Based at the Moffitt Cancer Center, Florida, Cancer Ecology is a small research group led by David Basanta. We are mathematical modellers who work with biologists and clinicians, trying to understand the ecology of tumors and the evolutionary dynamics of cancer progression and resistance to treatment.

Adaptive therapy in bone metastatic prostate cancer - a window through agent-based models

Adaptive therapy in bone metastatic prostate cancer - a window through agent-based models

Adaptive therapies in cancer, pioneered by my colleague at Moffitt’s IMO Bob Gatenby, work under the assumption that tumors already contain cells that are resistant to our clinical treatments. So rather than using those treatments in the conventional way, in a continuous fashion, it is better to alternate treatment and treatment vacations in an adaptive fashion, to ensure that there are always enough treatment-sensitive cells to keep the resistant cells in check.

Mathematical models, specially agent-based ones, are good theoretical frameworks in which to study adaptive therapies and, in this blog I have presented some ideas related to them previously [here, here, here and here]. These (and others) models tend to ignore the ecosystem that cancers inhabit which, in my view, is likely to affect if adaptive therapies work and how.

Fortunately we also have an agent-based model of the bone ecosystem in the context of prostate cancer (described here and here) that Arturo Araujo did with us some time ago.

This bone metastatic agent-based model was modified so to include two types of tumor cells: TGF-Beta producing (which also requires that cytokine for survival) and TGF-Beta neutral. Cells that can use TGF-Beta proliferate faster (as they can use TGF-Beta) but are sensitive to a treatment that targets them and spares the TGF-Beta neutral cells. How does this tumor look like when a) we don’t treat it, b) we use continuous treatment or c) we use an adaptive therapy?

Same tumor but 3 ways to go about it when it comes to treatment. 3 different results. The adaptive therapy tried here (right panel) did not work but it certainly did a better job than the continuous application of the treatment. Could it have performed better? There are no clear guidelines on how to define the window of treatment application in adaptive therapies. In this particular instance we are working to keep the volume between 20 and 30K cells (we are talking about a small section of what in reality would be a much larger tumor). Would different windows yield different results?

Same tumor but 3 ways to go about it when it comes to treatment. 3 different results. The adaptive therapy tried here (right panel) did not work but it certainly did a better job than the continuous application of the treatment. Could it have performed better? There are no clear guidelines on how to define the window of treatment application in adaptive therapies. In this particular instance we are working to keep the volume between 20 and 30K cells (we are talking about a small section of what in reality would be a much larger tumor). Would different windows yield different results?

CSBC-abstract figure.002.jpeg

Lessons? this is very preliminary but clearly the impact of changing the window of control for the adaptive therapy. A key finding is that, under these very idealized conditions, adaptive therapies can control the tumor for an indefinite amount of time. The example that did the best (59-60K cells, bottom-right panel) is also the narrowest window and the one closes to the simulation’s carrying capacity so whether this Is generalizable will require further investigation.

PSON pilot grant for Etienne

PSON pilot grant for Etienne