary for the field. What are your perspectives on what AI can bring to early cancer detection?
Well, I'll go back. You said machine learning and AI. I'll start with machine learning, and the example there, which I think is the one that I lived through, was in the stroke world.
And so the company that was started actually by my father-in-law and two Stanford scientists, Greg Albers and Roland...
Roland Cheese, I just got... We'll get into that. We'll come back to it, no problem. We'll come back to it. Dammer, Roland Dammer. They started doing machine learning on CT images for stroke. And we got to the point where the crossover happened. The machine was much better than the doctors. And that just happens because the machine learns the next one and the next one and the next one. Where you want to call machine learning and structured machine learning, unstructured, in the whole AI continuum, that's a whole discussion. But that started as long as 10 years ago.
And it's already there. Another example that's happened is we run as a family an eye clinic in Belize. So we, there's no healthcare in Belize. So we built this eye clinic with the help of a doctor who's a neighbor and Stanford, who's designated a global health program. But, and we rotate doctors through there. But what we've done is there's AI cameras now. And so we have a technician, just like if you go to Lenscrafter and you put your chin in and it takes a picture of your eye and it sends it to the cloud, and it runs what I'll call machine learning, but they call it AI, and comes back and says, you have diabetic retinopathy, and, you know, you better go start working on knocking off the Coca-Cola because you've got bad diabetes. So it's already happened. And the third story is Gary Glazer, who used to be the chair of radiology, he said, well, the first practice to go is gonna be pathology. And he said, the second one to go will be radiology. And I lived the radiology stuff because in Rapid with stroke, the neurosurgeons wanted the tool because the hospitals would make $50,000 to $100,000 per case. I mean, the numbers were just egregious. And the radiologists hated it because they saw the fact that the neurosurgeon didn't have to send it anywhere. So, you know, I think that that development will come where we'll just improve outcomes, improve outcomes. And in the case of Rapid, the major thing that that team did, which was not my doing, but I helped scale, was the global standard for stroke was six hours. And after six hours of a stroke, they wouldn't give you care because they thought you were done. And what the clinical trials with this technology proved is you can have mini strokes, as you might imagine,
And up to 24 hours. So the global standards changed because of this technology. The company grew rapidly, as you might expect, during that time. But there's no question in imaging and pathology, that's where we'll probably have the biggest impact to start. And do you think, you know, the idea that the machines are coming for the jobs, does that mean that people who now work in pathology and radiology, is it, is it that they're going to be out of work? Or is machine learning optimizing them to do what humans will always do better than machines?
Oh, I think it's the latter. I mean, I do think it's always going to be the two together. But, you know, reading, like, the pathologists on the Canary group.
Reading the slides at...
15 different institutions, there was the inter-reader variability was just incredible. All of a sudden you'd just give an algorithm and you send the algorithm to all 15 places and you get, whether the data is good or bad, it's all the same.


Right, and so I think in pathology for sure, it will vary very rapidly.
Change that field. What the mix is, I don't know. I mean, gosh, I was in the United lounge last week going to Belize and a robot came by picking up the glasses. Great big world. Right? The people weren't gone, but they weren't doing the bus person work. So maybe a practical example of how the mix changes. I don't know enough about pathology to tell you how the mix is going to change or radiology for that more about radiology, but the mix will certainly change. The machines are faster and better. Don, obviously, you're a sponge for knowledge. You do have a technical background in electrical engineering throughout your career in finance and technology. When is it important for you to read up yourself on cancer biology, on the technical literature? And when do you rely on, you know, your trusted partners, the scientists, the doctors, who basically tell you what you need to know?
Hi, it's a mix. I still read a lot, and, you know, I don't read very many novels because I read a lot during the day. I'm like, you know, put on Reacher on Amazon or something for me. So I do read a lot. And then our teams generate a lot of technical data. But, you know, when it comes to the teams, these guys are world-class scientists, and they do it every day. So I guess it's sort of 50-50. Dan, I want to ask some sort of macro political questions about public policy. And let's start first with, of course, your dual perspective, the Canadian healthcare system, the American healthcare system. What can each learn from each other? Let's focus first on the positives. What do you see as the best in Canada, in the United States, specifically as it comes to early cancer detection?
