Connecting the dots for a cure

Scientist Jacob Scott began as a physicist, then chose to become a radiation oncologist, and later a mathematician researching cancer. How does all that make sense? Listen as he explains what it's like to be the Cancer Connector, helping different disciplines work together in new ways to research a cure for cancer.
You can find the transcript of this episode below.

Check out Jacob Scott's blog

Many thanks to Dean Johanesen for the use of his song Walker's Pure Extract, and a big thank you to New World Brewery for the great brews and even better hospitality.


Episode transcript

Parmvir: Welcome ladies and gents to our podcast. So what we're trying to do is introduce a number of scientists and their research to you in a safe environment so that you can learn to love it in the same way that we do. For those of you who haven't heard anything from us so far, we are currently a merry band of four. That would be me. Parmvir, I'm working at USF Health. We have David and Arturo working at Moffitt Cancer Research Center, and we also have Angela, who is not a scientist, but our token nerd. She does all of the things that are not science. So basically all the hard work. Tonight's speaker is a very good friend of ours called Dr. Jacob Scott, who has probably had more career paths than the rest of us have had hot dinners. Tonight he's going to speak to us about his experiences as both a medic and as a scientist.

Jacob: So thanks very much for inviting me tonight, Parmvir and David, it's a pleasure to be here to try to communicate some, some of the science that we're doing at Moffitt cancer center to, to everyone, in a way that's a bit more comfortable and less less, constrained than the average scientific meeting. So I think, starting today it would be best to sort of chat about how I got where I am, which is sitting at a pub in Tampa. and, and through that, maybe someone can see some commonalities with their paths and, and, and, and find some, something there. So I began, as we all do, sitting in a high school classroom, and I was immediately motivated and caught on by the sort of hubris that the average 15 year old boy feels when confronted with the possibility of understanding the entire universe, with a set of sort of deterministic equations and in a physics class.

And I remember specifically my teacher at the time, Bob shirts, was, shooting across the class at a high speed side to side, opening and closing his arms, screaming, “expand, contract, expand, contract, expand contract”, trying to describe how sound waves traveled. And I was generally hooked. I remained sort of, blindfolded, if you will, to anything besides physics as I, I just sort of assumed that I'd found my path.

And went that way, taking only the minimum necessary of every other science until I got to college. And I studied astrophysics there thinking that that was sort of the way towards the greatest truth to study these amazing heavenly bodies. And you know, as an undergraduate, you're, you're shown the known world and, and very little else, you're showing what we know and you're shown, how we got there and you're sort of blinded a little bit from what we don't know and what's outside of that truth.

Quote unquote truth. And that's fine because you're 19 and you don't want to be muddled with the concept of the unknown. the, the college that I went to was the Naval Academy, and so we were sort of forced immediately afterwards to, go into a service. And so I ended up on a submarine where we studied. I did a bunch of, nuclear engineering and was sort of forced to, at that point, to do the most possible of applied sciences, which is the running of a vessel. And the, the making of war as well, and physics played directly into all these ventures, but at the same time, it didn't quite feel right. It didn't feel the same.

It didn't feel as though I was getting any closer to understanding the universe. It felt instead, like I was acting as a cog in a very big machine and it didn't feel right. It, it was what I was supposed to do and it's what I did. But at the same time, I felt that something was missing, which was the, the search for truth, the search for meaning.

And so when I finished my service after five years, I was sort of faced with what to do next. And I was very well trained doing the sort of Homer Simpson act, turning the lever when the thing goes red and preventing catastrophe and, and worse, I was also trained to turning the lever when the thing goes red and creating catastrophe with, with our missiles.

And that didn't, that didn't jive with my personal, morals at that time. So I was getting out. It didn't know what to do. I was enamored of this young woman who had asked to be my wife, and she'd assented, but we hadn't yet made that happen. I needed a job at the end of whatever path I chose, and so I was sort of faced with doing something I knew which I knew would not make me happy or trying something entirely different.

And so I applied to sort of every kind of graduate school there is because I didn't know anything about any of them. And was then faced with the difficult choice of how to, how to proceed. and I remember I was at a rock climbing gym and I was whining about this situation, but the person that was coming with that day happened to be an MD, which I didn't know. And here I am whining about my choices and he, he just literally dropped me off the face of the rock climbing gym and told me I was an idiot. And told me to go to medical school because there was more jobs there. And so I did. And I went off to medical school sort of blindly, not knowing much about it, but what I found was a huge rush.

