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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.

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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

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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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Authors and Affiliations

  1. Department of Integrated Mathematical Oncology, SRB, H. Lee Moffitt Cancer Center and Research Institute, 12902 Magnolia Dr., Tampa, FL, 33612, USA

    Jill Gallaher, Arturo Araujo, Jacob G. Scott & David Basanta

  2. Tumor Biology Department, SRB, H. Lee Moffitt Cancer Center and Research Institute, 12902 Magnolia Dr., Tampa, FL, 33612, USA

    Leah M. Cook & Conor C. Lynch

  3. Department of Genitourinary Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA

    Shilpa Gupta, Jasreman Dhillon & Julio Pow-Sang

  4. Department of Cancer Epidemiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA

    Jong Y. Park

  5. Wolfson Center for Mathematical Biology, Mathematical Institute, University of Oxford, Oxford, UK

    Jacob G. Scott

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  1. Jill Gallaher
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  2. Leah M. Cook
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  3. Shilpa Gupta
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  7. Jacob G. Scott
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  9. David Basanta
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  10. Conor C. Lynch
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Correspondence to David Basanta or Conor C. Lynch.

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Gallaher, J., Cook, L.M., Gupta, S. et al. Improving treatment strategies for patients with metastatic castrate resistant prostate cancer through personalized computational modeling. Clin Exp Metastasis 31, 991–999 (2014). https://doi.org/10.1007/s10585-014-9674-1

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  • DOI: https://doi.org/10.1007/s10585-014-9674-1

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