Chan Yiu Leung. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2006. / Includes bibliographical references (leaves 123-149). / Abstracts in English and Chinese. / Abstracts --- p.I / Acknowledgements --- p.VIII / List of Figures --- p.IX / Lists of Abbreviations --- p.X / Contents --- p.XII / Chapter Chapter One: --- Introduction and Literature Review --- p.1 / Chapter 1.1 --- Cancer in General --- p.1 / Chapter 1.2 --- Glioma --- p.3 / Chapter 1.2.1 --- Types of Glioma --- p.6 / Chapter 1.2.1.1 --- Astrocytomas --- p.6 / Chapter 1.2.1.2 --- Oligodendrogliomas --- p.8 / Chapter 1.2.1.3 --- Ependymomas --- p.9 / Chapter 1.2.2 --- Glioblastoma Multiforme (GBM) --- p.10 / Chapter 1.2.3 --- Molecular Biology of GBM --- p.11 / Chapter 1.2.4 --- Current Treatment for GBM --- p.15 / Chapter 1.3 --- HMG-Co A reductase inhibitors --- p.17 / Chapter 1.3.1 --- Pharmacology of HMG-Co A reductase inhibitor --- p.18 / Chapter 1.3.2 --- Epidemiological link between HMG-Co A Reductase Inhibitors and Cancer --- p.20 / Chapter 1.3.3 --- Current HMG-Co A reductase inhibitors research in cancer --- p.21 / Chapter 1.3.3.1 --- Inhibition of tumor cell growth --- p.21 / Chapter 1.3.3.2 --- Inhibition of Angiogenesis --- p.22 / Chapter 1.3.3.3 --- Anti-invasive effects of HMG-Co A reductase inhibitors.… --- p.23 / Chapter 1.3.3.4 --- Apoptosis induction by HMG-Co A reductase inhibitors --- p.24 / Chapter 1.3.4 --- In vivo efficacy and synergistic effects --- p.25 / Chapter 1.4 --- Tumor Necrosis Factor (TNF) related apoptosis-inducing Ligand (TRAIL) --- p.28 / Chapter 1.4.1 --- Molecular mechanisms of TRAIL-induced apoptosis --- p.29 / Chapter 1.4.2 --- Role for TRAIL in cancer therapy --- p.30 / Chapter 1.5 --- Objectives --- p.34 / Chapter Chapter 2 --- Methods and Materials --- p.35 / Chapter 2.1 --- Cell culture --- p.35 / Chapter 2.2 --- Cell proliferation detection (MTT) methods --- p.36 / Chapter 2.3 --- "Caspase 3,9 activities induced by lovastatin" --- p.37 / Chapter 2.4 --- Detection of apoptosis by Annexin V and PI staining --- p.39 / Chapter 2.5 --- Cell cycle analysis protocols --- p.41 / Chapter 2.6 --- DNA fragmentation ELISA detection kit protocols --- p.42 / Chapter 2.7 --- Reverse Transcription (RT) Polymerase Chain Reaction (PCR) --- p.44 / Chapter 2.8 --- Polymerase Chain Reaction (PCR) --- p.46 / Chapter 2.9 --- Bio-molecules extraction/purification protocols --- p.48 / Chapter 2.10 --- "Microarray analysis on lovastatin treated glioblastoma cells A172, M059J and M059K" --- p.51 / Chapter 2.10.1 --- Cells treatment and RNA extraction --- p.51 / Chapter 2.10.2 --- Synthesis of first strand cDNA --- p.53 / Chapter 2.10.3 --- Synthesis of second strand cDNA --- p.54 / Chapter 2.10.4 --- Purification of double stranded cDNA --- p.54 / Chapter 2.10.5 --- Synthesis of cRNA by in vitro transcription (IVT) --- p.55 / Chapter 2.10.6 --- Recovery of biotin-labelled cDNA --- p.56 / Chapter 2.10.7 --- Fragmentation of cRNA --- p.56 / Chapter 2.10.8 --- Preparation of hybridization reaction mixtures --- p.57 / Chapter 2.10.9 --- Loading of reaction mixtures into bioarray chambers --- p.58 / Chapter 2.10.10 --- Hybridization --- p.58 / Chapter 2.10.11 --- Post-hybridization wash --- p.59 / Chapter 2.10.12 --- 2.11.12Detection with streptavidin-dye conjugate --- p.59 / Chapter 2.10.13 --- Bioarray scanning and analysis --- p.61 / Chapter Chapter 3: --- Results --- p.62 / Chapter 3.1 --- Morphological effects of Lovastatin on human glioblastoma cells --- p.62 / Chapter 3.2 --- Anti-proliferation effects on glioblastoma cell lines --- p.64 / Chapter 3.3 --- Lovastatin-induced caspase3 and 9 activation in human glioblastoma cell lines --- p.69 / Chapter 3.4 --- Cell cycle determination by PI staining --- p.77 / Chapter 3.5 --- Quantification of apoptotic cell death by annexin V and propidium iodide staining --- p.79 / Chapter 3.6 --- Microarray analysis of lovastatin-modulated gene expression profiles --- p.82 / Chapter 3.7 --- Synergistic effects induced by lovastatin and Tumor Necrosis Factor related apoptosis-inducing Ligand (TRAIL) --- p.87 / Chapter 3.7.1 --- M059J and M059K glioblastoma cells was resistant to TRAIL attack --- p.87 / Chapter 3.7.2 --- Synergistic cell death was induced by lovastatin and TRAIL --- p.87 / Chapter 3.7.3 --- A combination of TRAIL and lovastatin induces