The Peter Attia Drive - #181 - Robert Gatenby, M.D.: Viewing cancer through an evolutionary lens and why this offers a radically different approach to treatment

Episode Date: October 25, 2021

Robert (Bob) Gatenby is a radiologist who specializes in exploring theoretical and experimental models of evolutionary dynamics in cancer and cancer drug resistance. He has developed an adaptive therapy approach for treating cancer which has shown promise in improving survival times with less cumulative drug use. In this episode, Bob explains what brought him into medicine, his search for organizing principles from which to understand cancer, and the mathematical modeling of other complex systems that led him to model the dynamics of tumor cell changes in cancer. He discusses his pilot clinical trial treating metastatic prostate cancer, in which he used an evolutionary game theory model to analyze patient-specific tumor dynamics and determine the on/off cycling of treatment. He describes how altering chemotherapy to maximize the fitness ratio between drug-sensitive and drug-resistant cancer cells can increase patient survival and explains how treatment of metastatic cancer may be improved using adaptive therapy and strategic sequencing of different chemotherapy drugs. We discuss: Bob’s unlikely path to medicine and disappointment with his medical school experience [1:45]; Rethinking the approach to cancer: using first principles and applying mathematical models [12:15]; Relating predator-prey models to cancer [26:30]; Insights into cancer gathered from ecological models of pests and pesticides [32:15]; Bob’s pilot clinical trial: the advantages of adaptive therapy compared to standard prostate cancer treatment [41:45]; New avenues of cancer therapy: utilizing drug-sensitive cancer cells to control drug-resistant cancer cells [48:15]; The vulnerability of small populations of cancer cells and the problem with a “single strike” treatment approach [56:00]; Using a sequence of therapies to make cancer cells more susceptible to targeted treatment [1:05:00]; How immunotherapy fits into the cancer treatment toolkit [1:15:30]; Why cancer spreads, where it metastasizes, and the source-sync trade off [1:20:15]; Defining Eco- and Evo-indices and how they can be used to make better clinical decisions [1:29:45]; Advantages of early screening for cancer [1:40:15]; Bob’s goals for follow-ups after the success of his prostate cancer trial [1:42:15]; Treatment options for cancer patients who have “failed therapy” [1:51:15]; More. Learn more: https://peterattiamd.com/ Show notes page for this episode: https://peterattiamd.com/RobertGatenby Subscribe to receive exclusive subscriber-only content: https://peterattiamd.com/subscribe/ Sign up to receive Peter's email newsletter: https://peterattiamd.com/newsletter/ Connect with Peter on Facebook | Twitter | Instagram.

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Now without further delay, here's today's episode. Everyone, my guess this week is Dr. Bob Gaitenby. Bob is a radiologist whose specialty is treating oncology through an evolutionary lens. Now, I stumbled across his work when doing research for another podcast and got completely transfixed down the rabbit hole of what he does. This is a simply fascinating podcast, and we get into what I believe is a very important way that we need to consider treating cancer. And I hope that this podcast will provide both some hope to people who themselves have cancer, but of equal importance

inspire some people who are on the front lines of cancer research in exploring another way to think about treating a disease for which frankly we haven't had a lot of great success over the past 50 years. Hey Bob, thanks so much for making time to chat today. I've been looking forward to this one for quite some time. Thanks for having me. I'll tell you, I kind of stumbled across your work in preparing actually for another podcast. I was doing another podcast some time ago and in the process of it actually came across

an article in Wired about you, which we'll obviously link to in the show notes. And that whole concept just quickly captivated me such that I made a note to my team and said, hey, at some point, this is someone I'm going to want to interview. And I'm going to want to talk about this entire approach to cancer and lo and behold, that led us to reach out to you. And that's how it came about. But it's interesting that it really started with this sort of rabbit hole, you can easily find yourself on in Google where you're trying to find out about this treatment for this type of cancer and this treatment and then all of a sudden you see

something and I don't know what led me to read that whole article, but I was very captivated by it. And I guess part of it is my own bias, which is, my background is very similar to yours. So tell folks a little bit about your background because your path to medicine was not, I guess, the standard path. No, I guess I, when I went to college, I wanted to be a physicist and spent most of that time really thinking that I should do that. And then I had some great physicists as mentors, and kind of came to the conclusion,

I'm not smart enough to do this. And I got to find something else to do. And of course, that was a great humanist, the Vietnam War era, and going to medical school was considered something that was really, you know, for humanity and something that I decided I would do. To my regret, by the way, but I did end up doing it. So you were at Princeton as an undergrad in the late 60s?

Late 60s are always 70s. Yes. And, you know, Richard Feynman, of course, also did his undergrad. Actually, no, no, did he do his PhD at Princeton and his undergrad at MIT, right? Yes. So was there still the shadow of Feynman there in the late 60s? Because he had just won his Nobel Prize in about 66, right? Absolutely.

And we were, was still the department chair, and he was, you know, one of the great physicists of his generation. Actually my TA won a field medal. Wow. Subsequently, you know, I was surrounded by really really smart people and it was a very enriching time. I mean, it was just very stimulating and intellectually exciting to be there at the time. And for people who don't know, the field's medal is like the Nobel Prize for mathematics. Mathematics does not have a Nobel Prize.

The field's medal is a substitute, although it comes with two caveats. One, the recipient has to be, I believe, under 40, correct. Yes. And the second caveat is it's only awarded, I believe, every four years instead of annually. Yeah, this is Witten, Dr. Witten, who has been very involved in string theory. So you decide to take this detour into medicine

aside from the altruism and the feeling that you weren't brilliant enough to win a Nobel Prize in physics, what else drew you to medicine? Well, that was pretty much it. It seemed like kind of the right thing to do. It was, again, that era was full of wanting to help the humanity and those kinds of exalted ideas about changing the world. So I guess I entered medical school with those things in mind, assuming that I would link the science there to helping people.

But now, given that, you know, the space race of the 1960s probably attracted some of the best and brightest people into engineering, which you were already in, did you at some point consider staying in engineering but not pursuing physics? No, and I don't know why. I can't quite tell you that. Somehow, medicine just kind of drew me.

The idea of it was so compelling that I felt like it was, I know, something I wanted to do. Now, I will tell you that I hated every minute of medical school, almost from the beginning, beginning looking at graduate schools to do something else, and ultimately decided to stay in it, but was disappointed by what I found there. Yeah, I think that's another part of your story that I can kind of relate to, which is not that I hated medical school.

I actually really enjoyed medical school in part because I think the environment I was in, I went to a med school that was in a nice part of the world. I met really, really wonderful friends and I was infinitely happier than any other point in my life. But I realized very quickly that in biology, you couldn't think your way out of every problem, the way you could in math or physics or engineering.

And I was, I'll tell you a funny story about how much I got humbled in my first semester of medical school. So as you'll recall from engineering, if you understand things from a first principle standpoint, you don't really have to memorize anything. And so it was always really funny when you had these engineering exams where they would allow you to take a cheat sheet in where you could write every formula under the sun on one piece of paper. And I was such an arrogant little schmuck in college that I took such great pleasure in writing only some stupid obvious formula like F equals MA, because I knew you had to turn the form in with your exam. And I wanted to demonstrate that all I needed to know was Newton's three laws, and I could

basically derive anything from them. You could come up with Coriolis acceleration if you understood calculus. So I get to medical school and first semester is histology among others. And like an idiot, I really believed I could intuit my way through histology without actually having to memorize everything. And I barely passed the exam. And I was like, oh my God, it's a new world here. I'm going to just have to commit to memorizing a whole bunch of things.

So that sort of foreshadowed some other issues I had with my own medical journey. Did you struggle with some of those things as well? Absolutely. Yeah. Almost identical experience. The other thing is I had 12 years of Catholic school before I went to college and was very familiar with catacism, this kind of idea, wrote memorization. And I thought, a clinical medicine in particular, was catacism, this kind of idea, wrote memorization. And I thought, quote, unquote, medicine in particular, was catacism like, in the sense that there are scripted questions and answers, and you're expected to memorize those.

And when asked the scripted question, you are required to make the scripted answer. And it's one of the things that I remember is, the scripted question was, why do cancers grow? And the scripted answer was, because cancer cells grow faster than normal cells. And that's totally wrong. I mean, that's so wrong at so many levels

that it's sort of hard to believe. And yet, you can ask a medical student these days, and some of them will still say that. And so it was less scientific than I expected, and more, almost like a trade school, in the sense that you know you learned from the masters these great unhigh, they would tell you that this is the way things are, and you would memorize that, and you'd you know go forth into the world with absolute certainty that you're right. into the world with absolute certainty that you're right. And it's very difficult to get physicians as a group

to just stop talking dogma. So just think about this for a minute. This is what your saying makes sense. I think that we were just so familiar. So it just became the way to think, which is to memorize. I memorize more than you do, kind of thing. And I bring from my memory, these little chunks of facts, that that really represents the height of intellectual ability.

One of the things I find I run into quite a bit is that if you say, you know, what you've been doing for the last five decades doesn't make any sense. It's hard, of course, it's hard for anybody to accept that. I mean, I get that. Just from a human point of view it's hard for anybody to accept that. I mean, I get that. Just from a human point of view, none of us wants to think that we've been doing something wrong, but it's hard to even get people to think about it. And of course, you probably are aware of books like the Doctors' Plague and the

Ghost Map that really talked about, and this was in the mid-19th century, that the medical communities are very, very conservative one, and even moving the medical community toward washing hands or using a septic technique did not occur overnight. In fact, it occurred over decades. It didn't even occur within the lifetime of the individual who proposed it. So, the individual who first proposed it basically dies in an insane asylum, literally in an insane asylum, for having been so rejected and cast out by the medical community. That's how long it took to make that the simple transition

of washing hands. It's a fascinating story. I'll share with you just one last story before we leave this point, which is by the time I'm in my residency, so now I'll call it, I'm six years, seven years into all of my medical training, really looking for any excuse to draw in some amount of mathematics to some problem solving. And lo and behold, I meet one of the critical care fellows when I'm doing an ICU rotation. And we really hit it off. He was an anesthesiologist, but he had a PhD in math. And he was the first person who could really put all of the critical care

equations into their truest form of physiologic differential equations. So he wasn't the one that just explained it through dogma, but he could really show you the theory. So he and I would stay up really late on call and you know, forgo sleep to instead go really deep, and he would pull out math books and physics books and papers and we would go over this stuff. And that really inspired me. So a couple of months later when I was doing another ICU rotation, I became sort of dumbfounded at how naively

we would dose antibiotics just based on guessing decay times. And I thought, well, surely there's a better way to do this. And lo and behold, there were equations that could really accurately describe how the plasma concentration of gentimice and would decay. And you needed a few more data points, but you could sample these things.

