Improving treatment strategies for patients with metastatic castrate resistant prostate cancer through personalized computational modeling
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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.
Keywords
Metastatic castrate resistant prostate cancer Bone metastasis Computational biology Genetic algorithms Heterogeneity Therapy sequence optimization Overall survivalList of abbreviations
- ADT
Androgen deprivation therapy
- AR
Androgen receptor
- GA
Genetic algorithm
- JAK/STAT
Janus kinase/Signal transducers and activators of transcription
- mCRPC
Metastatic castrate resistant prostate cancer
- NCCN
National comprehensive cancer network
- ODE
Ordinary differential equation
- PSA
Prostate serum antigen
- PTEN
Phosphatase and tensin homolog
- RANKL
Receptor activator of nuclear kappa B ligand
Notes
Acknowledgments
We would like to thank Drs. Alexander R. A. Anderson and Tom Sellers for the organization and support of the 2nd IMO workshop. This work was supported in part by the Moffitt Cancer Center and RO1CA143094
Conflict of interest
The authors disclose that they have no conflicts of interest.
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