Die vorliegende Arbeit ist eine retrospektive Kohortenstudie, welche alle Patienten einschloß, die im Zeitraum von 07/1988 bis 06/1997 aufgrund eines anaplastischen Astrozytoms oder eines Glioblastoma multiforme im damaligen Rudolf-Virchow-Klinikum in Berlin eine Strahlentherapie des Kopfes erhielten. Von den 111 Patienten erlitten im Beobachtungszeitraum 85 ein radiologisches Rezidiv. Die mediane Überlebenszeit betrug 9 Monate. 69 der Rezidive waren Zentralrezidive, 7 Randrezidive und 9 Fernrezidive. Auch die Rand- und Fernrezidive rezidivierten zusätzlich am Ort der Primärläsion. Es konnte gezeigt werden dass ein Sicherheitsabstand von 2-3 cm ausreicht um 90% der Rezidive vollständig zu erfassen und dass die lokale Kontrolle weiterhin das Hauptproblem bei der Behandlung dieser malignen Gliome ist. / This retrospective study reviews the data of 111 patients treated from 07/1988 to 06/1997 at the Rudolf-Virchow-Klinikum in Berlin. Both patients with anaplastic astrocytoma and glioblastoma multiforme were included. 85 patients showed radiological recurrence of tumour. Median survival was 9 months. 69 recurrences were central, 7 near and 9 distant recurrences. Near and distant recurrences were always multifocal, i. e. they recurred also at central locations. It was shown that a safety margin of 2-3 cm is sufficient to completely cover 90% of recurrent tumour. Local failure is still the primary difficulty in treating these malignant glioma.

Les phosphinosucres appelés aussi « phostines » sont des analogues phosphorés des sucres pyranoses et des C-arylglycosides. L'évaluation biologique de ses composés a révélé une activité anticancéreuse des phosphinosucres sur les cellules de glioblastome multiforme, un cancer particulièrement malin et invasif qui ne possède pas de solution thérapeutique. Dans le but de comprendre les mécanismes d'action des phosphinosucres et la stéréo-dépendance de leur activité biologique, la caractérisation des diastéréomères de « phostines » a été menée. Suite à cette détermination structurale, le développement de synthèses diastéréosélectives a permis d'obtenir un mélange fortement enrichi en diastéréomère le plus actif par une séquence réactionnelle qui a mis en jeu une réaction d'oxydation de phosphinosucres -hydroxylés en α-cétophosphinosucres, suivie d'une réduction diastéréosélective. Afin d'améliorer l'activité antiproliférative des phosphinosucres, une diversification chimique a été réalisée. Les variations du groupement aryle lié à l'atome de phosphore nous ont amené à développer une synthèse des aryl-hydrogénophosphinates qui a permis d'obtenir une large variété de ces composés. Par la suite, les aryl-hydrogénophosphinates obtenus ont été engagés dans la synthèse des « phostines ». De plus, des variations chimiques sur le carbone en position α de l'atome de phosphore ont été entreprises et ont permis l'élaboration de plusieurs composés (triflate, azido, amino, déoxy et triazolyles), puis finalement à l'analogue phosphinosucre du N-acétylglucosamine qui a présenté une importante activité anticancéreuse in vitro. / Phosphinosugars also called « phostines » are new cyclic phosphinates, analogs of carbohydrates and C-aryglycosides, with phosphorus atom mimicking the anomeric carbon. Biological screening tests of these compounds revealed an anticancer activity against glioblastoma multiform, a highly invasive and malignant tumor without curative therapy.With the aim of understanding the phosphinosugars mode of action and their stereo-dependent biological activity, characterization of four phosphinosugars diastereomers formed during the chemical process has been performed. After their structural determination, diastereoselective synthesis enabled us to obtain an enriched mixture of the most active diastereomer based on an oxidation of -hydroxyled phosphinosugars in corresponding -keto phosphinosugars followed by a diastereoselective reduction. Thereafter, antiproliferative activity of phoshinosugars was performed by chemical diversification. Modification of the aryl group linked to phosphorus atom led us to develop aryl-hydrogenophosphinate synthesis to create a broad variety of these structures. Then, the expected aryl-hydrogenophosphinates were used for phostines preparation. Furthermore, chemical modifications on the carbon in α position of phosphorus atom were led and furnished several new compounds (triflate, azido, amino, deoxy and triazolyl), as well as the phosphinosugar analog of N-acetylglucosamine which presented in vitro a high anticancer activity.