synergistic apoptosis in glioblastoma cells --- p.93 / Chapter 3.7.4 --- DNA fragmentation on glioblastoma cells --- p.98 / Chapter 3.7.5 --- Four TRAIL receptors mRNA expression profiles on glioblastoma cells --- p.102 / Chapter Chapter 4 --- Discussion --- p.105 / Chapter 4.1 --- Lovastatin exhibited anti-proliferation effects in human glioblastoma cells --- p.107 / Chapter 4.2 --- Lovastatin activated caspase 3 and caspase 9 in human glioblastoma cells --- p.108 / Chapter 4.3 --- Gene expression profile modulated by Lovastatin in human glioblastoma cells --- p.110 / Chapter 4.4 --- Lovastatin-sensitized TRAIL-induced apoptosis in human glioblastoma cells --- p.117 / Chapter Chapter Five: --- Conclusion and Future perspective --- p.121 / References --- p.122 / Appendix --- p.150

Le glioblastome multiforme (GBM) est la tumeur cérébrale la plus commune et létale chez l’adulte. Malgré les avancés fulgurantes dans la dernière décennie au niveau des thérapies contre le cancer, le pronostique reste inchangé. Le manque de spécificité des traitements est la cause première de la récurrence de cette tumeur. Une meilleure compréhension au niveau des mécanismes moléculaires et biologiques de cette tumeur est impérative. La découverte des cellules souches cancéreuses (CD133+) au niveau du GBM offre une nouvelle opportunité thérapeutique contre cette tumeur. Effectivement, les cellules CD133+ seraient responsables de l’établissement, le maintien et la progression du GBM. De plus, elles sont également la cause de la résistance du GBM faces aux traitements de radiothérapies. Ces cellules représentent une cible de choix dans le but d’éradiquer le GBM. L’oncogène BMI1 a été associé à plusieurs types de tumeurs et est également essentielle au maintien de différentes populations de cellules souches normales et cancéreuses. Une forte expression de BMI1 est observée au niveau du GBM et plus précisément, un enrichissement préférentiel de cette protéine est noté au niveau des cellules CD133+. L’objectif principal de cette thèse est d’évaluer le rôle potentiel de BMI1 dans le maintien et la radiorésistance des cellules souches cancéreuses (CSC), CD133+ du GBM. La fonction principale de BMI1 est la régulation négative du locus INK4A/ARF. Ce locus est impliqué dans l’activation de deux voies majeurs anti-tumorales : P53 et RB. Or, la perte de BMI1 induit in vitro une diminution des capacités prolifératives, une augmentation de la différentiation et de l’apoptose, ainsi qu’une augmentation de la radiosensibilité des CSC du GBM indépendamment de la présence du locus INK4A/ARF. Effectivement, deux tumeurs sur trois possèdent une délétion de ce locus, ce qui suggère que BMI1 possède d’autre(s) cible(s) transcriptionnelle(s). Parmi ces nouvelles cibles ont retrouve la protéine P21, un régulateur négatif du cycle cellulaire. De plus, la perte de BMI1 inhibe l’établissement d’une tumeur cérébrale lors d’études de xénogreffe chez la souris NOD/SCID. Également, une nouvelle fonction de BMI1 indépendante de son activité transcriptionnel a été démontrée. Effectivement, suite à l’induction d’un bris double brin (BDB) de l’ADN, BMI1 est rapidement recruté au niveau de la lésion et influence le recrutement des protéines de reconnaissance du dommage à l’ADN. La perte de BMI1 mène à un défaut au niveau de la reconnaissance et la réparation de l’ADN, alors que sa surexpression induit plutôt une augmentation de ces mécanismes et procure une radiorésistance. Ces résultats décrivent pour la première fois l’importance de BMI1 au niveau du maintien, de l’auto-renouvellement et la radiorésistance des CSC du GBM. Ainsi, ces travaux démontrent que la protéine BMI1 représente une cible thérapeutique de choix dans le but d’éradiquer le GBM, une tumeur cérébrale létale. / Glioblastoma multiform (GBM) is the most common and lethal primary brain tumor found in adults. Despite the advances made in the field of cancer therapy in the last decade, the median survival rate remains less than a year. Therefore, a better understanding of the molecular biology of GBM will reveal the mechanisms responsible for the initiation and progression of the tumor, and allow the development of new therapeutic strategies. GBM contains a minority cell population, characterized by tumor initiating cells expressing the stem cell marker, CD133. The