And so, sure enough, over the course of a couple of weeks, I built a model that would predict the exact right time to be checking for native levels to redose. To make a long story short, when I attempted to implement this, it was met with such resistance that I was actually threatened to be fired by one of the attendings at one point. I was actually going to predict that that would,

that's what happened. So when you were in medical school, what drew you to radiology or at the time did you go into radiology first and then radiation oncology or how did you make that choice? The radiology was a minimum game puzzle. You know, you know, you're a minimum information puzzle where you, you know, sort of get bits and pieces of things and try to put it together. And I really like that. It's an intellectual exercise. I know, somehow it appealed to me. And I was a very shy kind of retiring kid. Doing it with patients was very difficult for me.

It was a lot easier to work with films. It intellectually and psychologically met the kinds of things that I like to do. So it just felt like the right thing. And I'm happy I did it because that was really one of the few things that was still that I liked. Which I think is very common for people with our background. I had decided early on that I was going to go into surgery.

And one of the drawbacks, I guess, of deciding early on what you're going to do is it makes some of the other rotations you go through less enjoyable. But that wasn't true for radiology, because once I got to my month of radiology, I was, I mean, categorically obsessed with this field. Because first of all, all of the residents I met were basically engineers, mathematicians and physicists. So it heavily selects for those people. And an understanding of math and physics gave you an understanding of how the MRI machine worked in a way that you simply couldn't understand that without the background. And I remember thinking,

God, I wish there was a way to do both surgery and radiology. I agree. Yeah. So yeah, I found it very very appealing and enjoyed the residency and did enjoy people that I began working with in that setting. Where was it in your radiology career, if not sooner, that you began to start to think about cancer in a different way? My first job outside of training was at the Fox Chase Cancer Center in Philadelphia. And I hadn't really thought about cancer very much. And of course, we kind of had these very dogmatic views of cancer and what training I did get.

But when you work in a cancer center, I mean, you just want to help. I mean, it just, it felt like you want to make a contribution because this disease is just so awful. So I decided I would, I would spend more time learning about cancer and I would really just get textbooks and read it. And one of the things I started reading was the journal Cancer Research, which is the flagship journal of the ACR. And one of the things I found was that I would read an article and say, well, it's really a good article and that's really interesting and important.

And then I would read another article and have the same response, but then I would try to think of, well, how do these fit together? How do these relate to one another? And I couldn't see that. And there was no organizing principles that was involved. I mean, the authors themselves were not trying to put these together. They were simply making a sequence of observations. Each were really quite separate.

And as you know, in the physics world, these things had happened. The planetary motions, for example, Taikobra and others pretty much had the data, but the data was overwhelming and complicated. Kepler developed a kind of geometric interaction, and then ultimately it was Newton who developed first principles that could put all this together. And you know, similar with the bomber lines

in the early 20th century, the particle zoo in the mid 20th century, all of those required theoretical mastery of it. You could even make the case with Einstein's work around the photoelectric effect. I mean, most people think Einstein won the Nobel Prize for relativity, but it was actually for the photoelectric effect. I mean, most people think Einstein won the Nobel Prize for relativity, but it was actually for the photoelectric effect.

And he was not the first person to make the observation. He was simply the first person to put it all together. And I've always found that to be a very illustrative case of what really, what genius really is. It's that ability to assimilate information and pattern from what others can't see. And that, you know, Einstein and others are the examples. So I decided that maybe where I could contribute is to focus on developing first principles, developing a kind of framework of understanding, but recognizing that it really had to be mathematical.

I actually spent about a year re-learning the mathematics. I'd forgotten it all by then. Then worked on developing, you know, so what are the first principles? And I decided it was, you know, they have to be evolution in ecology. They're all living systems. Essentially obey the laws of Darwin and therefore cancer must do the same.

And so I sat down and I started writing population dynamic equations looking at cancer, looking at interaction of cancer with normal cells, and then the cancer cells with each other and competing them and that sort of thing. And so that was how I started it. Why were you so convinced, Bob, that this had to be done mathematically, as opposed to theoretically, but without the the quote unquote cumbersome mathematics

that comes with it. I think that I brought with it an appreciation of non-linearity, that human beings think linearly, and when complex systems have non-linear things like feedback loops, that so the linear thinking is if you do one, you get two, you do two, you get four, you get four, you do eight. You know, we're real good. The human brain is very good at that. But non-linear dynamics, we're really not good at all. A great example, which is also from Philadelphia, was Benjamin Franklin wanted to see a lunar eclipse one evening. But a Norreaster came in. Now a Norreaster, if you live in Philadelphia, you're familiar with these.

They, winds coming from the Norreath Northeast, hence the name. You know, these are often violent storms. And so it rolled in and he couldn't see the winter eclipse. Now, Franklin, like all scientists of his day, thought that the wind carried the storm. In effect, if you ask a child, how do you, how do you think storms move? They will say, well, it must be the wind blowing it, because it makes sense. It's intuitively obvious that that's the case. But what he talked to his brother in Boston about the eclipse, it turned out that the storm arrived in Boston after the eclipse was over. So in fact, the storm was going in the opposite direction of the wind.

And he was really the first scientist to recognize that obvious, that something that's intuitively clear must be happening is also wrong. So I think that in cancers, we see non-alignities all the time. And again, the feedback, the evolution dynamics of resistance, for example, is a good example of that. And we can't intuitively predict those things.

We need to actually understand first principles and the underlying mathematics to capture that piece of it. And so, I guess I was very involved in that kind of thing. And to me, it seemed obvious that we needed to do the math because the things that were being done in cancer treatment were often intuitively obvious, but we're clearly not working. So I mean, I may be putting retrospective analysis on that, but at the time it seemed that it really had to be understood mathematically. And of course, from a physics background, that's

just kind of a natural, you know, that theory has to be fundamentally about mathematics. This is just sort of a fun aside question. Why do you think that evolution gave us as humans the ability to understand linear systems quite well and absolutely no capability to understand non-linear systems? So for example, it's clear that we don't understand hyperbolic discounting. We just can't do it. Is it simply that evolution wasn't optimizing for that problem? And it really didn't, when it came down to reproduction and survival, linearity was sufficient?

That would be my guess that what we need to know to survive and put for it is sufficiently linear that we can probably, that's probably all that was needed. But I'd have to think more about that. I mean, what's linear in the world that is so important? But my sense is that relatively simple things that are related to eating and running away from predators and that sort of thing,

and running after mates are probably sufficiently linear that that was really all that was necessary. It's probably sufficiently linear that that was really all that was necessary. So as you're getting deeper into the mathematics of the biology, you're probably struck by the fact that you don't have a lot of colleagues in this space, right? There's not a conference for theoretical biologists, right? I know. And similar to the response of your colleagues to your model, pretty much all of my colleagues hated it. Thought the whole thing was ridiculous.

But were they even qualified to understand it? Probably not, but just on an intuitive level, they just couldn't understand why you would do this. And it's funny because if I had a dollar for every time someone said to me that cancer is too complicated to model, I wouldn't have to apply for grants anymore because that was kind of the prevailing wisdom. And I mean, the irony of that is that the argument itself is self-defeating. If it's that complicated, then you have to have mathematics. There's no way that the

human brain is going to understand complex systems without sufficient mathematics. Unfortunately, I think in there was a certain arrogance that you're also taught as physicians, which is that, well, it's too complicated to model, but my superior intellect will be able to master this and figure it out. I mean, I always got the feeling that there is that second cause in that statement that went unsaid,

but was part of this kind of idea that this confidence that we can understand this and you know we can step back and we can take care of this without the mathematics. So I think it takes some humility to say well I really need to look at the math models and the computer simulations to understand how I think this is going to happen. I don't know that we physicians have been taught humility sufficiently well to accept that. Another great example of that is look at other incredibly complex systems like economics. And of course, I don't know what the stats would be,

but certainly a sufficient number of the Nobel prizes awarded in that field are fundamentally based on mathematics, right? Whether it be game theory or otherwise, obviously a lot of them are behavioral as well. But nevertheless, I don't think anybody is suggesting that the models are sufficient in economics. In other words, that you can take an economic model, you can plug in all of the initial conditions and it will tell you the answer. If unemployment is this, if the rate of home price appreciation is this, if inflation is this, here are 50 starting variables, put them in the model and it will spit out GDP growth 10 years from now.

I don't think anybody is so delusional to believe that that's true, but it still doesn't minimize what the model can do for your understanding of the system. We like to talk about hurricane modeling and weather modeling in general, which is a classic example of how to master a complex dynamic system. You can be pretty sure that you can predict what's going to happen in the next 24 hours. After that, the complexity as well as stochastic changes are going to degrade the accuracy. So at each day, you get more data, and you just keep predicting forward.

It's not necessary, at least again, in cancer modeling to say, what's going to happen 10 years for now? It's just you need to know, what therapy should I use today? And for the next three months, say, and then after that, we'll get more data, we'll start again. But sometimes people expect more of say, and then after that, we'll get more data, we'll start again.

But sometimes people expect more of that of it than that. And like you said, that's this idea that we should be able to predict the entire course is not realistic. You know, I'm glad you brought up hurricanes and weather because, as you note, there's some of the most complicated models out there, and that has to do with the fact that they behave in many ways as these, you know, like Lorenz curves, right? So they are chaotic systems, and because they are so, so, so sensitive to initial conditions, they don't really behave well outside of very, very narrow Delta T windows, and you know, as we, I remember still, I remember in college when we began studying chaos, the first example you learned is about the butterfly that flaps its wings in Tokyo

that leads to the storm two weeks later in New York. And this is getting way ahead of ourselves, because we're gonna come back and go through it from an evolution perspective. But just at the outside, before I forget to ask this question, where would you put cancer models on the relative spectrum of, here's a really well-behaved

model on one side. Some linear regression model that works perfectly because you have infinite past data and the future scenarios don't deviate. That's like a monkey model. At the other end of the spectrum, you have the weather predicting, hurricane predicting model as the most chaotic. If that's a 1 and a 10, where does cancer biology behave? I think probably 8 or 9. I think it's more on the chaotic. I think there's a lot of stochasticity. There's also a lot of heterogeneity. And I think those things make

it difficult. So it's harder to predict, but not infinitely hard. I mean, I think we can do this, but we always have to have a level of humility and understanding that, first of all, evolution is very clever, and it likes to embarrass you. You can easily crash and burn, but it's not random either. There's predictability to it, and finding that point between them where you can predict with reasonable certainty and also sort of head your bets and ways that even if things don't go exactly as you plan, you can still benefit the patient from early recognition that it's not going the way you plan, recalibrating models, rethinking the underlying dynamics and then going forward as opposed to here's your your model, you know, just take one a day for the next 10 years and you'll be fine. I don't think we can we can be at that point. So let's go back to that first year, where you're you've relearned the mathematics.

