Cancer Treatment Simulations: Predicting Best Therapies

•Dynamic: Drugs in pharmacology are dynamic. The tumors adapt on multiple time scales. The time of day when drugs are administered matters. Dosing matters. Probably the simplest dynamic we can think about is when you treat a single cell with a drug – it is usually going to have some sort of a stress response. It's going to change the genes that it's expressing to try to help it deal with the fact that now you're trying to kill it. It does things like upregulate pumps that help it to pump the drug out of the cell. These are very well established mechanisms, and those are things that can really affect drug response, so are important to capture. The aspect of the dynamics that may be arguably the most important one to try to get a handle on is when we're thinking about what drugs we start with. Then as the subclonal makeup of that tumor changes, then what do we do? Do we attack the dominant subclone first? And then the ones that are initially in a lower proportion and maybe more aggressive, or do we take out those other low proportion ones first?

•Heterogeneity: Across every axis that you look at in cancer, there's heterogeneity. If you look across patients, it’s not just that a patient has prostate cancer, each patient’s tumor is unique. If you look within one patient's tumor, all of the cells within that tumor can be different. You can have different genetic subclones within that tumor that might respond differently to drugs. And even within the same genetic subclone, there is heterogeneity due to other random processes that happen in the cells. If you look at that tumor in a spatial sense, there are different microenvironmental factors, different oxygen concentrations, different immune local environments that can control drug responses.

