Matching Items (5)
Description
Patients with malignant brain tumors have a median survival of approximately 15 months following diagnosis, regardless of currently available treatments which include surgery followed by radiation and chemotherapy. Improvement in the survival of brain cancer patients requires the design of new therapeutic modalities that take advantage of common phenotypes. One such phenotype is the metabolic dysregulation that is a hallmark of cancer cells. It has therefore been postulated that one approach to treating brain tumors may be by metabolic alteration such as that which occurs through the use of the ketogenic diet (KD). The KD is high-fat, low-carbohydrate diet that induces ketosis and has been utilized for the non-pharmacologic treatment of refractory epilepsy. It has been shown that this metabolic therapy enhances survival and potentiates standard therapy in mouse models of malignant gliomas, yet the anti-tumor mechanisms are not fully understood.
The current study reports that KetoCal® (KC; 4:1 fat:protein/carbohydrates), fed ad libitum, alters hypoxia, angiogenic, and inflammatory pathways in a mouse model of glioma. Tumors from animals maintained on KC showed reduced expression of the hypoxia marker carbonic anhydrase 9 (CA IX), a reduction in hypoxia inducible factor 1-alpha (HIF-1α) and decreased activation of nuclear factor kappa B (NF-κB). Animals maintained on KC also showed a reduction in expression of vascular endothelial growth factor receptor 2 (VEGFR2) and decreased microvasculature in their tumors. Further, peritumoral edema was significantly reduced in animals fed the KC and protein analysis showed significantly altered expression of the tight junction protein zona occludens-1 (ZO-1) and the water channeling protein aquaporin-4 (AQP4), both of which have been implicated in malignant processes in glioma, including the formation of peritumoral edema in patients. Taken together the data suggests that KC alters multiple processes involved in malignant progression of gliomas. A greater understanding of the effects of the ketogenic diet as an adjuvant therapy will allow for a more rational approach to its clinical use.
The current study reports that KetoCal® (KC; 4:1 fat:protein/carbohydrates), fed ad libitum, alters hypoxia, angiogenic, and inflammatory pathways in a mouse model of glioma. Tumors from animals maintained on KC showed reduced expression of the hypoxia marker carbonic anhydrase 9 (CA IX), a reduction in hypoxia inducible factor 1-alpha (HIF-1α) and decreased activation of nuclear factor kappa B (NF-κB). Animals maintained on KC also showed a reduction in expression of vascular endothelial growth factor receptor 2 (VEGFR2) and decreased microvasculature in their tumors. Further, peritumoral edema was significantly reduced in animals fed the KC and protein analysis showed significantly altered expression of the tight junction protein zona occludens-1 (ZO-1) and the water channeling protein aquaporin-4 (AQP4), both of which have been implicated in malignant processes in glioma, including the formation of peritumoral edema in patients. Taken together the data suggests that KC alters multiple processes involved in malignant progression of gliomas. A greater understanding of the effects of the ketogenic diet as an adjuvant therapy will allow for a more rational approach to its clinical use.
ContributorsWoolf, Eric C (Author) / Scheck, Adrienne C (Thesis advisor) / Lake, Douglas F (Committee member) / LaBaer, Joshua (Committee member) / Arizona State University (Publisher)
Created2014
Description
Rapid intraoperative diagnosis of brain tumors is of great importance for planning treatment and guiding the surgeon about the extent of resection. Currently, the standard for the preliminary intraoperative tissue analysis is frozen section biopsy that has major limitations such as tissue freezing and cutting artifacts, sampling errors, lack of immediate interaction between the pathologist and the surgeon, and time consuming.
Handheld, portable confocal laser endomicroscopy (CLE) is being explored in neurosurgery for its ability to image histopathological features of tissue at cellular resolution in real time during brain tumor surgery. Over the course of examination of the surgical tumor resection, hundreds to thousands of images may be collected. The high number of images requires significant time and storage load for subsequent reviewing, which motivated several research groups to employ deep convolutional neural networks (DCNNs) to improve its utility during surgery. DCNNs have proven to be useful in natural and medical image analysis tasks such as classification, object detection, and image segmentation.
This thesis proposes using DCNNs for analyzing CLE images of brain tumors. Particularly, it explores the practicality of DCNNs in three main tasks. First, off-the shelf DCNNs were used to classify images into diagnostic and non-diagnostic. Further experiments showed that both ensemble modeling and transfer learning improved the classifier’s accuracy in evaluating the diagnostic quality of new images at test stage. Second, a weakly-supervised learning pipeline was developed for localizing key features of diagnostic CLE images from gliomas. Third, image style transfer was used to improve the diagnostic quality of CLE images from glioma tumors by transforming the histology patterns in CLE images of fluorescein sodium-stained tissue into the ones in conventional hematoxylin and eosin-stained tissue slides.
These studies suggest that DCNNs are opted for analysis of CLE images. They may assist surgeons in sorting out the non-diagnostic images, highlighting the key regions and enhancing their appearance through pattern transformation in real time. With recent advances in deep learning such as generative adversarial networks and semi-supervised learning, new research directions need to be followed to discover more promises of DCNNs in CLE image analysis.
Handheld, portable confocal laser endomicroscopy (CLE) is being explored in neurosurgery for its ability to image histopathological features of tissue at cellular resolution in real time during brain tumor surgery. Over the course of examination of the surgical tumor resection, hundreds to thousands of images may be collected. The high number of images requires significant time and storage load for subsequent reviewing, which motivated several research groups to employ deep convolutional neural networks (DCNNs) to improve its utility during surgery. DCNNs have proven to be useful in natural and medical image analysis tasks such as classification, object detection, and image segmentation.