I was 30 I had sort of done, some things. I had learned a lot about one field, but all of a sudden for the first time, I was thrusted at this brand new situation where I was a complete novice. The last time I had taken biology was, so this was 2004 and the last time I had taken anything that remotely considering biology, I'm not counting the effects of nuclear missiles on biological entities, was 11 years prior.

So. It was really a quite a lot of fun. Every class we took was brand new information. It was like being a kid in a candy shop in a way. and then medical school progresses as it does, and you're sort of forced to try to find a path. And it was a, a difficult thing. I didn't want to turn my back on the first 27 years of my life or 28 years of my life in which I studied physics and reactors and particles and sort of these, central truths, if you will.

But at the same time, I'd found that taking care of people. I'm thinking about a length scale that was more meaningful, seemed to seem to jive with my person. And also as a person who had just gotten out of the service, many of my friends were dying in service to the country, and it seemed like a waste to not use this new knowledge to sort of fend that off.

And so I ended up choosing a field in medicine, radiation oncology, which sort of, seemed like a nice mixture of my previous skills. It was some nuclear engineering, there was some particle physics and there was taking care of patients, sick patients with cancer. and so I chose the field because of its crossover with my previous life in physics.

But what I ended up really grabbing my soul, if you will, it's a bit of a nebulous word to use at a talk like this. But what really ended up grabbing me was, was the patients themselves and the bravery that is necessary to face a diagnosis of cancer and wake up the next day and decide to have a life anyway.

And, and that sort of motivates me to be a better physician. So I set off training, at Moffitt cancer center to be a radiation oncologist. And I thought that everything was all right and sort of a flashback to the previous decision in my life of whether to be a physician or a scientist. the physician who I was rock climbing with, I had kept in touch with him.

And when I finally chose radiation oncology, which as a side note, is a well remunerated specialty, he said, well, you only have one decision left in your life, whether you want an outboard motor or an inboard motor on your boat. And to me that sounded a little bit like acquiescence, and a little bit like not the way I wanted to live the rest of my life, sort of doing my job for money.

And I don't think he meant it that way, but it resonated with me that way. And I think from that day forward, I sort of started looking at my job a bit more critically. And I have to say I was, equally disappointed and encouraged. Because sort of what you learned during medical school is that evidence based medicine, that is taking care of patients in a way that has been tested by clinical trials is the, is the pinnacle of what we do as physicians.

We don't do anything unless it's been proven previously. But the caveat to that is that you don't do anything unless it's been proven previously. And in cancer, at least what's been proven previously is that we're not extremely good at what we do. And deviating from that path in the clinic is malpractice. And it's not a service to your patients. And so for me, the day to day practice of my job seemed to be not necessarily the best way for me to spend my energies. and so I started looking for outlets for research outlets to ask creative questions. And it's difficult to ask creative questions in clinical trials because it's not appropriate.

And so I looked toward, the science toward science again, and my previous life it was physics, as I mentioned, but. that was not really the standard path for a physician, for a medic. And so I was offered a position in a lab, a biology lab from a close friend, and I took it and I spent about three months totally wasting his time and money, killing cells accidentally rather than on purpose dumping reagents into the wrong dish and generally dreaming about questions whilst failing to answer the ones that were put in front of me.

And I realized that biology is really hard and it's something that I could never do because of my lack of attention to detail and just it's just not suited to me. And I sort of became a bit frustrated because it seemed like the only avenue open for me for research, and I didn't really know what else to do. So I sort of went back to the clinic and decided to harden myself to this truth and become a clinical triallist and go forward. And then, I sort of accidentally happened upon a group of, mathematicians working at our cancer center, which seemed like a ludicrous proposition to me that anyone would be doing mathematics about cancer that wasn't statistics, seemed like a waste of time, but I decided to go to their talk anyway and see what was going on.

And, I was immediately, immediately hooked. It was a group of people who were asking the sorts of questions that I was taught to ask as a physicist, and doing it in a way that made sense to me, but doing it in a way that was, grounded in first principles and built upon rigorous statements that could be chased one after the other.

And then even more exciting to me was the fact that the field was wide open. It felt like being a physicist in the 17 hundreds. What I experienced in college was that everything really was known and that you could study the stars, but we knew why they moved. And the best you could come up with, with some sort of spectral analysis of something that was so far away that it would never matter. And, and to me, that was frustrating to a lot of folks. Clearly that's, a career and a lovely one. But it seemed to me a difficult proposition. And so when I found out that there was folks sort of asking these big questions in biology, I really was hooked. The problem was I had zero skills in the field.

