03 May Prostate Cancer: Mathematical Model Defines Growth of Bone Metastases
MedicalResearch: What are the main findings of the study?
Dr. Araujo: Using in vivo approaches it is often challenging to study the multiple simultaneous interactions occurring at various time points in the setting of bone metastasis. However, integrating biological data with a powerful computational model allowed us to build a tool that could not only mimic the in vivo growth of cancer in bone but also to determine how the disease was behaving at any given time point. The key finding for us was that the computational model demonstrated the phasic or cyclical nature of how the prostate cancers grow in bone. For example, a wave of osteoclast mediated bone resorption would be followed by sustained bone formation by osteoblasts, followed again by bone reposition. We think these findings could provide novel insights into when the best time to apply therapies might be in order to obtain maximum efficiency.
MedicalResearch: Were any of the findings unexpected?
Dr. Araujo: Our model suggests that, if an anti-RANKL therapy was reaching the prostate-bone microenvironment with 100% efficacy, it would be sufficient to eradicate bone metastases by completely preventing osteoclast formation. To get to 100% efficacy is clinically challenging but our model predicts that increasing the efficacy anti-RANKL therapy from 40% to even 80% would significantly impact the growth of the disease. This could be achieved by increasing the dosage but this increase in dosing could also cause unanticipated side-effects.
MedicalResearch: What should clinicians and patients take away from your report?
Dr. Araujo: The take away message is that mathematical models can be used in an integrative way with biological models and clinical data to generate powerful tools that will be clinically useful by prolonging the overall survival of patients with prostate to bone metastases. Given the phasic nature of the disease it is possible that, by applying currently used standard of care treatments in different sequences or combinations could greatly enhance the efficacy and in turn, the overall survival for the patients. This kind of models can be used to design powerful combinations of these treatments. There are also implications for the rapid assessment of new inhibitors and how applying them to the computational model could predict whether they will be successful or not.
MedicalResearch: What recommendations do you have for future research as a result of this study?
Dr. Araujo: The complexity of cancer heterogeneity is a major challenge facing cancer researchers and we believe that the flexibility of mathematical modeling offers us a real advantage in studying how cancers evolve in response to their microenvironment and importantly to applied therapies. Using patient derived information in regards to genetic mutation, it would be possible to begin tackling this heterogeneity and personalize computational models in a bid to provide better treatment options for each patient.