Metabolic Signatures of Prostate Cancer and Renal Cell Carcinoma using High-Resolution NMR and Hyperpolarized 13C MRI

Non-invasive techniques to assess metabolic reprogramming during cancer progression can be used to improve therapeutic selection and provide an early assessment of therapeutic response or resistance in individual patients. Prior studies have shown that metabolic reprogramming plays a key role in the development of prostate cancer and renal cell carcinoma (RCC). This dissertation further elucidates the metabolic alterations that occur in treatment-resistant prostate cancer and in patient-derived models of RCC using high-resolution nuclear magnetic resonance (NMR) spectroscopy and hyperpolarized (HP) 13C magnetic resonance imaging (MRI), with the goal of identifying new non-invasive diagnostic imaging tools. Glycolysis, metabolism of pyruvate and glutamate via the tricarboxylic acid (TCA) cycle, glutaminolysis, and glutathione synthesis are upregulated in castration-resistant prostate cancer (CRPC) compared to their androgen-dependent counterparts, using human cell lines as well a treatment-driven transgenic murine model. These metabolic alterations were reversed in castration-resistant murine tumors by treatment with a secondary androgen pathway inhibitor, apalutamide, suggesting that early metabolic responses to treatment can be monitored using non-invasive imaging techniques. Furthermore, treatment-emergent small cell neuroendocrine prostate cancer, a consequence of protracted treatment with primary androgen deprivation therapy and secondary androgen pathway inhibitors, exhibits significantly upregulated glycolysis, TCA cycle metabolism of pyruvate and glutamate, and glutaminolysis, as well as significantly altered redox capacity compared to castration-resistant prostate adenocarcinoma using patient-derived xenograft models. Finally, the metabolic differences associated with the tumor microenvironment were compared between various patient-derived models of RCC, finding that RCC patient-derived xenografts (PDXs) displayed higher redox capacity and were more proliferative than cells and tissue slices derived from the PDXs and maintained ex vivo. The work presented in this dissertation suggests that a combination of HP [1-13C]pyruvate, [2-13C]pyruvate, [5-13C]glutamine, and [1-13C]dehydroascorbate can be used to distinguish advanced prostate cancer and RCC subtypes in future HP 13C MRI of patients for improved treatment selection and monitoring

Magnetic Resonance Imaging Studies of Angiogenesis and Stem Cell Implantations in Rodent Models of Cerebral Lesions

Molecular biology and stem cell research have had an immense impact on our understanding of neurological diseases, for which little or no therapeutic options exist today. Manipulation of the underlying disease-specific molecular and cellular events promises more efficient therapy. Angiogenesis, i.e. the regrowth of new vessels from an existing vascular network, has been identified as a key contributor for the progression of tumor and, more recently, for regeneration after stroke. Donation of stem cells has proved beneficial to treat cerebral lesions. However, before angiogenesis-targeted and stem cell therapies can safely be used in patients, underlying biological processes need to be better understood in animal models. Noninvasive imaging is essential in order to follow biological processes or stem cell fate in both space and time. We optimized steady state contrast enhanced magnetic resonance imaging (SSCE MRI) to monitor vascular changes in rodent models of tumor and stroke. A modification of mathematical modeling of MR signal from the vascular network allowed for the first time simultaneous measurements of relaxation time T2 and SSCE MRI derived blood volume, vessel size, and vessel density. Limitations of SSCE MRI in tissues with high blood volume and non-cylindrically shaped vessels were explored. SSCE MRI detected angiogenesis and response to anti-angiogenic treatment in two rodent tumor models. In both tumor models, reduction of blood volume in small vessels and a shift towards larger vessels was observed upon treatment. After stroke, decreased vessel density and increased vessel size was found, which was most pronounced one week after the infarct. This is in agreement with two initial, recently published clinical studies. Overall, very little signs of angiogenesis were found. Furthermore, superparamagnetic iron oxide (SPIO) labels were used to study neural stem cells (NSCs) in vivo with MRI. SPIO labeling revealed a decrease in volume of intracerebral grafts over 4 months, assessed by T2* weighted MRI. Since SPIO labels are challenging to quantify and their MR contrast can easily be confounded, we explored the potential of in vivo 19F MRI of 19F labeled NSCs. Hardware was developed for in vitro and in vivo 19F MRI. NSCs were labeled with little effect on cell function and in vivo detection limits were determined at ~10,000 cells within 1 h imaging time. A correction for the inhomogeneous magnetic field profile of surface coils was validated in vitro and applied for both sensitive and quantitative in vivo cell imaging. As external MRI labels do not provide information on NSC function we combined 19F MRI with bioluminescence imaging (BLI). The BLI signal allowed quantification of viable cells whereas 19F MRI provided graft location and density in 3D over 4 weeks both in the healthy and stroke brain. A massive decrease in number of viable cells was detected independent of the microenvironment. This indicates that functional recovery reported in many studies of NSC implantation after stroke, is rather due to release of factors by NSCs than direct tissue replacement. In light of these indirect effects, combination of the imaging methods developed in this dissertation with other functional and structural imaging methods is suggested in order to further elucidate interactions of NSCs with the vasculature

