Molecular Characteristics of Neuroblastoma with Special Reference to Novel Prognostic Factors and Diagnostic Applications

Molecular Characteristics of Neuroblastoma with Special Reference to Novel Prognostic Factors and Diagnostic Applications Department of Medical Biochemistry and Genetics Annales Universitatis Turkuensis, Medica-Odontologica, 2009, Turku, Finland Painosalama Oy, Turku, Finland 2009 Background: Neuroblastoma, which is the most common and extensively studied childhood solid cancer, shows a great clinical and biological heterogeneity. Most of the neuroblastoma patients older than one year have poor prognosis despite intensive therapies. The hallmark of neuroblastoma, biological heterogeneity, has hindered the discovery of prognostic tumour markers. At present, few molecular markers, such as MYCN oncogene status, have been adopted into clinical practice. Aims: The aim of the study was to improve the current prognostic methodology of neuroblastoma, especially by taking cognizance of the biological heterogeneity of neuroblastoma. Furthermore, unravelling novel molecular characteristics which associate with neuroblastoma tumour progression and cell differentiation was an additional objective. Results: A new strictly defined selection of neuroblastoma tumour spots of highest proliferation activity, hotspots, appeared to be representative and reliable in an analysis of MYCN amplification status using a chromogenic in situ hybridization technique (CISH). Based on the hotspot tumour tissue microarray immunohistochemistry and high-resolution oligo-array-based comparative genomic hybridization, which was integrated with gene expression and in silico analysis of existing transcriptomics, a polysialylated neural cell adhesion molecule (NCAM) and poorly characterized amplicon at 12q24.31 were discovered to associate with outcome. In addition, we found that a previously considered new neuroblastoma treatment target, the mutated c-kit receptor, was not mutated in neuroblastoma samples. Conclusions: Our studies indicate polysialylated NCAM and 12q24.31 amplicon to be new molecular markers with important value in prognostic evaluation of neuroblastoma. Moreover, the presented hotspot tumour tissue microarray method together with the CISH technique of the MYCN oncogene copy number is directly applicable to clinical use. Key words: neuroblastoma, polysialic acid, neural cell adhesion molecule, MYCN, c-kit, chromogenic in situ hybridization, hotspot

Functional Study of Oncogenic Transcription Factor ERG and its Signaling in Prostate Cancer

TMPRSS2–ERG is the most frequent type of genomic rearrangement present in prostate tumors, in which the 5- prime region of the TMPRSS2 gene is fused to the ERG oncogene. TMPRSS2, containing androgen response elements (AREs), is regulated by androgens in the prostate. The truncated TMPRSS2-ERG fusion transcript is overexpressed in half of the prostate cancer patients. The formation of TMPRSS2-ERG transcript is an early event in prostate carcinogenesis and previous in vivo and in vitro studies have shown ectopic ERG expression to be associated with increased cell invasion. However, the molecular function of ERG and its role in cell signaling is poorly understood. In this study, genomic rearrangement of ERG with TMPRSS2 was studied by using comparative genomic hybridization (CGH) in prostate cancer samples. The biological processes associated with the ERG oncogene expression in prostate epithelial cells were studied, and the results were compared with findings observed in clinical prostate tumor samples. The gene expression data indicated that increased WNT signaling and loss of cell adhesion were a characteristic of TMPRSS2- ERG fusion positive prostate tumor samples. Up- regulation of WNT pathway genes were present in ERG positive prostate tumors, with frizzled receptor 4 (FZD4) presenting with the highest association with ERG overexpression, as verified by quantitative reverse transcription-PCR, immunostaining, and immunoblotting in TMPRSS2-ERG positive VCaP prostate cancer cells. Furthermore, ERG and FZD4 silencing increased cell adhesion by inducing active β1-integrin and E-cadherin expression in VCaP cells. Furthermore, we found a novel inhibitor, 4-(chloromethyl) benzoyl chloride which inhibited the WNT signaling and induced similar phenotypic effects as observed after ERG or FZD4 down regulation in VCaP cells. In conclusion, this work deepens our understanding on the complex oncogenic mechanisms of ERG in prostate cancer that may help in developing drugs against TMPRSS2-ERG positive tumors.

