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Regulation and specificity of the polycation-stimulated protein phosphatases
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ISSN: 07707703 ISBN: 9061862736 Year: 1988 Volume: 5 Publisher: Leuven University Press

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Epithelial ovarian cancer : molecular and clinical predictors for platinum resistance.
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Year: 2012 Publisher: Leuven KU Leuven. Faculteit Geneeskunde. Departement Oncologie

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The evaluation of new biomarkers in gynaecological tumours : The emerging role of Proteomics.
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ISBN: 9789090266817 Year: 2012 Publisher: Leuven KU Leuven. Faculteit Geneeskunde. Departement Oncologie

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Transcriptomic/proteomic approach to detect biomarkers in endometriosis.
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Year: 2011 Publisher: Leuven K.U.Leuven. Faculteit Geneeskunde

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Endometriosis is an estrogen dependent multi-factorial disease which affects around 10% of women of reproductive age. It is defined as the presence of endometrium-like tissue in sites outside the uterine cavity. Notwithstanding, the etiology and pathogenesis of endometriosis remain uncertain. Combinations of genetic, hormonal, environmental and immunological factors play a role in the pathogenesis of this disorder. Until today, no semi- or non-invasive test has been developed for the diagnosis of endometriosis. The gold standard for the diagnosis of pelvic disease is surgical assessment by laparoscopy. The most important goal of a non-invasive diagnostic test is to identify women with endometriosis who might benefit from surgical treatment for endometriosis-associated pain or subfertility.The overall aim of this thesis was firstly to investigate the pathogenesis of endometriosis using macroscopically normal peritoneum and eutopic endometrium from women with and without endometriosis and secondly, to discover new biomarkers in order to develop a semi- or non-invasive diagnostic test for endometriosis, using endometrium and plasma samples from women with and without endometriosis.We found increased IL-6 mRNA and reduced IL-12 mRNA expression in macroscopically normal peritoneum. This altered gene expression may concurrently contribute to the pathogenesis of endometriosis via enhanced inflammation and via a reduction of natural killer (NK) cytotoxicity. The reduced ferritin mRNA expression in macroscopically normal peritoneum from women with endometriosis suggests that iron overload may be limited to endometriosis lesions and not extend to normal peritoneum. This study indicates that the immunobiology of macroscopically normal peritoneum is relevant to understand the pathogenesis of endometriosis.Proteomic Surface Enhanced Laser Desorption/Ionisation (SELDI-TOF) mass spectrometry (MS) analysis of plasma samples allowed the diagnosis of endometriosis using 5 protein or peptide peaks with high sensitivity (minimal-mild=75%, moderate-severe=98%) and high specificity (minimal-mild=86%, moderate-severe=81%), based on the analysis of a training and test set using a randomization approach. The peak with the highest intensity (2.189Da) was decreased in women with moderate-severe endometriosis when compared to controls and was identified as fibrinogen beta chain peptide. In our combined endometrium microarray and SELDI-TOF MS analysis we showed a clear difference in gene expression in menstrual phase compared to early luteal phase in patients with and without endometriosis. In our endometrium proteomics part of the study we were able to classify minimal-mild versus control using 5 protein or peptide peaks with a sensitivity of 94% and specificity of 100% and when combining minimal-severe with sensitivity of 91% and specificity of 80%. In this dissertation we propose a semi- and non-invasive way to diagnose endometriosis with a high sensitivity and high specificity.

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Manifold learning for visualization, prioritization, and data fusion of Mass Spectrometry Imaging data
Authors: --- --- ---
Year: 2021 Publisher: Leuven KU Leuven. Faculty of Engineering Science

