Browsing by Subject "Prostate Cancer"

Now showing 1 - 7 of 7
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    Application of targeting nucleases to modeling prostate cancer gene rearrangements
    (2014-06) Nyquist, Michael D.
    Advanced prostate cancer (PCa) treated with androgen deprivation therapy (ADT) eventually relapses to an ADT-resistant disease referred to as castration resistant PCa (CRPC). Recent integrative analyses of PCa genomes have led to the elucidation of potential subtypes that are revelatory to the development of PCa as well as the mechanisms of resistance to ADT and CRPC progression. These studies have confirmed that alterations in the androgen receptor (AR) signaling axis are central to CRPC progression, and have uncovered complex mechanisms by which AR and other components of the AR signaling axis affect, and are affected by, genomic changes and epigenetic transformations. Among the most frequent alterations in CRPC are direct alterations in the AR gene. These AR gene alterations include AR amplification, point mutations, and more recently AR gene rearrangements leading to expression of truncated, constitutively active AR splice variants that are impervious to ADT. Fortunately, the recent development of transcription activator-like effector nucleases (TALENs) has allowed researchers to tailor the genomes of their model systems more rigorously than ever before. This dissertation presents studies centered on genome engineered cell lines modeling intragenic-AR rearrangements that are associated with the production of androgen receptor splice variants. In the second chapter, two AR rearrangements associated with ARv567es expression were recreated in PCa cell line R1-AD1 using targeting nucleases. These engineered cell lines expressed high levels of Arv567es that recapitulated the full length AR transcriptome and drove androgen independent growth. In chapter three, AR rearrangements associated with high AR-V7 expression in CRPC cell lines CWR-R1 and 22Rv1 were induced and gene-corrected, respectively, using targeting nucleases. We found that a deletion contained in AR intron 1 of CWR-R1 was not sufficient to induce AR-V7 expression. However, genetic correction of a duplicated AR intron 3 region found in 22Rv1 decreased AR-V7 levels. These models were developed to determine the underlying mechanisms of AR variant production as well as provide a novel platform with which to study AR variant DNA binding, transcriptional regulation, and clinically relevant aspects of PCa such as biomarker research and precision medicine.
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    Prostate Cancer
    (2011-08-03) Bulau, Cassandra
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    Prostate Cancer Prevention: Do I need Finasteride?
    (2010-07-22) Shelver, Jon
    Despite reports, Finasteride is not yet recommended for routine prevention of prostate cancer. A recent study has raised new questions that are still being answered. In the mean time, the best approach is to continue an individualized monitoring plan developed between you and your doctor.
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    Targeting CD133 In Androgen Receptor Indifferent, Neuroendocrine Differentiated Aggressive Variant Prostate Cancer
    (2019-06) Glumac, Paige M.
    An increasing number of men are developing a lethal, non-androgen receptor (AR) driven form of prostate cancer (PCa) known as aggressive variant prostate cancer (AVPC). Therapeutic options for AVPC are limited, and the development of novel therapeutics is significantly hindered by the inability to accurately monitor the disease through imaging. This underscores the critical need to develop improved imaging agents for AVPC. Targeted imaging agents, such as those developed for prostate-specific membrane antigen (PSMA) have made significant progress in imaging metastatic prostate adenocarcinoma; however, numerous studies have shown that non-AR driven prostate cancer does not express PSMA. Thus, there is an urgent unmet need to identify novel antigens and targeted imaging agents for the detection and monitoring of this lethal form of PCa. In these studies, we have identified the pentaspan transmembrane glycoprotein, CD133, as a targetable antigen that is overexpressed on the surface of non-AR driven, neuroendocrine-differentiated prostate cancer. Additionally, we have developed a novel antibody, termed HA10 IgG, which was found to bind to a glycosylation-independent epitope on CD133. HA10 IgG was validated in numerous cell lines and demonstrated similar or more accurate binding to CD133 when compared to a frequently used commercial antibody in vitro. To assess the imaging potential of HA10 IgG, the antibody was labeled for near-infrared and positron emission tomography imaging. Our CD133 probe was validated in imaging studies and shown to be highly selective for CD133-expressing PCa cells, suggesting its potential as a non-invasive imaging agent for lethal, non-AR-driven AVPC.