A cell spot microarray method for high-throughput biology

High-throughput screening of cellular effects of RNA interference (RNAi) libraries is now being increasingly applied to explore the role of genes in specific cell biological processes and disease states. However, the technology is still limited to specialty laboratories, due to the requirements for robotic infrastructure, access to expensive reagent libraries, expertise in high-throughput screening assay development, standardization, data analysis and applications. In the future, alternative screening platforms will be required to expand functional large-scale experiments to include more RNAi constructs, allow combinatorial loss-of-function analyses (e.g. genegene or gene-drug interaction), gain-of-function screens, multi-parametric phenotypic readouts or comparative analysis of many different cell types. Such comprehensive perturbation of gene networks in cells will require a major increase in the flexibility of the screening platforms, throughput and reduction of costs. As an alternative for the conventional multi-well based high-throughput screening -platforms, here the development of a novel cell spot microarray method for production of high density siRNA reverse transfection arrays is described. The cell spot microarray platform is distinguished from the majority of other transfection cell microarray techniques by the spatially confined array layout that allow highly parallel screening of large-scale RNAi reagent libraries with assays otherwise difficult or not applicable to high-throughput screening. This study depicts the development of the cell spot microarray method along with biological application examples of high-content immunofluorescence and phenotype based cancer cell biological analyses focusing on the regulation of prostate cancer cell growth, maintenance of genomic integrity in breast cancer cells, and functional analysis of integrin protein-protein interactions in situ.

Functional Genomic Approaches for Deciphering Plant-Virus Interactions

Plant-virus interactions are very complex in nature and lead to disease and symptom formation by causing various physiological, metabolic and developmental changes in the host plants. These interactions are mainly the outcomes of viral hijacking of host components to complete their infection cycles and of host defensive responses to restrict the viral infections. Viral genomes contain only a small number of genes often encoding for multifunctional proteins, and all are essential in establishing a viral infection. Thus, it is important to understand the specific roles of individual viral genes and their contribution to the viral life cycles. Among the most important viral proteins are the suppressors of RNA silencing (VSRs). These proteins function to suppress host defenses mediated by RNA silencing and can also serve in other functions, e.g. in viral movement, transactivation of host genes, virus replication and protein processing. Thus these proteins are likely to have a significant impact on host physiology and metabolism. In the present study, I have examined the plant-virus interactions and the effects of three different VSRs on host physiology and gene expression levels by microarray analysis of transgenic plants that express these VSR genes. I also studied the gene expression changes related to the expression of the whole genome of Tobacco mosaic virus (TMV) in transgenic tobacco plants. Expression of the VSR genes in the transgenic tobacco plants causes significant changes in the gene expression profiles. HC-Pro gene derived from the Potyvirus Y (PVY) causes alteration of 748 and 332 transcripts, AC2 gene derived from the African cassava mosaic virus (ACMV) causes alteration of 1118 and 251transcripts, and P25 gene derived from the Potyvirus X (PVX) causes alterations of 1355 and 64 transcripts in leaves and flowers, respectively. All three VSRs cause similar up-regulation in defense, hormonally regulated and different stress-related genes and down-regulation in the photosynthesis and starch metabolism related genes. They also induce alterations that are specific to each viral VSR. The phenotype and transcriptome alterations of the HC-Pro expressing transgenic plants are similar to those observed in some Potyvirus-infected plants. The plants show increased protein degradation, which may be due to the HC-Pro cysteine endopeptidase and thioredoxin activities. The AC2-expressing transgenic plants show a similar phenotype and gene expression pattern as HC-Pro-expressing plants, but also alter pathways related to jasmonic acid, ethylene and retrograde signaling. In the P25 expressing transgenic plants, high numbers of genes (total of 1355) were up-regulated in the leaves, compared to a very low number of down-regulated genes (total of 5). Despite of strong induction of the transcripts, only mild growth reduction and no other distinct phenotype was observed in these plants. As an example of whole virus interactions with its host, I also studied gene expression changes caused by Tobacco mosaic virus (TMV) in tobacco host in three different conditions, i.e. in transgenic plants that are first resistant to the virus, and then become susceptible to it and in wild type plants naturally infected with this virus. The microarray analysis revealed up and down-regulation of 1362 and 1422 transcripts in the TMV resistant young transgenic plants, and up and down-regulation of a total of 1150 and 1200 transcripts, respectively, in the older plants, after the resistance break. Natural TMV infections in wild type plants caused up-regulation of 550 transcripts and down-regulation of 480 transcripts. 124 up-regulated and 29 down-regulated transcripts were commonly altered between young and old TMV transgenic plants, and only 6 up-regulated and none of the down-regulated transcripts were commonly altered in all three plants. During the resistant stage, the strong down-regulation in translation-related transcripts (total of 750 genes) was observed. Additionally, transcripts related to the hormones, protein degradation and defense pathways, cell division and stress were distinctly altered. All these alterations may contribute to the TMV resistance in the young transgenic plants, and the resistance may also be related to RNA silencing, despite of the low viral abundance and lack of viral siRNAs or TMV methylation activity in the plants.

