Dynamics of prostate cancer stem cells with diffusion and organism response

A hybrid spatiotemporal model of PCa dynamics and insights into optimal therapeutic strategies

Using a hybrid cellular automaton with stochastic elements, we investigate the effectiveness of multiple drug therapies on prostate cancer (PCa) growth. The ability of Androgen Deprivation Therapy to reduce PCa growth represents a milestone in prostate cancer treatment, nonetheless most patients eventually become refractory and develop castration-resistant prostate cancer. In recent years, a "second generation" drug called enzalutamide has been used to treat advanced PCa, or patients already exposed to chemotherapy that stopped responding to it. However, tumour resistance to enzalutamide is not well understood, and in this context, preclinical models and in silico experiments (numerical simulations) are key to understanding the mechanisms of resistance and to assessing therapeutic settings that may delay or prevent the onset of resistance. In our mathematical system, we incorporate cell phenotype switching to model the development of increased drug resistance, and consider the effect of the micro-environment dynamics on necrosis and apoptosis of the tumour cells. The therapeutic strategies that we explore include using a single drug (enzalutamide), and drug combinations (enzalutamide and everolimus or cabazitaxel) with different treatment schedules. Our results highlight the effectiveness of alternating therapies, especially alternating enzalutamide and cabazitaxel over a year, and a comparison is made with data taken from TRAMP mice to verify our findings.

Neuroendocrine Transdifferentiation in Human Prostate Cancer Cells: An Integrated Approach

Prostate cancer is highly sensitive to hormone therapy because androgens are essential for prostate cancer cell growth. However, with the nearly invariable progression of this disease to androgen independence, endocrine therapy ultimately fails to control prostate cancer in most patients. Androgen-independent acquisition may involve neuroendocrine transdifferentiation, but there is little knowledge about this process, which is presently controversial. In this study, we investigated this question in a novel model of human androgen-dependent LNCaP cells cultured for long periods in hormone-deprived conditions. Strikingly, characterization of the neuroendocrine phenotype by transcriptomic, metabolomic, and other statistically integrated analyses showed how hormone-deprived LNCaP cells could transdifferentiate to a nonmalignantneuroendocrine phenotype. Notably, conditioned media from neuroendocrine-like cells affected LNCaP cell proliferation. Predictive in silico models illustrated how after an initial period, when LNCaP cell survival was compromised by an arising population of neuroendocrine-like cells, a sudden trend reversal occurred in which the neuroendocrine-like cells functioned to sustain the remaining androgen-dependent LNCaP cells. Our findings provide direct biologic and molecular support for the concept that neuroendocrine transdifferentiation in prostate cancer cell populations influences the progression to androgen independence.