scholarly journals Mathematical analysis and simulation study of a phase-field model of prostate cancer growth with chemotherapy and antiangiogenic therapy effects

2020 ◽  
Vol 30 (07) ◽  
pp. 1253-1295 ◽  
Author(s):  
Pierluigi Colli ◽  
Hector Gomez ◽  
Guillermo Lorenzo ◽  
Gabriela Marinoschi ◽  
Alessandro Reali ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Cell Death ◽  
Tumor Growth ◽  
Phase Field ◽  
Clinical Studies ◽  
Advanced Prostate Cancer ◽  
Antiangiogenic Therapy ◽  
Specific Antigen ◽  
Cytotoxic Chemotherapy ◽  
Cancer Growth

Chemotherapy is a common treatment for advanced prostate cancer. The standard approach relies on cytotoxic drugs, which aim at inhibiting proliferation and promoting cell death. Advanced prostatic tumors are known to rely on angiogenesis, i.e. the growth of local microvasculature via chemical signaling produced by the tumor. Thus, several clinical studies have been investigating antiangiogenic therapy for advanced prostate cancer, either as monotherapy or in combination with standard cytotoxic protocols. However, the complex genetic alterations that originate and sustain prostate cancer growth complicate the selection of the best chemotherapeutic approach for each patient’s tumor. Here, we present a mathematical model of prostate cancer growth and chemotherapy that may enable physicians to test and design personalized chemotherapeutic protocols in silico. We use the phase-field method to describe tumor growth, which we assume to be driven by a generic nutrient following reaction–diffusion dynamics. Tumor proliferation and apoptosis (i.e. programmed cell death) can be parameterized with experimentally-determined values. Cytotoxic chemotherapy is included as a term downregulating tumor net proliferation, while antiangiogenic therapy is modeled as a reduction in intratumoral nutrient supply. An additional equation couples the tumor phase field with the production of prostate-specific antigen, which is a prostate cancer biomarker that is extensively used in the clinical management of the disease. We prove the well posedness of our model and we run a series of representative simulations leveraging an isogeometric method to explore untreated tumor growth as well as the effects of cytotoxic chemotherapy and antiangiogenic therapy, both alone and combined. Our simulations show that our model captures the growth morphologies of prostate cancer as well as common outcomes of cytotoxic and antiangiogenic mono therapy and combined therapy. Additionally, our model also reproduces the usual temporal trends in tumor volume and prostate-specific antigen evolution observed in experimental and clinical studies.

scholarly journals Tissue-scale, personalized modeling and simulation of prostate cancer growth

2016 ◽  
Vol 113 (48) ◽  
pp. E7663-E7671 ◽  
Author(s):  
Guillermo Lorenzo ◽  
Michael A. Scott ◽  
Kevin Tew ◽  
Thomas J. R. Hughes ◽  
Yongjie Jessica Zhang ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Tumor Growth ◽  
Modeling And Simulation ◽  
Specific Antigen ◽  
Growth Patterns ◽  
Diffusion Reaction ◽  
Patient Specific ◽  
Cancer Growth ◽  
Major Health Problem ◽  
Computed Tomography Images

Recently, mathematical modeling and simulation of diseases and their treatments have enabled the prediction of clinical outcomes and the design of optimal therapies on a personalized (i.e., patient-specific) basis. This new trend in medical research has been termed “predictive medicine.” Prostate cancer (PCa) is a major health problem and an ideal candidate to explore tissue-scale, personalized modeling of cancer growth for two main reasons: First, it is a small organ, and, second, tumor growth can be estimated by measuring serum prostate-specific antigen (PSA, a PCa biomarker in blood), which may enable in vivo validation. In this paper, we present a simple continuous model that reproduces the growth patterns of PCa. We use the phase-field method to account for the transformation of healthy cells to cancer cells and use diffusion−reaction equations to compute nutrient consumption and PSA production. To accurately and efficiently compute tumor growth, our simulations leverage isogeometric analysis (IGA). Our model is shown to reproduce a known shape instability from a spheroidal pattern to fingered growth. Results of our computations indicate that such shift is a tumor response to escape starvation, hypoxia, and, eventually, necrosis. Thus, branching enables the tumor to minimize the distance from inner cells to external nutrients, contributing to cancer survival and further development. We have also used our model to perform tissue-scale, personalized simulation of a PCa patient, based on prostatic anatomy extracted from computed tomography images. This simulation shows tumor progression similar to that seen in clinical practice.

scholarly journals Experience with degarelix in the treatment of prostate cancer

2012 ◽  
Vol 5 (1) ◽  
pp. 11-24 ◽  
Author(s):  
Neal D. Shore
Keyword(s):  
Prostate Cancer ◽  
Gnrh Agonist ◽  
Clinical Studies ◽  
Advanced Prostate Cancer ◽  
Gnrh Antagonist ◽  
Serum Alkaline Phosphatase ◽  
Specific Antigen ◽  
Progression Free Survival ◽  
Similar Efficacy ◽  
First Line