Quantitative Magnetic Resonance Imaging of Tissue Microvasculature and Microstructure in Selected Clinical Applications

This thesis is based on four papers and aims to establish perfusion and diffusion measurements with magnetic resonance imaging (MRI) in selected clinical applications. While structural imaging provides invaluable geometric and anatomical information, new disease relevant information can be obtained from measures of physiological processes inferred from advanced modelling. This study is motivated by clinical questions pertaining to diagnosis and treatment effects in particular patient groups where inflammatory processes are involved in the disease. Paper 1 investigates acquisition parameters in dynamic contrast enhanced (DCE)-MRI of the temporomandibular joint (TMJ) with possible involvement of juvenile idiopathic arthritis. High level elastic motion correction should be applied to DCE data from the TMJ, and the DCE data should be acquired with a sample rate of at least 4 s. Paper 2 investigates choices of arterial input functions (AIFs) in dynamic susceptibility contrast (DSC)-MRI in brain metastases. AIF shapes differed across patients. Relative cerebral blood volume estimates differentiated better between perfusion in white matter and grey matter when scan-specific AIFs were used than when patient-specific AIFs and population-based AIFs were used. Paper 3 investigates DSC-MRI perfusion parameters in relation to outcome after stereotactic radiosurgery (SRS) in brain metastases. Low perfusion prior to SRS may be related to unfavourable outcome. Paper 4 applies free water (FW) corrected diffusion MRI to characterise glioma. Fractional anisotropy maps of the tumour region were significantly impacted by FW correction. The estimated FW maps may also contribute to a better description of the tumour. Although there are challenges related to post-processing of MRI data, it was shown that the advanced MRI methods applied can add to a more accurate description of the TMJ and of brain lesions.Doktorgradsavhandlin

Development of a mathematical model to estimate intra-tumor oxygen concentrations through multi-parametric imaging

Background Tumor hypoxia is involved in every stage of solid tumor development: formation, progression, metastasis, and apoptosis. Two types of hypoxia exist in tumors—chronic hypoxia and acute hypoxia. Recent studies indicate that the regional hypoxia kinetics is closely linked to metastasis and therapeutic responses, but regional hypoxia kinetics is hard to measure. We propose a novel approach to determine the local pO2 by fusing the parameters obtained from in vivo functional imaging through the use of a modified multivariate Krogh model. Methods To test our idea and its potential to translate into an in vivo setting through the use of existing imaging techniques, simulation studies were performed comparing the local partial oxygen pressure (pO2) from the proposed multivariate image fusion model to the referenced pO2 derived by Green’s function, which considers the contribution from every vessel segment of an entire three-dimensional tumor vasculature to profile tumor oxygen with high spatial resolution. Results pO2 derived from our fusion approach were close to the referenced pO2 with regression slope near 1.0 and an r2 higher than 0.8 if the voxel size (or the spatial resolution set by functional imaging modality) was less than 200 μm. The simulation also showed that the metabolic rate, blood perfusion, and hemoglobin concentration were dominant factors in tissue oxygenation. The impact of the measurement error of functional imaging to the pO2 precision and accuracy was simulated. A Gaussian error function with FWHM equal to 20 % of blood perfusion or fractional vascular volume measurement contributed to average 7 % statistical error in pO2. Conclusion The simulation results indicate that the fusion of multiple parametric maps through the biophysically derived mathematical models can monitor the intra-tumor spatial variations of hypoxia in tumors with existing imaging methods, and the potential to further investigate different forms of hypoxia, such as chronic and acute hypoxia, in response to cancer therapies

Co-Clinical Imaging Resource Program (CIRP): Bridging the translational divide to advance precision medicine

