Our knowledge of the mechanisms driving the progression of prostate cancer is growing. Although several new therapies that target both the cancer cells and the supporting microenvironment and are likely to increase overall survival rates for men with mCRPC, new challenges are also emerging, particularly within the context of tumor heterogeneity.
Heterogeneity is a key aspect of cancer evolution and is a clinical reality in many cancers, including prostate cancer.124-126 Greater heterogeneity facilitates the evolution of the treatment resistance of cancer but also gives the cancer a number of phenotypic strategies that allow for growth in select microenvironments (eg, bone).
Emerging studies suggest that most patients would be best served by therapies tailored toward cancer cells harboring common aberrations as well as by therapies geared toward smaller subpopulations who could potentially become the dominant-resistant population.127
The therapies described herein constitute new ways in which to expand the number of potential options for the treatment of heterogeneous bone metastatic CRPCs. However, a challenge emerging with the advent of these therapies is how to rationally design a treatment strategy for individual patients. Current guidelines from the National Comprehensive Cancer Network provide recommendations for applying the sequence of existing therapies to patients with mCRPC based on individual patient parameters.
However, some studies suggest that altering the sequence or the combination of existing therapies can have a profound impact on overall survival rates.128
To circumvent costly and time-consuming clinical trials assessing the combination and sequence alterations of a new line of targeted therapies currently in clinical trials, alternative approaches are required. In this regard, integrating computational models and genetic algorithms with individual patient-derived biological data might lead to the rapid optimization of therapy choice and sequence. In the preclinical setting, the power of this integrated approach has been demonstrated.
Recent studies have discovered how appropriate drug combinations guided by com putational models could minimize prostate cancer progression in vivo.129 Therefore, the refinement and validation of these approaches may assist in overcoming the challenges posed by cancer heterogeneity.
Metastatic castration-resistant prostate cancer is an incurable disease, but the advent of new therapies, combined with an enhanced understanding of the underlying biology, suggests that significant improvement in overall survival is within reach.
An increase in the number of available treatment options will be challenging from a clinical perspective with regard to patient stratification and in selecting the optimal therapy sequence, combination, or both.
However, integrating computational models and genetic algorithms based on individual patient data may help overcome this challenge and allow for the delivery of individualized treatment for patients with this disease.
Jeremy S. Frieling, David Basanta, PhD, and Conor C. Lynch, PhD
From the Departments of Tumor Biology (JSF, CCL) and Integrated Mathematical Oncology (DB), H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida.
Submitted August 6, 2014; accepted September 11, 2014.
Address correspondence to Conor C. Lynch, PhD, Department of Tumor Biology, Moffitt Cancer Center, 12902 Magnolia Drive, SRB-3, Tampa, FL 33612. E-mail: [email protected]
No significant relationships exist between the authors and the companies/organizations whose products or services may be referenced in this article.
This work was supported in part by funding support that Dr Lynch received from the National Cancer Institute (RO1CA143094).
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