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Over the past decade, multiple function genomic datasets studying chromosomal aberrations and their downstream implications on gene expression have accumulated across a variety of cancer types. With the majority being paired copy number/gene expression profiles originating from the same patient groups, this time frame has also induced a wealth of integrative attempts in hope that the concurrent analysis between both genomic structures will result in optimized downstream results. Borrowing the concept, this dissertation presents a novel contribution to the development of statistical methodology for integrating copy number and gene expression data for purposes of predicting treatment response in multiple myeloma patients.

Traditional antidepressants, which act on the serotonin, dopamine, and norepinephrine systems, require many weeks to produce a therapeutic effect and are not effective for every patient. A sub-anesthetic dose of the anesthetic agent ketamine, a glutamate N-methyl-D-aspartate receptor antagonist, has been shown to produce a rapid and robust antidepressant effect in treatment-resistant major depressive disorder (MDD). As depressive symptoms typically return after one week following a single infusion, recent work has begun to focus on methods for prolonging the effects. Repeated infusions on a specific dosing schedule are being explored, however, the early identification of treatment responders and non-responders would be beneficial for optimized treatment selection within this population. The mechanisms underlying ketamine’s rapid effects conceivably involve the regulation of altered glutamatergic signaling in MDD, though this is not yet completely understood. Understanding of the central mechanisms mediating ketamine’s rapid antidepressant effects may be increased through the use of non-invasive electroencephalographic measures, including resting electroencephalography (EEG) and the mismatch negativity (MMN) event-related potential. These measures have been shown to be altered in depressed individuals and are sensitive to ketamine administration. The primary objectives of this study were to 1) examine acute changes in EEG- and MMN-derived indices, immediately post- and two hours postinfusion, with a sub-anesthetic ketamine dose in comparison to an active placebo (midazolam), and 2) to examine their relationships with early and sustained antidepressant treatment response to ketamine within an eight week clinical trial involving three study phases. Ketamine decreased measures of scalp-level alpha and theta resting activity, immediately postinfusion, and increased gamma immediately and two hours postinfusion. An increase in source-localized anterior cingulate activity two hours postinfusion was also observed. Regarding the MMN, ketamine reduced frontal amplitudes as well as theta event-related oscillations and source-localized peak frontal generator activity. Measures of resting theta and change in gamma, as well as left frontal MMN amplitude, theta event-related oscillations, baseline left phase locking factor, and baseline right inferior temporal lobe activity were predictive of decreases in depressive symptoms at both early and sustained treatment time points. Alpha power was predictive of decrease in suicidal ideation, though the relationship with baseline and early change in symptoms was stronger. These findings contribute to our understanding of the role of baseline and ketamine-induced changes in both resting and task-evoked electrophysiological measures, and may have the potential to act as non-invasive biomarkers of antidepressant response prediction to glutamatergic agents.

Hepatitis C Virus (HCV) infects 170 million people worldwide. The current treatment regimen, which is combination therapy with pegylated interferon (PegIFN) and Ribavirin (Rib), cures only 50% of the patients infected with the most prevalent HCV genotype. Therefore, there is a pressing need to understand the molecular mechanism of interferon resistance and to develop a prognostic tool to predict who will respond to treatment before initiation of therapy. It has been firmly established that the virus-host interaction plays an important role in determining treatment outcomes. My thesis investigated the host factors that are involved in interferon resistance with an aim to provide insights into the molecular mechanism of IFN resistance. cDNA microarray analysis identified 18 differentially expressed hepatic genes from pretreatment liver tissues of responders (Rs) and non-responders (NRs). Based on the differential expression levels of these 18 genes, a prognostic tool was developed to predict who will respond to therapy, with a positive predicting value (PPV) of 96%. Most of these 18 genes are interferon stimulated genes (ISGs) and they are more highly expressed in NR livers, indicating that preactivation of interferon signaling in the pre-treatment liver tissues contributes to NR. 3 out of the 18 genes are involved in an ubiquitin-like ISG15/USP18 signaling pathway that plays an important role in interferon response. Over-expression of USP18 and ISG15 in the pretreatment liver tissues of NR promotes HCV production and blunts interferon anti-HCV activity. There exists a distinct cell-type specific ISG activation in the pretreatment liver tissues of Rs and NRs. Up-regulation of the two ISGs that I tested (ISG15 and MxA) was found mainly in hepatocytes in NRs while ISG activation was preferentially observed in macrophages in Rs. Taking all these data together, pre-activation of interferon signaling and cell-type specific gene activation in the pretreatment liver tissues of patients infected with HCV are associated with treatment non-response. HCV exploits the host interferon system to favour its persistence by enhanced replication /secretion stimulated by a few ISGs (ISG15, USP18) in response to IFN. The developed prognostic tool can be used to stratify patients for treatment and the novel insights of the molecular mechanism of IFN resistance in HCV patients offer potential drug targets for future development.

