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Updated: Apr 25, 2026
A Bioluminescent and Fluorescent Orthotopic Syngeneic Murine Model of Androgen-dependent and Castration-resistant Prostate Cancer
Published on: March 6, 2018
Improving treatment strategies for patients with metastatic castrate resistant prostate cancer through personalized
Jill Gallaher1, Leah M Cook, Shilpa Gupta
1Department of Integrated Mathematical Oncology, SRB, H. Lee Moffitt Cancer Center and Research Institute, 12902 Magnolia Dr., Tampa, FL, 33612, USA.
Abstract:
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.
Insights
Personalized computational models can optimize prostate cancer treatment sequences. This approach aims to improve survival for men with metastatic castrate-resistant prostate cancer (mCRPC).
Area of Science:
- Oncology
- Computational Biology
- Bioinformatics
Background:
- Metastatic castrate-resistant prostate cancer (mCRPC) leads to most prostate cancer deaths, with a median survival of two years.
- Skeletal metastases are common in mCRPC, and current treatments are largely palliative.
- Treatment sequencing for mCRPC is complex, depending on patient history and response.
Purpose of the Study:
- To explore the use of personalized computational models and treatment optimization algorithms for mCRPC.
- To enhance oncologists' ability to select optimal therapy sequences for maximizing patient survival.
- To demonstrate how computational approaches can predict treatment response in heterogeneous mCRPC bone metastases.
Main Methods:
- An integrated team approach involving clinicians, researchers, and mathematicians.
- Development of personalized computational models incorporating patient-specific parameters.
- Utilization of genetic algorithms to predict responses to various treatment sequences.
Main Results:
- An example demonstrating the application of computational models and genetic algorithms was generated.
- The models predict the response of heterogeneous mCRPC bone metastases to standard and targeted therapies.
- The study highlights the potential for optimizing treatment sequences.
Conclusions:
- Personalized computational models and treatment optimization algorithms offer a promising strategy for mCRPC management.
- These models can aid oncologists in selecting optimized therapy sequences to improve patient survival.
- Further refinement of these computational tools is crucial for extending survival in mCRPC patients.