The National Institutes of Health\u27s (National Cancer Institute) precision medicine initiative emphasizes the biological and molecular bases for cancer prevention and treatment. Importantly, it addresses the need for consistency in preclinical and clinical research. To overcome the translational gap in cancer treatment and prevention, the cancer research community has been transitioning toward using animal models that more fatefully recapitulate human tumor biology. There is a growing need to develop best practices in translational research, including imaging research, to better inform therapeutic choices and decision-making. Therefore, the National Cancer Institute has recently launched the Co-Clinical Imaging Research Resource Program (CIRP). Its overarching mission is to advance the practice of precision medicine by establishing consensus-based best practices for co-clinical imaging research by developing optimized state-of-the-art translational quantitative imaging methodologies to enable disease detection, risk stratification, and assessment/prediction of response to therapy. In this communication, we discuss our involvement in the CIRP, detailing key considerations including animal model selection, co-clinical study design, need for standardization of co-clinical instruments, and harmonization of preclinical and clinical quantitative imaging pipelines. An underlying emphasis in the program is to develop best practices toward reproducible, repeatable, and precise quantitative imaging biomarkers for use in translational cancer imaging and therapy. We will conclude with our thoughts on informatics needs to enable collaborative and open science research to advance precision medicine

Quantitative PET-CT Perfusion Imaging of Prostate Cancer

Functional imaging of 18F-Fluorocholine PET holds promise in the detection of dominant prostatic lesions. Quantitative parameters from PET-CT Perfusion may be capable of measuring choline kinase activity, which could assist in identification of the dominant prostatic lesion for more accurate targeting of biopsies and radiation dose escalation. The objectives of this thesis are: 1) investigate the feasibility of using venous TACs in quantitative graphical analysis, and 2) develop and test a quantitative PET-CT Perfusion imaging technique that shows promise for identifying dominant prostatic lesions. Chapter 2 describes the effect of venous dispersion on distribution volume measurements with the Logan Plot. The dispersion of venous PET curves was simulated based on the arterio-venous transit time spectrum measured in a perfusion CT study of the human forearm. The analysis showed good agreement between distribution volume measurements produced by the arterial and venous TACs. Chapter 3 details the mathematical implementation of a linearized solution of the 3-Compartment kinetic model for hybrid PET-CT Perfusion imaging. A noise simulation determined the effect of incorporating CT perfusion parameters into the PET model on the accuracy and variability of measurements of the choline kinase activity. Results indicated that inclusion of CT perfusion parameters known a priori can significantly improve the accuracy and variability of imaging parameters measured with PET. Chapter 4 presents the implementation of PET-CT Perfusion imaging in a xenograft mouse model of human prostate cancer. Image-derived arterial TACs from the left ventricle were corrected for partial volume and spillover effects and validated by comparing to blood sampled curves. The PET-CT Perfusion imaging technique produced parametric maps of the choline kinase activity, k3. The results showed that the partial volume and spillover corrected arterial TACs agreed well with the blood sampled curves, and that k3max was significantly correlated with tumor volume, while SUV was not. In summary, this thesis establishes a solid foundation for future clinical research into 18F-fluorocholine PET imaging for the identification of dominant prostatic lesions. Quantitative PET-CT Perfusion imaging shows promise for assisting targeting of biopsy and radiation dose escalation of prostate cancer

3D Microfluidic model for evaluating immunotherapy efficacy by tracking dendritic cell behaviour toward tumor cells

Immunotherapy efficacy relies on the crosstalk within the tumor microenvironment between cancer and dendritic cells (DCs) resulting in the induction of a potent and effective antitumor response. DCs have the specific role of recognizing cancer cells, taking up tumor antigens (Ags) and then migrating to lymph nodes for Ag (cross)-presentation to naïve T cells. Interferon-α-conditioned DCs (IFN-DCs) exhibit marked phagocytic activity and the special ability of inducing Ag-specific T-cell response. Here, we have developed a novel microfluidic platform recreating tightly interconnected cancer and immune systems with specific 3D environmental properties, for tracking human DC behaviour toward tumor cells. By combining our microfluidic platform with advanced microscopy and a revised cell tracking analysis algorithm, it was possible to evaluate the guided efficient motion of IFN-DCs toward drug-treated cancer cells and the succeeding phagocytosis events. Overall, this platform allowed the dissection of IFN-DC-cancer cell interactions within 3D tumor spaces, with the discovery of major underlying factors such as CXCR4 involvement and underscored its potential as an innovative tool to assess the efficacy of immunotherapeutic approaches

2020 Conference Abstracts: Annual Undergraduate Research Conference at the Interface of Biology and Mathematics

Schedule and abstract book for the Twelfth Annual Undergraduate Research Conference at the Interface of Biology and Mathematics Date: October 31 - November 1, 2020Location: The 2020 conference was conducted remotely due to COVID-19 concerns, utilizing the sococo platform that allows personal avatars to move between rooms and sessions, interact in small groups and also participate in zoom sessions.Keynote Speaker: Gerardo Chowell, Population Health Sciences, Georgia State Univ. School of Public Health, AtlantaFeatured Speaker: Olivia Prosper, Mathematics, Univ. of Tennessee, Knoxvill