Well, I think, certainly on a funding level, you know, we still are doing better at NCI funding for early detection in all cancers versus in Canada, for the UK for that matter as well. The government doesn't fund much of any research, you know. What they fund is the public health care system, so they have to rely on people like Cancer Research UK and others. You know, I grew up in Canada, and my family's in Vancouver now, and my sister's a nurse, and she'll tell you at least on the day-to-day care and watching my mom go through it.
Things are slow.
But they're ubiquitous. And things here are fast and expensive. But I think technology is the thing that will change in the United States, where if it's 50 bucks in the dentist's office some 10 years from now, I think that we will adopt those technologies very quickly. You know, an example on one thing that happened in Canada with one of the teams, the ovarian team, one of their big successes is the discovery that the fallopian tubes are really the primary source of serous ovarian cancer, which is the most deadly of the ovarian cancers. And that was discovered, as all ironies in the world, by the surgeon of my mother. So, you know, unbelievable. The poor woman, you know, came out and said, I can't get it, and my mom died, and then we funded her anyways. But that practice was...
absorbed into Canada first because taking out the fallopian tubes takes five more minutes of OR time.
And that is an expense. And in Canada, they went, yeah, well, we're going to do that. And it's only now that that's translated into U.S. care because people are pushing back on the five minutes in the OR.
So, you know, one is a little bit altruistically driven, but clunky and slow. And, you know, on this side, very financially driven, can be very fast if you're wealthy, and is terrible if you're poor. I mean, you just don't get any care, as you know. So in the best-case scenario, the Canadian and American systems would absorb the best of each, where it would be fast and ubiquitous, if I understand correctly. Is there an international model? Is there like, you know, a Finland or a Sweden or a Japan? Is there some society in the world that is doing a good job in both of these areas?
Em...
Not that I know of.
I think it's one or the other, you know, the Scandinavian countries are well known for their healthcare systems, but there's no research money and very little innovation that's going on. So, you know, China is a huge opportunity. Like our lung cancer program, we ended up with a biomarker panel, five different things that determine lung cancer. And that clinical trial is now being funded by the Chinese government because it's 10,000 people. So I suspect because of both their commitment to machine learning and AI in China, the enormous number of people and even being able to cull the rich people in, we'll have big impacts in China in this field in the coming years.
I wonder if you can walk me through, so, you know, it's unfortunate that there isn't a perfect country out there that can be a model. Let's say you weren't the founder of Canary, let's say you were, you know, the builder of a new country.
What would you want to see? What would be the public policy initiatives that would get people into the clinic, that would encourage a culture of going to see your doctor, that would produce the kind of outcomes that you dream of?
Wow, David, that's a big question. I have never started a country, so...
Certainly, you'd want to focus on access.

For people
As it pertains to early detection, you know, Pap smears are, of course, a very important early detection test, and some cultures just...
Aren't having it. And in some cases, in Mexico, they figured out if they go with mobile pap smear run only by women in buses to the church, that they can get. So there's, there's, you know, no one size fits all by culture or by country. I think you just have to be mindful of...
who you're dealing with and what their issues are. You know, right now...
Colonoscopy is the gold standard. It's not that, I mean, people think it's a terrible procedure. It's not. The prep is no fun, but the procedure itself...
It's a twilight experience and you're in the lobby. So I would just make sure that both information and access were out there, and we still are failed. I mean, my God, my cousin died.
Five years ago in Canada.
Guys, and...
And some of the public policy stuff gets tied up in the cost of doing this, and so you take a hard look at what public policy is, but the sad news is a life has a price, and that's how many of these decisions are made in terms of whether we deploy these tests. So let's delineate that in terms of what public policy can do to encourage people to go see the doctor. Let's delineate that by what should people be doing who are non-symptomatic, and this is just sort of protocol, and what should people be doing who are symptomatic?