This thesis proposes using DCNNs for analyzing CLE images of brain tumors. Particularly, it explores the practicality of DCNNs in three main tasks. First, off-the shelf DCNNs were used to classify images into diagnostic and non-diagnostic. Further experiments showed that both ensemble modeling and transfer learning improved the classifier’s accuracy in evaluating the diagnostic quality of new images at test stage. Second, a weakly-supervised learning pipeline was developed for localizing key features of diagnostic CLE images from gliomas. Third, image style transfer was used to improve the diagnostic quality of CLE images from glioma tumors by transforming the histology patterns in CLE images of fluorescein sodium-stained tissue into the ones in conventional hematoxylin and eosin-stained tissue slides.
These studies suggest that DCNNs are opted for analysis of CLE images. They may assist surgeons in sorting out the non-diagnostic images, highlighting the key regions and enhancing their appearance through pattern transformation in real time. With recent advances in deep learning such as generative adversarial networks and semi-supervised learning, new research directions need to be followed to discover more promises of DCNNs in CLE image analysis.
ContributorsIzady Yazdanabadi, Mohammadhassan (Author) / Preul, Mark (Thesis advisor) / Yang, Yezhou (Thesis advisor) / Nakaji, Peter (Committee member) / Vernon, Brent (Committee member) / Arizona State University (Publisher)
Created2019
Description
A coincidence reporter construct, consisting of the p21-promoter and two luciferase genes (Firefly and Renilla), was constructed for the screening of drugs that might inhibit Olig2's tumorigenic role in glioblastoma. The reporter construct was tested using an Olig2 inhibitor, HSP990, as well as short hairpin RNA targeting Olig2. Further confirmatory analysis is needed before the reporter cell line is ready for high-throughput screening at the NIH and lead compound selection.
ContributorsCusimano, Joseph Michael (Author) / LaBaer, Joshua (Thesis director) / Mangone, Marco (Committee member) / Mehta, Shwetal (Committee member) / Barrett, The Honors College (Contributor) / Department of Chemistry and Biochemistry (Contributor)
Created2014-05
Description
Glioblastoma is the most aggressive and lethal brain tumor, due to its resistance to current conventional therapy. The resistance to chemo- and radiotherapy has been attributed to a special population of cells known as glioma stem cells. Previous literature has shown the importance of a Central Nervous System-restricted transcription factor OLIG2 in maintaining the tumor-propagating potential of these glioma stem cells. OLIG2's function was further elucidated, with its pro-mitogenic function due to its ability to negatively regulate the p53 pathway by suppressing the acetylation of the p53 protein's C terminal domain. Past work in our lab has confirmed that one of OLIG2's partner proteins is Histone Deacetylase 1 (HDAC1). In vitro experiments have also shown that targeting HDAC1 using hairpin RNA in glioma stem cells negatively impacts proliferation. In a survival study using a murine glioma model, targeting Hdac1 using hairpin RNA is shown to reduce tumor burden and increase survival. In this paper, we demonstrate that silencing Hdac1 expression reduces proliferation, increases cell death, likely a result of increased acetylation of p53. Olig2 expression levels seem to be unaffected in GSCs, demonstrating that the Hdac1 protein ablation is indeed lethal to GSCs. This work builds upon previously collected results, confirming that Hdac1 is a potential surrogate target for Olig2's pro-mitotic function in regulating the p53 pathway.
ContributorsLoo, Vincent You Wei (Author) / LaBaer, Joshua (Thesis director) / Mehta, Shwetal (Committee member) / School of Molecular Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2017-05
Description
Glioma is a devastating, invasive form of brain cancer with a 36-month median overall survival. The highest grade tumors, glioblastomas, have an even shorter prognosis of about 15 months. A glioma often requires an intense combination of treatments including surgery, chemotherapy, and radiotherapy, often resulting in very damaging side effects. Due to their sensitive location in the brain, which is often difficult to access because of the skull, gliomas are most often visualized using magnetic resonance imaging (MRI), a non-invasive imaging method. Because high grade gliomas (HGGs) are highly aggressive and recurrence is common, patients diagnosed with these tumors stand to significantly benefit from novel, advanced MRI techniques that can lead to better patient-specific tumor characterization and improved response assessment. Magnetic resonance elastography (MRE) is a MRI-based method that measures the mechanical properties of tissue, and has the potential to significantly enhance the ability to distinguish malignant vs. healthy brain tissue by determining spatial differences in physical stiffness. We investigated whether the addition of MRE to standard clinical glioma MRI protocols would provide a more accurate understanding of the extent of tumor invasion. Using routinely available T2-weighted and contrast enhancing T1-weighted clinical MRI images, the Swanson lab has developed the Proliferation-Invasion (PI) model of brain tumor growth. Using this model, we quantify the relative diffusion (D) and proliferation (�) of tumor cells as D/�. Clinical MRIs were segmented in order to parameterize the model and determine these tumor growth metrics for each patient in our retrospective study. Next, we compare these tumor growth metrics with MRE features of physical stiffness of malignant tissue to determine whether there are correlations with the PI model's kinetic parameters. We hypothesized that MRE stiffness measurements would be associated with the PI model of glioma growth and may provide additional patient-specific tumor characterization information useful for optimally choosing treatment and understanding treatment response. MRE has the potential to be a useful addition to the clinical management of glioma and be integral to further understanding tumor growth and invasiveness.
ContributorsYee, Sara (Author) / Swanson, Kristin (Thesis director) / Hu, Leland (Committee member) / Rickertson, Cassandra (Committee member) / Massey, Susan (Committee member) / Jackson, Pamela (Committee member) / School of Life Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05