Adaptive Cancer Therapy: Using Evolution to Fight Drug Resistance

•Low dose: Hit the tumors with enough treatment to perturb their system, but not so much to kill the sensitive cells and leave the resistant cells to proliferate. The cancer group is heterogeneous and the resistant cells can be controlled by keeping the sensitive cells around.

•Combinations (first strike second strike): Lacking magic bullets, metastatic cancers can be cured through a strategic combination of pretty good bullets. None of these bullets could by themselves cure the cancer, but the combination could.

•Sequencing (not a combina combination cocktail): If you have a combination cocktail, especially as a first strike, you're applying the therapy to the largest possible population. The heterogeneity is such that almost certainly you will find tumor cells that can be resistant to the combination. It is better to hit the cancer with therapies in sequence, as each knocks the population down and can drive it to an extinction.

•Mathematical models : Having a hypothesis and a simulation of what should happen based on evolutionary theory helps in understanding why and enables insights from much smaller trial cohorts. To apply this strategy in real life we asked Bob to give his off-the-cuff thoughts on the case of advanced cancer patient Brian McCloskey, who is currently on abiraterone, the drug that Bob used in his study. Bob felt that because Brian currently has a low tumor volume that he is a candidate for an attempt at extinction, to hit his cancer with a ladder of 3 different drugs to successively knock down the population, with the hope that it could be nudged down the vortex. Requests

•Do you have any feedback on Bob’s principles for treatment strategy?

•This adaptive strategy seems intuitive, yet it’s not widely practiced. What are the barriers or objections to it? The information and opinions expressed on this website or platform, or during discussions and presentations (both verbal and written) are not intended as health care recommendations or medical advice by Cancer Patient Lab/Prostate Cancer Lab, its principals, presenters, participants, or representatives for any medical treatment, product, or course of action. You should always consult a doctor about your specific situation before pursuing any health care program, treatment, product or other course of action that might affect your health. — Meeting Notes Brian McCloskey: I'm super excited to have Dr. Bob Gatenby with us today. Bob is the co- director of the center of excellence for evolutionary therapy and the department chair of diagnostic imaging at the Moffitt Center. He's also a radiologist who specializes in exploring theoretical and experimental models of evolutionary dynamics in cancer and cancer drug resistance. He's developed an adaptive therapy approach for treating cancer, which has shown promise in improving survival times with less cumulative drug use. He's also led the formation of a program called integrative mathematical oncology, which brings together a group of applied mathematicians to collaborate with tumor biologists and clinical oncologists. And the goal of tha that is to develop non-linear dynamic systems, to examine the physiology of tumors, incorporating factors, such as phenotype evolution, intercellular communication pathways, and the interaction of microenvironmental factors, including therapies for prostate cancer patients. We are particularly fortunate to have Bob join us because his adaptive therapy has been applied in prostate cancer specifically, a study, including the dosing strategy for abiraterone, a novel hormone therapy, which I happen to be on currently. And I know some of the other patients that are on here have been on that drug as well. So to summarize, Bob is really a breath of fresh air. We're so fortunate to have him here. He has very novel ideas around cancer treatment that challenge conventional practice, particularly around the notion of maximum tolerable dose. Bob Gatenby: Apologies for this formal presentation. Uniquely in cancer centers, we have a department of mathematics. We now have nine applied mathematicians and two evolutionary biologists. Our goal is to use a physics-related paradigm, where we have theoreticians and experiments working together in complex dynamic systems like cancer. The physicists have taught us for centuries that this is really the only way you could address these kinds of problems. That's what we're trying to do. I'm going to talk about two topics:

•An evolution-based game-theoretical approach to treating metastatic cancers for control or cure.