In terms of the government encouraging, providing incentives to get people to go. Well, let me back up once and say, look.
We want to differentiate not so much, at least in early detection, symptomatic versus non. If you're symptomatic, go to the damn hospital, right? Or go to the ER or go to your doctor. But with the genetic tools that you referred to earlier, we now have like three, about two big buckets of people who are normal risk, we think, and then we have two buckets of high risk people. We have people with family histories, two or more people with breast or ovarian cancer, you're high risk in your family. And then we have genetic testing, BRCA1, BRCA2, a whole variety of different genetic weaknesses in the body. Well, that's where we want to start. So if I started my country, I'd be more focused on how do we help people understand if they're high risk and get them into the care path, as opposed to going, well, you know, I don't want to do that. And if...
If I had convinced my cousin, which I didn't, that he was high risk because he had, my dad had it, and I maybe had it, he might have been able to get care. But I think in that case, he ran up to the barrier of he wasn't 50 yet. And when he turned 50, he got the colonoscopy, and then there he was with stage four colon cancer. So part of where, you know, where we're focused is, can you bring these tests, at least in ovarian and pancreas, to the high-risk community because you can delineate those both genetically and from family history.
Don, what do you see as the future? What do you see as, you know, all of the things that you've learned? What's most important to emphasize 2025 looking ahead?
Well, if I, let me...
Phrase the question this way, if I had to bet on where the best outcomes would come bang for the dollar, it would be in molecular imaging.
I really think giving surgeons tools, with pancreas cancer, the confounding issue is pancreatitis. If we could even do imaging that said, oh boy, you have pancreas cancer, not pancreatitis, that would move people down the care path much faster. With ovarian cancer, imaging can do it. With prostate cancer, it's becoming clear that imaging is really going to help. I mean, my God, if you understand how a biopsy is done on the prostate, blind someone poking your prostate 12 times with a needle where the sun don't shine and doing it blindly, now image-guided biopsies there. So if I had to say where the technology that will, at this next 10 years, make a big difference, it'll be in imaging. And much of that imaging needs AI imaging, of course, to be successful. But that'll be the tool that we'll put in the quiver that we don't have right now.
Don, I think the last thing we can cover in today's discussion, it's really, you know, it's unfortunate, it's in the headlines, the, you know, federal government funding for cancer specifically is, is, is...
It's a grave situation right now. So first, how did we get ourselves to this position, culturally and politically? What's the way forward given what's happened so far?
Well...
As I understand it, it's not just cancer, right? It's the National Institute of Health has some 30-odd different institutes, of which NCI is the biggest at about a quarter of the budget. And that all happened through the Clinton years, where as president, he increased the NIH budget radically. So the money was there.
Where I think the community lost its way is the way it works is that if I get a grant, an R01 grant, let's call it, where as a scientist I get $250,000 a year for three years, my institute gets the overheads associated with their institute paid by NIH. So I'm going to get the 750K over three years from NCI, but NIH is going to give Stanford 60% more of that. What is that? 450 grand. Well, it got a perverse incentive got created, which is if you could get away with convincing the panel that you needed those overheads, they give them to you. Well, all of a sudden, so on one side of the coin, you'll have people argue, well,
My scientists don't have to do photocopies anymore. My scientists have grant writer support because of the overheads. The Fred Hutch is 73%, for gosh sakes. And I used to argue with Lee because I'd say, you know, he'd give me the photocopier thing. And I'd say, Lee, at Cisco, our overheads were 4%. And the first CEO, John Morgridge, had an assistant that he shared with four people. And he'd photocopy his own stuff. And I'd say, John, why are you doing that? He said, because I only need two. And if I had an assistant, I'd probably do 20. And so it's a little perverse.