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Mass Spectrometry Imaging (MSI) is a powerful molecular imaging technology that can detect the spatial distribution of molecules in a tissue section. Because MSI does not require any a priori labeling, the technique has become very popular for the explorative comparison of metabolites, lipids, peptides and proteins between various tissue regions. Since it has been shown that tumor heterogeneity plays an important role in tumor biology, it has become clear that we need to elucidate the spatial distribution of molecules. MSI can therefore be of significant relevance in predicting cancer progression and treatment response, which often remains a challenge in today's clinical practice. A single measurement can however lead to complex and high dimensional data with file sizes in the gigabyte and even the terabyte range. As such manually exploring the data is becoming infeasible and support from computational methods is required.The focus of this work is therefore the development and application of computational methods to MSI data. Specifically we have concentrated on the topics related to non-linear dimensionality reduction, the prioritization of molecules measured per tissue region, and data fusion of MSI with the corresponding histology or microscopy image. For the non-linear dimensionality reduction, we make use of Uniform Manifold Approximation and Projection (UMAP) through which we achieve excellent visualizations of MSI data as demonstrated by the corresponding histology or microscopy images. We have conducted an extensive evaluation regarding the performance and results in comparison to other dimensionality reduction methods. To this end, we have used spatial autocorrelation and spectral similarity as a benchmark. In addition, we have empirically evaluated a number of different distance metrics, where we show that the choice of a particular distance metric might impact the visualization outcome. Building further on the obtained visualizations using UMAP, we proposed a bi-directional dimensionality reduction approach to prioritize the molecules driving these observations. This approach enables the prioritization of m/z-values in individual tissue samples but also across different tissue samples through the incorporation of both spatial and spectral information. The approach was demonstrated for tissue samples obtained from healthy mouse pancreas tissue. Finally, we introduce the correspondence-aware manifold learning paradigm for data fusion of molecular imaging data with the corresponding microscopy images. This enables us to bring the molecular information to a higher spatial resolution. As such these visualizations can play an important role in the digital pathology field for the quick assessment of a complete MSI dataset from a pathologist's perspective. We have shown that using this approach it becomes possible to identify a single infiltrating plasma cell amongst a group of phenotypically different (epithelial) cells. The identification of single aberrant cells is of crucial importance to evaluate a wide range of pathologies, in particular in cancer.

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Identification of PPP2R4 as a novel candidate tumor suppressor : evidence from PTPA gene trapped mice and human tumors
Authors: --- --- --- ---
Year: 2014 Publisher: Leuven KU Leuven. Faculty of Medicine

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Protein Phosphatase 2A (PP2A) complexes counteract diverse kinase-driven oncogenic pathways. Accumulating clinical evidence further underscores impaired PP2A function/activity in diverse cancers, sustaining its suspected tumor suppressor function. Nevertheless, whether loss of PP2A activity is sufficient for tumorigenesis in vivo has remained elusive. Here, we describe development of spontaneous malignancies in mice (haplo)deficient for Ppp2r4, encoding a PP2A chaperone (PTPA) essential for generation of active PP2A holoenzymes. PTPA-deficient tissues show reduced PP2A activity and methylation, selectively affecting specific PP2A holoenzymes. Complementary analyses of protein phosphorylation and gene expression revealed heterogeneous activation of diverse oncogenic signaling pathways in the tumors, underscoring that decreased PP2A activity affects multiple targets. Importantly, cancer database surveys revealed heterozygous PPP2R4 deletion at strikingly high frequency in several human cancer types. Furthermore, cancer-derived PPP2R4 mutants showing impaired PP2A-C binding in cellulo or impaired PTPA activity in vitro were unable to rescue transformation of PTPA-depleted human HEK-TER cells. Our data provide the first compelling in vivo evidence that impaired PP2A activity is sufficient to promote tumor development and establish PPP2R4 as a novel haploinsufficient tumor suppressor gene in multiple human cancers. These findings ultimately validate PP2A as a bona fide tumor suppressor and target for tumor suppressor reactivation therapies.

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Dissertation
Multi-class Prediction of Tumor Heterogeneity in Mass Spectrometry Imaging (MSI)
Authors: --- --- ---
Year: 2021 Publisher: Leuven KU Leuven. Faculteit Ingenieurswetenschappen

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Mass spectrometry is being applied in the field of pathology and is under development. Matrix Assisted Laser Desorption Ionization is one of the technologies in this field that grant the analysis of biological tissues through the analysis of molecular masses combined with spatial information. This technique creates large datasets that can provide information about the analyzed samples, but working with such huge datasets is still very challenging. The visualization of these datasets can provide valuable information for pathologists. Several methods exist to extract information from the raw data. In this work, two of these methods, namely principal component analysis (PCA) and uniform manifold approximation and projection (UMAP) are investigated with the use of human lymphoma and mouse pancreatic datasets. Two different variants of UMAP, the non-parametric (i.e. unsupervised) and the parametric (i.e. semisupervised) variant, are applied and compared. Furthermore, the importance of normalization is discussed, with considerations for implementing it. The results reveal that there are differences between nonparametric and parametric UMAP, this was probably due to a difference in iterations. The analyses are visualized through RGB hyperspectral visualization, allowing for a case-by-case comparison. Normalization shows its use by increasing the illumination and the contrast of the visualizations. Depending on the nature of the datasets, the normalization applications affect the number of clusters.