•An incidental theme is my concerns about limitations in the clinical investigation gold standard of double-blind randomized trials, which I think have some benefits, but also some drawbacks. I think we're seeing those drawbacks now as we come into this era in which we're moving away from maximum tolerated dose treatments. Currently, personalized cancer care is all about molecular targets. Consider this example of a woman with metastatic lung cancer. She has EGFR mutations identified, and there are targeted therapies for that. You give it, and she gets a great response. This is a beautiful example of the problem that if you keep giving it, almost inevitably, the tumor will come back. It will evolve resistance and return, leading to treatment failure, and ultimately to the death of the patient. The basic idea here is that this evolution of resistance is patient-specific, drug-specific, and tumor-specific. This should be part of the personalized medicine initiative. The general principle is that by applying drugs in this way, the cancer cells will inevitably access the selection for resistance. The human genome will find ways to get around it, but it's the proliferation of that population that is meaningful. A small population of resistant cells is meaningless. It becomes meaningful only when it proliferates sufficiently to become a clinically evident cancer. One cc of tumor usually is about a billion cancer cells. So this requires a considerable amount of proliferation, and those are dynamic and subject to Darwinian forces, and that's where we want to focus. One of the approaches you can use is a game theoretic model – treat cancer treatment as a game. The oncologist is playing against the tumor. The oncologist plays the game by applying a therapy, and the tumor plays the game by applying an adaptive strategy. When you look at this as a game theoretic, the oncologist actually has two enormous advantages. One is that he or she plays first. The cancer cannot begin to evolve a resistance until the oncologist has placed a therapy down. This is called a Stackelberg game, and it's the equivalent of playing white pieces in chess – the oncologist always leads the game. The second, and probably more important advantage, is that the oncologist is sentient and can play dynamically – she or he can anticipate the future and can change therapies on the fly. Depending on the evolutionary dynamics that are going on in the tumor cancers, any evolving population can never anticipate the future. It can never adapt to an environment that it's not seen before. The problem is that the dogma in cancer for the last 50 years has been that treatment is applied continuously and at the maximum tolerated dose until progression. That's the standard approach. But in doing that, you'll notice that the oncologist loses both advantages because the oncologist simply plays the same move over and over again, applies the same therapy again and again, and the cancer cells do not have to change their adaptive strategies. They can just keep adapting to that. The other thing is that because the therapy has changed only when the cancer is observed to grow, you're ceding control of the game to the cancer. The cancer is leading the game, and the oncologist is following. Part of what we're trying to do here is to use these advantages in a dynamic way, by understanding the underlying evolution. If we restate the game from an evolutionary point of view, suppose we start with a mixed population of cells, most of which are sensitive: if you give a maximum tolerated dose, you get a great response: the tumor shrinks away. But what is left behind – almost inevitably – is a small population of cells that are resistant. If you keep giving the same treatment over and over again, you're treating resistant cells, and you're having no effect on them. Even though the tumor may be not visible, you may not be seeing this growth because it's below the resolution. It is in fact growing, and the conviction of the oncologist that the tumor is under control with this therapy is really an illusion. Eventually you get proliferation that you can see. This is a very well known evolution dynamic. It's called competitive release. And as the name suggests, what's happening is that by using this approach, you're maximally killing off the cells that are sensitive, leaving behind the cells that are resistant, and eliminating all of their potential competitors. To them now they're only competing against each other, and that's bad for the patient because that means that these guys are slowly growing, getting better and better at what they do. An alternative approach, one of many, is what's called adaptive therapy, which is to deliberately pull your punches. You could apply treatment, but you specifically want to keep intact a sufficient population of sensitive cells. You treat only enough to knock the tumor back a little bit, and then you stop treatment. You pull it away and the tumor will grow. But since you're not applying a selection for resistance, and because in general, the sensitive cells, which do not have the burden of the resistance mechanisms that the resistant cells have, the sensitive cells have a fitness advantage. So when the tumor grows back, you recapitulate the initial population, meaning that you still have a large number of sensitive cells. You treat again, cycling the minimum necessary therapy to keep the cells in check. The goal here is to use the sensitive cells that you can control to control the resistant cells that you cannot control. This is the basic idea. Mathematically, this is using the sensitive cells as a forcing function. This is not a passive approach. You're driving them forward and backward. The idea is to use those oscillating dynamics to create a nearly steady state. We did preclinical experiments, but the first group that we applied this adaptive therapy approach on was abiraterone therapy for men with metastatic, castrate-resistant prostate cancer. Most of the cells in a typical prostate cancer at the beginning require exogenous androgen produced by the body. When you start to knock those down with the initial androgen deprivation therapy, some of those tumor cells begin to make their own testosterone, so they can proliferate and become resistant. Abiraterone blocks that enzyme, so that they are no longer making testosterone. This is typically a second line therapy. The time to radiographic progression typically is in the range of 8 to 60 months, depending on pretreatment. We established a mathematical model for this. The details are not important, but just to give you some idea, although you could look at this in a variety of ways, we just used a very simple model. We have a population of cells:

•T+ cells that require exogenous testosterone.

•TP cells that make testosterone. These are the ones sensitive to abiraterone.

•T- cells that proliferate independent of testosterone. These are the bad guys that we want to suppress. We have a mathematical model. There's an interesting dynamic here because the TP cells not only make testosterone, they spill it into the environment. The T plus cells can use that. That's called cheating. They have the testosterone as a common good, so that can complicate some of these dynamics. We have two mathematicians and an evolutionary biologist here as part of the group working on this. We developed a payoff matrix. Jingsong Zhang is the very brave oncologist who brought this into clinical practice. It was a very simple protocol. We said that you would give abiraterone, and when the PSA got to 50% of pretreatment value, then you stop, and you let the tumor go. And when the PSA comes back to what it was pre-treatment, you start again. You cycle this over and over again. Our mathematical models predicted that we could control the tumor for 2 to 20 cycles, and this actually turned out to be incorrect. I'll talk about why it was incorrect shortly. We completed the trial. There was no funding for this. We scraped money together to do this. We had 17 patients that completed the trial. We compared those to 16 patients who had the same 50% PSA decline with the initial abiraterone dose, but who then got standard of care dosing. These groups were quite similar demographically.