Deconvoluting the Complexity of Bone Metastatic Prostate Cancer via Computational Modeling

Principal Investigator: ARAUJO-GUTIERREZ, ARTURO
Program: PCRP
Proposal Number: PC141612
Award Number: W81XWH-15-1-0184
Funding Mechanism: Postdoctoral Training Award
Partnering Awards:
Award Amount: $119,831.61


Principal Investigator's (PI) Long-Term Career Goals: The PI's long-term career goal is to contribute to the development of computational tools that will accelerate the translation of knowledge from the laboratory to the clinic. The PI's specific research interest is in prostate cancer (PCa) to bone metastasis because he believes that only by creating tools to better understand the dynamics of molecular and cellular interactions driving the cancer can we hope to develop new and better curative strategies for this currently incurable disease. The PI completed, in 2013, his PhD in Modeling Biological Complexity at University College London, one of the leading universities in the world. There he gained theoretical, computational, and wet-lab training that enabled him to graduate with a highly interdisciplinary dissertation that helped tease apart and finally understand complex key aspects of cancer. However, during his PhD, the PI became aware of an existing disconnect between theoretical, experimental, and clinical research, and the need for interdisciplinary scientists that have the skills necessary to navigate seamlessly across those areas. Aiming to address this unmet need with his acquired interdisciplinary skill set, the PI applied for a postdoctoral position at the Integrated Mathematical Oncology (IMO) department at the H. Lee Moffitt Cancer Center & Research Institute (HLMCC), Tampa, FL; one of the few institutes in the world where this kind of pioneering translational research is possible. Highly driven and eager to make a difference, the PI sought the mentorship of IMO faculty Dr. David Basanta, an internationally recognized expert in computational models of evolution in cancer, and Dr. Conor Lynch, an expert on bone metastatic prostate cancer in the Cancer Center's Tumor Biology Department. They helped the PI to quickly grasp the challenges of prostate cancer to bone metastases and how a biologically motivated and parameterized computational model could address questions that experimental and computational models alone could not. Through the combination of his interdisciplinary talents and viewpoints, the PI successfully bridged an important gap between theory, experiments, and the clinic with an integrated computational model. This work yielded, in a short period of time, published results that were well received by the prostate cancer research community. Results from this research have shed new light on the failure of the current standard of care to treat this disease and have offered new avenues for clinical research. This integrated computational model has the potential to be transformed into a clinically relevant tool for the innovative development, combination, and quick assessment of new therapies to treat bone metastatic prostate cancer. The aim of this and the PI's future research is then to produce platforms like this that will allow us to translate biological discoveries into clinical results through computational modeling.

Research Project: Existing work has shown that the signaling molecule TGF-beta is key for the survival and growth of prostate cancer cells in bone; the bone matrix being one of the richest sources of TGF-beta in the human body. Importantly, data from the HLMCC clinic shows that biopsies of prostate to bone metastases are made up of cancer cells with different levels of TGF-beta production and consumption. Elucidating the precise role of TGF-beta and how to best harness it will be important in determining the best kind of patient-specific treatment. The PI proposes to build on his existing platform to model a diverse cancer cell population using de-identified clinical samples that could enable us to study a promising experimental treatment based on a TGF-beta inhibitor. Model outputs will be validated in the laboratory and will be crucial in explaining whether this treatment could work and how to better deliver it to patients in terms of scheduling and dosage. Compared to traditional experimental research, computational experimentation is fast, inexpensive and, as evidenced by the PI's body of work, very useful in the lab by helping biologists design better experiments, reducing the need for animal experiments, cutting down costs and expediting the translation of knowledge from the bench into the clinic.

Expected Outcomes: Based on the PI's previous body of work, he will be able to harness existing and ongoing biological and clinical experiments so that they can be seamlessly integrated into a computational framework. This framework will tell us the nature of the tumors that could be treated with TGF-beta inhibition and the better way to treat different patients with different types of metastases. The PI has found that the main advantage of the generation of this kind of biologically relevant computational model is that it opens a window into the inner life of the cells, their interactions, and their behaviors across time and space, features that are difficult to determine using traditional biological approaches. The PI expects that the impact of a TGF-beta inhibitor will be different in a homogeneous tumor population compared to a heterogeneous one. The PI will use this opportunity to advance his knowledge in his interdisciplinary field and to actively help bridge the gap between theoreticians, experimentalists, and clinicians.