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INVESTIGATION OF THE PATHOGENESIS AND DIAGNOSIS OF ENDOMETRIOSIS
Authors: --- --- --- ---
Year: 2019 Publisher: Leuven KU Leuven. Faculty of Medicine

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Endometriosis is a benign, chronic, gynaecological disorder defined as the growth of endometrial-like tissue outside the uterus. The disease is highly prevalent, affecting about 10% of women of reproductive age and up to 50% of women with pelvic pain and/or infertility. Understanding the pathogenesis of endometriosis and finding a non-invasive diagnosis have been identified as research priorities. The most common theory on the origin of endometriosis states that during menstruation endometrial cells and tissue fragments flow backwards through the oviducts into the peritoneal cavity where they can implant, grow and develop into endometriotic lesions. We detected endometrial cells in the peritoneal fluid of some but not all women regardless of the presence of endometriosis (Chapter 2), indicating that other factors besides retrograde menstruation must be involved in the pathogenesis of this complex disease. It has been postulated that endometrial stem cells are the true instigators of the disease. We investigated the transcriptome of endometrial mesenchymal stem cells of women with and without endometriosis and found differences in gene expression conferring an increased survival capacity in endometriosis (Chapter 3). Till today, the gold standard of diagnosis is through laparoscopic surgery with histological confirmation of endometrial glands and stroma in excised lesions. The lack of a non-invasive diagnosis contributes to a diagnostic delay of approximately 10 years. To reduce this delay, we aimed to validate previously reported biomarkers for the development of a semi- or non-invasive diagnostic test in endometrium or peripheral blood plasma, respectively. We could not confirm the previously reported high diagnostic value of PGP9.5-positive endometrial nerve fibers (Chapter 4), nor of the previously developed prediction models containing CA-125, VEGF, Annexin V and glycodelin or sICAM-1 (Chapter 5). Validation of biomarkers for endometriosis remains challenging due to the existing patient heterogeneity, pre-analytical variability in sample collection and storage, analytical/technical variability of research immunoassays and differences in data interpretation and statistical analysis. For biomarker discovery we used the proteomic mass spectrometry method Orbitrap LC-MS as hypothesis-generating tool for untargeted biomarker discovery and identified 15 putative endometriosis biomarkers that warrant further validation (Chapter 6.1). We used antibody arrays as a hypothesis-driven approach in which up to 1000 of pre-specified proteins can be identified in one reaction (Chapter 6.2). However, our results were not repeatable nor reproducible and therefore, we could not identify any new biomarker candidates. Finally, the single marker myeloperoxidase was investigated as an endometriosis biomarker because of its role as a marker of inflammation. There was no difference in specific myeloperoxidase levels between women with endometriosis and women with other benign gynecological conditions, rendering this marker ineffective as endometriosis biomarker (Chapter 6.3). In conclusion, endometriosis is a complex disease with an unclear pathogenesis. Biomarkers for endometriosis have remained elusive due to heterogeneity in assay methodology and patient characteristics. Initiatives to stimulate collaboration between research groups can contribute to collecting larger numbers of well-defined patient samples in order to increase the quality of biomarker research.

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Improved classification of partially labeled data in Imaging Mass Spectrometry through integration of morphological information
Authors: --- --- ---
Year: 2019 Publisher: Leuven KU Leuven. Faculteit Ingenieurswetenschappen