Presumably, at some point, you say I want to look in nature and see where mathematics has already come up with an elegant way to describe a similar problem. Is that what led you to the study of predator prey models? Just the equal evolution dynamics in general. It's very appealing because of course they're living systems. The ecology of swamps and that's what I think is very complicated. And yet they can more or less master these complex relationships. So that was appealing to me. It was also appealing to me because when you work in a cancer center, the cancer almost

takes on a persona, like an evil entity kind of thing, almost magical in its ability to overcome anything that the physicians do. If you talk to oncologists, many of them will have that. They may not say it, but they have that kind of sense of it. And to me, just saying that cancers have to obey their winning loss, that they're not magic, they're not unfathomable. They simply are really good evolutionary machines. And we can master this, we understand evolution, and we can get on top of this.

It's not something that we cannot understand. Because before that, it just felt like we have no basis to understand this. It just happens and we do this and this happens. So to me gave it some deterministic quality, some cause-effect relationships that at least to me were comforting. We could deal with this. So explain to folks how the standard, relatively simple predator prey models work. The swamp is too complicated because you might have multiple predators and multiple pray, but let's start with first-order differential equations, second-order differential equations

where you've got one, you've got an isolated ecosystem where you've got foxes and hairs. How does that dynamic work? Well, the hairs convert local vegetation to babies, to baby hairs. They re-produce the foxes or whatever the predator is going to eat the rabbits. And of course, so then you get these these population rises and falls because if the predator eats too many of the prey, then its population will tend to decline. It's its population declines, the predator population expands. And so you can see these discycling effect that goes on for prolonged periods of time. And there are at least some data that have suggested that this is the case.

But as with everything that's living, it's always more complicated than that. There's always various factors that come into play. But at least that was really, I think, probably the first of really kind of recognized population models that began to be applied to nature. It's interesting to see that. And predator prey models are things that we use a little bit like for the immune system, where the immune system is kind of a predator and it's chasing after the cancer cells. But of course there's important differences in that. The predator eats the prey and gains substrate from that. Whereas in the immune system, it kills the cancer cells, but it loses substrate, it doesn't eat them up. And in fact, if anybody's eating anything, it's probably the cancer cells, but it loses substrate, doesn't eat them up. And in fact, if anybody's eating anything,

it's probably the cancer cells are swooping up elements of their brethren that the macromolecules that they're getting spilled into the environment. Again, it's one of those things that's an appealing model, the predator prey model, an immunotherapy, and yet there are important distinctions that you have to recognize that that make the biology different and in some ways can give advantages to the prey that you don't really expect.

So when you move on to a more complicated system like the swamp, right, where you've got, you know, you've got everything from algae to bacteria to small fish and then you have to deal with how much sunlight is coming in and what's the temperature. I mean, now it starts to look a little bit more like the human body, where why is there an algal, you know, bloom here that basically consumes all of the oxygen and rapidly kills all the fish versus a system that can be somewhat in a sustained setting where you never fully get rid of the algae, but the fish can live and there's kind of a beautiful chain of carbon fixation that goes from

algae to fish. How did you get to the point where you could look at that and say we can now model this for human cancer given that this is far more likely how it behaves. Again, it's simplifying. The best we can do is sort of a cartoon. And it's interesting to think about why. I mean, ecologists that are looking at a new ecosystem will begin by asking a very simple question, what's the birth rate and death rate of each species that's present? We don't know that in the cancer. I mean, it's not enough. That's not data that we get.

What's the carbon cycle? What's the iron cycle? What's the nitrogen? What are all these cycles? How can we watch these substrate pass through individuals? And how does that work? We don't get that. So, we call this kind of turn pale when they start asking these questions.

And realize that we have cancer dialysis and have never thought in those terms. I mean, something as simple as what is the birth rate and the death rate of the cancer population? It's astonishing to me that we don't know that. And things like that, you know, what's the growth rate of the tumor? What are we kind of dealing with even in a first order kind of estimate? We often don't do. It's kind of astounding to me that we do things very crudely. You know, the evolutionary biologist and ecologist take a form or sophisticated view of these interactions than we do.

An interesting fact is that if you are a pesticide manufacturer, you are required by law to submit a resistance management plan. You have to identify what are the mechanisms of resistance, and how do I plan to prevent that from occurring? You can introduce cancer drugs, I mean, well what catch drugs are routinely approved without any knowledge of what the resistance mechanism is, much less how you're going to manage that in a patient. So, again, this odd kind of disconnect that in some ways, I think the ecologist, evolutionary

biologist, have pushed ahead of us and we're just trying to catch up with them in terms of understanding of taking their sophisticated models and applying them to cancer. So let's talk about the ecological models of pests and pesticides, because that's something that I think gave you a big insight, correct? Yes. One of the things that I had run across was just kind of a story on the internet news somewhere about the Diamond Back Moth. And the story was that it was first recognized, I think it's in Indiana, somewhere in the

Midwest in the mid-19th century. And the Diamond Back Moth has the, I guess, the honor of having received and struck by every pesticide developed in the modern era. And what it has done is absolutely nothing. In fact, it spread all over the, the Dombat wants spread all over the country to Europe and to Asia and it's just everywhere. And in the 1980s, they were covering Dianne Wackwaths that were not susceptible to any known pesticide. I mean, they had become resistant to all of them. To me, this was interesting because, so you'd be able to sort of look at this whole process.

And of course, farmers for centuries used pesticides freely. And the idea is you dump as much of it on your fields as you can. You want to get rid of as many of the pests as you can. But what people realize is that by doing that, you're selecting for resistance. Explain to people why that's true, because it is counterintuitive. I think most people would say, well, gosh, if I'm a farmer, and I've got my acre of corn here, and there are a million moths that have descended on this acre, I want to get every one of them eradicated. And the best odds of doing that,

wouldn't that just be using all of the pesticide I can just shy of killing my own crop? Yes, and that's one of those things that is intuitively appealing, but not necessarily true. And the reason is that you're dealing with a very large population. And this is not a uniform population. This is biology, which means that there's heterogeneity within that population. And there are moths in that population that are extremely sensitive to the pesticide.

And there's going to be moths in that population that are not very sensitive to the pesticide. Okay, so let's put some numbers to it. So I just gave you a million moths. You dump the best pesticide imaginable on them. What if we assume that there's a spectrum and 20% of them die at the first whiff of the pesticide, 60% of them eventually get clubbed to death, but 20% of them, I don't know, they seem somewhat immune. Is that, let's just use those numbers, right?

Sure. So, short term, you did good. Because you got rid of 80% of them. Right. Now dumb pesticide on them again. So that population that's 20%, now has a whole field that's open to it. It has no competitors. So it can rapidly expand. And it's taking its genome with it.

And so, it's offspring will also be somewhat heterogeneous. But they will definitely be shifted more toward the resistant one. So now you dump your massive amounts of pesticide and maybe at 5% of them that it die and maybe you get 20% of them that eventually die, but 80% of them don't. And you just keep doing it. So year one or season one looks good. And again, intuitively, like a really good idea, but the long term effect is that now you've got pests

that you can't control anymore. You have a pesticide that doesn't work. And so in the longer term, what you've done is you've created a species you can no longer control. Now you have to find a new pesticide and there's always some cross-resistance and in any large biological population, there's sufficient heterogeneity

that it's most likely that you're gonna have resistance. So if it worked, and part of this is say, well, yeah, okay, but how do you know that there's resistance and It's simple if it worked there wasn't resistance but you know from from experience and Over and over and over again what what you found was that you use this large dose Pesticide you can get a short-term gain, but in the long-term it doesn't work So believe your eyes. So this actually can get a short-term gain, but in the long-term, it doesn't work.

So believe your eyes. So this actually, again, ecologists that was about, you know, way ahead of us here, they said, well, there's got to be something different to this. And so, in the next administration, this is a long time ago, same time that they started the one cancer, using large doses of pesticide was essentially not allowed. And they started to do this thing they called integrated pest management. And the idea with this is that you recognize that you can't eradicate the pests. That there's simply no, just historically, there's nothing that's ever done that before.

So let's assume that's not going to happen this time. Let's give enough pesticide that we're going to knock this population low enough that they're not going to do very much crop damage. But don't do more than that. There's a number of ways you can do that. So for example, they would take a field and they would put pesticide on three quarters of the field and leave one quarter alone. So you would knock this population down, but you would not, to this last quarter of the field, you're applying no selection for resistance. And so these guys would then move out to the rest of the field and you know, you'd eventually get this low population, but you're not selected for resistance. There's another bit of this, and that is that

the resistance costs something. Be able to deal with a toxic pesticide or toxic drug. You know, you have to have the molecular machinery necessary to deal with it. You have to be able to repair your DNA. If it's damaged, you have to be able to pump it out, which is what all of them do. I mean, there's a number of different mechanisms, but all those mechanisms have a cost. It doesn't necessarily have to be a big cost. And in the cost-benefit ratio, when you've got a lot of this drug around, those guys are clearly, you know, the benefit greatly outweighs the cost. But when there's no drug present, then the benefit does not away the cost.

And so now, the guys that are not resistant don't have to carry that machinery around with them are more fit. And so there's this then subtle competition that goes on. So you'll be selecting for resistance, and let's say the three quarters of the field. But when the ones that are sensitive move into that area, they have the benefit of not having this pesticide being applied to them anymore. There's no selection for resistance, and their fitness advantage will be such that when the pest population comes back to something close to what it was beginning, it's as sensitive as it was in the beginning. Will it actually get better? I mean, is there a scenario here under which, if I'm understanding you correctly,

you... the moths that will be able to best resist the pesticide, they, that comes at a cost, and the cost is molecular machinery that is only there to allow them to survive the toxicity of the pesticide, but not reproduce better or acquire food better. So the more quickly you can get non-pesticide resistant moths back into their ecosystem, who presumably, in the absence of pesticide, now have a fitness advantage over them because

they don't have the extra baggage they don't need in a non-pesticide world. They'll actually out-compete them. So is there a scenario where in year one, 20% of the moths are resistant to the pesticide? But if you manage it correctly in year five, that number is 10% because they have actually been out competed by the other moths. Yeah, so where were you 20 years ago? Because only recently has your scenario come up. And I guess we're getting a little ahead of ourselves, but we've done a study now in

men with prostate cancer, or we've used a drug. And what we did was that we gave the drug until the tumor responded to 50% of its pre-treatment value, pulled it away, let the tumor come back up. But again, similar to this tool, we talked about, there's no pesticide being applied, there's no drug being applied, there's no selection for resistance, and so the sensitive guys are supposed to come back up and so grow with expensive resistance. Now, for a very long time, our model said that what would happen is that every time the sensitive population came down, the resistant population would go up a little bit.