The purpose of this research is to validate various forms of mathematical modeling of glioblastoma multiforme (GBM) expressed as differential equations, numerically. The first work was involved in the numerical solution of the reaction-convection model, efficacy of which is expressed in terms of survival time. It was calculated using simple numerical scheme for the standard-of-care treatment in clinics which includes surgery followed by the radiation and chemotherapy. Survival time using all treatment options increased significantly to 57 weeks compared to that of surgery close to 14 weeks. It was also observed that survival time increased significantly to 90 weeks if tumor is totally resected. In reaction-diffusion model using simple numerical scheme, tumor cell density patterns due to variation in patient specific tumor parameters such as net proliferation rate and diffusion coefficient were computed. Significant differences were observed in the patterns while using dominant diffusion and proliferation rate separately. Numerical solution of the tumor growth model under the anti-angiogenic therapy revealed some impacts in optimum tumor growth control however it was not significant. / Includes bibliography. / Thesis (M.S.)--Florida Atlantic University, 2016. / FAU Electronic Theses and Dissertations Collection

Uma das principais áreas de pesquisa em Biologia de Sistemas refere-se à descoberta de redes biológicas a partir de conjuntos de dados de microarrays. Estas redes consistem de um grande número de genes cujos níveis de expressão afetam os outros genes de vários modos. Nesta tese, apresenta-se uma nova maneira de analisar os conjuntos de dados de microarrays, com base nos diferentes tipos de ciclos encontrados entre os genes das redes de co-expressão construídas com dados quantificados obtidos a partir dos microarrays. A entrada do método de análise é formada pelos dados brutos, um conjunto de genes de interesse (por exemplo, genes de uma via conhecida) e uma função (ativador ou inibidor) destes genes. A saída do método é um conjunto de ciclos. Um ciclo é um caminho fechado com todos os vértices (exceto o primeiro e o último) distintos. Graças à nova forma de encontrar relações entre os genes, é possível uma interpretação mais robusta das correlações dos genes, porque os ciclos estão associados a mecanismos de feedback, que são muito comuns em redes biológicas. A hipótese é que feedbacks negativos permitem encontrar relações entre os genes que podem ajudar a explicar a estabilidade do processo regulatório dentro da célula. Ciclos de feedback positivo, por outro lado, podem mostrar a quantidade de desequilíbrio de uma determinada célula em um determinado momento. A análise baseada em ciclos permite identificar a relação estequiométrica entre os genes da rede. Esta metodologia proporciona uma melhor compreensão da biologia do tumor. Portanto, as principais contribuições desta tese são: (i) um novo método de análise baseada em ciclos; (ii) um novo método de classificação; (iii) e, finalmente, aplicação dos métodos e a obtenção de resultados práticos. A metodologia proposta foi utilizada para analisar os genes de quatro redes fortemente relacionadas com o câncer - apoptose, glicólise, ciclo celular e NF B - em tecidos do tipo mais agressivo de tumor cerebral (Gliobastoma multiforme - GBM) e em tecidos cerebrais saudáveis. A maioria dos pacientes com GBM morrem em menos de um ano, essencialmente nenhum paciente tem sobrevivência a longo prazo, por isso estes tumores têm atraído atenção significativa. Os principais resultados nesta tese mostram que a relação estequiométrica entre genes envolvidos na apoptose, glicólise, ciclo celular e NF B está desequilibrada em amostras de GBM em comparação as amostras de controle. Este desequilíbrio pode ser medido e explicado pela identificação de um percentual maior de ciclos positivos nas redes das primeiras amostras. Esta conclusão ajuda a entender mais sobre a biologia deste tipo de tumor. O método de classificação baseado no ciclo proposto obteve as