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Images revealed by a range of staining techniques remain at the base of histopathology practice, and form the key input for expert pathologists to define regions of interest on samples or reach diagnostic conclusions about abnormalities of the specimen or on the nature of the disease. Analysis of the rich dataset behind mass spectrometry imaging (MSI) of the same specimens does not present with a similar convenient visual guidance. This research explores the hypothesis that information extracted from stained images can facilitate the interpretation of MSI data on the same sample by providing clues that will drive the subsequent analysis onto the optimal choice of sample regions or selection of mass/charge (m/z) values. To this purpose, stained images and MSI data are processed in a parallel feature extraction and labeling pipeline, and the output of those pipelines is driving an ultimate joint analysis based on clustering techniques. This report shows that texture carries the more relevant image information. The extraction of texture information using a Haralick transformation variant, and subsequent segmentation based on a spatially aware waterflood algorithm delivers an unsupervised labeling of the image. Adding the original density information into the pipeline improves the detection of structural features. The vast pool of information inside a full MSI dataset can be summarized with a digitization method based on both spatial and spectral dimensions, a fingerprint extracting only high relevant peaks per pixel and a Term Frequency / Inverse Document Frequency (TF-IDF) and k-means clustering and labeling algorithm borrowed from the field of text analysis. A visualization of the full MSI data is obtained much faster than published t-SNE or UMAP techniques with full retention of the link to real mass values. Integrating the features of both initial pipelines using another variant of the TF-IDF and k-means clustering methodology confirms the research hypothesis on 1 of the 2 provided test samples. Alignment of the source features at pixel level seems to be a main reason for not achieving proof of concept on the second sample. Both the intermediate image and MSI labeling, the image alignment methodology and the final clustering of the successful sample present attractive options for further analysis and follow-up research.

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Dissertation
Proteomics in cancer research : methods and applications of ProteinChip arrays.

