Abstract
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.
Highlights
The prostate is an exocrine gland of most mammals’ male reproductive system
Since prostate cells and their malignant counterparts require androgen stimulation to grow, prostate cancer can be treated by androgen deprivation therapy (ADT), a type of hormone therapy
Extra than testing with flat/Jeffreys priors, in the numerical nested sampling approach, we explore the parameter space logarithmically to avoid divergences, and once we reach a statistically significative sample, i.e., above the Poisson noise fluctuation (∼ Npat) shaping the posterior probability distribution function (PDF), we proceed to implement the posterior as a prior for the patients analyzed in the dataset; we reiterate by implementing a recursive determination of the prior (Fig. 2b, c)
Summary
The prostate is an exocrine gland of most mammals’ male reproductive system. The normal prostate is dependent on androgens, testosterone and 5αdihydrotestosterone (DHT), for development and maintenance (Feldman and Feldman 2001). Since prostate cells and their malignant counterparts require androgen stimulation to grow, prostate cancer can be treated by androgen deprivation therapy (ADT), a type of hormone therapy. This therapy reduces androgen-dependent (AD) cancer cells by preventing their growth and inducing cellular apoptosis. Intermittent androgen deprivation (IAD) therapy, whereby treatment is cycled on and off, is often used as an alternative to ADT to delay treatment resistance. We consider models of intermittent therapy due to clinical interest and solve the inference problem using longitudinal PSA data from the Canadian Prospective Phase II Trial of IAD for locally advanced prostate cancer.
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