Intermittent Hormone Therapy Models Analysis and Bayesian Model Comparison for Prostate Cancer

被引:10
作者
Pasetto, S. [1 ]
Enderling, H. [1 ,2 ,3 ]
Gatenby, R. A. [1 ,4 ]
Brady-Nicholls, R. [1 ]
机构
[1] H Lee Moffitt Canc & Res Inst, Dept Integrated Math Oncol, 12902 Magnolia Dr, Tampa, FL 33612 USA
[2] H Lee Moffitt Canc & Res Inst, Dept Radiat Oncol, 12902 Magnolia Dr, Tampa, FL 33612 USA
[3] H Lee Moffitt Canc Ctr & Res Inst, Dept Genitourinary Oncol, 12902 Magnolia Dr, Tampa, FL 33612 USA
[4] H Lee Moffitt Canc & Res Inst, Dept Radiol, 12902 Magnolia Dr, Tampa, FL 33612 USA
基金
美国国家卫生研究院;
关键词
Prostate cancer; Intermittent hormone therapy models; PROSPECTIVE PHASE-II; ANDROGEN SUPPRESSION; SENSITIVITY-ANALYSIS; MATHEMATICAL-MODEL; MEN; RESISTANCE; ALGORITHM; GROWTH;
D O I
10.1007/s11538-021-00953-w
中图分类号
Q [生物科学];
学科分类号
07 ; 0710 ; 09 ;
摘要
The prostate is an exocrine gland of the male reproductive system dependent on androgens (testosterone and dihydrotestosterone) for development and maintenance. First-line therapy for prostate cancer includes androgen deprivation therapy (ADT), depriving both the normal and malignant prostate cells of androgens required for proliferation and survival. A significant problem with continuous ADT at the maximum tolerable dose is the insurgence of cancer cell resistance. In recent years, intermittent ADT has been proposed as an alternative to continuous ADT, limiting toxicities and delaying time-to-progression. Several mathematical models with different biological resistance mechanisms have been considered to simulate intermittent ADT response dynamics. We present a comparison between 13 of these intermittent dynamical models and assess their ability to describe prostate-specific antigen (PSA) dynamics. The models are calibrated to longitudinal PSA data from the Canadian Prospective Phase II Trial of intermittent ADT for locally advanced prostate cancer. We perform Bayesian inference and model analysis over the models' space of parameters on- and off-treatment to determine each model's strength and weakness in describing the patient-specific PSA dynamics. Additionally, we carry out a classical Bayesian model comparison on the models' evidence to determine the models with the highest likelihood to simulate the clinically observed dynamics. Our analysis identifies several models with critical abilities to disentangle between relapsing and not relapsing patients, together with parameter intervals where the critical points' basin of attraction might be exploited for clinical purposes. Finally, within the Bayesian model comparison framework, we identify the most compelling models in the description of the clinical data.
引用
收藏
页数:36
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