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Inleiding De afronding van het humane genoomproject luidde offcieel een nieuw tijdperk in, dat van het proteoom. Er onstond een nieuwe discipline, proteomics of de analyse van het proteoom, de verzameling van alle eiwitten in een weefsel, cel of organisme op een gegeven ogenblik en onder bepaalde fysiologische omstandigheden. Eiwitten zijn direct verantwoordelijk voor de regulatie van biochemische processen en abberante of ontregelde eiwitexpressie ligt aan de basis van vele ziekten, waaronder kanker. Kennis van kanker gerelateerde eiwitten biedt daarom niet enkel diagnostische perspectieven, het laat ons ook toe om nieuwe moleculen te ontwikkelen die specifiek interageren met de ontregelde eiwitten en zodoende de progressie van de ziekte kunnen remmen. In het lopend onderzoek van het Laboratorium voor Experimentele Oncologie bestonden twee onopgehelderde proteoom vraagstellingen. De eerste vraagstelling was of er kanker specifieke eiwitvarianten bestaan van de Vasculaire Endotheliale Groeifactor A (VEGF-A). VEGF-A speelt een belangrijke rol in de groei, uitzaaiing en resistentie van tumoren, maar is tevens cruciaal voor het onderhoud van een gezond bloedvatenstelsel. Kennis van tumorspecifieke VEGF-A varianten zou mogelijks toelaten om de therapeutische index van anti-VEGF-A therapieën te verbeteren. Een tweede vraagstelling was of Gastrointestinale Stromale Tumoren (GISTs) die snel resistentie ontwikkelen tegen Imatinib systematisch andere eiwitten tot expressie brengen in vergelijking met GISTs waarbij Imatinib resistentie eerder zeldzaam voor komt. Methodologie De belangrijkste uitdagingen voor proteomics zijn de bijzondere proteoomcomplexiteit, de relatief lage eiwit expressieniveaus en de immense inter en intra- individu variabiliteit. Daardoor is er in eerste instantie nood aan een gevoelige en reproduceerbare methodologie, geschikt voor een relatief grote staaldoorvoer. Wij besloten om de bruikbaarheid van ProteinChip technologie voor de studie van bovenvermelde proteomics vraagstellingen na te gaan. ProteinChip technologie maakt gebruik van retentiechromatografische arrays voor het weerhouden van subgroepen van eiwitten uit complexe stalen zoals lichaamsvloeistoffen of weefselextracten. Deze eiwitten worden vervolgens massapectrometrisch geanalyseerd door middel van Surface Enhanced Laser Desorption/ Ionization Time-of-Flight Mass Spectrometry (SELDI TOF MS). Resultaten De hoogmoleculaire gewichts varianten van VEGF-A konden geassocieerd worden met verschillende maligniteiten, terwijl de laag moleculaire VEGF-A varianten eerder algemeen bleken voor te komen, ook in normale fysiologische omstandigheden. Een vergelijkende proteoomanalyse bij GISTs wees uit dat 21 SELDI pieken statistisch significant verschilde in intensiteit tussen een groep GISTs met hoge waarschijnlijkheid op Imatinib resistentie en een groep GISTs met laag risico op resistentie ontwikkeling. Een multivariaat statistische analyse wees bovendien uit dat 14 extra SELDI pieken verdere aandacht verdienden. In totaal werden 40 eiwitten geïdentificeerd en de differentiële expressie van een selectie van drie van deze eiwitten kon bevestigd worden door middel van western blot analyse: het 40 kDa ATP-ase domein van Hitte Shock Proteïne 70 (HSP70), Cu/Zn Superoxide. Conclusies en discussie De therapeutische index van anti-VEGF-A therapiën zou mogelijks kunnen verbeterd worden door specifieke inhibitie van de tumor gerelateerde hoog moleculaire gewichts varianten van VEGF-A. Onze gegevens wijzen erop dat de laag moleculaire gewichtsvarianten van VEGF-A een belangrijke rol spelen in normale fysiologie en dus bijgevolg best onaangeroerd blijven om nevenwerkingen te minimaliseren. Stress gerelateerde eiwitten zouden een belangrijke rol kunnen spelen in de ontwikkeling van Imatinib resistentie bij GISTs. Deze eiwitten staan onder centrale controle van de Hitte Shock Factor 1 (HSF1), een eiwit waartegen reeds inhibitoren beschikbaar zijn, hetgeen nieuwe therapeutische perspectieven biedt voor de behandeling van Imatinib resistente GISTs. Ook de inhibitie van de signaaltransductie via de eiwitten 14-3-3 zeta en Calmodulin biedt mogelijks nieuwe therapeutische perspectieven. ProteinChip technologie is zeer geschikt gebleken voor zowel de gerichte analyse van eiwitten als voor de differentiële proteoomanalyse tussen verschillende patiëntengroepen. Introduction The completion of the human genome project officially introduced the era of the proteome. The new discipline of proteomics was born, which refers to the analysis of the proteome or the collection of all proteins that are expressed in a tissue, cell or organism at a given point in time and under certain physiological conditions. Proteins are directly involved in the regulation of biochemical processes and aberrant or deregulated protein expression is related with many diseases, including cancer. Knowledge of cancer related proteins not only offers diagnostic perspectives, but also allows development of new molecules which specifically interact with the deregulated proteins and are thereby able to inhibit cancer progression. Two unanswered proteomic questions existed at the Laboratory of Experimental Oncology. The first question was whether cancer specific variants of the Vascular Endothelial Growth Factor A (VEGF-A) exist. VEGF-A plays an important role in tumor growth, metastasis and resistance, but is also crucial for the maintenance a healthy vascular system. Knowledge of tumor specific VEGF-A variants might enable us to increase the therapeutic index of anti-VEGF-A therapies. A second proteomic question was whether Gastrointestinal Stromal Tumors (GISTs) that easily develop resistance to treatment with Imatinib express different proteins than GISTs that rarely develop such resistance.  