Bone metastases (BM) are a source of significant mortality for patients with advanced prostate and renal cancer, which is mainly caused by treatment failure. The interaction between cancer and bone cells has recently emerged as a key mediator of treatment resistance, but the lack of appropriate experimental systems prevents rigorously addressing its involvement. Mouse models of BM have identified key mechanisms of therapy response and are indispensable to investigating the pathophysiology of this challenging disease. However, they are limited in addressing the complexity of drug testing (e.g. number of combinations and observation time) in a time-cost effective manner. The integration of ce...

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Bone metastases (BM) are a source of significant mortality for patients with advanced prostate and renal cancer, which is mainly caused by treatment failure. The interaction between cancer and bone cells has recently emerged as a key mediator of treatment resistance, but the lack of appropriate experimental systems prevents rigorously addressing its involvement. Mouse models of BM have identified key mechanisms of therapy response and are indispensable to investigating the pathophysiology of this challenging disease. However, they are limited in addressing the complexity of drug testing (e.g. number of combinations and observation time) in a time-cost effective manner. The integration of central biological findings with mathematical modeling allows exploring an unlimited amount of experimental combinations and has been applied to successfully model the impact of treatment on cancer progression. The goal of our project is to create a computer replica of prostate and renal BM that incorporates tumor and bone cells, and is generated by integrating mouse models, advanced multiphoton microscopy and mathematical modeling. Then, we will use this system to test the response to joint administration of Radium223 and cabozantinib, two drugs currently applied in patients as single agents. This combination is expected to significantly improve outcome based on the ability of these drugs to target reciprocal niches of therapy resistance; however, finding the best conditions for their application is a complex task that demands a huge effort in terms of time and lab resources. Our in vivo-inspired computational model of BM will allow us to investigate a great number of parameters and variables that would not be possible to address using mouse models only. As a result, we expect to identify optimal conditions that maximize effectiveness and provide a strong rationale for this combined regimen, with the goal of rapid clinical translation to directly impact patient care.

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