Do I think there needs to be a better structure? I do. I think, like many things that are happening right now in the administration, it's too draconian. If you said to people, look, we're going to get to 30%, and you've got three years to do it, I think people could absorb that, understand, start making changes in the hiring practice, the level of buildings that they build, and the like. But right now, in our community, people are gobsmacked. They don't know whether to you-know-what or steal second base. So people are kind of frozen. They don't even know if grants today, on March 25th, that they've been awarded are going to get the NIH funding pulled from them. So it's frozen the whole community. Do I think, like many things the administration are doing, I think directionally, perhaps correct, but the way and the brutality is just, you'll never get the proper result that you're after. Is that to say, Don, that already we're seeing the negative impacts?
That we're already seeing experiments on hold, that there are cures and therapies that might not happen because this has already taken place? There's certainly hiring freezes and building, you know, addition freezes and the like. So at an inf-


structure level, yes. You know, will it stop a drug development pipeline? That I can't comment on. I doubt it. You know, with the pharmacy money pull, but it's all institutions are reeling from this, especially the big ones. I think Memorial Sloan Kettering is closer to 80%. So I don't know how you lose half your budget without losing a bunch of productivity.
Don, the irony in all of this historically is, you know, you got involved, you brought your own success and generosity to this at a time when federal funding was ascendant. What's the message now if, in order to close this gap, if it's not coming from government sources?
Where do you get it from? What is the role of private benefactors? Are you in touch with people who have the means to do what they can at least to backfill some of these budgets?
Well, yes, and, you know, the best example is the Bill Bowes Foundation continues to support us, and at an annual level, we renew with them. This new found big foundation, you know, I mean, just to be very candid, many of the major donors we have have passed over the last five to seven years as they were in their 80s. But yeah, we continue to fundraise and reach out.
But private will never be able to replace 60, or let's call it 40% of $30 billion. I mean, that's the shortfall. So it's at least $12, if not $15 billion a year. What my current approach is, if I continue to fund certain labs, they're going to continue to be able to move forward independent of whatever happens in the administration because I provide some certainty. I can't provide complete certainty, but I can provide certainty that this new imaging lab is going to be funded for three years at UC San Diego.
Finally, Don, last question for today, the idea that, you know, imitation is the finest form of flattery. Given how unique the founding vision of the Canary Foundation has been,
Where have you seen others sort of take up that model? What have been sort of sister organizations or parallel endeavors that have been inspired by what you've accomplished over these past 25 years?
Well, I think the two I already referred to, one is the Oregon system, and they have a very unique model, I'll just add, where they don't go for government grants.
The billion dollars, you come in as a scientist and you get your full budget, and away you go. Cambridge is the other one I'm very proud of, but we've now, there's now initiatives at MIT, there's initiatives at Harvard. So...
The word's getting out, David, that there's an opportunity here, and what I think was viewed as an intractable problem 25 years ago is viewed as, we can get there from here. And then we'll wait and see to your AI question.
Are we gonna be able to create inference models out of the biomarker lake that gets developed? Maybe we can. I don't know. Wouldn't that be amazing? But right now, you know, as I said, you know, six guys and a goat, we're thousands of people that believe that we can get this done. And we have industry now pulling and we have environments where the scientists can also make money if they're so entrepreneurial oriented. Don, is that to say that now this problem is no longer viewed as intractable, thinking back to where you were in the late 90s, early 2000s? Is that the greatest success of this whole story, just moving the ball forward where so many people share your vision that we really do have a handle on this now? Yeah, it's exactly right. I mean, early on, we gave postdocs to the American Cancer Society in early detection because no one would come into the field. Yeah. So we lured people into the field. Now, I think they believe, indeed, the success that they've seen. So, you know, the water's warm. Come on in. That's great. Well, Don, this has been a wonderful initial overview conversation. Next time we'll go back. We'll develop your own personal background, the all importance of your family's experience to what you accomplished next. We'll take the story from there. Thank you.
All right, you're very good at this, David. Made it easy.