And when the sensitive population started going, we took the drug away. What the sensitive population grow, the resisted population would plateau. And what we then assume is that at each cycle, you would have this plateau, so stepwise, it would just keep going up, and at some point you would lose control inevitably.

All of our models said that if we did this somewhere between two and 20 cycles, the tree would fail. Wow, that's a huge variance though, two to 20 cycles. That could be the difference between a few months and a few years. Why such a difference? At time, we didn't know what the fraction of resistant population was at the beginning. So that was probably the biggest thing. So if it's 1%, that takes longer than if it's, let's say, 20%, 30%.

And there are some other things like we weren't really entirely sure what the proliferation rate was. And again, I mean, things like birth and death, we just estimated those. So we did this a pilot trial to see if this worked. So we gave this kind of intermittent therapy, and we compared it to patients that just got the standard maximum tolerated dose until progression.

The difference between the groups was 16 months. So it was the median time progression for standard of care was 14 months, which is pretty much what's in the literature for the adaptive therapy group. It was the 30 months, which was great. But then we said, but of the 20, four patients are now out five years

and they're still cycling. And this is patients with metastatic prostate cancer? That's correct. So what the models said, and this is now gets very nerdy, but perhaps the most important thing that came out of this trial was the number seven, because the ratio of the fitness measure for the sensitive cells was sevenfold that of the resistant cells. And we had estimated it at two or three. So it's a big difference.

And it's a big difference in favor of the healthy cell. Exactly. And so what we see then is that when the sensitive cells go down, resistant cells go up, sensitive cells go up, we expect that them to plateau. But in fact, they're going down. They go down, resistance cells go up, sensitive cells go up. We expected them to plateau. But in fact, they're going down. They go down. And what we found was that if you in fact had three cycles where you hit that sweet spot

exactly, the resistance cells progressively went toward extinction. So three successive cycles is a critical enough mass that you can drive that resistant cell, presumably to a place where, and by the way, is it limited by substrate? Is that the most important factor that is creating the fitness differential? Yes, we think. Now, it's going to be, they're competing for space and substrate. And of course, and then the tumor environment is one in which usually the substrate delivers very poor because the blood flow isn't very good. So substrate is probably the most glumining factor. But again, we don't know that, I mean, this is again one of those things that I wish we

knew more about because in some ways we were just working in the dark. But, and so when we went back and looked at these four patients that have gone far beyond anything that you would expect for this treatment, there's very rare that someone would go five years on this treatment using standard of care. What we found was that each of them had these three sequences cycles where we, where the model predicted that they drove the the resistant population to extinction or something very close to extinction. And none of the others had the three cycles?

No, and that was the key thing that we learned from this, and which is one of the great advantages of having the math models because you can self-cortique. Now, now you don't just take the results of the cohort and say, well, this is nice. We did 16 months better. We can also look at what we did and say, what did we do right and what did we do wrong? And what the model said was that we had built into the protocol some components

that as a result, we did not hit those three consecutive cycles perfectly. And universally, we overtreated. If we had cut back on therapy, we would have done still better. And the monos predicted that every patient in both cohorts in this study, we could have gained control of the tumor indefinitely. What was the other cohort? The other cohort received standard of care. Oh, oh, oh, but this was not randomized. That's correct.

It was a contemporarieship. And what were you following? Was your metric PSA? PSA. That was probably where the biggest mistake we made is that we were following the PSA, but then we required that when the PSA went down, that it would be confirmed by radiographic studies. And we were getting the PSA every month,

but you're constrained by insurance to get radiographic studies every three months. And so what happened is that the PSA would get to 50%, but we didn't do, we didn't change therapy until we got the- The radiographic finding. So you were- You were lagging.

For two months, they kept getting treatment and they kept driving down. So we were, we were killing off too many of the resistance of the sensitive cells. And therefore they were not able to, you were impeding their fitness. Of course, this is what the model said is you guys are idiots. You know, if you just stayed with the PSA, you would have gotten far better results than you did. And you got pretty good results anyway. Your question goes to something that we honestly

never thought about before, which is that we thought we could use the sensitive cells to control the resistance cells. You can actually use the sensitive cells to destroy the resistance cells. To eradicate the current. Now, I never thought about that, but this is now opening up whole new avenues of therapy, at least theoretically, meaning least potentially, because there's a lot of cases where the initial

therapy for cancers gets great results. I mean, they talk about tumors going into remission, And of course, what happens is the tumor eventually comes back. What we know then is that the resistance cells are there, but they're in a very small number. And you can then estimate that they must be way, way, way less fit than the sensitive cells in the absence of treatment. So that fitness difference is, it must be very large. So using a number like seven as the ratio is not unreasonable. In fact, it's highly likely. And so now we've got this situation where maybe we can start to think about using these very effective therapies. Right now what we do is we give the therapy, tumor kind of disappears,

can't see it, and what do we do? We'll keep giving the same therapy. We give it until progression. Well, as we're doing that, we've knocked the tumor to the ground, but we just keep giving it the same treatment. And of course, what we're doing, we're just closing the gap, we're reducing the fitness ratio from 7, 6, 5, 4, 3, 2, 1, and it'll flip on you. Yeah, so now suddenly we've got all we've got is resistance cells. I can keep treating that all I want. That's not going to work, but these are highly vulnerable to additional perturbations. So the thing is, instead of just giving this treatment, getting the cancer populations to something that's very,

very small, and then just keep training them. At that point, hit them again. Do something different. Apply additional perturbations. One of my colleagues likes to say, this is, we're like boxing here. You knock your opponent down and you go to your corner. The referee counts them when the opponent's back up, then you start again. And this is not a boxing match.

It's a knife fight. This is to the death. And that means that when you knock your opponent down, you don't stand back and wait. You go with hack. And I think that we've, that's an approach, and we've called that an extinction approach, that we think is something that has not been explored yet

But it's interesting that if you look at anthropocene extinctions if you look at how our species has killed off other species This is the way it happens. There's usually an initial perturbation, but then after that when you get a small population you get a sequence of smaller perturbations that drive it to extinction It's a two-step kind of process. What's interesting is that you cause extinction through a sequence of perturbations, none of which by itself could cause extinction. In cancer, we're always looking for the magic bullet, the one that will eradicate the cancer and not the normal cells. And for century, we've been looking for magic bullets, but maybe all we need is a series of pretty good bullets.

So that's a different sort of thinking process, but it is hard to sell that, especially to a medical community in which new drug development is what's most incentivized. So using the same drug, in the case of our protocol, it's using a drug called Aberaterone, which is off patent, nobody's making any money on this. So there's no pharma company that's going to support the clinical trials.

It's really, it's kind of an orphan drug. So there's no natural supporter for this. And the same is true with a lot of these other things that we're really just talking about using drugs that already exist, about using them better. Again, there's just not a, there's not kind of a natural support for it within the medical community. Well, D2 for a second, Bob, and talk about how this logic fits into an area that, frankly, I've never considered until now. I've just taken it for granted, which is the use of antibiotics

and bacteria. So standard thinking is you have a bacterial infection, you give the antibiotic, and there's some magic number, which is you're going to give it for X number of days. It's a 10-day course. Now three days into that 10-day course, the patient has defervest, they feel completely better. The tissue culture is negative, but we continue to give the antibiotic for seven more days because our fear is, or what we were told was, if you don't, you will generate resistance. But based on what you're telling me now, that might not be true. In fact, it might be that giving that seven additional days of antibiotic might be exactly

the thing that is leading to resistance. Is that, am I misunderstanding? I think that's certainly true. I mean, I don't work with infections, but there are people that are looking at this exact issue. How do you prevent the development of resistance of bacteria? And worldwide, it's a big problem with malaria and tuberculosis and things like that. And so the World Health Organization actively uses evolution-based mathematical models to plan how they deliver treatment.

And a lot of it is, as I understand it, and again, this isn't my area, is that they are trying to kind of do this kind of adaptive approach where you just keep the resistant population low. Now, there are some exceptions where my understanding is that some people have advocated regional attempts to eradicate a population

and again, a little bit like the enthropocene extinctions. So Bob is one difference, I guess, as the more I think about this, between antibiotics and bacteria versus chemotherapy and cancer. I mean, you could live with billions of cancer cells in your body and be totally fine. Is that true with bacteria? Is that true? Could you have a billion merse floating around your bloodstream and still be fine? Or do we need complete extinction there in a way that we don't with cancer?

You know, I don't know. We have to get a, someone that's expert in this. The one thing to remember is that by the time you have a cancer, it's defeated your immune system, whereas the bacteria often are still very different from our normal cells, and therefore, I think more targetable by the immune system. I think there's probably a slightly different dynamic in that that may change things, but I would defer to the people that are doing this. I can give you some names of people that are interested in this topic. Well, I'm glad to hear that people are studying this because antibiotic resistant bacteria are a huge problem in the hospital, right?

And we've heard it, you know, again, we've heard about these super bugs. And you can't help but wonder based on what you're saying if a big part of the problem was we're creating them by over-treating in situations when we don't need to, using a drug for a lot longer than we need to. Let's give people a sense of some numbers because I remember when I was in medical school, I spent a few months at the NIH and the NCI and got all these amazing assignments to do. And what my favorite assignment to do as the medical student was to look up all of the literature on cancer growth rates.

And they gave me this assignment because of my background in math. And so it was sort of like I could go digging in all the literature on, you know, starting with the most trivial sort of logistic models to the Gompertz model and all of these other models. But one of the things that really is interesting is just how many cancer cells you need to actually even be detected, right? It's about a billion cells in a centimeter cube. That's usually the number that's used, yeah.

You couldn't even... There's no clinical... Outside of what we now have as liquid biopsies. There's no clinical way to even detect cancer shy of about a billion cells. And this speaks to the nonlinearity, right, to go from a million to a billion versus a billion to a trillion. Like these are, it's hard for people to wrap their head around what's involved in those things. And both from a growth perspective, because that's not linear, and a size perspective,

because you're dealing with something that's growing with a cube power, not a square power. Where do you see this being an issue? Because the numbers are so enormous, right? And this gets to your question of, if we talked about the example I used earlier, there being like a million moths, well, the heterogeneity there is already significant. But once you're talking about tens of billions of cells, by definition, the heterogeneity is probably even more significant, correct? Absolutely. And it's really important. I think there's two factors to think about.