And so, I approached Sandy Anderson, who's the head of the group there, and I was lulled in by some of David's, who's one of our organizers here is, elegant game theory models to sort of try to, make some inroads and ended up starting all over yet again when I joined these folks and, and I again experienced the same rush starting as a brand new grad student at the age of 36 in mathematics.

And I guess, I guess the rush that I feel working with this group is that I can take whatever question suits me as a clinician. Whatever big question I seem to see and immediately abstracted and answer it in a way that could be meaningful. I'm not tied to any specific biological tool set or my own skillset.

I can abstract anything I like and write down a formalism, a mathematical sentence, if you will, to try to make scientific progress. And so you remember the scientific method we all learned in high school. It's all about deriving new hypotheses. I found that the hypothesis would come quite quite freely in the clinic, but they're sometimes, often, they're quite often very difficult to test in a laboratory because sometimes the questions, don't tightly follow others, but when one is freed from that by the ability to do things mathematically in a, in a sort of creative way,  it's a freeing moment.

Cancers has a thousand open questions. We've had the war on cancer since the forties, and we've invested an immense amount of money in the study. Actually just the other day, David showed me an Atlantic article that suggested that dumping more money into our current system, our current business system that is cancer research may not be the right answer.

It seems like what we're selecting for, because the system is set up and success in the scientific system is based on marginal successes. And, if you look at the, the government's way of funding science through the national institutes of health through these other tax and government funded organizations, it's really, they're quite risk averse.

And you can think of them as, as like a large company, they don't want to invest in crazy ideas. What they want to do is sort of make sure that what they invest in succeeds and a 1% increase in six or 1% increase in knowledge to them is a good investment because you invest a whole lot of money and you get a tiny bit of return. Well, that's a good thing.

But in a disease state like cancer where we know so little of the overall situation, incrementally advancing is really not a success at all. Specifically, if you think about how, how each time we answer a new question, like in any field, you really sort of open up the amount of unknown by quite a bit.

So if you answer one question today, you think of two more tomorrow that you didn't know. And if you're advance is incremental, but yet you're opening out the space of the unknown by double what you've advanced. It's sort of almost a losing venture. And so it's, it's a difficult situation to find ourselves in because the funding situation requires success. And yet the successes we have, the marginal successes that we're, we're forced to try to make are almost getting us further behind the eight ball. And so we find ourselves in a situation where we have million dollar therapies, which, give us incremental or no gain at all. And, and that's sort of the great success you can have as a, as a researcher.

And so to try to answer these questions in a different way. Seems to be meaningful. So together with some of the other researchers in the integrated mathematical oncology group, we've identified some, some big questions in cancer specifically, how does it spread not just what genes mediate the spread, which is the question typically asked today, but sort of what first principles govern the spread of cancer.

And so when we think of a cancer or a tumor arising inside someone, there's the, event in which it happens, which by the way, we don't understand. And then there's its growth where it begins, which by the way, we don't really understand. And then there's the idea that it can spread throughout the body and not just where it starts, so if you take a lung cancer, for example, it grows in the lung, it can spread to nearby lymph nodes, which are part of the immune system. And then further it can spread through the bloodstream to distant organs like the brain or the bones or the liver. And it's this step, this spread that accounts for, I think the recent number is 90% of death from the disease, and yet we know almost nothing about it.

It's sort of been cloaked in mystery, that's tied up in our fact that we, we can't really measure things because a lot of times patients will show up it having already happened, and it's very difficult to sort of try to understand a process that's already happened. It's actually impossible. And so what we've tried to do is to create abstract models by which we can try to understand this process. And an abstract model is all well and good, and an equation is a beautiful thing. And you can do all the maths you like, but if you can't generate something that's directly testable by an experimentalist, your theories are really just sort of rabble rousing.

And if you think about what a theory or a model is, so what we do is we were called mathematical modelers. And so if you think about what a mathematical model is, it's, it's just a mathematical way of doing something that all of us do and we all have done since birth, which is to create and try to understand the world around us.

And so each of us goes through life every day, building models of the way we experience the world. Even if you're a non scientist, you have to realize that everything you experience is understood by you in a unique way. Every person around you experiences the same things that you experienced, but in a slightly different manner.