mesmas métricas de desempenho como uma rede neural, um método clássico de classificação. No entanto, o método proposto tem uma vantagem significativa em relação às redes neurais. O método de classificação proposto não só classifica as amostras, fornecendo diagnóstico, mas também explica porque as amostras foram classificadas de uma certa maneira em termos dos mecanismos de feedback que estão presentes/ausentes. Desta forma, o método fornece dicas para bioquímicos sobre possíveis experiências laboratoriais, bem como sobre potenciais genes alvo de terapias. / One of the main research areas in Systems Biology concerns the discovery of biological networks from microarray datasets. These networks consist of a great number of genes whose expression levels affect each other in various ways. We present a new way of analyzing microarray datasets, based on the different kind of cycles found among genes of the co-expression networks constructed using quantized data obtained from the microarrays. The input of the analysis method is formed by raw data, a set of interest genes (for example, genes from a known pathway) and a function (activator or inhibitor) of these genes. The output of the method is a set of cycles. A cycle is a closed walk, in which all vertices (except the first and last) are distinct. Thanks to the new way of finding relations among genes, a more robust interpretation of gene correlations is possible, because cycles are associated with feedback mechanisms that are very common in biological networks. Our hypothesis is that negative feedbacks allow finding relations among genes that may help explaining the stability of the regulatory process within the cell. Positive feedback cycles, on the other hand, may show the amount of imbalance of a certain cell in a given time. The cycle-based analysis allows identifying the stoichiometric relationship between the genes of the network. This methodology provides a better understanding of the biology of tumors. As a consequence, it may enable the development of more effective treatment therapies. Furthermore, cycles help differentiate, measure and explain the phenomena identified in healthy and diseased tissues. Cycles may also be used as a new method for classification of samples of a microarray (cancer diagnosis). Compared to other classification methods, cycle-based classification provides a richer explanation of the proposed classification, that can give hints on the possible therapies. Therefore, the main contributions of this thesis are: (i) a new cycle-based analysis method; (ii) a new microarray samples classification method; (iii) and, finally, application and achievement of practical results. We use the proposed methodology to analyze the genes of four networks closely related with cancer - apoptosis, glucolysis, cell cycle and NF B - in tissues of the most aggressive type of brain tumor (Gliobastoma multiforme – GBM) and in healthy tissues. Because most patients with GBMs die in less than a year, and essentially no patient has long-term survival, these tumors have drawn significant attention. Our main results show that the stoichiometric relationship between genes involved in apoptosis, glucolysis, cell cycle and NF B pathways is unbalanced in GBM samples versus control samples. This dysregulation can be measured and explained by the identification of a higher percentage of positive cycles in these networks. This conclusion helps to understand more about the biology of this tumor type. The proposed cycle-based classification method achieved the same performance metrics as a neural network, a classical classification method. However, our method has a significant advantage with respect to neural networks. The proposed classification method not only classifies samples, providing diagnosis, but also explains why samples were classified in a certain way in terms of the feedback mechanisms that are present/absent. This way, the method provides hints to biochemists about possible laboratory experiments, as well as on potential drug target genes.

