Hormone-refractory tumors are the most deadly form of prostate cancer. There is currently no cure for patients with these very aggressive and highly metastatic tumors. Thus, the design of effective therapies for patients with such advanced prostate cancer is very essential for increasing patient survival and quality of life. It also is absolutely critical for therapy development to understand how different cell signaling pathways give rise to this deadly disease. In fact, sensitive imaging technology could give insight into these molecular mechanisms in a small animal model of hormone-refractory prostate cancer. Moreover, in vivo monitoring of tumor development could also assess immediate responses to prostate cancer therapy. Hence, bioluminescence imaging has already become a powerful tool for imaging tumor development in animals, but quantitative imaging has not been possible yet. Animals still need to be sacrificed at the endpoint of therapy in order to determine the actual tumor mass for therapy evaluation. Therefore, the development of a novel tomographic bioluminescence imaging method, where tumor volume can directly be determined in vivo and at all time points during studies, is of utmost significance for developing such highly effective therapies. The main hypothesis of this application is that a bioluminescence image reconstruction method will provide detailed volume information about tumor growth/regression at all intermediate time points during therapy in a small animal model of hormone-refractory prostate cancer. Our technology will help testing what specific drugs and therapies could block hormone-refractory prostate cancer in vivo and, thus, would significantly aid therapy development and facilitate its translation into the clinic. We will focus on two specific aims to support the hypothesis. First, we will develop a multi-spectral and multiple-view Bayesian image reconstruction method with an entropic prior and an evolution strategy for sampling the global search space of the unknown bioluminescent source distributions inside tissue. Our method will employ a high-order radiative transfer model based on the SPN equations and an adaptive grid refinement method for curved geometries. We will also implement a MRI and CT co-registration method with an animal bed for including anatomical tissue structure into the image reconstruction process. Second, we will reconstruct changes in tumor volume during therapy in a Nkx3.1/Pten mouse model of hormone-refractory prostate cancer. We will study the pharmacological manipulation of the Akt/mTOR and B-Ref/Erk Map kinase signaling pathways by determining small changes in tumor volume at various time points during therapy.
A Bayesian bioluminescence image reconstruction method will help testing what specific drugs and therapies can block hormone-refractory prostate cancer in small animal models. It will not only significantly aid therapy development in pre-clinical studies but will also facilitate its translation into the clinic. Therefore, the proposed imaging method will have a high probability of directly impacting the treatment of male patients with the deadliest form of prostate cancer and would increase survival rate.
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