Bone metastatic prostate cancer is fatal and causes extensive pathological bone growth and destruction by manipulating osteoblasts and osteoclasts respectively. Understanding the factors driving the disease can help identify new therapeutic targets. Experimental approaches have dissected many of the molecular mechanisms involved but how factors at different scales collectively impact complex multi-cellular interactions over time is a significant challenge. Integrating molecular, cellular and clinical information into computational models offers a novel and powerful way to overcome this road-block: cell processes and mechanisms from different scales can be broken down into discrete and continuous components and used to parameterize a multi-scale model. This model can then be used to examine the temporal dynamics emerging from the multi scale cellular interactions. Rationale: Using our own and published data, we generated a novel computational model of normal bone remodeling controlled by TGF? and RANKL. Seeding the model with a metastatic prostate cancer cell generated in silico lesions that qualitatively and quantitatively mimic the pathophysiology of the human disease. As expected, our preliminary model confirmed the role of TGF? but predicted novel roles for cyclical osteoclast infiltration and mesenchymal stem cells in prostate cancer growth. Based on these emerging data, we believe that the enhancement of the computational model with cellular and molecular factors that control prostate cancer-bone interaction (PTHrP, Wnts, interleukins, chemokines and polarizing macrophages) will allow us to predict the key circuits driving the behavior of bone metastatic prostate cancer and to define new therapeutic strategies to treat this disease. We will test this hypothesis using the following integrated approaches: Approaches:
In Aim 1, an enhanced molecular and cellular multiscale computational model will be developed and the key circuits driving bone metastatic cancer growth will be predicted.
In Aim 2, we will predict the role of cyclical osteoclast infiltration and macrophage polarization (M1/M2) in promoting prostate cancer growth.
Aim 3 will dissect the role of MSC recruitment in prostate cancer induced osteogenesis in silico. Importantly, the predictions generated in each aim will be tested with relevant in vivo rodent models of bone metastatic prostate cancer and validated in human clinical specimens. Quantitative biological values will be used to recalibrate the computational model in the event that outputs are discordant. Innovation/Impact: Our innovative studies will; 1) generate a robust and dynamic multi-scale computational model of the prostate cancer-bone microenvironment, 2) will define novel roles for monocyte derived cells (osteoclasts/M1/M2 macrophages) in driving prostate cancer growth, 3) will define new roles for MSCs in promoting prostate cancer induced osteogenesis, 4) will predict the key circuits driving bone metastatic prostate cancers and 5) will predict and test curative strategies for eradicating bone metastatic prostate cancer.
Prostate cancer frequently spreads to the skeleton where it creates incurable painful bony lesions that greatly affect the patient's quality of life. This application will generate a powerful multiscale computational model integrating data from the clinical, molecular and cellular scales to predict curative strategies for the eradication of bone metastatic prostate cancer. We will achieve this goal by; 1) determining the key cellular and molecular mechanisms driving prostate cancer metastasis to the bone growth; 2) interrogating the role of monocyte derived osteoclasts and macrophages in driving the growth of the disease and; 3) identifying how prostate cancers promote pathological bone formation through the validation of key model-generated predictions.
| Araujo, Arturo; Cook, Leah M; Lynch, Conor C et al. (2018) Size Matters: Metastatic Cluster Size and Stromal Recruitment in the Establishment of Successful Prostate Cancer to Bone Metastases. Bull Math Biol 80:1046-1058 |
| Kaznatcheev, Artem; Vander Velde, Robert; Scott, Jacob G et al. (2017) Cancer treatment scheduling and dynamic heterogeneity in social dilemmas of tumour acidity and vasculature. Br J Cancer 116:785-792 |
| Cook, Leah M; Araujo, Arturo; Pow-Sang, Julio M et al. (2016) Predictive computational modeling to define effective treatment strategies for bone metastatic prostate cancer. Sci Rep 6:29384 |
| Gallaher, Jill; Cook, Leah M; Gupta, Shilpa et al. (2014) Improving treatment strategies for patients with metastatic castrate resistant prostate cancer through personalized computational modeling. Clin Exp Metastasis 31:991-9 |