Modelo multinível é uma ferramenta estatística cada vez mais popular para análise de dados com estrutura hierárquica. O objetivo deste trabalho é propor um método para realizar a predição de observações de novos grupos usando modelos de regressão logística multinível com 2 níveis. Além disso, é apresentado e comparado dois métodos de estimação para o modelo multinível: Quase-verossimilhança Penalizada (QVP) e Quadratura de Gauss-Hermite (QGH). A idéia central está baseada no trabalho de (Jiang e Lahiri, 2006) no qual se propõe o uso do chamado melhor estimador empírico para o efeito aleatório. Através deste estimador, utilizou-se a parte fixa do modelo em conjunto com uma estimativa do desvio padrão do efeito aleatório para fazer a predição de observações de novos grupos, encontrando a probabilidade estimada dessa observação apresentar o evento de interesse, dadas suas características. / Multilevel model is an statistical tool which is becoming more and more popular in data analysis with hierachical structure. The purpose of this dissertation is to present a method to make a prediction of new group observation in multilevel logistic regression models with 2 levels. Besides, were presented and compared two estimation methods for multilevel model: Penalized Quase-likelihood and Gauss-Hermite Quadrature. The central idea is based on the paper of Jiang and Lahiri (2006), which is presented the empirical best estimator for the random effect. Through this estimator was used the fixed part of the model with an estimative of the standard deviation of the random effect to find the estimated probability of this observation presenting the target event, in accordance with its characteristic.

Hepatitis C Virus (HCV) infects 170 million people worldwide. The current treatment regimen, which is combination therapy with pegylated interferon (PegIFN) and Ribavirin (Rib), cures only 50% of the patients infected with the most prevalent HCV genotype. Therefore, there is a pressing need to understand the molecular mechanism of interferon resistance and to develop a prognostic tool to predict who will respond to treatment before initiation of therapy. It has been firmly established that the virus-host interaction plays an important role in determining treatment outcomes. My thesis investigated the host factors that are involved in interferon resistance with an aim to provide insights into the molecular mechanism of IFN resistance. cDNA microarray analysis identified 18 differentially expressed hepatic genes from pretreatment liver tissues of responders (Rs) and non-responders (NRs). Based on the differential expression levels of these 18 genes, a prognostic tool was developed to predict who will respond to therapy, with a positive predicting value (PPV) of 96%. Most of these 18 genes are interferon stimulated genes (ISGs) and they are more highly expressed in NR livers, indicating that preactivation of interferon signaling in the pre-treatment liver tissues contributes to NR. 3 out of the 18 genes are involved in an ubiquitin-like ISG15/USP18 signaling pathway that plays an important role in interferon response. Over-expression of USP18 and ISG15 in the pretreatment liver tissues of NR promotes HCV production and blunts interferon anti-HCV activity. There exists a distinct cell-type specific ISG activation in the pretreatment liver tissues of Rs and NRs. Up-regulation of the two ISGs that I tested (ISG15 and MxA) was found mainly in hepatocytes in NRs while ISG activation was preferentially observed in macrophages in Rs. Taking all these data together, pre-activation of interferon signaling and cell-type specific gene activation in the pretreatment liver tissues of patients infected with HCV are associated with treatment non-response. HCV exploits the host interferon system to favour its persistence by enhanced replication /secretion stimulated by a few ISGs (ISG15, USP18) in response to IFN. The developed prognostic tool can be used to stratify patients for treatment and the novel insights of the molecular mechanism of IFN resistance in HCV patients offer potential drug targets for future development.

Modelo multinível é uma ferramenta estatística cada vez mais popular para análise de dados com estrutura hierárquica. O objetivo deste trabalho é propor um método para realizar a predição de observações de novos grupos usando modelos de regressão logística multinível com 2 níveis. Além disso, é apresentado e comparado dois métodos de estimação para o modelo multinível: Quase-verossimilhança Penalizada (QVP) e Quadratura de Gauss-Hermite (QGH). A idéia central está baseada no trabalho de (Jiang e Lahiri, 2006) no qual se propõe o uso do chamado melhor estimador empírico para o efeito aleatório. Através deste estimador, utilizou-se a parte fixa do modelo em conjunto com uma estimativa do desvio padrão do efeito aleatório para fazer a predição de observações de novos grupos, encontrando a probabilidade estimada dessa observação apresentar o evento de interesse, dadas suas características. / Multilevel model is an statistical tool which is becoming more and more popular in data analysis with hierachical structure. The purpose of this dissertation is to present a method to make a prediction of new group observation in multilevel logistic regression models with 2 levels. Besides, were presented and compared two estimation methods for multilevel model: Penalized Quase-likelihood and Gauss-Hermite Quadrature. The central idea is based on the paper of Jiang and Lahiri (2006), which is presented the empirical best estimator for the random effect. Through this estimator was used the fixed part of the model with an estimative of the standard deviation of the random effect to find the estimated probability of this observation presenting the target event, in accordance with its characteristic.

