Improving treatment strategies for patients with metastatic castrate resistant prostate cancer through personalized computational modeling.

Metastatic castrate resistant prostate cancer (mCRPC) is responsible for the majority of prostate cancer deaths with the median survival after diagnosis being 2 years. The metastatic lesions often arise in the skeleton, and current treatment options are primarily palliative. Using guidelines set forth by the National Comprehensive Cancer Network (NCCN), the medical oncologist has a number of choices available to treat the metastases. However, the sequence of those treatments is largely dependent on the patient history, treatment response and preferences. We posit that the utilization of personalized computational models and treatment optimization algorithms based on patient specific parameters could significantly enhance the oncologist's ability to choose an optimized sequence of available therapies to maximize overall survival. In this perspective, we used an integrated team approach involving clinicians, researchers, and mathematicians, to generate an example of how computational models and genetic algorithms can be utilized to predict the response of heterogeneous mCRPCs in bone to varying sequences of standard and targeted therapies. The refinement and evolution of these powerful models will be critical for extending the overall survival of men diagnosed with mCRPC.