Global dynamics of a model of joint hormone treatment with dendritic cell vaccine for prostate cancer

Rutter, Erica M.; Kuang, Yang

doi:10.3934/dcdsb.2017050

Cited by 24 publications

(26 citation statements)

References 42 publications

(52 reference statements)

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“…In computational studies, they show a small advantage of combining IAS with immunotherapy over CAS with immunotherapy. Subsequently, Rutter and Kuang [69] extend their model to study the effects that drug dosage amounts and frequencies of administration on the time to relapse. Their computational and mathematical analysis of the model show the possibility of tailoring the vaccine dosage to the patient's effective immune system to maximize the effectiveness of treatment.…”

Section: Peng Et Al (2016)mentioning

confidence: 99%

Review: Mathematical Modeling of Prostate Cancer and Clinical Application

et al. 2020

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We review and synthesize key findings and limitations of mathematical models for prostate cancer, both from theoretical work and data-validated approaches, especially concerning clinical applications. Our focus is on models of prostate cancer dynamics under treatment, particularly with a view toward optimizing hormone-based treatment schedules and estimating the onset of treatment resistance under various assumptions. Population models suggest that intermittent or adaptive therapy is more beneficial to delay cancer relapse as compared to the standard continuous therapy if treatment resistance comes at a competitive cost for cancer cells. Another consensus among existing work is that the standard biomarker for cancer growth, prostate-specific antigen, may not always correlate well with cancer progression. Instead, its doubling rate appears to be a better indicator of tumor growth. Much of the existing work utilizes simple ordinary differential equations due to difficulty in collecting spatial data and due to the early success of using prostate-specific antigen in mathematical modeling. However, a shift toward more complex and realistic models is taking place, which leaves many of the theoretical and mathematical questions unexplored. Furthermore, as adaptive therapy displays better potential than existing treatment protocols, an increasing number of studies incorporate this treatment into modeling efforts. Although existing modeling work has explored and yielded useful insights on the treatment of prostate cancer, the road to clinical application is still elusive. Among the pertinent issues needed to be addressed to bridge the gap from modeling work to clinical application are (1) real-time data validation and model identification, (2) sensitivity analysis and uncertainty quantification for model prediction, and (3) optimal treatment/schedule while considering drug properties, interactions, and toxicity. To address these issues, we suggest in-depth studies on various aspects of the parameters in dynamical models such as the evolution of parameters over time. We hope this review will assist future attempts at studying prostate cancer.Prostate cancer (PCa) is the leading cause of cancer in US men (behind non-melanoma skin cancers), as well as the second leading cause of cancer-related mortality in men. The American Cancer Society estimates that in 2020, 191,930 Americans will be diagnosed with prostate cancer, with approximately 21.5% of all new cancer cases in men. The average five-year survival rate for all cases of prostate cancer is about 99%, which is a 31% point higher than in the 1970s [1]. However, once cancer metastasizes, the five-year survival rate drops to 30%, an increase of 10 percentage-point compared to the 1970s [2]. While the incidence of prostate cancer in the US is decreasing (see Figure 1), one possible cause is doctors' discouragement of early PCa diagnosis due to the fear of overtreating cancer. Globally, the incidence of prostate cancer is increasing, especially among developing nations [3].

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Section: Peng Et Al (2016)mentioning

confidence: 99%

Review: Mathematical Modeling of Prostate Cancer and Clinical Application

et al. 2020

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“…Nöroblastoma, sarkoma, hepatoselüler karsinoma, melanoma, deri skuamöz hücreli karsinom, prostat kanseri gibi kanser türlerin tedavisinde; astım, diyabet, otoimmün hastalıkların tedavisinde kullanılmaktadır. [12][13][14][15][16][17][18]…”

Section: Dendr‹t‹k Hücrelerunclassified

“…Aralıklarla uygulanan androjen yoksunlu¤u tedavisinin hastaların yaam kalitesini iyiletirdi¤i ve tümör direncini azalttı¤ı bilinmektedir. Rutter ve Kuang, [17] çalımalarında DH aılarıyla bir araya getirilen androjen hormon tedavisi modelini incelemilerdir. Aının aralıklarla uygulanmasından ziyade, yıllık dozajın sabit tutulması, daha sık uygulanan enjeksiyonların daha etkili oldu¤unu bulmulardır.…”

Section: Dendr‹t‹k Hücre ‹Zolasyonuunclassified

Dendritic cells and clinical applications in transplantation

Erdal¹

2017

FNG & Bilim Tıp Transplantasyon Dergisi

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Dendritic cells are immunogenic and tolorogenic cells that present the first stage antigen in the immune system. Dendritic cells are usually found in peripheral tissues. Blood and skin are the most easily accessible structures to examine these cells. Dendritic cells are most commonly used in vaccine production and development. In this review we aim to examine the current status in dendritic cell vaccines and to investigate the progress of phase studies from a retrospective point of view.

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“…Patients that are treated by immunotherapy dendritic vaccines develop an immune response following vaccination. Some patients experience a significant decline in their PSA level, and others experience stabilization of their previous progressing disease [19,21,22].…”

Section: Introductionmentioning

confidence: 99%