Pharmacological ascorbate, intravenous administration of high-dose vitamin C aimed at peak plasma concentrations ~ 20 mM, has recently re-emerged, after a controversial history, as a potential anti-cancer agent in combination with standard-of-care radiation and chemotherapy-based regimens. The anti-cancer effects of ascorbate are hypothesized to involve the auto-oxidation or metal-catalyzed oxidation of ascorbate to generate H2O2, and preclinical in vitro and in vivo studies in a variety of disease sites demonstrate the efficacy of adjuvant ascorbate. Furthermore, phase I clinical trials in pancreatic and ovarian cancer have demonstrated safety and tolerability in combination with chemotherapy and preliminary results suggest therapeutic efficacy. Both preclinical in vitro and in vivo studies as well as phase I clinical trials suggest a cell-intrinsic mechanism of selective toxicity of cancer cells as compared to normal cells; however, the mechanism(s) for cancer cell-selective toxicity remain unknown. The current study aims to investigate the preclinical therapeutic efficacy of pharmacological ascorbate in combination with standard cancer therapies in three novel disease sites: non-small cell lung cancer (NSCLC), glioblastoma multiforme (GBM), and some histological subtypes of sarcoma. In vitro experiments demonstrate cancer cell-selective susceptibility to pharmacological ascorbate as compared to normal cells of identical cell lineages. Furthermore, in vivo murine xenograft models of NSCLC, GBM, and fibrosarcoma demonstrate therapeutic efficacy of pharmacological ascorbate in combination with chemotherapy and/or radiation as compared to chemotherapy and/or radiation alone without any additional therapeutic toxicity. Additionally, a phase I clinical trial in GBM subjects demonstrates the safety and tolerability of ascorbate in combination with radiation and temozolomide therapy. Although not powered for efficacy, preliminary results suggest that ascorbate may be efficacious in these subjects (median survival 18.2 months vs. 14.6 months in historical controls), and, importantly, that ascorbate therapy may be independent of MGMT promoter methylation status (median survival 23.0 months vs. 12.7 months in historical controls with absent MGMT promoter methylation). Preliminary results from a phase II clinical trial of ascorbate in combination with carboplatin/paclitaxel chemotherapy in advanced stage NSCLC subjects also demonstrate promising preliminary results related to efficacy (objective response rate (ORR) 29% and disease control rate (DCR) 93% vs. historical control ORR 15-19% and DCR 40%). In addition to demonstrating the potential efficacy of pharmacological ascorbate in combination with standard anti-cancer therapies, this work demonstrates that the selective toxicity of ascorbate may be mediated by perturbations in cancer cell oxidative metabolism. Increased mitochondrial-derived O2- and H2O2 disrupts cellular iron metabolism, resulting in increased iron uptake via Transferrin Receptor and a larger intracellular labile iron pool. The larger pool of labile iron in cancer cells underlies the selective sensitivity of cancer cells to ascorbate toxicity through pro-oxidant chemistry with ascorbate-produced H2O2. This mechanism is further supported by the finding of increased levels of O2- and labile iron in patient lobectomy-derived NSCLC tissue as compared to adjacent normal fresh frozen tissue. Together, these studies demonstrate the feasibility, selective toxicity, tolerability, and potential efficacy of pharmacological ascorbate in NSCLC, GBM, and sarcoma therapy and propose that further investigations of tumor and systemic iron metabolism are required to determine if these alterations can be exploited to enhance therapeutic efficacy or serve as therapeutic biomarkers.

Das Glioblastoma multiforme gehört zu den ZNS-Tumoren neuroepithelialen Ursprungs. Es zeichnet sich durch ein multiformes Zellbild, einen geringen Differenzierungsgrad und eine schnelle Krankheitsprogression aus. Trotz mikrochirurgischer Entfernung und anschließender Radiochemotherapie entwickeln die Patienten im Durchschnitt nach 7 Monaten einen Rezidivtumor und haben eine mittlere Überlebenszeit von 14,6 Monaten. Die Rezidivneigung stellt somit ein großes Problem in der Behandlung von Glioblastompatienten dar. In früheren Arbeiten konnte nachgewiesen werden, dass die Rezidivtumore eine andere Zellzusammensetzung und auch ein aggressiveres Wachstumsverhalten als deren Primärformen aufweisen. Ziel dieser Arbeit war es, zu prüfen ob mittels 2D-Gelelektrophorese und anschließender MALDI-TOF-Massenspektrometrie Unterschiede im Proteinexpressionsmuster zwischen Gewebeproben vom Primärtumor eines Glioblastoms WHO Grad IV und dem korrespondierendem Rezidivtumor eines Patienten detektierbar sind. Hierbei wurden 43 Proteine als differentiell exprimiert erkannt, von denen mit Hilfe der MALDI-TOF-Massenspektrometrie sechs genauer charakterisiert wurden. Vier der sechs Proteine waren im Rezidivtumor erhöht: EnoylCoA-Hydratase, ATP-Synthase Untereinheit d, Tropomyosin alpha-3-Kette Isoform 2 und Cathepsin D. Die anderen zwei waren im Rezidivtumor niedriger ausgeprägt: Nukleosid-Diphosphatkinase A und L-3-Phosphoserin-Phosphatase. Eine weitere Untersuchung mittels Western-Blot-Analyse bestätigte, dass Cathepsin D (als eines der sechs charakterisierten Proteine) tatsächlich auch in den Rezidivtumoren dreier weiterer Patienten stärker exprimiert war als in den korrespondierenden primären Glioblastomen.