And it's because of their understanding of it. And it's because of the way they have built up their own understanding. So the way that your friend thinks about you is probably quite different than the way you think your friend thinks about you. And this is all based on the idea that our understanding systems are built on these constructs in our own minds.

In science, what we try to do is formalize these thought constructs. And so by working together with more than one person, we try to write down in as formal a way as we can, the way we think something works. And so by doing this in a mathematical way, we attempt to write something down in a very rigorous way.

So it's all, it's all well and good to write down a cartoon to draw a picture of how you think something works. And for many, many years, this has worked in biology. It's worked in meteorology. It's worked in almost every science to write down an idea, a cartoon model of the way things work. But now that we're better at sort of measuring things, these models have started to not suffice.

So meteorology is a great example. In the 1920s before the advent of radar or computers, people had sort of these predictive models of what the weather would look like. It was based on what the weather was like yesterday in Tucson and what the weather was like last week in Phoenix. There's a guy named Louis Fry Richardson in 1922 who suggested that you could get a room full of computers, and by that he meant people doing maths. and if you connected enough of them and they had access to real data about the weather that you could predict in a better way, what they were doing at the time. But in 1922 they didn't have any way to better predict or connect these people. And I think we find ourselves in a sort of similar situation today is that we've been moving along as well as we can in biology and measuring things and having our marginal successes, but it hasn't really been done in concert with an overall grand plan as to how to understand things and further the people whose job it is to do the computation, speak a very different language and having their heads have very different understanding, a very different model system than those who are measuring. And if we're all trying to inform a different idea, if we're all talking about something that's slightly different or radically different, then the idea of us putting that together into a meaningful structure as a team is hard to do.

And so I think that moving forward at groups like ours and the biologists with whom we work are trying to create a central model, a central idea that we all hash out together and work on together. And by doing so, we can be both rigorous and test our ideas and move forward. And I think that the, the end result of this as it is, is the end goal of personalized medicine. And the end questions that we're really trying to uncover is what it is we ought to be measuring. And I think that the, the biological world has moved towards smaller and smaller and smaller scales, molecules, genes down even to the atom, if you will. And in physics, that's the way that we've gone. And the models have worked.

But in biology, this, the model systems that we understanding. From a mathematical, a rigorous way, don't do a very good job of scaling from that level to the person. And so currently the models that we work on largely in our group are, are sort of above a higher scale. We don't think so much about the very smallest level of biological measurement, but instead of the sort of larger scale and the cell level, and by trying to work with biologists to ask new types of questions, we can build models that we hope will inform the biology in a new way.

And so we're working on something like metastasis as I suggested, and we were trying to write down using, an engineering model of, of networks. And, but just by writing it down in a new way, we've had the opportunity to ask some questions, which over the last hundred years haven't have yet to be asked.

It's funny because you show a model like we're playing with, to someone who studies networks for a living and they giggle at, it's, how rudimentary and silly it is. But you show the same model to someone who has been studying metastasis from a biological world, and their whole mind is blown by the simplicity and obviousness of it.

And it's this sort of connecting of disciplines where I think mathematical modeling and cancer is sort of working to drive forward the science specifically to, to bring together a conversation between a mathematical and physical sciences with the biologists who are doing the actual work. And I think that's where we're heading.

From there. I think there's lots of things going on, but, but I'll open it to questions.

Parmvir: Thank you very much for that, Jacob. We're actually going to take some of the questions that we've got from people. One of the things you just mentioned is this is a, a weird area for anyone who's even for a biologist.

They've been doing something over and over and over again. Do you think that we actually need to understand cancer to be able to cure it.

Jacob: So that's a great question. And I think that sort of points to literally the crux of what we're doing. So I consider myself to be a theorist, and the end goal of a theorist is understanding, but there's an entire other branch of science, which is prediction, and they're sort of approaching the problems from different directions.

One's from the top down, which I would argue is the predictive science. And the other is the ground up, which is the theoretical side. And I think the question that you've asked. Really gets at that exact situation. Do we need to have a perfect understanding from the ground up to affect any better change or is it good enough to forget about understanding entirely and just work our way from the top down?

And I think the answer is really unclear at this time. So previous to about 15 years ago, I'd say everyone was going top down, and that was the, that was the case in almost all sciences. And you can do okay that way. But I think we're finding more and more that in cancer at least. I think maybe we've hit a plateau is what I'm nervous about.

If you look at death rates in the last 40 years, they're about flat. We've made some big advances, and the idea that big data is going to come in and save us is, is is tempting, but I'm nervous that, but I'm nervous at the top down perspective won't get us where we needed to go and that a greater understanding will be needed.

