Mathematical modeling of prostate cancer progression in response to androgen ablation therapy

Abstract

Prostate cancer progression depends in part on the complex interactions between testosterone, its active metabolite DHT, and androgen receptors. In a metastatic setting, the first line of treatment is the elimination of testosterone. However, such interventions are not curative because cancer cells evolve via multiple mechanisms to a castrate-resistant state, allowing progression to a lethal outcome. It is hypothesized that administration of antiandrogen therapy in an intermittent, as opposed to continuous, manner may bestow improved disease control with fewer treatmentrelated toxicities. The present study develops a biochemically motivated mathematical model of antiandrogen therapy that can be tested prospectively as a predictive tool. The model includes "personalized" parameters, which address the heterogeneity in the predicted course of the disease under various androgen-deprivation schedules. Model simulations are able to capture a variety of clinically observed outcomes for "average" patient data under different intermittent schedules. The model predicts that in the absence of a competitive advantage of androgen-dependent cancer cells over castration-resistant cancer cells, intermittent scheduling can lead to more rapid treatment failure as compared to continuous treatment. However, increasing a competitive advantage for hormone-sensitive cells swings the balance in favor of intermittent scheduling, delaying the acquisition of genetic or epigenetic alterations empowering androgen resistance. Given the near universal prevalence of antiandrogen treatment failure in the absence of competing mortality, such modeling has the potential of developing into a useful tool for incorporation into clinical research trials and ultimately as a prognostic tool for individual patients.castration resistance | continuous androgen ablation | intermittent androgen ablation | rapid antiandrogen cycling

A multiscale road map of cancer spheroids – incorporating experimental and mathematical modelling to understand cancer progression

“…This approach modelled the outcome of either continuous or intermittent treatment and predicted that continuous therapy leads to a disease-free survival of up to five years, whereas an observed increase in androgen-dependent cancer cells suggests that intermittent therapy promotes androgen resistance. As this in silico strategy is based on patient-derived data, it could be used as a personalised prognostic tool (Jain et al, 2011). These computational modelling approaches for breast and prostate cancer further demonstrate the usefulness of in silico techniques in simulating cancer cell behaviour.…”

“…Another biochemically based mathematical model of antiandrogen therapy dissected the heterogeneity of prostate cancer progression, and is suitable as a predictive tool when personalised parameters are incorporated (Jain et al, 2011). Here, the personalised input parameters address the dynamics of cancer growth and progression, including that of healthy prostate cells, androgen-dependent and castrate-resistant cancer cells.…”

“…These in silico techniques can predict therapy outcomes (e.g. cell-cycle and non-cell-cycle-specific therapeutics, hormone therapy and radiotherapy), surgical efficacy and cancer cell kinetics, thus being able to propose the therapeutic intervention that yields the best possible patient outcome (Jain et al, 2011;Kohandel et al, 2006;Marcu et al, 2005;Montalenti et al, 1998;Panetta, 1997;Rockne et al, 2010).…”

“…Such computational models are designed to improve our knowledge of the 'pathophysiological phenomena' of cancer, to screen chemoand radiotherapeutics, and in some cases to help us develop surgical strategies to arrest the growth of primary tumours and disease progression to secondary sites (Brown and Palmer, 2009;Kohandel et al, 2006;Montalenti et al, 1998;Panetta, 1997). The majority of previous mathematical models have focused on breast and prostate cancer, as both cancer types have the highest incidence rates in females and males, respectively (Chakrabarti et al, 2012;Jain et al, 2011;Siegel et al, 2012). However, there are a few studies that mathematically model ovarian cancer and address relevant aspects of tumour growth and angiogenesis, which indicates the efficacy of predictive techniques in hypothesis testing related to anti-cancer agents, such as cellcycle and non-cell-cycle-specific therapeutics (Brown and Palmer, 2009;Kohandel et al, 2006;Montalenti et al, 1998;Panetta, 1997).…”

Mathematical Models of Androgen Resistance in Prostate Cancer Patients under Intermittent Androgen Suppression Therapy

“…Many mathematical models have studied the dynamics of prostate cancer during ADT or IAS [11][12][13][14][15][16][17][18]. A detailed review of some of these models are presented in the recent book of Kuang et al [19].…”