CD133+ GBM cells are responsible for tumor initiation, maintenance, progression and resistance to chemo/radiotherapy. The CD133+ cells represent a valuable and specific therapeutic target against GBM. The Polycomb (PcG) group family of transcriptional repressors have been involved in a vast range of cancers. The PcG protein and oncogene BMI1 is the best-characterized PcG protein. The implication of BMI1 in normal and cancer stem cell survival, self-renewal and maintenance has been thoroughly investigated. BMI1 is highly expressed in GBM and more precisely; it is enriched specifically in CD133+ cell populations. The main goal of this thesis was to elucidate the potential role of BMI1 in GBM CD133 + cancer stem cell (CSC) maintenance and radioresistance. The main function of BMI1 is to repress the expression of the genes encoded by the INK4A/ARF locus, which is implicated in the activation of two major tumor suppressor pathways, P53 and RB. However, BMI1 depletion in vitro induces a reduction in proliferation potential, as well as an increase in differentiation, apoptosis, and radiosensitivity regardless of INK4A/ARF status. Indeed, two-thirds of all tumors posses a deletion of this locus, suggesting that BMI1 regulates other targets. P21, a cell cycle regulator, was identified as a new BMI1 target. Moreover, we have observed that the loss of BMI1 inhibits the establishment of a cerebral tumor in a xenograft mouse model. In addition to transcription related activity, we identified a new transcription independent function of BMI1. After the induction of a DNA double-strand-break, BMI1 is rapidly recruited to the damage site and influences the recruitment of DNA damage response proteins. Furthermore, defects in DNA damage recognition and repair are observed after BMI1 knockdown. Consistent with these results, BMI1 overexpression induces DNA damage response and increases radioresistance potential. These results emphasize for the first time the requirement of BMI1 for the maintenance, self-renewal, and radioresistance in GBM CSC, thus providing a potential target for future therapeutic strategies against GBM.

Changes in gene expression programs play a central role in cancer. Chromosomal aberrations such as deletions, duplications and translocations of DNA segments can lead to highly significant positive correlations of gene expression levels of neighboring genes. This should be utilized to improve the analysis of tumor expression profiles. Here, we develop a novel model class of autoregressive higher-order Hidden Markov Models (HMMs) that carefully exploit local data-dependent chromosomal dependencies to improve the identification of differentially expressed genes in tumor. Autoregressive higher-order HMMs overcome generally existing limitations of standard first-order HMMs in the modeling of dependencies between genes in close chromosomal proximity by the simultaneous usage of higher-order state-transitions and autoregressive emissions as novel model features. We apply autoregressive higher-order HMMs to the analysis of breast cancer and glioma gene expression data and perform in-depth model evaluation studies. We find that autoregressive higher-order HMMs clearly improve the identification of overexpressed genes with underlying gene copy number duplications in breast cancer in comparison to mixture models, standard first- and higher-order HMMs, and other related methods. The performance benefit is attributed to the simultaneous usage of higher-order state-transitions in combination with autoregressive emissions. This benefit could not be reached by using each of these two features independently. We also find that autoregressive higher-order HMMs are better able to identify differentially expressed genes in tumors independent of the underlying gene copy number status in comparison to the majority of related methods. This is further supported by the identification of well-known and of previously unreported hotspots of differential expression in glioblastomas demonstrating the efficacy of autoregressive higher-order HMMs for the analysis of individual tumor expression profiles. Moreover, we reveal interesting novel details of systematic alterations of gene expression levels in known cancer signaling pathways distinguishing oligodendrogliomas, astrocytomas and glioblastomas.

