Clinical & Experimental Metastasis

, Volume 31, Issue 8, pp 991–999 | Cite as

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

  • Jill Gallaher
  • Leah M. Cook
  • Shilpa Gupta
  • Arturo Araujo
  • Jasreman Dhillon
  • Jong Y. Park
  • Jacob G. Scott
  • Julio Pow-Sang
  • David Basanta
  • Conor C. Lynch


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.


Metastatic castrate resistant prostate cancer Bone metastasis Computational biology Genetic algorithms Heterogeneity Therapy sequence optimization Overall survival 

List of abbreviations


Androgen deprivation therapy


Androgen receptor


Genetic algorithm


Janus kinase/Signal transducers and activators of transcription


Metastatic castrate resistant prostate cancer


National comprehensive cancer network


Ordinary differential equation


Prostate serum antigen


Phosphatase and tensin homolog


Receptor activator of nuclear kappa B ligand



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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Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  • Jill Gallaher
    • 1
  • Leah M. Cook
    • 2
  • Shilpa Gupta
    • 3
  • Arturo Araujo
    • 1
  • Jasreman Dhillon
    • 3
  • Jong Y. Park
    • 4
  • Jacob G. Scott
    • 1
    • 5
  • Julio Pow-Sang
    • 3
  • David Basanta
    • 1
  • Conor C. Lynch
    • 2
  1. 1.Department of Integrated Mathematical Oncology, SRBH. Lee Moffitt Cancer Center and Research InstituteTampaUSA
  2. 2.Tumor Biology Department, SRBH. Lee Moffitt Cancer Center and Research InstituteTampaUSA
  3. 3.Department of Genitourinary OncologyH. Lee Moffitt Cancer Center and Research InstituteTampaUSA
  4. 4.Department of Cancer EpidemiologyH. Lee Moffitt Cancer Center and Research InstituteTampaUSA
  5. 5.Wolfson Center for Mathematical BiologyMathematical Institute, University of OxfordOxfordUK

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