Glioblastoma multiforme (GBM) ist der nach WHO am gefährlichsten eingestufte Hirntumor astrozytären Ursprungs. Patienten versterben ohne Behandlung etwa drei bis sechs Monaten nach Diagnose, die derzeitig modernste Behandlung mit Chemo-Radiotherapie verlängert das mediane Überleben auf 12-15 Monate. Trotz intensiver Forschung gibt es zurzeit keine realistische Heilungschance. Bislang erfolgt der Großteil der Forschung an Zellkulturen oder mit Hilfe von Tiermodellen, bei denen ein Tumor künstlich erzeugt wird. Dabei ergeben sich Probleme für die Übertragung der Ergebnisse auf den Menschen. Zellkulturen werden z.B. als sogenannte Monolayer-Kulturen gehalten, was bedeutet, dass ihnen der natürliche Gewebeverband und die für Signalling-Wege wichtige extrazelluläre Matrix fehlen. Außerdem werden solche Langzeitkulturen häufig subkultiviert und mutieren dadurch in Richtung einer klonalen Linie, was zwar Ergebnisse leichter reproduzierbar macht, aber nicht die Situation im Patienten widerspiegelt. Tierversuche implizieren zwar den Gewebeverband im Körper, jedoch müssen die dafür verwendeten Nager immunsupprimiert sein, so dass sie den induzierten Tumor nicht abstoßen. Dies erzeugt wiederum ein künstliches Umfeld. In diesem Projekt wird untersucht, ob sich humane GBM-Gewebe als sogenannte Slice-Kultur halten lassen und als Testsysteme zur Untersuchung der Wirkung von Chemotherapeutika sowie Bestrahlung geeignet sind. Bei dieser Kultivierungsmethode wird das Gewebe in Scheiben (Slices) geschnitten, wobei alle Zellen im Verband sowie die 3D-Struktur erhalten bleiben. Wegen des humanen Ursprungs entfällt das Problem des Speziesunterschiedes. Das Gewebe wird direkt aus dem Operationssaal ins Labor transferiert und weiterverarbeitet. Wir konnten bislang zeigen, dass Slice-Kulturen von humanem GBM über mindestens zwei Wochen in Kultur vital bleiben und ihre ursprüngliche charakteristische Morphologie beibehalten. Etablierte Behandlungsmethoden wie die Gabe von Temozolomid oder Röntgenbestrahlung zeigen auch in kultivierten Slices bekannte Effekte wie Induktion von DNA-Doppelstrangbrüchen, Reduktion von Proliferation und Aktivierung des Apoptose-Enzyms Caspase 3. Eine neue Therapieoption besteht seit einigen Jahren in der Bestrahlung mit Kohlenstoffionen (12C), die an der GSI Helmholtzzentrum für Schwerionenforschung in Darmstadt entwickelt und getestet wurde. Derzeit wird diese Therapie sehr erfolgreich an soliden Tumoren im Kopf- und Halsbereich angewendet und soll nun auf weitere Tumorarten ausgedehnt werden. Eine Kooperation mit der dortigen Biophysik-Gruppe wurde initiiert, um humane GBM-Slices mit 12C zu bestrahlen. Bislang wurde das entsprechende Setup etabliert und erste Experimente durchgeführt. Die ersten Ergebnisse wurden kürzlich publiziert. Weiterhin soll nun geprüft werden, ob das Ansprechen der GBM Slice-Kulturen mit dem Überleben der Patienten korreliert bzw. ob resistente Kulturen aus Patienten stammten, die schlecht auf die Therapie reagierten. Außerdem sollen überlebende Zellen in den Slices nach Behandlung auf ihre molekularen Eigenschaften geprüft werden, um Hinweise auf die Mechanismen der Tumorresistenz zu erhalten. Langfristig könnten diese Slice-Kulturen genutzt werden, um neuartige Wirkstoffe in der Vorklinik zu prüfen oder eine optimierte, personalisierte Therapie für Patienten zu ermitteln.

