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An agent-based model of prostate Cancer bone metastasis progression and response to Radium223

BACKGROUND: Bone metastasis is the most frequent complication in prostate cancer patients and associated outcome remains fatal. Radium223 (Rad223), a bone targeting radioisotope improves overall survival in patients (3.6 months vs. placebo). However, clinical response is often followed by relapse an...

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Autores principales: Casarin, Stefano, Dondossola, Eleonora
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7325060/
https://www.ncbi.nlm.nih.gov/pubmed/32600282
http://dx.doi.org/10.1186/s12885-020-07084-w
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author Casarin, Stefano
Dondossola, Eleonora
author_facet Casarin, Stefano
Dondossola, Eleonora
author_sort Casarin, Stefano
collection PubMed
description BACKGROUND: Bone metastasis is the most frequent complication in prostate cancer patients and associated outcome remains fatal. Radium223 (Rad223), a bone targeting radioisotope improves overall survival in patients (3.6 months vs. placebo). However, clinical response is often followed by relapse and disease progression, and associated mechanisms of efficacy and resistance are poorly understood. Research efforts to overcome this gap require a substantial investment of time and resources. Computational models, integrated with experimental data, can overcome this limitation and drive research in a more effective fashion. METHODS: Accordingly, we developed a predictive agent-based model of prostate cancer bone metastasis progression and response to Rad223 as an agile platform to maximize its efficacy. The driving coefficients were calibrated on ad hoc experimental observations retrieved from intravital microscopy and the outcome further validated, in vivo. RESULTS: In this work we offered a detailed description of our data-integrated computational infrastructure, tested its accuracy and robustness, quantified the uncertainty of its driving coefficients, and showed the role of tumor size and distance from bone on Rad223 efficacy. In silico tumor growth, which is strongly driven by its mitotic character as identified by sensitivity analysis, matched in vivo trend with 98.3% confidence. Tumor size determined efficacy of Rad223, with larger lesions insensitive to therapy, while medium- and micro-sized tumors displayed up to 5.02 and 152.28-fold size decrease compared to control-treated tumors, respectively. Eradication events occurred in 65 ± 2% of cases in micro-tumors only. In addition, Rad223 lost any therapeutic effect, also on micro-tumors, for distances bigger than 400 μm from the bone interface. CONCLUSIONS: This model has the potential to be further developed to test additional bone targeting agents such as other radiopharmaceuticals or bisphosphonates.
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spelling pubmed-73250602020-06-30 An agent-based model of prostate Cancer bone metastasis progression and response to Radium223 Casarin, Stefano Dondossola, Eleonora BMC Cancer Research Article BACKGROUND: Bone metastasis is the most frequent complication in prostate cancer patients and associated outcome remains fatal. Radium223 (Rad223), a bone targeting radioisotope improves overall survival in patients (3.6 months vs. placebo). However, clinical response is often followed by relapse and disease progression, and associated mechanisms of efficacy and resistance are poorly understood. Research efforts to overcome this gap require a substantial investment of time and resources. Computational models, integrated with experimental data, can overcome this limitation and drive research in a more effective fashion. METHODS: Accordingly, we developed a predictive agent-based model of prostate cancer bone metastasis progression and response to Rad223 as an agile platform to maximize its efficacy. The driving coefficients were calibrated on ad hoc experimental observations retrieved from intravital microscopy and the outcome further validated, in vivo. RESULTS: In this work we offered a detailed description of our data-integrated computational infrastructure, tested its accuracy and robustness, quantified the uncertainty of its driving coefficients, and showed the role of tumor size and distance from bone on Rad223 efficacy. In silico tumor growth, which is strongly driven by its mitotic character as identified by sensitivity analysis, matched in vivo trend with 98.3% confidence. Tumor size determined efficacy of Rad223, with larger lesions insensitive to therapy, while medium- and micro-sized tumors displayed up to 5.02 and 152.28-fold size decrease compared to control-treated tumors, respectively. Eradication events occurred in 65 ± 2% of cases in micro-tumors only. In addition, Rad223 lost any therapeutic effect, also on micro-tumors, for distances bigger than 400 μm from the bone interface. CONCLUSIONS: This model has the potential to be further developed to test additional bone targeting agents such as other radiopharmaceuticals or bisphosphonates. BioMed Central 2020-06-29 /pmc/articles/PMC7325060/ /pubmed/32600282 http://dx.doi.org/10.1186/s12885-020-07084-w Text en © The Author(s) 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research Article
Casarin, Stefano
Dondossola, Eleonora
An agent-based model of prostate Cancer bone metastasis progression and response to Radium223
title An agent-based model of prostate Cancer bone metastasis progression and response to Radium223
title_full An agent-based model of prostate Cancer bone metastasis progression and response to Radium223
title_fullStr An agent-based model of prostate Cancer bone metastasis progression and response to Radium223
title_full_unstemmed An agent-based model of prostate Cancer bone metastasis progression and response to Radium223
title_short An agent-based model of prostate Cancer bone metastasis progression and response to Radium223
title_sort agent-based model of prostate cancer bone metastasis progression and response to radium223
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7325060/
https://www.ncbi.nlm.nih.gov/pubmed/32600282
http://dx.doi.org/10.1186/s12885-020-07084-w
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