Computational Model Can Predict Therapy Outcomes in Prostate Cancer With Bone Metastasis

Key Points

  • A computational model that simulates bone metastasis of prostate cancer has the potential to rapidly assess experimental therapy outcomes and determine personalized medicine for patients with prostate cancer.
  • The application of clinically used bisphosphonates and anti-RANKL therapies to the computational model found that the anti-RANKL therapy fared better than bisphosphanates.
  • The model predicted that improving the efficacy of anti-RANKL delivery to the prostate cancer–bone microenvironment might yield better outcomes.

Active prostate cancer cells in the bone environment can disrupt the bone remodeling process, promoting a “vicious cycle” of extensive bone destruction and formation that yields nutrients allowing prostate cancer cells to grow. To simulate this complex biologic process, researchers have integrated biologic and computational approaches to generate a hybrid cellular automata model of normal bone matrix homeostasis and the prostate cancer bone microenvironment. The computational model has the potential to predict the impact of potential therapies on cancer cells and normal cells of the bone and individualize treatment for patients with prostate cancer. The study by Araujo et al is published in Cancer Research.

To develop the computational model, the researchers created simulations of different cell types involved in bone metastasis of prostate cancer, including two types of bone cells called 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 and RANKL.

Study Findings

When the researchers introduced a single metastatic prostate cancer cell to the model, it was able to simulate bone metastasis 7 out of 25 times, accurately creating the vicious cycle. In addition to developing the computational model, the researchers grew prostate cancer cells that metastasize to bone in mice. They found that the tumor growth rate predicted by the computational model was comparable to the tumor growth rate in mice, which validated the model simulations.

Predicting Treatment Outcomes

To test if the computational model could predict treatment outcomes, the researchers applied two standard-of-care treatments: bisphosphonates and an anti-RANKL therapy. Anti-RANKL therapy was found to fare better than bisphosphonates. “At 100% efficacy, bisphosphonates inhibited cancer progression while, in contrast to clinical observations in humans, anti-receptor activator of nuclear kappa B ligand (anti-RANKL) therapy completely eradicated the metastases in the model,” wrote the researchers. “Lowering the efficacy of the anti-RANKL therapy yielded clinically similar results suggesting that better targeting or dosing could improve patient survival…We have generated a computational model framework that can be tailored for the rapid assessment of putative therapies and the delivery of precision medicine to patients with prostate to bone metastases,” concluded the researchers.

“By integrating mathematics with robust biological data, we are beginning to develop powerful tools that allow us to rapidly assess how factors contribute to prostate cancer progression in bone,” Arturo Araujo, PhD, postdoctoral fellow in the Department of Integrated Mathematical Oncology at the Moffitt Cancer Center in Tampa, Florida, said in a statement. “Ultimately, we feel that the ability to customize these models based on inputs from each patient’s cancer biopsy will help medical oncologists determine the best treatment strategies, so that significant improvements in survival and quality of life can be made.”

Dr. Araujo and Conor C. Lynch, PhD, of H. Lee Moffitt Cancer Center, are the corresponding authors for the Cancer Research article.

The study was funded with a grant from the Department of Defense New Idea Award and the National Cancer Institute. The study authors report no conflicts of interest.

The content in this post has not been reviewed by the American Society of Clinical Oncology, Inc. (ASCO®) and does not necessarily reflect the ideas and opinions of ASCO®.