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Uma das principais áreas de pesquisa em Biologia de Sistemas refere-se à descoberta de redes biológicas a partir de conjuntos de dados de microarrays. Estas redes consistem de um grande número de genes cujos níveis de expressão afetam os outros genes de vários modos. Nesta tese, apresenta-se uma nova maneira de analisar os conjuntos de dados de microarrays, com base nos diferentes tipos de ciclos encontrados entre os genes das redes de co-expressão construídas com dados quantificados obtidos a partir dos microarrays. A entrada do método de análise é formada pelos dados brutos, um conjunto de genes de interesse (por exemplo, genes de uma via conhecida) e uma função (ativador ou inibidor) destes genes. A saída do método é um conjunto de ciclos. Um ciclo é um caminho fechado com todos os vértices (exceto o primeiro e o último) distintos. Graças à nova forma de encontrar relações entre os genes, é possível uma interpretação mais robusta das correlações dos genes, porque os ciclos estão associados a mecanismos de feedback, que são muito comuns em redes biológicas. A hipótese é que feedbacks negativos permitem encontrar relações entre os genes que podem ajudar a explicar a estabilidade do processo regulatório dentro da célula. Ciclos de feedback positivo, por outro lado, podem mostrar a quantidade de desequilíbrio de uma determinada célula em um determinado momento. A análise baseada em ciclos permite identificar a relação estequiométrica entre os genes da rede. Esta metodologia proporciona uma melhor compreensão da biologia do tumor. Portanto, as principais contribuições desta tese são: (i) um novo método de análise baseada em ciclos; (ii) um novo método de classificação; (iii) e, finalmente, aplicação dos métodos e a obtenção de resultados práticos. A metodologia proposta foi utilizada para analisar os genes de quatro redes fortemente relacionadas com o câncer - apoptose, glicólise, ciclo celular e NF B - em tecidos do tipo mais agressivo de tumor cerebral (Gliobastoma multiforme - GBM) e em tecidos cerebrais saudáveis. A maioria dos pacientes com GBM morrem em menos de um ano, essencialmente nenhum paciente tem sobrevivência a longo prazo, por isso estes tumores têm atraído atenção significativa. Os principais resultados nesta tese mostram que a relação estequiométrica entre genes envolvidos na apoptose, glicólise, ciclo celular e NF B está desequilibrada em amostras de GBM em comparação as amostras de controle. Este desequilíbrio pode ser medido e explicado pela identificação de um percentual maior de ciclos positivos nas redes das primeiras amostras. Esta conclusão ajuda a entender mais sobre a biologia deste tipo de tumor. O método de classificação baseado no ciclo proposto obteve as mesmas métricas de desempenho como uma rede neural, um método clássico de classificação. No entanto, o método proposto tem uma vantagem significativa em relação às redes neurais. O método de classificação proposto não só classifica as amostras, fornecendo diagnóstico, mas também explica porque as amostras foram classificadas de uma certa maneira em termos dos mecanismos de feedback que estão presentes/ausentes. Desta forma, o método fornece dicas para bioquímicos sobre possíveis experiências laboratoriais, bem como sobre potenciais genes alvo de terapias. / One of the main research areas in Systems Biology concerns the discovery of biological networks from microarray datasets. These networks consist of a great number of genes whose expression levels affect each other in various ways. We present a new way of analyzing microarray datasets, based on the different kind of cycles found among genes of the co-expression networks constructed using quantized data obtained from the microarrays. The input of the analysis method is formed by raw data, a set of interest genes (for example, genes from a known pathway) and a function (activator or inhibitor) of these genes. The output of the method is a set of cycles. A cycle is a closed walk, in which all vertices (except the first and last) are distinct. Thanks to the new way of finding relations among genes, a more robust interpretation of gene correlations is possible, because cycles are associated with feedback mechanisms that are very common in biological networks. Our hypothesis is that negative feedbacks allow finding relations among genes that may help explaining the stability of the regulatory process within the cell. Positive feedback cycles, on the other hand, may show the amount of imbalance