One is that is stochasticity. If you have a small population, small changes in birth and death rates, can't be quite significant. You know, small populations can go extinct based on very small changes. The other thing is what's called the ALI effect. And that is that in a classic evolutionary model as a population grows, the fitness should be decreasing because you're getting toward the caring capacity

of the environment. But ALI and others have found that it increases. And the reason is that there's an advantage in groups, in herds, for example. I mean, you can, the obvious advantages that they can gather themselves to defeat predators, but they can also, there's sort of leadership issues and other things. In tumors, there's a couple of things. One is they have to make blood vessels. And that means that there's small groups of them have to get together and make the endogenic factors that they then send to the blood vessels to

to bring a blood vessel. We, and it's probably not a single cell that can do that. It's to be a loosely organized system. The development of X-Chow matrix that you know, the tumors, it's just not tumor cells. They make a collagen and other things that go in between the cells that provide a kind of a structure for them to live on, they have to make that. And it can't just be anything. They probably act together for that. And then they also probably act together to defeat the immune system. They can secret things.

So there's a number of things that they can act together on. Tumor cells, small populations, may not grow as fast as large populations, and they may be more vulnerable to treatment. They may not be as able to deal with immunotherapy or target therapy or even chemotherapy because they don't have the capacity to act together. So those two things are, I think, important factors in understanding treatment. And this is a little bit why, you know, I was talking about when, you know, there are

therapies now that can take large cancers and drive them down so that they become not detectable by CT scans, which means that their population has to be very small. Those guys are now vulnerable to dynamics related to stochasticity and olefax, that the larger populations are not necessarily responsive to. And one of the things that's very interesting, if you take something like neoadjuvant therapy, where you take a big breast cancer, for example, you make it small and then take it out. What you find is that the tumor isn't a ball that just shrinks, so you have to start with a big ball, you have a smaller ball. In fact, it fragments. And so what you get are little islands of tumor cells surrounded

by fibrosis or an acrosis or something like that. So these guys are islands, you know, these are highly vulnerable. And so I think this is when we should crank it up and kill it. I think tumors are vulnerable at this point. But I think that the way we've done things, which is this dogma in an oncology, you know, continuous, maximum tolerated dose until progression.

And as you just said, you can't see it, if you're waiting for progression, you won't see that until you have billions of cells. I mean, it has to be big enough that you can image or feel. And that's at least a CC of tumor and it's probably two or three CCs. By that time, now, when you change therapy, you're doing with a really big population,

whereas you could have been dealing with a small fraction of it a few months earlier when you couldn't see it. Now that oncologists always get, well, I have to be able to see something to be able to understand whether it's effective or not. You know, my therapy is effective. Yeah, and that's, you know, I get that, but you're not going to be able to do that in this setting. What's interesting is that the pediatric oncologist

learned this a long time ago. This is how they cure leukemia. They give an induction therapy, and then they immediately changed to a different therapies. So what they learned was that even after induction therapy, when there was no apparent tumor in the bone marrow or in the blood, that if you didn't do anything, the kids would all relapse.

And so they hid it immediately with another group of drugs and then another group of drugs. So this is first strike, second strike kind of approaches, but they're not, they can't miss their outcome. They can only measure their outcome in the long term by saying, well, we've cured this kid. Right, they can't say which drug was more effective in that cocktail.

Right, and you'll probably never know. There's this thing called the extinction vortex that they talk about in extinctions where when you've got a small population, there's typically a sequence of perturbations and they tend to be self-centeredizing. In other words, they further down the population you push, the more sensitive it is to stochastic

and alleofax. And so now you can never really tease apart those dynamics. It just all goes into extinction eventually. And the goal then is not to try to really put these things together in a really additive way, except to say that all these guys together will tend to push this to this population to extinction. Again, it's a different strategy and it's a little less precise than I guess we would like, but that is the nature, I think, of the extinction process as we've learned it. I mean, it's interesting because whenever you say extinction, everybody thinks about the dinosaurs, and I would argue even that some of our therapy

is that kind of single event, single cause extinction, application of massive evolutionary force to the species and you wipe it out. And of course the problem is you also wipe out most of the land animals on earth. So it's indiscriminate. Whereas what we've learned from enthropocene extinctions from our own species, eradicating other species, you know, as unfortunate as it is, we've been able to study those in detail and have learned that in fact, most extinctions are multi-caused, multi-event. It's not the dinosaurs. They are the exception, not the rule. And so when we want to think about an enthropocene extinction of cancer cells, which is, which is, are you going to be what there be is?

We should probably be thinking more about these multi-caus approaches rather than trying to find a magic bullet that could cure the cancer by itself because you always have these side effects. So far, we've not found a magic bullet. Well, and to your point, the bigger issue with the dinosaur analogy is not even so much that it's the exception and not the rule. It's the collateral damage. When there's a single strike success, the collateral damage changes the planet. If what killed the dinosaurs when it killed them came on our planet today, it would probably kill a lot of other things as well. Absolutely. Sometimes when I tell people about it, I said, 100 grams of tumor,

which is still a pretty small tumor. He is going to have 100 billion cells, let's say, which is far more than the human population on Earth. Think about, could you do one thing? Could you apply one event that would kill all the people on earth and nothing else? I mean, probably something could be done, but I think that would be very difficult and it's a kind of a gruesome analogy, but at least it kind of makes the point that the idea of a magic bullet, I think, is maybe not being achievable result.

What role does time play in the sequencing of this? So let's go back to the example of pediatric oncology where you could sequence, you know, you go induction therapy and then you go with a series of therapies to follow. So single shots, right? So one, two, three, four, five, six, versus all six at once. Let's put aside the toxicity for a moment because a big reason you wouldn't do that is the toxicity to the child. And a lot of these, you know, you have synergistic side effects that are intolerable. But just thinking about this from a theoretical standpoint, would there be an advantage or disadvantage to one of those approaches? It would certainly be reasonable to say, well, if we've got these three different therapies,

why don't we put them together and give them all at once? And college has been doing this for a long time. And the results have been pretty much uniform in that. What we find is that they do better than one, the tumor is controlled for a longer period of time. But the result is still that they die of the disease. That the tumor comes back and they die now. And you can add more four, know, four or five drugs. And what tends to happen is that you get this diminishing return

so that you just start getting more toxicity and no more increased benefit. One of the things to think about is suppose you have two drugs and you're applying it to a 10 billion population, you know. So what is the probability that there are going to be cells present that are resistant to both? Do you have a big denominator? But now let's say you have a million. So let's say you have one drug, you get it, you know, you really do a great job, you down to a million cells,

or let's say 10 million cells. Now you add the second drug. So now it's what it's the probability that within this 10 million that you will have some resistance, knock it down again, give a third drug. Now it's the probability, let's say of 100,000. So you see,

you're confused by that still, because if you say we could take one drug and go from 10 billion to 100 million, and then hit it with another drug to go from 100 million to a million, and then a third drug to go from a million to a eradication. Wouldn't those be independent events in the sense that if you just hit the 10 billion cells with all three drugs, shouldn't you still be able to stratify that resistance pattern? What is it about sequencing those that would create a treatment advantage? There's two things. One is you get, as you get smaller, you're with killing the tumor cells

that are sensitive. But remember that the ones that are resistant are also have to deploy mechanisms of resistance. Being able to go right back to that discussion, and I guess to your point, they're going to be more fragmented and therefore have less of the group network effect. Right. And so now, when you've got a small population,

subtle changes in birth and death rates will be sufficient to drive it to extinction. That makes sense. And as I talk to people about potentially designing this, even though there may be resistance, that you can still use the resistance to your benefit. And some people have talked about then adding things like environmental changes.

So let's take an anti-androgenic so that you're now you're delivering less blood and you're creating a more difficult substrate environment. There's people that have used an acid, people have used, you know, a number of different, these aren't typically common medical practice, but what individuals are doing based on their own exploration through Google and other other websites is that, and they use various metabolic things, and I think they're potentially advanced to that. Again, in combination with this in a way that makes sense. So far most of what we've been speaking about

is really more or less one class of drug with a slightly different take on another one. Would you been talking about chemotherapy? These are chemicals that are aimed at disproportionately killing cells that in theory are dividing, right? So let's go back to what you said earlier. The thing we learned in medical school that was incorrect was cancer cells divide more quickly than regular cells. Those of us that pay attention today know that that's not actually true at all.

The fundamental difference between a cancer cell and a non-cancer cell is the response to cell cycle signaling. It's that a normal cell doesn't divide slower. It just, when it's told to stop dividing, it stops dividing. Cancer cell doesn't. When it's told to stop dividing, it says, piss off, I'm going to keep dividing. That's a very good point. I mean, that really is. And evolutionarily what this is, is that it has a self-defined fitness function. Its proliferation is dependent on its own properties as the interact with the environment, not on its instructions from the tissue. Normal tissue cannot evolve because its birth and death

is dependent on tissue controls. That's right. So knowing that chemotherapy, which is the earliest form of drug in the modern arsenal against cancer, says, how can we kill a cell that is not responding to cell cycle signaling? And the first shot across the bow is, well, let's just kill anything that's dividing. Because we know that normal cells are less likely to be dividing. And cancer cells are more likely to be dividing and cancer cells are more likely to be dividing. So let's target different cycles of the DNA proliferation phase.

And that's why, of course, most people who are getting chemotherapy have side effects to the normal tissues that relate to that. So the mucosal ulcers, the hair loss, the skin damage, nail thinning, all of that stuff is because those are tissues, even though they're normal, that are dividing more rapidly, and they're being smacked as part of the collateral damage. You mentioned another class of drugs, courtesy of someone named Judith Folkman, who was really one of the first people to point out this idea that you've raised, which is cancer cells have another challenge that they face, which is cancer cells have another challenge that they face, which is they have to get the blood vessels to bring in the substrate,

the glucose that they need to replicate, make energy and make building blocks to make more cells. And what if we target those things, these vascular and ethereal growth factors? And those drugs haven't turned up to be very successful, by the way. So they're billion dollar blockbuster drugs, you know, a vast and probably the first, if I recall. And they've extended median survival like a few months here and there, but they really haven't been blockbuster drugs.

We haven't talked about immunotherapy, right? So we haven't talked about the cutroas of the world how they fit into that But it seems to me and then there's others of course, right? We can talk about the few cancers where a single gene mutation Exists and you can target it with like a Gleevec or something like that, but it seems that you could use selective chemotherapies and target on top of that anti-VED Jeff. And the real question then becomes timing, right?