•Multiple Pathways : Gene expression isn’t linear, it’s more a network. Multiple pathways intersect to explain how the cancer evolves and behaves. How do you build simulation models to describe cancer dynamics? Simulation models can be empirical (based on observations of experience, per the scientific method) or mechanistic (based on a theory of how the system is structured and works). Mechanistic models are preferable because they can predict, fill in blanks, and are interpretable. Empirical models depend on large, clean datasets to infer patterns. Biochemistry provides biochemical models which can be built upon. When will simulation models be ready for clinical use? Simulation models are in the world of research and basic science. There's too much uncertainty in the models to be clinically informative at this point. That's both from a technical perspective, because the models need to be big, and we just don't have a lot of the formalisms and computational tools to do it. Alex Feltus: “Marc’s stuff is probably years away from being truly translational. But I think Marc’s stuff is the stuff that's going to change everything.” 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 Brad Power: This is going to be a discussion about simulations and how they might in the future guide cancer treatment decisions. I'm pleased we have Marc Bertwhistle and Alex Feldus to lead this discussion. I got to know Alex through Pete Kane and through Bill Paseman, for whom Alex was running a hackathon. Bill Paseman has a rare kidney cancer. Alex introduced us to Marc. We're interested in learning about how simulations might help guide treatment decisions. We have identified that personalized solutions for patients are often off label, particularly for drug combinations. There are not enough randomized clinical trials that say, “this drug combination is indicated for this use, or this drug combination is inside the standard of care.” How could you have confidence in making such a prescription if you were the treating physician? How would you feel good about something that's not obvious because there's good clinical evidence to support it? At least some confidence might come from understanding simulations or models of cancer and its dynamics, and how it progresses, and how it's impacted, and putting everything together into a simulation model. Marc Birtwistle: I'm excited and nervous. I've never talked to a forum like this before. I was struggling a little bit on how to talk about the work that we do in my lab and how to present it. Hopefully it's useful and informative for everybody here. I am an associate professor here in the chemical and biomolecular engineering department at Clemson. I'm a chemical engineer by training, but I've been working in systems biology and signal transduction and cancer systems biology pretty much for my entire research career. An engineer with that sort of thinking, but also blending that with cancer biology and trying to do research. One of the big questions that the whole field is interested in is how we match anticancer drug combinations to patients. I heard Brad talk about that in a way that really resonated with me – it's really not a very well established or known thing. Some quick Google searching will give you a lot of information about what drugs could be used, or sometimes are used, for particular types of cancer, for pretty much any cancer type. I put some information here on the left for breast cancer. You can see dozens of them. Some are traditional chemotherapies, some are more targeted chemotherapies. The reason why these are used is because there is some clinical evidence that they are effective. But there's obviously still room to improve because cancer is still quite a deadly disease for many, many people. I wanted to highlight a pretty common knowledge, but combinations of two or three or sometimes even more drugs are used. Doctors use many different combinations, and it's not clear that any particular drug combination is the best. How do we actually make any traction on that problem? One of the big roadblocks in the way of that, is to get solid numbers on how many anticancer drugs there are that are FDA approved right now. It was a hard number to pinpoint. There are at least hundreds of them that are approved and maybe sometimes drugs used off label that aren't for anticancer indications, but could be used by physicians. Let's just say that you had a hundred anticancer drugs, and you wanted to figure out different three-way combinations. A little bit of math will tell you there's over 150,000 different three-way combinations. We need some way to reduce that search space to answer this question. One of the ways to do that currently is what we call targeted therapy. The idea is that if we know something about the genomics of somebody's tumor – the mutations that are driving the tumor's behavior – maybe we can directly match a drug to that mutation. There are lots and lots of examples of this that have been developed over the past several decades. One of the original ones was imatinib for a BCR-ABL fusion protein mutation. If you have a HER2 positive breast cancer, there are several drugs available, one of which is a monoclonal antibody called trastuzumab or Herceptin. If you have a melanoma and you have a particular point mutation in BRAF called BRAF V600E, there are multiple small molecule kinase inhibitors that are available, one of which is called dabrafenib. You can go on and on with the examples of targeted therapies that have been developed. They have done a good job, but there's still room for improvement. I'd like to highlight a couple of reasons why there's room for improvement, and what some of that improvement might look like. This paper shows this Kaplan-Meier survival plot (depicting survival time) if you combine two different inhibitors to treat melanoma, one is inhibiting BRAF in patients who have the BRAF mutation with the drug dabrafenib that I just showed, and then combining it with a drug called trametinib, a MEK inhibitor. These proteins essentially act in a signaling pathway. A signaling pathway is a system of proteins that work together in cells to send signals that eventually control what a cell does. Like, is it going to divide? Is it going to die? Is it going to move? Is it going to grow? Things like that. BRAF activates MEK, and MEK activates this protein ERK, which ends up doing a lot of that signal transmission in the cell to tell a cell to grow or not grow. By inhibiting these pathways, you can turn off a signal, but why would you turn off a signal and then turn off a signal that's directly downstream of it? It really doesn't make much sense genetically that a drug combination like this would be effective, but clinically it was shown to be quite effective because patients survived longer. What's really going on there? What's going on in a cell is not so simple as a linear pathway. What's been shown is that some of these BRAF inhibitors can activate a different isoform of the protein called CRAF, and then CRAF can activate the pathway in a parallel manner. There are also things like feedback loops inside signal transduction systems like engineered systems. We have negative feedback loops to help us suppress the effective noise. This RAF-MEK-ERK pathway has such negative feedback loops that help to fight the effects of something like a drug to keep that pathway on. Although genomically targeted therapies work for some people that have a mutation, it doesn't always work for everybody that has the mutation. It can also eventually fail due to development of resistance. Both of these points can be seen from any Kaplan-Meier survival plot, like this one here, or other ones that highlight the effectiveness of targeted therapies. My other point is that it doesn't always directly inform combination therapy because of this linear pathway thinking. Why would a drug combination targeting the same pathway actually be effective? There's a lot more that we need to consider to understand those sorts of things and to be able to predict them. I think targeted therapy helps us to reduce that search space, but we still need better ways. And one of the ways that we should be focusing on is really understanding more how biochemical networks drive response to therapy. One of the ways I think about this problem in my lab is to use genomics as a foundation because we know genetics really drives the behavior of most tumors. But instead run that information through some understanding of a biochemical network, and use that to better inform what kind of drugs might be useful or effective for a particular patient. In doing so, I like to talk a little bit about models, and the way that I think about models. Usually I'm talking to an audience that doesn't work with mathematical models or simulation models very much. I like to start out by talking about experimental models, two of which that are very widely used in cancer research are mouse models and cell line models. We can learn a lot about the basic biology of cancer or how particular types of cancer could be sensitive or resistant to different drugs or drug combinations by using experimental models that have some recapitulation of the eventual human cancer that we care about. One of the problems though with experimental models is that the large combinatorial drug space, if we're talking about, as I mentioned before, two- and three-, maybe even more, drug combinations, and the sheer number of anticancer drugs, we just can't do that many experiments to take a brute force approach of understanding that mapping from genetics to drug combinations. That's where this idea of simulation models comes in. It’s an experimental model that is just a more convenient way of looking at or analyzing a system that we care about. Those simulation models are in principle the same, and they're very much used across engineering. I highlighted a couple of examples here: a flight simulator – if people want to understand how changes to an aircraft might show up; if we want to try to understand how to design earthquake-proof buildings. We use simulation models to try to help us with that. Or if you want to land something on Mars, it really helps to have good simulation models, because that's really hard to test before you get there. My point is that simulation models facilitate the design choices across engineering. Maybe this is something that we can start to try to develop in the context of oncology. It wouldn't be easy, but we can think about it, and that's where my lab tries to sit much more in a basic science sense at this point. How do we actually build those models, and what should we build into those models to have it be “good”? What if we had it though? What could we do? If we had a good tumor simulation model, we could start to answer two kinds of questions in my vision: (1) given a patient we could prioritize what types of drugs might be useful, or we could even start talking about what types of dosing or scheduling might be better than others, or (2) if we're taking more from the perspective of drug development, if we're given a drug, what sort of patients should or should not respond? We could start to make more informed decisions about inclusion or exclusion from clinical trials, or given a drug we're trying to develop, what are some other drugs that might be good or not good to combine with that drug? What do we need to capture in such a model? There are lots of aspects of cancer biology that we know are important, but are just hard to get a handle on in terms of building a simulation model. There is also lots of just incomplete knowledge, and it makes it really challenging. But we know there are several important aspects that tend to drive response to therapy, and I listed four of them here. Some of the examples I highlighted before highlight the fact that we need to think about the systems that are actually driving drug responses inside of cells, not just as genetic mutations, but what is the system where this genetic mutation lives, and how does that drive that cellular decision to grow or divide or die? Most tumors aren't driven by one mutation; there are multiple mutations. I've seen estimates of around four to seven different mutations, and the fact that we have different driver mutations probably means that we need multiple drugs to hit them. We also need to consider that these are dynamic systems. Drugs in pharmacology in general are dynamic. The tumors adapt. They evolve on multiple time scales. One of the most challenging aspects of the disease that has become much more highlighted and understood in the past 10 years or so is the heterogeneity of the disease in every direction. Across every axis that you look at in cancer, there's heterogeneity. If you look across patients, not just every patient that has prostate cancer, their tumor is unique. It's not quite the same, even though they might have some shared features. If you look within one patient's tumor, all of the cells within that tumor can be different. You can have different genetic subclones within that tumor that might respond differently to drugs. And even within the same genetic subclone, there is heterogeneity due to other random processes that happen in the cells. And if you then look at that tumor more in a spatial sense, there are different microenvironmental factors, different oxygen concentrations, different immune local environments that can control drug responses. There are a lot of things that a good tumor simulation model should capture. When you're building a simulation model, there are lots of different options of formalisms and ways to go about it. Two very broad classes of models are mechanistic or empirical models. When you formulate a simulation model in a mechanistic way, inherently you have some ability to do what's called