of a certain cell in a given time. The cycle-based analysis allows identifying the stoichiometric relationship between the genes of the network. This methodology provides a better understanding of the biology of tumors. As a consequence, it may enable the development of more effective treatment therapies. Furthermore, cycles help differentiate, measure and explain the phenomena identified in healthy and diseased tissues. Cycles may also be used as a new method for classification of samples of a microarray (cancer diagnosis). Compared to other classification methods, cycle-based classification provides a richer explanation of the proposed classification, that can give hints on the possible therapies. Therefore, the main contributions of this thesis are: (i) a new cycle-based analysis method; (ii) a new microarray samples classification method; (iii) and, finally, application and achievement of practical results. We use the proposed methodology to analyze the genes of four networks closely related with cancer - apoptosis, glucolysis, cell cycle and NF B - in tissues of the most aggressive type of brain tumor (Gliobastoma multiforme – GBM) and in healthy tissues. Because most patients with GBMs die in less than a year, and essentially no patient has long-term survival, these tumors have drawn significant attention. Our main results show that the stoichiometric relationship between genes involved in apoptosis, glucolysis, cell cycle and NF B pathways is unbalanced in GBM samples versus control samples. This dysregulation can be measured and explained by the identification of a higher percentage of positive cycles in these networks. This conclusion helps to understand more about the biology of this tumor type. The proposed cycle-based classification method achieved the same performance metrics as a neural network, a classical classification method. However, our method has a significant advantage with respect to neural networks. The proposed classification method not only classifies samples, providing diagnosis, but also explains why samples were classified in a certain way in terms of the feedback mechanisms that are present/absent. This way, the method provides hints to biochemists about possible laboratory experiments, as well as on potential drug target genes.

Uma das principais áreas de pesquisa em Biologia de Sistemas refere-se à descoberta de redes biológicas a partir de conjuntos de dados de microarrays. Estas redes consistem de um grande número de genes cujos níveis de expressão afetam os outros genes de vários modos. Nesta tese, apresenta-se uma nova maneira de analisar os conjuntos de dados de microarrays, com base nos diferentes tipos de ciclos encontrados entre os genes das redes de co-expressão construídas com dados quantificados obtidos a partir dos microarrays. A entrada do método de análise é formada pelos dados brutos, um conjunto de genes de interesse (por exemplo, genes de uma via conhecida) e uma função (ativador ou inibidor) destes genes. A saída do método é um conjunto de ciclos. Um ciclo é um caminho fechado com todos os vértices (exceto o primeiro e o último) distintos. Graças à nova forma de encontrar relações entre os genes, é possível uma interpretação mais robusta das correlações dos genes, porque os ciclos estão associados a mecanismos de feedback, que são muito comuns em redes biológicas. A hipótese é que feedbacks negativos permitem encontrar relações entre os genes que podem ajudar a explicar a estabilidade do processo regulatório dentro da célula. Ciclos de feedback positivo, por outro lado, podem mostrar a quantidade de desequilíbrio de uma determinada célula em um determinado momento. A análise baseada em ciclos permite identificar a relação estequiométrica entre os genes da rede. Esta metodologia proporciona uma melhor compreensão da biologia do tumor. Portanto, as principais contribuições desta tese são: (i) um novo método de análise baseada em ciclos; (ii) um novo método de classificação; (iii) e, finalmente, aplicação dos métodos e a obtenção de resultados práticos. A metodologia proposta foi utilizada para analisar os genes de quatro redes fortemente relacionadas com o câncer - apoptose, glicólise, ciclo celular e NF B - em tecidos do tipo mais agressivo de tumor cerebral (Gliobastoma multiforme - GBM) e em tecidos cerebrais saudáveis. A maioria dos pacientes com GBM morrem em menos de um ano, essencialmente nenhum paciente tem sobrevivência a longo prazo, por isso estes tumores têm atraído atenção significativa. Os principais