“I don’t think we know how to build these (mechanistic simulation) models (that would predict drug response or resistance for individual patients). There’s too much uncertainty in the models themselves 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.” – Marc Birtwistle
Marc Birtwistle, PhD, Associate Professor of Chemical and Biomolecular Engineering and Bioengineering, and Alex Feltus, PhD, Professor, Department of Genetics and Biochemistry, Clemson, led a discussion on “Basic Research into Simulation Models that Could Eventually Guide Clinical Decisions”.
What are the complex decisions faced by advanced cancer patients that simulation models might help?
There is much room for improvement in making treatment decisions for advanced cancer patients. For example, although genomically-targeted therapies work for some people that have a mutation, it doesn’t always work for everybody that has the mutation. A treatment can also eventually fail due to development of resistance. Personalized drug combinations can offer better outcomes, but there is no evidence for most of the many potential combinations from randomized clinical trials. If we had a good tumor simulation model, we could prioritize what types of drugs might be useful for a given patient, or we could even start talking about what types of dosing or scheduling might be better than others.
What are the challenges in developing simulation models to describe cancer dynamics?
- 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.”