Development of a mathematical model that predicts the outcome of hormone therapy for prostate cancer

Hirata, Yoshito; Bruchovsky, Nicholas; Aihara, Kazuyuki

doi:10.1016/j.jtbi.2010.02.027

Cited by 114 publications

(156 citation statements)

References 33 publications

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“…Following the above consideration, we proposed a modified model that contains three phenotypes of cancer cells as illustrated in figure 8 (Hirata et al 2010). One phenotype corresponds to AD cells and the other two correspond to AI cells.…”

Section: Application To Clinical Data Analysismentioning

confidence: 99%

“…Following the developments of the fundamental models of IAS therapy, we have proposed a method to analyse real time-series data of serum PSA concentrations based on a piecewise linear model (Hirata et al 2010). To complement this study, we explain why the piecewise linear model, instead of conventional models, is necessary for characterizing actual observations of clinical PSA data.…”

Section: Introductionmentioning

confidence: 99%

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Mathematical modelling of prostate cancer growth and its application to hormone therapy

Tanaka

Hirata

Goldenberg³

et al. 2010

Phil. Trans. R. Soc. A.

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Hormone therapy in the form of androgen deprivation is a major treatment for advanced prostate cancer. However, if such therapy is overly prolonged, tumour cells may become resistant to this treatment and result in recurrent fatal disease. Long-term hormone deprivation also is associated with side effects poorly tolerated by patients. In contrast, intermittent hormone therapy with alternating on-and off-treatment periods is a possible clinical strategy to delay progression to hormone-refractory disease with the advantage of reduced side effects during the off-treatment periods. In this paper, we first overview previous studies on mathematical modelling of prostate tumour growth under intermittent hormone therapy. The model is categorized into a hybrid dynamical system because switching between on-treatment and off-treatment intervals is treated in addition to continuous dynamics of tumour growth. Next, we present an extended model of stochastic differential equations and examine how well the model is able to capture the characteristics of authentic serum prostate-specific antigen (PSA) data. We also highlight recent advances in time-series analysis and prediction of changes in serum PSA concentrations. Finally, we discuss practical issues to be considered towards establishment of mathematical model-based tailor-made medicine, which defines how to realize personalized hormone therapy for individual patients based on monitored serum PSA levels.

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Section: Application To Clinical Data Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Mathematical modelling of prostate cancer growth and its application to hormone therapy

Tanaka

Hirata

Goldenberg³

et al. 2010

Phil. Trans. R. Soc. A.

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“…[14][15][16][17][18][19] The model of Hirata et al 17 reproduces the actual quantitative changes in the tumor marker, prostate specific antigen ͑PSA͒. With this model, we were able to approximate conditions for both averting and delaying relapse, but we did not obtain the optimal scheduling of the on-and off-treatment periods of IAS.…”

Section: Introductionmentioning

confidence: 99%

“…Accordingly, in this paper, we consider the design and testing of an ideal program for the administration of IAS in the treatment of prostate cancer. The objective is to solve the model of Hirata et al 17 analytically and use the analytical solutions to derive conditions for checking the rate of progression to androgen-independent relapse. By efficiently structuring the ratio of on-and off-treatments, we are able to produce a design principle that leads to the most favorable treatment outcome.…”

Section: Introductionmentioning

confidence: 99%

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Hybrid optimal scheduling for intermittent androgen suppression of prostate cancer

Hirata¹,

Bernardo²,

Bruchovsky³

et al. 2010

Chaos: An Interdisciplinary Journal of Nonlinear Science

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We propose a method for achieving an optimal protocol of intermittent androgen suppression for the treatment of prostate cancer. Since the model that reproduces the dynamical behavior of the surrogate tumor marker, prostate specific antigen, is piecewise linear, we can obtain an analytical solution for the model. Based on this, we derive conditions for either stopping or delaying recurrent disease. The solution also provides a design principle for the most favorable schedule of treatment that minimizes the rate of expansion of the malignant cell population. © 2010 American Institute of Physics. ͓doi:10.1063/1.3526968͔ Drug resistance is a critical factor in limiting the success of treating various types of malignancy. In the case of prostate cancer, hormonal therapy results in a significant response in about 95% of patients at the initiation of treatment. Despite impressive responses, such treatment, when applied on a continuous basis is not curative and eventually culminates in life-threatening androgenindependent disease. In contrast, intermittent hormone therapy with alternating on-and off-treatment periods is a possible clinical strategy to delay progression to a hormone refractory state. Despite extensive clinical experience with this approach, little or no information is available to guide decisions concerning the ideal protocol for any given patient. In this paper, we employ the mathematical model recently described by Hirata et al.17 to design and validate a protocol that optimizes the on-and off-scheduling of intermittent androgen suppression in the treatment of prostate cancer. We show that an optimal schedule can be obtained in terms of an on-off feedback control action that aims to minimize the expanding rate of tumor growth. Our formula is based on a retrospective analysis, and accordingly, its utility will have to be confirmed in a prospective clinical trial.

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Early Diagnosis of Complex Diseases by Molecular Biomarkers, Network Biomarkers, and Dynamical Network Biomarkers

Liu

Wang

Aihara

et al. 2013

Medicinal Research Reviews

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254

157

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Many studies have been carried out for early diagnosis of complex diseases by finding accurate and robust biomarkers specific to respective diseases. In particular, recent rapid advance of high-throughput technologies provides unprecedented rich information to characterize various disease genotypes and phenotypes in a global and also dynamical manner, which significantly accelerates the study of biomarkers from both theoretical and clinical perspectives. Traditionally, molecular biomarkers that distinguish disease samples from normal samples are widely adopted in clinical practices due to their ease of data measurement. However, many of them suffer from low coverage and high false-positive rates or high false-negative rates, which seriously limit their further clinical applications. To overcome those difficulties, network biomarkers (or module biomarkers) attract much attention and also achieve better performance because a network (or subnetwork) is considered to be a more robust form to characterize diseases than individual molecules. But, both molecular biomarkers and network biomarkers mainly distinguish disease samples from normal samples, and they generally cannot ensure to identify predisease samples due to their static nature, thereby lacking ability to early diagnosis. Based on nonlinear dynamical theory and complex network theory, a new concept of dynamical network biomarkers (DNBs, or a dynamical network of biomarkers) has been developed, which is different from traditional static approaches, and the DNB is able to distinguish a predisease state from normal and disease states by even a small number of samples, and therefore has great potential to achieve "real" early diagnosis of complex diseases. In this paper, we comprehensively review the recent advances and developments on molecular biomarkers, network biomarkers, and DNBs in particular, focusing on the biomarkers for early diagnosis of complex diseases considering a small number of samples and high-throughput data (or big data). Detailed comparisons of various types of biomarkers as well as their applications are also discussed.

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Development of a mathematical model that predicts the outcome of hormone therapy for prostate cancer

Cited by 114 publications

References 33 publications

Mathematical modelling of prostate cancer growth and its application to hormone therapy

Mathematical modelling of prostate cancer growth and its application to hormone therapy

Hybrid optimal scheduling for intermittent androgen suppression of prostate cancer

Early Diagnosis of Complex Diseases by Molecular Biomarkers, Network Biomarkers, and Dynamical Network Biomarkers

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