Team Science and prostate cancer

Cancer researchers often, at least these days, talk about Team Science as our best hope to understand and treat cancer. I agree. But team science is easier to explain than to do: it involves bridging scientific communities that rarely speak to each other.

If you look at the figure accompanying this post (and beautifully sketched by Dr. Arturo Araujo) you will find the way we are trying to address this problem at Moffitt. To no one’s surprise it involves using mathematical oncology as the glue between our understanding of the biology of cancer and the clinical data from the hospital. It means that we can try to understand the complex mechanisms that drive cancer, personalise them with patient data, and help clinicians come with better ways to use existent but also new treatments.

Today our group received good news in the shape of a pilot grant from the Moffitt Cancer Center to explore these ideas in the context of prostate cancer. Together with the lab of Dr. Conor Lynch and the clinical team of Dr. Julio Powsang (as well as many other talented experimental, mathematical and clinical researchers) we will start utilising the abundant clinical data (generously donated by) from Moffitt’s patients to parameterise, test and improve a mathematical model that will help Dr. Powsang and colleagues treat prostate cancer patients.

These are exciting times to be in cancer research. Researchers have gathered experimental data and hypotheses for decades but I believe that integrated and multiscale mathematical models will catalyse discoveries in ways that we have not seen before.

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Evolutionary game theory without interactions

An evolutionary game without interactions from Artem Kaznatcheev. Is a game without interactions still a game?

Theory, Evolution, and Games Group

When I am working on evolutionary game theory, I usually treat the models I build as heuristics to guide intuitions and push the imagination. But working on something as practical as cancer, and being in a department with many physics-trained colleagues puts pressure on me to think of moving more towards insilications or abductions. Now, Philip Gerlee and Philipp Altrock are even pushing me in that direction with their post on TheEGG. So this entry might seem a bit uncharacteristic, I will describe an experiment — at least as a theorist like me imagines them.

Consider the following idealized protocol that is loosely inspired by Archetti et al. (2015) and the E. coli Long-term evolution experiment (Lenski et al., 1991; Wiser et al., 2013; Ribeck & Lenski, 2014). We will (E1) take a new petri dish or plate; (E2) fill it with a fixed mix of nutritional medium…

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Making model assumptions clear

This week I am in Columbus, Ohio, in a meeting organised by Sandy Anderson and Trevor Gram on tumour heterogeneity. It’s always a pleasure to leave Tampa in the middle of the winter to fly north so I must confess it has been a productive week.

Initially I was planning to just attend their meeting [link]. But unfortunately for the attendees I was asked to give a talk so I took this opportunity to talk about three different projects.

1. The first one is a theoretical study of the role of hard edges and boundaries in tumours and how they change the evolutionary dynamics of the tumour. Artem Kaznatcheev has blogged extensively about this [link[link][link]. The gist is that edges select for different phenotypes than the core or the growing boundary.
2. The second one is what is known as Team Science. Together with an oncologist, a pathologist, a radiation oncologist, a GU surgeon, an epidemiologist, experimental biologists and mathematical modellers, we are trying to change how prostate cancer patients with bone metastases are treated. The idea is that if we could measure heterogeneity in the patients tumour we could use a mathematical model to optimise their treatment. We have published the approach recently [link] so you can find more online.
3. The third one is an agent-based model of prostate cancer in bone metastases. Is a more complex model than the one mentioned before. The idea is less to guide clinical treatment and more to try to understand biology. Knowing how the tumour grows in the bone will allow us to find how to stop it. The idea then is to try to capture all the key cell types and microenvironment features and see what tumour cells do with them. We have done a lot of new work with this model but a description of it can be found here [link].

It this last model that draw the most attention from the biologists. Many biologists do not mind how clever the mathematical model may look but do care about the assumptions we make. This is because it’s easy to find papers in favour or against any assumption. So making these assumptions clear during the presentation is important. Without that many biologists will not trust us. I will argue though, that cancer biologists are working with models as well and they should do a better job of explaining the assumptions and limitations of their models. They do a good job of showing where these models excel. But although showing assumptions in an in vivo model is harder is also important.