The application of cDNA and tissue microarray methods in the study of human carcinomas

Currently, numerous high-throughput technologies are available for the study of human carcinomas. In literature, many variations of these techniques have been described. The common denominator for these methodologies is the high amount of data obtained in a single experiment, in a short time period, and at a fairly low cost. However, these methods have also been described with several problems and limitations. The purpose of this study was to test the applicability of two selected high-throughput methods, cDNA and tissue microarrays (TMA), in cancer research. Two common human malignancies, breast and colorectal cancer, were used as examples. This thesis aims to present some practical considerations that need to be addressed when applying these techniques. cDNA microarrays were applied to screen aberrant gene expression in breast and colon cancers. Immunohistochemistry was used to validate the results and to evaluate the association of selected novel tumour markers with the outcome of the patients. The type of histological material used in immunohistochemistry was evaluated especially considering the applicability of whole tissue sections and different types of TMAs. Special attention was put on the methodological details in the cDNA microarray and TMA experiments. In conclusion, many potential tumour markers were identified in the cDNA microarray analyses. Immunohistochemistry could be applied to validate the observed gene expression changes of selected markers and to associate their expression change with patient outcome. In the current experiments, both TMAs and whole tissue sections could be used for this purpose. This study showed for the first time that securin and p120 catenin protein expression predict breast cancer outcome and the immunopositivity of carbonic anhydrase IX associates with the outcome of rectal cancer. The predictive value of these proteins was statistically evident also in multivariate analyses with up to a 13.1- fold risk for cancer specific death in a specific subgroup of patients.

Improving Transportation and Warehousing Efficiency with Simulation-Based Decision Support Systems

Transportation and warehousing are large and growing sectors in the society, and their efficiency is of high importance. Transportation also has a large share of global carbondioxide emissions, which are one the leading causes of anthropogenic climate warming. Various countries have agreed to decrease their carbon emissions according to the Kyoto protocol. Transportation is the only sector where emissions have steadily increased since the 1990s, which highlights the importance of transportation efficiency. The efficiency of transportation and warehousing can be improved with the help of simulations, but models alone are not sufficient. This research concentrates on the use of simulations in decision support systems. Three main simulation approaches are used in logistics: discrete-event simulation, systems dynamics, and agent-based modeling. However, individual simulation approaches have weaknesses of their own. Hybridization (combining two or more approaches) can improve the quality of the models, as it allows using a different method to overcome the weakness of one method. It is important to choose the correct approach (or a combination of approaches) when modeling transportation and warehousing issues. If an inappropriate method is chosen (this can occur if the modeler is proficient in only one approach or the model specification is not conducted thoroughly), the simulation model will have an inaccurate structure, which in turn will lead to misleading results. This issue can further escalate, as the decision-maker may assume that the presented simulation model gives the most useful results available, even though the whole model can be based on a poorly chosen structure. In this research it is argued that simulation- based decision support systems need to take various issues into account to make a functioning decision support system. The actual simulation model can be constructed using any (or multiple) approach, it can be combined with different optimization modules, and there needs to be a proper interface between the model and the user. These issues are presented in a framework, which simulation modelers can use when creating decision support systems. In order for decision-makers to fully benefit from the simulations, the user interface needs to clearly separate the model and the user, but at the same time, the user needs to be able to run the appropriate runs in order to analyze the problems correctly. This study recommends that simulation modelers should start to transfer their tacit knowledge to explicit knowledge. This would greatly benefit the whole simulation community and improve the quality of simulation-based decision support systems as well. More studies should also be conducted by using hybrid models and integrating simulations with Graphical Information Systems.