How do you cycle those on and off to always be maximizing the gap in fitness? So how do you maximize that fitness ratio? By the way, did your model predict that there's something specific about the number seven? Because if I recall you said your model thought you would end up between two and three, you actually ended up at seven. In retrospect, does the model now tell you seven is the tipping point? It's somewhere around there. We've looked at that at the transition as you go from zero to two to three to seven. It's in that range. I don't know exactly where it is. And it's certainly going to vary from tumor to tumor, but it's in that it's certainly I would guess at the five-ish range. Okay. And so going back to my question, do you think, for example, the anti-vegetrugs

play a critical role in this when the tumor is scattered? The way I'm thinking about it, right, this is, I've anthropomorphized everything you've said into a society, right, which is the United States is 330 million people. It's a mighty nation. And if anybody attacks us in theory, we can mount a response in a coordinated fashion, et cetera, et cetera, et cetera. And this analogy may be too stupid by the time it comes out of my mouth, but basically, if you decimated the population by 90%, and there were only 33 million people left,

presumably we would no longer continue to be the United States of America. We would be a whole bunch of disparate tribes in total chaos. Therefore, we'd be a heck of a lot more susceptible. Is that fair assessment? Yes, and that's the in the military, they talk about defeating your enemy into tail. Meaning that if they are fragmented, you don't have to deal with the whole population. You could just do it one at a time. And yes, I think those are the case, that's the case. And when you look at tumors after neo-edgment therapy, these small little populations look like they're 100,

maybe 1000 cells, not bad millions of cells. Wow. And they're widely separated. They're not ascending cells back and forth very much, if at all. So it's a very different dynamic than these continuous tumors that, you know, where if you take, if you, you know, chop out this corner of the tumor for some reason and all the cell's die, they, everything just sort of moves in there and eventually it repopulates.

You don't have that luxury in these fragmented populations. And that's one of the major components of enthropocene extinctions, the Ethan, for example, which was all up and down the Northeast coast when the European settlers arrived, was finally just limited to Martha's Island. And although it was protected by the humans there, a series of things happened. It had a couple of bad winters.

There was a fire in one of the, a series of things happened. A couple of bad winters, there was a fire in part of their habitat, and there was a disease developed. And although the humans protected it so that its population grew considerably, it was still in only Martha's vineyard. And these perturbations, essentially what they did, again, it was at the extinction vortex, several things. And you can't point to any one of them as the cause, but, but together they what did out. You know, if you, if they had had another colony, someone else, you know,

they would still be alive, but that just didn't happen. So. So where do you think immunotherapy fits into the toolkit here, given that it's, it tends to be more binary than chemotherapy, right? So immunotherapy, I'm making this up because it varies by histology, but let's just say you take people in the best case scenario to people that have checkpoint mutations, right? So someone that has a PD1 mutation, the response rate to those people for an anti-PD1 drug like Ketruda is very high and very durable. I mean far more than anything

you see with chemotherapy. Conversely, if they don't have the PD1 mutation, the drug is useless. So what does that tell us about the opportunity to use immune-based therapies? And by the way, we borrow the term adaptive therapy again here, right? So you have adaptive therapies within immunotherapy, which are not to be confused with what you're describing as adaptive therapy. Yeah, I think it's going to be a critical component of all this. And as you say, sometimes immunotherapy can stand on its own. It's the closest thing to a magic bullet that I think we have. The group I work with tends to view,

immunotherapy with the exception of those unusually responsive tumors as your closer. And this is our baseball technology that when you get the population that's small, we think that alleFX are particularly important in immunostrants, in your response to immune system. Meaning evasion from the immune system. Right. And so bringing the immune system

in when you've got the tumor on the mat, it's small, fragmented, I think, that will be the most effective closer that we have to essentially wipe it out. I could be wrong, but that's in theory, this is the one you bring in to win the game. And by the way, that could be going back to Gen 1 immunotherapy, which is interleukin 2. Interleukin 2 had about a maybe a 10 to 20% response rate in melanoma, a particularly immune

sensitive cancer, but that was treating patients with full-blown metastatic melanoma. treating patients with full-blown metastatic melanoma, maybe it would look very different in patients who were NED or who were, you know, visibly without disease. And there are some, I know that there have been some experiments that have suggested that cells that survive chemotherapy are more vulnerable to immunotherapy.

And ultimately, that would be the game that you'd want to play here, is that you, what you want to do is apply a therapy and you know that the resistance strategy might be the following thing, you know, this is how it's going to evolve to become resistant and then attack the Achilles heel that's exposed by this immune response. We had a person here do a, that's kind of the University of this now, they did a P53 vaccine in, in lung cancer patients that were, had been treated it with multiple different things. And, and it didn't really get an effect. I mean, there was minimal efficacy, one patient got a partial response, none of the others did,

although they did generate immune cells to the vaccine. They then went on and gave them chemotherapy. And the response rate to the chemotherapy was 60 to 70%, astonishing number for patients at that stage in their disease. It should have been less than 5%. And the patients that had the best immune response

to the vaccine were the ones that were most responsive to therapy, suggesting that those that responded, you know, they developed adaptive strategies. And those adaptive strategies then made them more vulnerable to toxicity. I mean, it's appealing to think that maybe they downregulated P53 so that they were not expressing the target. And of course, P53 is important in survival mechanisms and that sort of thing. So perhaps that was a mechanism. I'm just speculating, of course. But that's the kind of thinking that I would like to apply here.

So that we're not isolating you do this therapy and then you do this therapy and you do this therapy, all of them in isolation, but start to put strategic strings of therapy together where the adaptive strategies that you're you're going to select for with one become vulnerable to two and three and so that you're putting them together in a strategic kind of thoughtful way rather than just kind of putting them together in a kind of haphazard or intuitive way. And again, that's where math models can I think can be very helpful. No, that actually makes a ton of sense. I want to talk about another feature of cancer, which is what I call the source sync trade-off. I'm sure you've thought a lot about this.

I endlessly thought about this and really never came up with a great insight. Let's start from first principles. There aren't that many cancers that can kill you without spreading. Let's list the ones that can. Brain cancer can kill you without spreading. So glioblastoma, multi-formae can kill you and it doesn't leave the brain. Primary hepatic cancer can kill you without spreading. Hepatocelular carcinoma can kill you. I suppose lung cancer can kill you without spreading, though it's less common. Am I missing an example? Is there some other cancer that can kill you shy of metastasizing? This is an example. Is there some other cancer that can kill you shy of metastasizing? I think if you've for all intents and purposes, yes.

Okay. You're right. Okay. So that's principle number one. Then let's go to observation number two, or observation number one, I suppose. So there are certain cancers that are very deadly, that leave tissues that other cancers never come to. So breast cancer, prostate cancer,

pancreatic cancer, colon cancer. These are deadly cancers, which come from an environment that are not especially hospitable to cancers. Because when a woman gets breast cancer, the first thing that cancer wants to do is get out and kill her by going to her bones, her brain, her lungs, her liver. Pancreatic cancer virtually always wants to go to the liver and that's where it kills. Colin cancer wants to go to the lungs, wants to go to the liver, and wants to go to the brain

sometimes, and that's where it kills. But rarely does a cancer go to the breast, or go to the pancreas, or go to the colon, or go to the prostate. Prostate virtually always wants to kill you by going to the bones. So you see what I'm going with this? You have this, and I could go on for hours with all of my observations.

I think the brain is another one, right? It's an enormously attractive place for cancers to go to, but a primary cancer from the brain never seems to leave. Now, theoretically, there's an exception to that. I suppose there are some vascular tumors of the brain that can leave, but they're not really brain parankomal tumors. Do you have an insight into what explains this? And more importantly, does the answer to that

question or the inference of an answer to that question offer an insight into the environment in which tumours thrive and therefore what we might be able to do about it? The short answer is I don't know. Let me say what I'm certain of and that is this is not a planned event. We tend to give catch cells that sort of anthropomorphic kind of you know they are first they go out and prepare a metastatic site and then they send their cells out to it. That doesn't happen. And evolving population can never adapt to conditions it has not seen before. It cannot plan to make metastases.

And it certainly can't plan to feather its nest in some distant sight before sending out its scouts. That's not happening. But we know in nature that we see introductions all the time. And we know from nature that species of introductions are sometimes successful and sometimes not. And it has to do with the way their phenotype interacts with the local adaptive landscape.

And can they adjust or not? And we know that sometimes it takes several introductions before one occurs, sometimes it never happens, sometimes they can expand and then die out. Again, they're just basic rules that they have to live by. So, for example, you can see species coming out of the Amazon, they get onto the Amazon collects trees and things that are floating down, the animals are on it, and so they go out. And then anything that's in front of the Amazon is going to be receiving more of these, so more likely to see a metastatic monkey or something like that from the middle of the Amazon. So the pancreas spills into the portal vein, which then goes into the liver, and so it's delivering a lot of its cells to the liver. So it makes sense then

that it tends to be tacitized there. If only because it's just sending a lot of cells there. Right, the pancreas makes sense, but how do we make sense of the breast? How do we make sense of the prostate going to the bone disproportionately? I think we just suggest general principles that there's something about some of the breast cancer cells seem to be able to set up shop and bones, some of them in the lung and some of the, and for reasons that are not yet clear. But you mentioned actually something that's, I think, important in source sync. And I think that's a dynamic.

And that's something that we've been talking about recently, source habitats, sink habitats. The idea that you've got habitats that have very good blood supply, that they produce a lot of cells, and then now they're producing too many cells for the spatial environment, and those cells have to go out. And one can think of those within the cells, so that you've got areas with poor blood flow, and areas of good blood flow next to one another,

and you can imagine cells migrating between them. And this can set up dynamics that are very interesting, but you could also think about a breast cancer cell that's got, let's say it's near a blood vessel, and as you might expect it. So, now it's getting big. If there's too many cells, they crowd into the blood vessels. It sends them out. And so now you've got perhaps synch habitats somewhere else,

and there may be some coupling that goes on in ways that, honestly, I don't understand, but I think that we can sort of make up rules for that. But for sure, what we know is that the limiting factor in this is the dynamics at the metastatic site that if you inject cancer cells into mice, that nearly all of them die at the metastatic site that if you inject cancer cells into mice, that nearly all of them die at the metastatic sites, only a very small percentage of them that will form even a few cells in a smaller percentage of those that ultimately form more cells, that form of population.