Les cancers de la prostate sont classés en deux catégories. Les cancers de haut grade se distinguent des cancers de bas grade par une plus forte agressivité et un pronostic plus mauvais. Lorsqu’ils deviennent résistants à l’hormonothérapie, les cancers de haut grade sont traités par une chimiothérapie basée sur les taxanes. Néanmoins, les taux de réponse restent faibles. Il existe donc un réel besoin quant à l'identification d'alternatives thérapeutiques qui soient spécifiques de ce type de tumeur. Dans cette optique, notre travail a été de proposer une telle alternative par une approche qui prenne en compte la génétique spécifique des cancers de haut grade. Nous avons exploité une signature de 86 gènes dont le niveau d’expression permet de discriminer entre les tumeurs de haut et de bas grade. Par une approche in silico originale utilisant la banque de données du NCI, nous avons identifié 382 corrélations entre le niveau d’expression de 50 gènes et la sensibilité à 139 agents antiprolifératifs. Parmi ces corrélations, nous avons identifié une signature de 9 gènes qui est spécifique de la réponse à l’oxaliplatine. Cette signature a été confirmée sur le plan fonctionnel dans les lignées cancéreuses prostatiques DU145 et LNCaP. Nous avons donc fourni la preuve de concept que notre approche permet d’identifier de nouvelles molécules pouvant être utilisées en alternative aux taxanes pour traiter spécifiquement les cancers de haut grade. Cette stratégie permet aussi d’identifier de nouveaux marqueurs (gènes) régulant la sensibilité à certains médicaments. Nos résultats démontrent par exemple le rôle des gènes SHMT, impliqués dans la régulation du métabolisme monocarboné, dans la sensibilité spécifique à l’oxaliplatine par un mécanisme qui fait intervenir, du moins en partie, une dérégulation du niveau de méthylation global de l’ADN. / Prostate cancers are classified in two categories. High grade cancers are distinguished from low grade cancers by their higher agressivity and worse prognostic. When they become refractory to hormone therapy, high grade cancers are treated with a taxane-based chemotherapy. However, response rates remain low. Therefore, there is a real need for the discovery of new therapeutic alternatives which are specific for this type of tumors. For that purpose, our work aimed at proposing such an alternative with a strategy that took into account the high grade genetic background. We exploited a signature of 86 genes for which expression level could distinguish between low grade and high grade tumours. With an original in silico approach, we searched the NCI databases and identified 382 correlations between 50 genes and the sensitivity to 139 antiproliferative agents. Among these, a signature of 9 genes was able to specifically predict cell response to oxaliplatin. This signature was validated at the functional level in two prostate cancer cell lines, DU145 and LNCaP. We have thus provided the proof-of-concept that our approach allows the identification of new drugs that can be used alternatively to taxanes in order to specifically treat high grade prostate cancers. This strategy also allows the identification of new markers (genes) regulating the sensitivity to various drugs. Our results demonstrate for example the implication of SHMT genes, which are involved in the regulation of the one-carbon metabolism, in the specific sensitivity to oxaliplatin, by a mechanism which involves, at least in part, the deregulation of the global level of DNA methylation.

Soňa Fraňková: Role of host-dependent factors in prediction of antiviral treatment response in chronic hepatitis C Abstract Hepatitis C virus infection represents a leading cause of liver disease in western countries. The primary goal of HCV therapy is elimination of the virus, i.e. sustained virological response (SVR) achievement. Genetic factors have long been suspected of playing a crucial role in determining response to IFN-α-based therapies, but pretreatment predictors of response were only poorly defined and did not allow personalization of therapy. The aim of the thesis is to describe the role of host-dependent factors in prediction of antiviral treatment response in chronic hepatitis C in specific groups of patients. First, we focused on the role of the IFNG -764G/C promoter variant in SVR achievement. We did not prove that this variant predicted SVR in Czech HCV-infected individuals. Next, we focused on the role of IL28B and IFNL4 in HCV-infected patients: we confirmed that the IL28B rs12979860 CC genotype slows down the progression of liver fibrosis in chronic HCV infection and that IFNL4 ss469415590 TT|ΔG genotyping does not bring a better prediction of treatment success than IL28B rs12979860 in the Czech population. Third, we assessed prediction of treatment response in HCV positive liver...