Changes in gene expression programs play a central role in cancer. Chromosomal aberrations such as deletions, duplications and translocations of DNA segments can lead to highly significant positive correlations of gene expression levels of neighboring genes. This should be utilized to improve the analysis of tumor expression profiles. Here, we develop a novel model class of autoregressive higher-order Hidden Markov Models (HMMs) that carefully exploit local data-dependent chromosomal dependencies to improve the identification of differentially expressed genes in tumor. Autoregressive higher-order HMMs overcome generally existing limitations of standard first-order HMMs in the modeling of dependencies between genes in close chromosomal proximity by the simultaneous usage of higher-order state-transitions and autoregressive emissions as novel model features. We apply autoregressive higher-order HMMs to the analysis of breast cancer and glioma gene expression data and perform in-depth model evaluation studies. We find that autoregressive higher-order HMMs clearly improve the identification of overexpressed genes with underlying gene copy number duplications in breast cancer in comparison to mixture models, standard first- and higher-order HMMs, and other related methods. The performance benefit is attributed to the simultaneous usage of higher-order state-transitions in combination with autoregressive emissions. This benefit could not be reached by using each of these two features independently. We also find that autoregressive higher-order HMMs are better able to identify differentially expressed genes in tumors independent of the underlying gene copy number status in comparison to the majority of related methods. This is further supported by the identification of well-known and of previously unreported hotspots of differential expression in glioblastomas demonstrating the efficacy of autoregressive higher-order HMMs for the analysis of individual tumor expression profiles. Moreover, we reveal interesting novel details of systematic alterations of gene expression levels in known cancer signaling pathways distinguishing oligodendrogliomas, astrocytomas and glioblastomas.

Glioblastoma multiforme (GBM) is the most common and aggressive primary brain tumor. The current standard-of-care treatment including surgery, radiation and temozolomide (TMZ) chemotherapy does not prolong the survival satisfactorily. Here we have tested the feasibility, efficacy and safety of a potential gene therapy approach using AAV as gene delivery vehicle for treatment of GBM. Interferon-beta (IFNβ) is a cytokine molecule also having pleiotropic anticancerous properties. Previously it has been shown by our group that AAV mediated local (intracranial) gene delivery of human IFNβ (hIFNβ) could be an effective treatment for non-invasive human glioblastoma (U87) in orthotopic xenograft mouse model.But as one of the major challenges to treat GBM effectively in clinics is its highly invasive property, in the current study we first sought to test the efficacy of our therapeutic model in a highly invasive human GBM (GBM8) xenograft mouse model. One major limitation of using the xenograft mouse model is that these mice are immune-compromised. Moreover, as IFNβ does not interact with cross-species receptors, the influence of immune systems on GBM remains largely untested. Therefore to test the therapeutic approach in an immune-competent mouse model, we next treated a syngeneic mouse GBM model (GL261) in an immune-competent mouse (C57B6) with the gene encoding the species-matched IFNβ (mIFNβ). We also tested if combination of this IFNβ gene therapy with the current standard chemotherapeutic drug (TMZ) is more effective than any one of the therapeutic modes alone. Finally, we tested the long term safety of the AAV-mIFNβ local gene therapy in healthy C57B6 mice. Next, we hypothesized that global genetic engineering of brain cells expressing secretory therapeutic protein like hIFNβ could be more beneficial for treatment of invasive, migratory and distal multifocal GBM. We tested this hypothesis using systemic delivery of AAV9 vectors encoding hIFNβ gene for treatment of GBM8 tumor in nude mice. Using in vivo bioluminescence imaging of tumor associated firefly luciferase activity, long term survival assay and histological analysis of the brains we have shown that local treatment of AAV-hIFNβ for highly invasive human GBM8 is therapeutically beneficial at an early growth phase of tumor. However, systemic delivery route treatment is far superior for treating multifocal distal GBM8 tumors. Nonetheless, for both delivery routes, treatment efficacy is significantly reduced when treated at a later growth phase of the tumor. In syngeneic GL261 tumor model study, we show that local AAV-mIFNβ gene therapy alone or in combination with TMZ treatment can provide significant survival benefit over control or only TMZ treatment, respectively. However, the animals eventually succumb to the tumor. Safety study in the healthy animals shows significant body weight loss in some treatment groups, whereas one group shows long term survival without any weight loss or any noticeable changes in the external appearances. However, histological analysis indicates marked demyelinating neurotoxic effects upon long term exposures to mIFNβ over-expressions in brain. Overall, we conclude from this study that AAV-IFNβ gene therapy has great therapeutic potential for GBM treatment in future, but the therapeutic window is small and long term continuous expression could have severe deleterious effects on health.

<p>Herein, the development, optimization, and evaluation of quantitative techniques are presented for dynamic PET studies in cancer imaging applications. Dynamic PET image analysis techniques are first applied to 18F-fluoroethyltyrosine (FET) PET imaging of glioma and brain metastasis patients. In a second application, dynamic PET image analysis techniques are applied to 68Ga-PSMA-11 PET imaging for primary prostate cancer patients. Overall, the application of dynamic PET imaging techniques supports improved clinical outcomes and enhanced clinician confidence for treatment modifications. </p>