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    Targeting the Tumor Stroma Using a Monoclonal Antibody Platform Technology
    (2021-04) Hintz, Hallie
    1 in 9 men will be diagnosed with prostate cancer during their lifetime and it remains the second leading cause of cancer death among American men. Men who fail standard-of-care androgen deprivation therapy (ADT) and progress to metastatic castration resistant prostate cancer (mCRPC) are left with few therapeutic options. Current second line therapies only provide a small survival benefit and there is a critical unmet need for new and innovative approaches to treat mCRPC. Imaging is a crucial aspect of mCRPC clinical management used for the detection of recurrent or distant disease. The development of new therapies is also dependent on accurate imaging modalities for patient staging and evaluating treatment response. Our research shows fibroblast activation protein alpha (FAP) is a relevant target for imaging and treating mCRPC. FAP is emerging as the next pan-cancer target given its upregulated expression in cancer associated fibroblasts (CAFs) and localization to the tumor microenvironment. Here we document the discovery and validation of a monoclonal antibody that selectively binds to FAP. The lead antibody, B12, was identified from a naïve murine single-chain variable fragment antibody phage display library screened against recombinant human FAP. The heavy and light chains of B12 were cloned into full-length human immunoglobulin 1 vectors and expressed as a chimeric monoclonal antibody (B12 mAb). B12 mAb was shown to detect FAP expression in cell lines and was rapidly internalized by FAP-expressing cells in vitro. B12 mAb demonstrated cross-reactivity with murine FAP, but not with the highly homologous protease human dipeptidyl peptidase IV. PET/CT imaging with [89Zr]Zr-B12 mAb demonstrated high tumor uptake and long-term retention of the probe in several preclinical animal models. Furthermore, we show its superiority to other clinically investigated imaging probes which suggests clinical translation of B12 mAb as a non-invasive mCRPC imaging probe. Next, we evaluated the therapeutic potential of B12 mAb as an antibody-dependent cell-mediated cytotoxicity (ADCC) inducing agent in combination with an engineered NK-92MI CD64 cell therapy. The immunotherapy demonstrated selective cytotoxicity in vitro and treatment effectively controlled tumor growth in an animal model. Furthermore, we engineered B12 mAb as an antibody-drug conjugate (ADC) and showed cytotoxic effect in several in vitro and in vivo solid tumor models. Overall, this research represents a platform technology for the development of theranostics targeting FAP that could provide urgently needed therapies and imaging probes for mCRPC patients.
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    Therapeutic Targeting of Intrinsically Disordered Androgen Recptor Functional Domains in Prostate Cancer
    (2015-04) Brand, Lucas
    Prostate cancer (PCa) is a leading cause of morbidity and mortality in the United States, and contributes to a significant healthcare burden due to an overall lack of curative interventions for advanced-stage disease. Because PCa is largely insensitive to cytotoxic chemotherapy, the androgen receptor (AR) has long been the primary therapeutic target for the clinical management of locally advanced and metastatic PCa. Problematically, targeting AR signaling via androgen deprivation or treatment with AR antagonists is associated with progression to lethal, castration-resistant prostate cancer (CRPC) via a variety of molecular mechanisms that alter AR expression and function. However, CRPC is marked by a continued reliance on AR expression and activity. Thus, new modes of intervention with ability to durably repress AR activity in advanced CRPC are an unmet clinical need. In Chapter 1, we review the problem of castration resistance through a new paradigm of genetic rearrangements that produce truncated AR variants (ARV), which confer resistance to all current forms of AR-based PCa therapy. In Chapter 2, we discuss a novel AR inhibitor that directly targets the AR NH2-terminal transcriptional activation domain (NTD), but with significant off-target effects due to the lack of specificity for the intrinsically disordered NTD. In Chapter 3, we characterize the differences in NTD utilization between full length AR and ARV. Finally, in Chapter 4, we discuss a brief history of AR targeting in PCa, and offer a perspective on how future translational studies can approach the problem of intrinsic disorder in the NTD to develop new interventions with more durable and lasting mechanisms of action.
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    Voxel-wise Classification of Prostate Cancer Using Multi-parametric MRI Data
    (2019-06) Jin, Jin
    As a continuously developing tool for the diagnosis and prognosis of prostate cancer, multi-parametric magnetic resonance imaging (mpMRI) has been widely used in a variety of prostate cancer-related topics. While current research has shown the great potential of mpMRI in detecting prostate cancer, further investigation is needed for modeling some specific features of mpMRI, including the anatomic difference between different regions of a prostate, the spatial correlation between voxels within each prostate image, and the difference in the distribution of the observed mpMRI parameters between patients. This dissertation focuses on novel statistical methods for the voxel-wise classification of prostate cancer using mpMRI data. Systematic modeling frameworks will be proposed to improve cancer classification by incorporating the aforementioned features of mpMRI. Three topics are discussed in depth: (1) development of a general Bayesian modeling framework that can incorporate the various mpMRI features; (2) how to model the mpMRI features in the proposed Bayesian framework, preferably in a computationally efficient manner; (3) development of an alternative approach to accounting for the mpMRI features, which uses a multi-resolution modeling technique to account for the regional heterogeneity, and is flexible to be extended to more complex classification problems for prostate cancer. The solutions are presented in the following order. In Chapter 2, we propose a Bayesian hierarchical modeling framework that allows complex distributional assumptions for the various data components. Based on the modeling framework, two approaches will be proposed for modeling the heterogeneity between regions of the prostate, which can be combined with a spatial Gaussian kernel smoother to account for residual spatial correlation and reduce random noise in the data. In Chapter 3, we add additional layers in the hierarchical model to model the spatial correlation structure and between-patient heterogeneity. Modeling the spatial correlation structure is computationally challenging and even infeasible for our mpMRI data set, due to the large number of voxels within each image. Three scalable spatial modeling approaches are then proposed for the correlation between voxels. In Chapter 4, we develop an alternative, machine learning-based method to account for the mpMRI features: a super learner with an ensemble learning technique is utilized to combine base learners trained in multi-resolution sub-regions. Specific algorithms will be introduced for both the classification of binary cancer status, and a more complex problem: classification of an ordinal outcome that indicates the clinical significance of prostate cancer. Method performance will be illustrated by simulation studies and applications to in vivo data that motivated the method's development.