It doesn't mean that with our current understanding, we can't cure any patients cause clearly we can, but I think until we have a greater understanding of the disease process itself, we're not going to make very many more gains.

Parmvir: One of the other things that you've mentioned is that obviously you feel it's important for people who are trained within medicine to be able to apply different aspects of science. How easy is it for you personally to switch from being the applied physician to being able to use something as abstract as mathematics.

Jacob: So actually wrote down in my, on my notes for what I was trying to say today. One of the lines was, but really, who am I fooling? And I think the point there is that I will never be able to be a mathematician in the way that a mathematician is.

Nor do I think can any physician, with the exception of a very rare gem who might've been a mathematician in a previous life. But I think that my role instead is to sort of understand enough of the language of the mathematicians to frame the questions in the clinic properly. And that's really the difficulty, I think, in interdisciplinary science. And, you know, for the last 20 years or so, we've been talking about, Oh, we need multidisciplinary teams. We need multidisciplinary teams. If you don't have any multidisciplinary teams, you're not going to get forward. And in almost every field, not just science. The problem is, is that you can't just put a mathematician and a clinician and a biologist at a table together and expect magic to happen.

The problem is you need, say again, it's the start of a joke, yet it was put up a clinician, a meth. So what we need instead is we also need some people there who are translating. We need sort of the C3PO's, if you will. And whilst I'm not gold nor shiny, I think that's sort of going to be my role is, is trying to understand the questions and understand the answers in such a way that the real experts can do their work. And so I think it's impossible to be an effective, truly effective mathematician and a truly effective physician. I hope to be something in between.

Parmvir: Do you think your life is more meaningful as this physician-scientist hybrid than as a pure physician or a pure scientist?

Jacob: That's easy. Yeah. For me, yes, a hundred percent but that's me and that doesn't mean everybody should, should aim for that. I think that it's a, it's a sort of strange place because you have to accept that you're never going to be the best to any of the things that you do. And for some people that's an impossible task, especially in the highly specialized fields of medicine and mathematics, where once you've gotten to the doctoral level, you've sort of proven at least one thing, which is that you can be really good at something.

For a lot of folks who've gotten to that level in any one field, being less than the best in the room is a hard task. And I think in order to be a physician scientist, you have to almost day one, accept that that's your fate.

Parmvir: Since this is a very weird field, I think for people to say, okay, well you're using maths to try and cure cancer, do you find that this also reflects in how you try and apply for research funding?

Jacob: Well, the beauty of my current situation is that I'm a, I'm funded by others as a student, but watching my friends and colleagues around me, I'm trying to apply for this sort of, new type of funding. It is very difficult because the current, as I spoke about the Atlantic article earlier that was talking about dumping money into the system, it was actually, it was about Clifton Leaf's new book. What he said there is what we discussed previously, which is the idea that the current funding bodies are set up to fund success at any cost. And if you're choosing success at any cost, it means that you're looking for any variety of success and you're eschewing failure entirely.

And I think what we're doing is automatically considered risky because I mean, you know, to an outsider, it does sound sort of like hubris to suggest that I could write down one or two mathematical sentences that fully describes something as complicated as a disease or that from those sentences, I could gain anything of consequence that a biologist didn't get in the last 10 years hacking away at it.

And so yeah, it's hard because, we're trying to suggest that's what's been happening so far. We're not saying that it's wrong by any stretch, but we're saying that it needs an adjunct and we're saying that it would benefit from a new relationship. And so anytime you have an established situation and you try to horn, in your opinion, it's difficult, but I think we're doing it for the best reasons.

Parmvir: And of course, I'm sure with the sequester that everybody feels that potentially their science is going to be the one that's going to, feel the pinch.

Jacob: Yeah. But of course ours is the a the way forward, so we shouldn't be.

Parmvir: Of course. Of course.

Well, thank you very much, Jacob, for your time and thank you very much to everyone for listening.

As we say, this is going to be a regular monthly thing, so please look out for who our future scientists are going to be. And of course remember to ask your questions via any one of our social media sites. Thanks again.

[Musical outro]

Parmvir: Having moved over to the UK to carry out part of his PhD. We suddenly looked on Facebook one day and we see this picture of Jacob and we think the poor man has been mugged. He's got this massive scar across his forehead. He's got two black eyes.
It turns out he's just developed a love for rugby.