But I think this is again, is that small population dynamics that we've talked about. There's stochastic effects, a Lea effects, you know, there's a lot of statistical problems with going from a single cell to a cancer that's significant. We are very lucky for that because we know that human cancers are frequently dumping millions

of cells into the blood and at mastatic sites, metastases are relatively rare when you think that. You know, people with early stage 1 cancer, you know, you can find cancer cells in their bone marrow, and yet they don't get bone marrow metastasis. Breast cancer is the same kind of thing. You can do bone marrow biopsies on women getting mastectomies for apparently localized disease. And maybe 30 to 40 percent of them will have breast cancer cells in their bone marrow. And yet all of them do not develop metastatic disease in the bones. Thank goodness because it seems like for a variety of reasons. And how much of that do you attribute to the ole effects and the stochastic variability that says,

like, they're just just not gonna be fit enough to take up residence there versus some other inherent principle of like the genetic robustness of the tumor itself because a lot of those women may go on to get adjuvant therapy and then it becomes a question of how successful was the adjuvant therapy? Yes, exactly. And I think that's where if you look at breast cancer, for example, what we know is that adjuvant therapy? Yes, exactly. And I think that's where, if you look at breast cancer,

for example, what we know is that adjuvant therapy will reduce the probability of metastasis, but not eliminated. It's best a small effect. And we need to do better with that. It's a great point, because how do we treat breast cancer with adjuvant therapy? Well, we give them platinum or some drug

for some period of time, and that's it? Well, why don't we give a sequence of drugs? Why don't we take advantage of, because we know for sure we're dealing with small populations, and we know for sure that we can cause them to go extinct, at least some of the time. So why don't we optimize how we do this

instead of simply pick up a drug from the shelf and administer it continuously for six months. This is another good example of where I think we've not thought through the eco-evolutionary dynamics of what we're trying to treat. Yeah, it's basically a paint by numbers approach, right, which is paint by numbers is we're just going to do it this way and versus I'm going to think through this strategically. We have a pretty good sense that the more cancer cells a person has in their body, the more mutations they have. So the difference between a billion cells, i.e. one CC of cancer versus a trillion cells, which is like an almost fatal load of cancer,

is an enormous number of mutations. And therefore, it's not only bad from the mass effect, but also from now the fitness of it, right? You have so, you just have such a genetically, a bad genetically diverse population. So you have enormous heterogeneity within the genetic badness for lack of a smarter word. Can I add that you also have diversity of their ecosystem? Yeah, yeah, yeah, no, no, no, but let's do that. Yeah, so let's talk about these two indices

of eco and evo indices, which you've written about in a very recent paper. So explain for folks what these mean, because these are not the easiest concepts to get your head around sometimes. So when you talk to a cancer biologist about evolution, they say it's mutation selection. You get a random mutation and that the cancer cells accumulate these mutations, occasionally they provide a benefit, and that's what causes the tumor to expand. The problem with that approach is that it assumes the environment is stable.

They're all doing the same environment. Now, the alternative is to say, and I think the more evolutionary appropriate thing to say is, that there is tremendous variation in blood flow and other factors within the tumor. the revolutionary appropriate thing to say is that there is tremendous variation in blood flow and other factors within the tumor. And as the tumor goes into these areas of say, well, blood flow, the environment is entirely different.

And so the environment is applying selection forces that are totally different from, let's say, down the road where the blood vessels are really good. And the edge of the tumor, these cells are competing with the normal cells internally, they're competing with other tumor cells. So again, entirely different environments. And so, the more evolutionary model, in my opinion, is to say that the different environments within the tumor

are the ones that give rise to different phenotypes, which in turn give rise to different phenotypes, which in turn give rise to different genotypes. So that in this case, the genes aren't causing the evolution. The genes are the consequences of evolution. What do the ways to think about this is to think about whether a modern day Darwin sitting on the beagle in the, with his microwave machine. He's got all these fancy molecular biology equipment.

And Seyors just brought random samples of finches to this modern Darwin. They ground them up, put them through the machine, got billions and billions and billions of data points. Could they have written origin of the species from that data? And I think the answer is no. And I think that the reason is that what Darwin saw was that the beak of the bird and the seed matched.

There's a morphologic matching that makes common sense. This isn't magic. It's just that beak's got to be bigger to pick up that seed. He paired phenotype to environment. To environment, to environmental section. That's the logical way that evolution occurs. The genes are kind of downstream of that.

Multiple genes, the beak could be under the control of a few genes. But how would you distinguish these genetic changes and say, well, there's the beak there. Oh, that means there must be a big seed there. Isn't it safe to say we might not even know? Like, isn't it safe to say that if you took the two closest finches, the one that went from the beak like this to the beak like this, and you sequenced them both, you might not be able to extract which genetic differences between those accounted for the beak? In fact, I'd be surprised if you could. No, I completely agree.

And I think that when we talk about cancer as a disease of the genes, it's more complicated than that. And I think that the intense effort to characterize the genetics of tumors has had some benefit. But it also has some disadvantages because you lose that morphologic matching of the environment and the morphology of the cell, which is more common sense.

And I think in the long term, it helps us understand it better. I mean, one of the questions one of my colleagues likes to ask is, who's never a shabbyologist, how many niches do you think are present in a cancer, and how many species are present? I mean, it's certainly not one. Virtually never is it going to be one, but is it nine, is it a hundred, is it a million? So when we think about tumors of clay, it's a starting from a single cell or small number, but it's specieating all over the place as it gets in different environments. But the speciation isn't magic, and it's not just due to a random mutation, it's because there are environmental variations. And trying to understand that I think would help us a lot

in terms of really getting a handle on what the cancer is doing. And the reverse of that is, I mean, so take cancer cells out of a patient, put them in a dish, and now you start to go home and culture. Well, they're no longer being attacked by the immune system, they no longer have to worry about antigenesis. I mean, it's an entirely different... Right, they don't have the edge effects that you described earlier, where some of them have a selective advantage, vis-a-vis access to substrate, whereas others are more competing

with the tumors at the inside. They're subjected to entirely different environmental selection forces. Now we take those tumors out, we do molecular biology studies, and we say, oh, well, this came from a lung cancer. That means lung cancer must interact with this to do this. When, in fact, we're dealing with cells that have evolved far past, you know, they're far different from the cancer cells that were present,

that makes it up under the enormous amount of the cancer literature right now. And I don't know that that's useful. I don't know that you can extrapolate from those results to anything that goes on in vivo. And it's also, I think, has led to a very confusing literature, at least in my opinion. You take genes that have been extensively evaluated. And it's like, they could do anything. According to the literature, they have any function you can name. They can find everywhere. So that's becoming non-useful.

If you can't really sort of pin down what it's role is in BIVO, in a patient, you've got a lot of literature, but I don't know that you have a lot of knowledge. How does this fit into the EVO index? How do you, which basically looks at the diversity versus genetic change over time? How do you then use that to clinically make a decision? I don't know. I mean, in dealing with a very heterogeneous population, and I think this is your question, it does, as you increase the heterogeneity, does that increase the, what say,

its resistance to therapy or the likelihood? And I think that's probably true, but I don't know how that can be taken into account right now in a clinical setting. And I'm sure that someone smarter than me is gonna figure this out. So in other words, we can't do something as simple as biopsy patients to get a sense of drug resistance

versus non-resistance because it immediately takes them out of the environment in which it matters. And we get back to this problem of, well, we can't really do this in vitro. The nice thing, I mean, you get information about the tumor, but those cells are dead. What's in the tumor is now going to rapidly diverge from there, because the minute you touch it with therapy or, you know,

some other perturbation, it's evolving and changing very rapidly. So, again, it's like the hurricane going on further and further. Whatever the data you got in day one is important for day two, but becomes less important in day three and day four and day five and ultimately then having nothing to do with having no predictive capacity at all for the hurricane. So I think one of the things that I think we have to be careful

about is how we use data. I mean, there's a lot of interest in circulating DNA, and circulating tumor cells, and that sort of thing. But there's very little information about where they come from, exactly. Is the DNA coming from cells that have died because they're less fit? So those are the losers in the evolution game. So we and I care about those. Or are they the winners? In that case,

we do care about them. And how are they representative of all the different rates of sales? Is this a distribution of DNA representative of the distribution of populations? Or I think that's highly unlikely. And so I think that we do have to be thinking about using imaging as a way to look at the intratumal revolution over time. It's a non-destructive way to do it. But we have to be able to take the macroscopic scale images that we can get from radiologic studies and bridge the scale to a microscopic level that can tell us about what's going on at the cellular and molecular level. And what kind of tool could do that?

Lanscape ecologists have developed technology where they take satellite images, and they can look at them and say, and again, this is simplifying things quite a bit, but they could say, well, can we generate a species map from these high level images? And the way they tend to do it is that they look for habitats. They identified distinctive areas within the image. Usually, there's five types. Every part of the image is one of these five, let's say. So instead of trampling through this entire state or however, you know, large area, you just tram through one of the habitats and say, what are the species distribution here? And then you extrapolate.

I think that those kinds of things, and again, part of the reason is that in the images we see areas that have good blood flow, poor blood flow, we might even see areas of temporal variations and blood flow, we can see a demon, we can see a lot of different things. We may be able to find habitats. And again, we won't see the cells, but we can see them indirectly in the sense that we know that, for example, in an area that where the blood flow is varying frequently, that there's certain types of phenotypes. They're going to be adapting to that environment. And so we can at least make that extrapolation and make those kinds of suggestions. certain types of phenotypes, they're going to be adapting to that environment. And so we can at least make that extrapolation and make those kinds of suggestions.

It may be we can do better than that and even start to think about molecular properties of the cells that are present, at least in a general way. It's not perfect, but at least another mechanism, perhaps in combination with blood circulating to ourselves and or cell-free DNA and so on. So cell-free DNA, all those things, combining with that, I think probably neither alone is going to be sufficient, but together they may be sufficient that you can understand intratural malevolution during therapy, which is really a key piece of

clinical data that we currently cannot get. What inference do you draw from your work with respect to the importance or lack there of early, early screening? In other words, do you now have a point of view one way or the other from the mainstream view, which truthfully is that screening is kind of important, but probably not that important, right? I mean, there's only five cancers for which probably the American Cancer Society would even offer. Certainly, the U.S. Task Force on Preventative Services would even offer a point of view on screening.

ProState, we've basically thrown our hands up and said, eh, talk to your doctor. So we have a point of view on lung cancer and smokers, mammography, cervical cancer, and colon cancer. But given that we now have many more tools to go looking for cancer early, whether it be liquid biopsies and self-redeNA or far, far better imaging studies, does your work point you one way or the other towards having a better shot at treating cancer by getting it early? It would make sense.

Again, the smaller the population, the more likely you could cause extinction. But that said, I'm not, I don't think really qualified to comment on the screening program. I'm very supportive of screening, it just intuitively makes sense. But that's just from a personal point of view, I'm not based on any work that I've done. So when I hear what you were saying, it makes me think you should be doubling down on screening. Right? It makes me think that notwithstanding the challenges

of screening, which are both economic and the psychological toll of false positives, which invariably happened the earlier and earlier you look for a cancer, the trade-off is those effects that we've described, which is you are going after a group of hunter-datherer colonies rather than the United States of America, right? You have a much easier chance, and they've had far less time to accumulate mutations, and they are far less interconnected.