Methods The most important challenges for proteomics are the high complexity of the proteome, the relatively low protein expression levels and the enormous inter and intra individual variability. The most important requirement for proteomic analysis is therefore a sensitive and reproducible method that enables a relatively high sample throughput. We decided to investigate the usefulness of ProteinChip technology for the study of the proteomic questions mentioned above. ProteinChip technology is based on the principle of retention chromatography for the fractionation of proteins from complex samples such as body fluids or tissue extracts. These proteins are subsequently analysed with Surface Enhanced Laser Desorption/ Ionization Time-of-Flight Mass Spectrometry (SELDI TOF MS). Results The high molecular weight varieties of VEGF-A were associated with different types of malignancies, while the low molecular weight VEGF-A varieties rather seemed common and were also observed in normal physiological conditions. A comparative proteomic analysis of GISTs pointed out to 21 SELDI peaks that were statistically significantly differentially expressed between a group of GISTs with a high risk for development of Imatinib resistance and a group with a low risk for development of such resistance. Multivariate analysis further pointed out to 14 additional interesting SELDI peaks. In total, forty proteins were identified and the differential expression pattern of a selection of three of these proteins could be confirmed with western blot analysis: the 40 kDa ATP-ase domain of Heat Shock Protein 70 (HSP70), Cu/Zn Superoxide Dismuatase (SOD1) and Protein 14-3-3 Zeta. Conclusions and discussion The therapeutic index of anti-VEGF-A therapies might be improved by specific inhibition of tumor associated high molecular weight VEGF-A varieties. Our data indicate that the low molecular weight varieties of VEGF-A play an important role in normal physiology and should therefore be spared by anti-VEGF-A therapies to minimize the risk of adverse effects. Stress related proteins might play an important role in the development of Imatinib resistance of GISTs. The fact that these proteins are regulated by one and the same factor, the Heat Shock Factor 1 (HSF1), offers interesting perspectives for the treatment of Imatinib resistant GISTs, especially because inhibitors of HSF1 already exist. In addition, the inhibition of signal transduction pathways controlled by 14-3-3 zeta and Calmodulin also seems promising. ProteinChip technology has showed to be suitable for both the targeted analysis of proteins as for the analysis of differential protein expression between different groups of patients. Het genoom of de verzameling van alle genen van een organisme bevat de informatie over hoe alle eiwitten van dat organisme dienen opgebouwd te worden. Het genoom kan men dus beschouwen als het receptenboek van het leven. Onze genen op zich vervullen echter geen enkele functie. De eiwitten beschreven in onze genen daarentegen zorgen voor structuur en biologische activiteit en maken ons uiteindelijk tot wat we zijn. Het doctoraatsonderzoek beschreven in deze thesis startte ongeveer tegelijkertijd met de afronding van het humane genoomproject, waarbij alle menselijke genen werden in kaart gebracht. Deze gebeurtenis staat bekend als een belangrijke mijlpaal in de biomedische geschiedenis, omdat de kennis van het genoom een nodige voorwaarde is om functionele genoomanalyse te kunnen verrichten. Functionele genoomanalyse betekent de studie van de genen die betrokken zijn in biologische en biomedische processen. Door middel van functionele genoomanalyse kunnen we nieuwe inzichten verwerven in onopgeheldere ziekteprocessen zoals kanker. Het proteoom of de verzameling van alle eiwitten van een organisme is als eindprodukt van het genoom het studieonderwerp bij uitstek om inzichten te verwerven in het functionele deel van het genoom. De ontrafeling van het proteoom vereist echter inzet van zogenaamde “state-of-the-art” analytische technieken, die de afgelopen jaren slechts met mondjesmaat ter beschikking zijn gekomen van de wetenschap. Het doel van dit doctoraatsproject was de bruikbaarheid na te gaan van ProteinChip technologie voor de studie van het proteoom van humane tumoren. Daarbij werden verschillende methoden ontwikkeld voor zowel gerichte als “blinde” analyses van eiwitten in tumorweefsels. Deze methoden bleken achteraf ook nuttig te zijn voor de studie van andere biomedische vraagstellingen buiten het domein van de oncologie (bv. ziekte van Alzheimer, endometriose, ...). The genome, or the collection of all genes of an organism contains the information that is required to build all proteins of this organism. The genome may therefore be regarded as the book of recipes of life. Genes do not have any function of their own. The proteins that are encoded by the genome provide structure and are biologically active. The doctoral research described in this thesis started almost simultaneously with the completion of the human genome project, in which all human genes were mapped. This event is regarded as an important milestone in the history of biomedical sciences, because it enables functional genome studies. Functional genome analysis refers to the study of the genes that are involved in biological and biomedical processes. Functional genome analysis enables us to generate new insights into disease processes such as cancer. The proteome or collection of all proteins of an organism is the most appealing subject for functional genome analysis, because it is the end product of the genome. Proteomics refers to the study of the proteome and is by many considered as the most important research strategy in the post-genomic era. Proteomics requires a different approach than genome analysis and above all the use of state-of-the-art technologies such as mass spectrometry. In this doctoral research project, the usefulness of ProteinChip technology was explored for applications in the laboratory of experimental oncology. Several methods were developed for both targeted and blind analysis of proteins in tumor tissues. These methods have already showed to be useful to generate new insights in the molecular biology of cancer and for the development of novel diagnostic tests. ProteinChip technology has also been used successfully to generate new insights in other diseases such as Alzheimer disease and Endometriosis. 

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