resultados nesta tese mostram que a relação estequiométrica entre genes envolvidos na apoptose, glicólise, ciclo celular e NF B está desequilibrada em amostras de GBM em comparação as amostras de controle. Este desequilíbrio pode ser medido e explicado pela identificação de um percentual maior de ciclos positivos nas redes das primeiras amostras. Esta conclusão ajuda a entender mais sobre a biologia deste tipo de tumor. O método de classificação baseado no ciclo proposto obteve as mesmas métricas de desempenho como uma rede neural, um método clássico de classificação. No entanto, o método proposto tem uma vantagem significativa em relação às redes neurais. O método de classificação proposto não só classifica as amostras, fornecendo diagnóstico, mas também explica porque as amostras foram classificadas de uma certa maneira em termos dos mecanismos de feedback que estão presentes/ausentes. Desta forma, o método fornece dicas para bioquímicos sobre possíveis experiências laboratoriais, bem como sobre potenciais genes alvo de terapias. / One of the main research areas in Systems Biology concerns the discovery of biological networks from microarray datasets. These networks consist of a great number of genes whose expression levels affect each other in various ways. We present a new way of analyzing microarray datasets, based on the different kind of cycles found among genes of the co-expression networks constructed using quantized data obtained from the microarrays. The input of the analysis method is formed by raw data, a set of interest genes (for example, genes from a known pathway) and a function (activator or inhibitor) of these genes. The output of the method is a set of cycles. A cycle is a closed walk, in which all vertices (except the first and last) are distinct. Thanks to the new way of finding relations among genes, a more robust interpretation of gene correlations is possible, because cycles are associated with feedback mechanisms that are very common in biological networks. Our hypothesis is that negative feedbacks allow finding relations among genes that may help explaining the stability of the regulatory process within the cell. Positive feedback cycles, on the other hand, may show the amount of imbalance of a certain cell in a given time. The cycle-based analysis allows identifying the stoichiometric relationship between the genes of the network. This methodology provides a better understanding of the biology of tumors. As a consequence, it may enable the development of more effective treatment therapies. Furthermore, cycles help differentiate, measure and explain the phenomena identified in healthy and diseased tissues. Cycles may also be used as a new method for classification of samples of a microarray (cancer diagnosis). Compared to other classification methods, cycle-based classification provides a richer explanation of the proposed classification, that can give hints on the possible therapies. Therefore, the main contributions of this thesis are: (i) a new cycle-based analysis method; (ii) a new microarray samples classification method; (iii) and, finally, application and achievement of practical results. We use the proposed methodology to analyze the genes of four networks closely related with cancer - apoptosis, glucolysis, cell cycle and NF B - in tissues of the most aggressive type of brain tumor (Gliobastoma multiforme – GBM) and in healthy tissues. Because most patients with GBMs die in less than a year, and essentially no patient has long-term survival, these tumors have drawn significant attention. Our main results show that the stoichiometric relationship between genes involved in apoptosis, glucolysis, cell cycle and NF B pathways is unbalanced in GBM samples versus control samples. This dysregulation can be measured and explained by the identification of a higher percentage of positive cycles in these networks. This conclusion helps to understand more about the biology of this tumor type. The proposed cycle-based classification method achieved the same performance metrics as a neural network, a classical classification method. However, our method has a significant advantage with respect to neural networks. The proposed classification method not only classifies samples, providing diagnosis, but also explains why samples were classified in a certain way in terms of the feedback mechanisms that are present/absent. This way, the method provides hints to biochemists about possible laboratory experiments, as well as on potential drug target genes.