Degarelix is a gonadotrophin-releasing hormone (GnRH) antagonist for the first-line treatment of androgen-dependent advanced prostate cancer. It has a direct mechanism of action that blocks the action of GnRH on the pituitary with no initial surge in gonadotrophin or testosterone levels. Degarelix is the most extensively studied and widely available GnRH antagonist worldwide. Clinical studies have demonstrated similar efficacy to the GnRH agonist leuprolide in achieving testosterone suppression in patients with prostate cancer. However, degarelix produces a faster suppression of testosterone and prostate-specific antigen (PSA), with no testosterone surge or microsurges, thus preventing the risk of clinical flare in advanced disease. Clinical trials have demonstrated that degarelix can offer improved disease control when compared with a GnRH agonist in terms of superior PSA progression-free survival (suggesting that degarelix likely delays progression to castration-resistant disease), and a more significant impact on bone serum alkaline phosphatase and follicle-stimulating hormone. Degarelix is generally well tolerated, with no reports of systemic allergic reactions in any clinical studies. In conclusion, degarelix offers clinicians a rational first-line hormonal monotherapy option for the management of advanced prostate cancer.

scholarly journals Optimal control of cytotoxic and antiangiogenic therapies on prostate cancer growth

2021 ◽  
pp. 1-50
Author(s):  
Pierluigi Colli ◽  
Hector Gomez ◽  
Guillermo Lorenzo ◽  
Gabriela Marinoschi ◽  
Alessandro Reali ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Optimal Control ◽  
Combined Therapy ◽  
Antiangiogenic Therapy ◽  
Cytotoxic Chemotherapy ◽  
Cancer Growth ◽  
Antiangiogenic Therapies ◽  
Control Framework ◽  
Optimal Drug ◽  
Delivery Strategies

Prostate cancer can be lethal in advanced stages, for which chemotherapy may become the only viable therapeutic option. While there is no clear clinical management strategy fitting all patients, cytotoxic chemotherapy with docetaxel is currently regarded as the gold standard. However, tumors may regain activity after treatment conclusion and become resistant to docetaxel. This situation calls for new delivery strategies and drug compounds enabling an improved therapeutic outcome. Combination of docetaxel with antiangiogenic therapy has been considered a promising strategy. Bevacizumab is the most common antiangiogenic drug, but clinical studies have not revealed a clear benefit from its combination with docetaxel. Here, we capitalize on our prior work on mathematical modeling of prostate cancer growth subjected to combined cytotoxic and antiangiogenic therapies, and propose an optimal control framework to robustly compute the drug-independent cytotoxic and antiangiogenic effects enabling an optimal therapeutic control of tumor dynamics. We describe the formulation of the optimal control problem, for which we prove the existence of at least a solution and determine the necessary first-order optimality conditions. We then present numerical algorithms based on isogeometric analysis to run a preliminary simulation study over a single cycle of combined therapy. Our results suggest that only cytotoxic chemotherapy is required to optimize therapeutic performance and we show that our framework can produce superior solutions to combined therapy with docetaxel and bevacizumab. We also illustrate how the optimal drug-naïve cytotoxic effects computed in these simulations may be successfully leveraged to guide drug production and delivery strategies by running a nonlinear least-square fit of protocols involving docetaxel and a new design drug. In the future, we believe that our optimal control framework may contribute to accelerate experimental research on chemotherapeutic drugs for advanced prostate cancer and ultimately provide a means to design and monitor its optimal delivery to patients.

Prostate Cancer

2018 ◽  
Author(s):  
Kathryn M. Wilson ◽  
Lorelei Mucci
Keyword(s):  
Prostate Cancer ◽  
Risk Factors ◽  
Family History ◽  
Prostate Specific Antigen ◽  
Advanced Prostate Cancer ◽  
Advanced Disease ◽  
African Ancestry ◽  
Specific Antigen ◽  
Dietary Factors ◽  
Increased Risk

Prostate cancer is among the most commonly diagnosed cancers among men, ranking second in cancer globally and first in Western countries. There are marked variations in incidence globally, and its incidence must be interpreted in the context of diagnostic intensity and screening. The uptake of prostate-specific antigen screening since the 1990s has led to dramatic increases in incidence in many countries, resulting in an increased proportion of indolent cancers that would never have come to light clinically in the absence of screening. Risk factors differ when studying prostate cancer overall versus advanced disease. Older age, African ancestry, and family history are established risk factors for prostate cancer. Obesity and smoking are not associated with risk overall, but are associated with increased risk of advanced prostate cancer. Several additional lifestyle factors, medications, and dietary factors are now emerging as risk factors for advanced disease.

Sign in / Sign up

Export Citation Format

Share Document