Model predicts therapy outcomes for prostate cancer with bone metastasis
A new computational model that simulates bone metastasis of prostate cancer has the potential to rapidly assess experimental therapy outcomes and help develop personalized medicine for patients with this disease, according to new data.
"Bone remodeling is a balanced and extremely well-regulated process that controls the health of our bones and the levels of circulating calcium," said Leah M. Cook, PhD, postdoctoral fellow at the Moffitt Cancer Center in Tampa, Florida. "Active prostate cancer cells in the bone environment can speak the same language of the bone remodeling cells, and disrupt the delicate bone remodeling process. They promote extensive bone destruction and formation that in turn yields nutrients, allowing the prostate cancer cells to grow, thus creating a vicious cycle."
"The mathematical model we created simulates this vicious cycle, and allows us to predict the impact of potential therapies on cancer cells and normal cells of the bone," said Arturo Araujo, PhD, postdoctoral fellow at the Moffitt Cancer Center. "Unlike biological models, we can freeze the mathematical model at any time point in order to explore what each cell is doing at that particular point in time."
To create the computational model, which they call hybrid cellular automata, Araujo, Cook, and colleagues created simulations of different cell types involved in bone metastasis of prostate cancer, including two types of bone cells (osteoclasts and osteoblasts) and prostate cancer cells. They then created algorithms to simulate the interactions of these cells among themselves and with other bone metastasis-related factors in the microenvironment, including the proteins TGF-beta, RANKL, and other bone-derived factors.
The researchers found that when they introduced a single metastatic prostate cancer cell to the model, it was able to simulate bone metastasis seven out of 25 times, accurately recreating the vicious cycle. This phenomenon is difficult to reproduce using preclinical animal models, which is critical in determining the best time to apply therapies in order to obtain maximum efficiency, explained Araujo.
Further, the fact that the model failed to generate a bone lesion 18 out of 25 times reflects reality, where not every metastatic cancer cell that invades bone in prostate cancer patients succeeds in forming a viable lesion, he added.
Their work was published in Cancer Research (2014; 10.1158/0008-5472.CAN-13-2652).
In parallel to developing the computational model, the researchers grew prostate cancer cells that metastasize to bone in mice and found that the tumor growth rate predicted by the computational model was comparable to the tumor growth rate in mice, thus validating their simulations.
To test if the model could predict treatment outcomes, they applied two standard-of-care treatments, bisphosphonates and an anti-RANKL therapy, and found that the anti-RANKL therapy fared better than bisphosphonates, which is what is seen in prostate cancer patients with bone metastasis treated with these therapies. The model predicted that improving the efficacy of anti-RANKL delivery to the prostate cancer-bone microenvironment might yield better outcomes.