Quantitative refinement of reaction-based biomodels

In the field of molecular biology, scientists adopted for decades a reductionist perspective in their inquiries, being predominantly concerned with the intricate mechanistic details of subcellular regulatory systems. However, integrative thinking was still applied at a smaller scale in molecular biology to understand the underlying processes of cellular behaviour for at least half a century. It was not until the genomic revolution at the end of the previous century that we required model building to account for systemic properties of cellular activity. Our system-level understanding of cellular function is to this day hindered by drastic limitations in our capability of predicting cellular behaviour to reflect system dynamics and system structures. To this end, systems biology aims for a system-level understanding of functional intraand inter-cellular activity. Modern biology brings about a high volume of data, whose comprehension we cannot even aim for in the absence of computational support. Computational modelling, hence, bridges modern biology to computer science, enabling a number of assets, which prove to be invaluable in the analysis of complex biological systems, such as: a rigorous characterization of the system structure, simulation techniques, perturbations analysis, etc. Computational biomodels augmented in size considerably in the past years, major contributions being made towards the simulation and analysis of large-scale models, starting with signalling pathways and culminating with whole-cell models, tissue-level models, organ models and full-scale patient models. The simulation and analysis of models of such complexity very often requires, in fact, the integration of various sub-models, entwined at different levels of resolution and whose organization spans over several levels of hierarchy. This thesis revolves around the concept of quantitative model refinement in relation to the process of model building in computational systems biology. The thesis proposes a sound computational framework for the stepwise augmentation of a biomodel. One starts with an abstract, high-level representation of a biological phenomenon, which is materialised into an initial model that is validated against a set of existing data. Consequently, the model is refined to include more details regarding its species and/or reactions. The framework is employed in the development of two models, one for the heat shock response in eukaryotes and the second for the ErbB signalling pathway. The thesis spans over several formalisms used in computational systems biology, inherently quantitative: reaction-network models, rule-based models and Petri net models, as well as a recent formalism intrinsically qualitative: reaction systems. The choice of modelling formalism is, however, determined by the nature of the question the modeler aims to answer. Quantitative model refinement turns out to be not only essential in the model development cycle, but also beneficial for the compilation of large-scale models, whose development requires the integration of several sub-models across various levels of resolution and underlying formal representations.

Data analysis tools and methods for DNA microarray and high-throughput sequencing data

The recent rapid development of biotechnological approaches has enabled the production of large whole genome level biological data sets. In order to handle thesedata sets, reliable and efficient automated tools and methods for data processingand result interpretation are required. Bioinformatics, as the field of studying andprocessing biological data, tries to answer this need by combining methods and approaches across computer science, statistics, mathematics and engineering to studyand process biological data. The need is also increasing for tools that can be used by the biological researchers themselves who may not have a strong statistical or computational background, which requires creating tools and pipelines with intuitive user interfaces, robust analysis workflows and strong emphasis on result reportingand visualization. Within this thesis, several data analysis tools and methods have been developed for analyzing high-throughput biological data sets. These approaches, coveringseveral aspects of high-throughput data analysis, are specifically aimed for gene expression and genotyping data although in principle they are suitable for analyzing other data types as well. Coherent handling of the data across the various data analysis steps is highly important in order to ensure robust and reliable results. Thus,robust data analysis workflows are also described, putting the developed tools andmethods into a wider context. The choice of the correct analysis method may also depend on the properties of the specific data setandthereforeguidelinesforchoosing an optimal method are given. The data analysis tools, methods and workflows developed within this thesis have been applied to several research studies, of which two representative examplesare included in the thesis. The first study focuses on spermatogenesis in murinetestis and the second one examines cell lineage specification in mouse embryonicstem cells.