Using mathematical biology to rationalise and personalise cancer treatments

A couple of years ago a few of us at Moffitt took part in what could be described as a series of synchronised and competitive hackathons. Basically we were put into different groups and asked to come with a question and a mathematical model that could be used to personalise treatments in the clinic. My group? We went on to see how mathematical models could be used to treat prostate cancer patients (2nd most common cancer among males) with metastases in the bone (which happens in 90% of the cases of patients that die of the disease).

figureWe recently published our ideas in a paper in Clinical and Experimental Metastases (online here and soon in bioarXiv). There we describe a mathematical model that captures the interactions between a heterogeneous metastasis (in terms of three possible mutations) and the bone microenvironment (see the figure above). The features of the tumour cells can be parameterised with patient-specific information whereas the rest of the parameters of this model were taken from literature.

What can you do with a model like this? I guess a lot of things but what we did is the following. We created a genetic algorithm, a tool that can optimise treatments, and we used it to see if we could optimise a sequent of treatments (including both conventional as well as new targeted treatments) for specific prostate cancer patients with metastases to the bone.

We are currently working on the validation of this approach using retrospective data but I am happy to see that the ideas are out there for any other team of scientists to use them as well. Our team included pathologists, oncologists, radiotherapy specialists, surgeons, experimental biologists and, of course, mathematical and computational biologists. It does take a diversity of talents to make these type of approaches work but it is worth the effort.

diagramWill this be better than the current approach used in the clinics and cancer hospitals? Our current results suggest that this is possible. Moreover, we think that mathematical models (that captures the biology of the disease better than current standards) can be the glue that joins biological understanding with clinical data and that alone should help move medicine from craft to science.




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