Far more vulnerable. Yeah, exactly. Why did you select prostate cancer for your trial? Was it simply because you had a great biomarker in PSA or was there anything else about the biology of prostate cancer that attracted you? Two things was that it had a great biomarker and it had a very brave oncologist willing to do it and you should not minimize the courage of the latter because it does go against the grain and the oncologists have to swim against the stream to do this.

And many of them are really not willing to do it. So I salute Jin Song's ang, who was the very brave oncologist that ran the trial. I did it very well. Well, given the success of that trial now, has that increased the appetite of others to consider doing this either in a larger scale with randomization for prostate cancer and or for other histologies? Minimally, there was a philanthropic fund in Europe that wants to fund a phase three trial in prostate cancer, so that's really good.

And I think that's really important, but I would say that at the same time, if anything, I get to feeling there's more resistance. I think just everybody's got their backup. It's interesting that article that I mentioned about showing how you can eliminate the resistance cells. I got this morning, actually, got the 10th rejection for that article. I mean, it just people just don't want to hear it. So I don't get the sense that there's any greater interest in that sense. If funding were available, and more importantly than funding, or at least equally important, would be oncologist collaboration, what other experiments would you want to

do? Well, I think there's some low hanging fruit. I think Ovarian cancer is one that could be treated using this technique very successfully. Again, they have a nice siramarker. I think that small cell lung cancer, it almost universally fatal disease. But one that is responsive extremely well to initial therapy. So any tumor that responds well to initial therapy to the point that you virtually cannot see any tumor

is one that in theory could be, I think we could eradicate, and then initial prostate cancer therapy. So pre-medestatic. No metastatic? Oh, metastatic, okay. Men that present with metastatic prostate cancer, they're treated with antigen deprivation therapy, and the PSA becomes normal or undetectable in 90% of these men.

And of course, what do we do? We just keep treating with antigeniporation therapy. Which by the way says nothing about the metabolic damage and the total metabolic derangement it brings on those men. So if they don't die from prostate cancer, they're gonna die from diabetes and the complications of fatty liver disease.

Yeah, I mean, they hate the therapy. So I think as soon as it normalizes, we should use additional therapies, hit it again. You know, this is the knife fight again. And that's a very common disease in a very common scenario. And it's interesting that I cannot get anyone, any oncologist to be interested in doing that.

To me, that's very low-hanging fruit that we should certainly try. Or we could try the cycling, but with the idea that we drive the resistance cells to extinction, also probably fairly easy to do, perhaps even easier to do, but with the idea that we get to the point where these men don't have to keep taking this the end of the deprivation therapy I mean it's funny because you're exactly right. I mean I I think there tends to be a the sense of it's not really Cancer therapy and it's certainly not like chemotherapy. I find it to be worse truthfully

For the future. It's miserable and We should not minimize the effect of this which is very significant and it's a pretty common problem So I think that there are some opportunities. I don't think, to be honest, I don't think this will be something that I'll see in my lifetime, but I do hope that the next generation of oncologists and cancer biologists

will try to at least bring another step forward. Is there a cancer that you would stay away from at the early stage? In other words, you obviously want to start with the ones where you think you have the highest likelihood of success. What would you put at the other end of that spectrum where you think, you know, that's gonna be

a really tough problem to solve. I think the toughest problem I can think of is glioblastoma one that you mentioned before. I just don't understand how, I mean, every other tumor virtually, the problem is not local control. If you control locally, you cure it. It's a mess to add to these.

GBM, you can't control it. It bothers me that there's something where we're missing in this, that I don't understand, but it feels like like it's an intimidating cancer and I would never try to get involved with it. I mean, I'd love to be able to make an impact to help people with the disease, but it's such a difficult problem that I would be willing to do that unless someone came up with a really good idea for treating it. I mean, I think part of the problem is there's the, quote unquote,

the simple mass effect problem, right, which is anything in the brain is problematic due to the inability of the organ to absorb growth. I think a second problem with GBM is the ubiquity of radiation that is understandably used to treat it, but it introduces mutagenesis, probably in an even greater rate. So now you have two layers. I think it puts a bit of an accelerator on its ability to out-compete its environment. It just introduces a new genetic heterogeneity. I don't know if that's right. I mean, that's just a theoretical argument. I don't know that that's been documented. Of course, the brain is inaccessible, so it's hard to see what goes on. But what we know is that you hammer this tumor. I mean, you take it out, you bulk it, most of it, give it radiation therapy, chemotherapy,

I mean, very little can survive that. And yet, it does. And the question is, is it because, as some people think, that tumor cells on the periphery sort of grow back into it, or is it because there's resistant cells that emerge, that are so tough, that you can virtually hit them with a hammer, and they just shrug it off. One of the things I'd suggest at one point, maybe we could do radiation first, then do the surgery and see if you can learn about how the cells change during radiation therapy that could give you some idea about whether they're evolving resistance, whether that's the major factor or not.

But it's very hard to convince anybody to do it. And the surgeons, of course, are willing to work on anything that's had radiation before it, but it's interesting because the neurosurgeon said, well, you could probably do it, but and nobody wants to do it. Well, everybody knows that's not what you're supposed to do, so it's never gone anywhere. And of course, I've not pushed it because I would be afraid of causing harm. What about other bad actors? I mean, I always put cancers in the category of, there's

the cancers that give cancer a bad name, like GBM is one of them, pancreatic adenoh is another. What would be your level of optimism or pessimism around pancreatic adenocarsinoma? Again, I sort of work with some people on this. I think there's still there's opportunities. Again, I don't think we understand enough about the biology. So, for example, classically, pancreatic cancers have a lot of fibrosis associated with it. One question is, is that fibrosis a host response or is it a tumor adapted strategy? Evasion, yeah. Now, nobody really seems to know that.

So, little things like that. So, should we promote fibroblasts? I mean, you know, classically, we try to reduce the fibroblast because there's this idea that you can get more therapy. And if you do that, but suppose it's a host response, maybe we should be giving fibroblast growth factor to them. And maybe they're competing against each other for space. And if you just promote the fibroblast a little bit, they'll kill off the tumor itself for you. I'm just saying that with every tumor we could ask questions because we often don't know really basic things about the underlying equi-fusual dynamics. And if we could learn more about them, I think we

could have some different solutions, some different strategies. I will point out that another brave pediatric oncologist that I work with, Damon Reed, has started a trial for kids with metastatic sarcoma. And as for giving cancer a bad name, this is one of those tumors that responds very well. This is a rabidomyocircoma response very well. It'll keep them a therapy.

And then comes back and they die. And of course, these are teenagers, young adults. I can't think of anything more tragic, sort of a younger children dying. And so he is actually trying extinction therapy protocol in a trial. So I just wanted to give him some recognition for doing this. And I got out of love to help these kids. I mean, there's no doubt Bob

that there gonna be people listening to this, who either themselves or their loved ones are progressing through therapy and basically looking down the barrel of no options. And so let's say someone's in that boat and they hear this and they say, well look, I'm basically being told by my oncologist, I've progressed through all my therapy or the term that oncologists use,

that some oncologists use that I don't particularly find as you've failed therapy. I hate that expression. Yeah. Now, let's say they're listening to this and they say, I want to find a doctor who can help me try this therapy. What would their options be? Well, you know, you have to find an oncologist willing to do this. And there are some who will work with them,

but there's a lot that will not. And it's a very difficult problem. And I can't help because, I mean, I'm an oncologist, and I'm not qualified to be prescribing anything for people. When people call me, I said, I'm happy to work with an oncologist. If your oncologist is willing to try something different, I'm happy to at least give them my best sense for what the underlying equivo-exaggerated amics are. Understanding, of course, that the lot of times we don't really know for sure, but this, again, in desperate situations, if they

want to try this, that's between them and their doctor. And as you know, this physicians are want to try this, that's between them and their doctor. And as you know, this physicians are very aware of medical legal issues are very afraid of doing anything that's non-standard because it does open them up to lawsuits and that sort of thing. And I understand that. And that's not a criticism, it's just the statement. So unfortunately, it's a very difficult problem

and it's a lot easier to deal with patients that are just presenting with cancer than the ones who have been through many therapies, unfortunately. And I, you know, my father died of a self-geal cancer and I remember this vividly, the desperation and the despair and everything that comes along with people at the end stages of the disease. And I would love to help.

I mean, I really wish I could, but unfortunately most of the time there's just nothing left to do. Bob, I have found your story really interesting and really provocative. And I think the frustrating thing about what you say is that there is, in my opinion, very little downside to trying it, it would be very easy to conduct clinical trials that would have a crossover arm where you could take this adoptive approach, pit it against a standard approach, and if the patients on the adoptive approach were progressing, you could cross them over. And by the way, vice versa,

when the patients who are progressing on the standard therapy too quickly, you would cross them over. In other words, when you think about some of the risks that we talk about in medicine, this actually strikes me as a relatively low risk proposition. The greatest risk is to our egos and to the dogma as opposed to the patients. And that's, to me, a bit of the unfortunate part of medicine is when we, then we're all

guilty of it. I can probably count 100 examples of where my ego gets in the way, but this is a particularly sad example of it when there is a disease that we've really had very limited success treating, right? You referenced Nixon earlier, right? The war on cancer is now in its 50th year. And we don't have a whole heck of a lot to show for it. Certainly not given the time and the resources that

have gone into it. I don't think anybody in that era, in the early 1970s, if they had a crystal ball and showed them what we have today would say, that's success. Yeah, I mean, we surely made some progress, but we have not made a lot of progress in the metastatic cancer population, which is still more or less as fatal as it was 50 years ago, and of course, well before then. I think it's important to try to rethink some of the things we do

that just keep doing the same thing over and over again and just keep looking for new drugs. It certainly, you know, do drugs. No, that is important, but I think we could do better with the old drugs, and I don't think that we've been really that incentivized. I'm not sure we've really taken a lot of time to think this. And again, this is perhaps a bit of what we started with.

Intuitively, it would seem like killing as many cancer cells as possible is the best approach for the patient. And in fact, in a nonlinear system intuition is often not wrong. But nevertheless, it seems like it's right. I mean, you just, you feel absolutely certain you're correct, even when your own eyes tell you that it's different. And that's hard. I mean, that's hard for all of us. Yeah, I mean, I think, look, I think that statement is a great place to end this, which is in nonlinear systems, your intuition can be very misleading. That's true in

life of which biology, especially this corner of biology happens to be exceedingly non-linear. And with that, Bob, I want to thank you for your generosity and time, and much more importantly, of course, the work you've done, which I think is helping a lot of people, and you're right. I mean, it might not put a dent in the world of cancer in the next decade, but as we alluded to earlier, sometimes some of the most interesting things in medicine take decades plural to take hold. So hopefully there are a number of people who are going to be able to pick up this baton and run with you and eventually even be able to carry it. Thank you. Thank you. It's been a pleasure.

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