TY - JOUR
T1 - An agent-based model of prostate Cancer bone metastasis progression and response to Radium223
AU - Casarin, Stefano
AU - Dondossola, Eleonora
N1 - Funding Information:
Dr. Casarin is supported by the John F. Jr. and Carolyn Bookout Presidential Distinguished Chair fund. Dr. Dondossola is supported by the AACR-Bayer Innovation and Discovery Grant, the MD Anderson Cancer Center Prostate Cancer SPORE (P50 CA140388–09) and Bayer HealthCare Pharmaceuticals (57440). The Genitourinary Cancers Program of the Cancer Center Support Grant (CCSG) shared resources at MD Anderson Cancer Center is supported by NIH/NCI award number P30 CA016672. The funders did not have any influence on any aspects of the study, including design, data collection, analyses, interpretation, or writing the manuscript.
Publisher Copyright:
© 2020 The Author(s).
PY - 2020/6/29
Y1 - 2020/6/29
N2 - 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.
AB - 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.
KW - In silico model
KW - Prostate cancer bone metastasis
KW - Radium 223
KW - Therapy optimization
KW - Therapy response
KW - Tumor growth
KW - Humans
KW - Radiation Tolerance
KW - Brachytherapy/methods
KW - Male
KW - Disease Progression
KW - Xenograft Model Antitumor Assays
KW - Animals
KW - Models, Biological
KW - Computer Simulation
KW - Prostatic Neoplasms/pathology
KW - Cell Line, Tumor
KW - Mice
KW - Tibia/diagnostic imaging
KW - Bone Neoplasms/diagnosis
KW - Intravital Microscopy
KW - Microscopy, Fluorescence
KW - Radium/administration & dosage
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UR - http://www.scopus.com/inward/citedby.url?scp=85087401674&partnerID=8YFLogxK
U2 - 10.1186/s12885-020-07084-w
DO - 10.1186/s12885-020-07084-w
M3 - Article
C2 - 32600282
AN - SCOPUS:85087401674
SN - 1471-2407
VL - 20
SP - 605
JO - BMC Cancer
JF - BMC Cancer
IS - 1
M1 - 605
ER -